Search results for: minimum root mean square (RMS) error matching algorithm

Authors: Maryam Ziaee

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This research proposes a new approach and provides an opportunity for manufacturing companies to produce large amounts of products that meet their prospective customers’ tastes, needs, and expectations and simultaneously enable manufacturers to increase their profit. Mass customization is the production of products or services to meet each individual customer’s desires to the greatest possible extent in high quantities and at reasonable prices. This process takes place at different levels such as the customization of goods’ design, assembly, sale, and delivery status, and classifies in several categories. The main focus of this study is on one class of mass customization, called optional customization, in which companies try to provide their customers with as many options as possible to customize their products. These options could range from the design phase to the manufacturing phase, or even methods of delivery. Mass customization values customers’ tastes, but it is only one side of clients’ satisfaction; on the other side is companies’ fast responsiveness delivery. It brings the concept of agility, which is the ability of a company to respond rapidly to changes in volatile markets in terms of volume and variety. Indeed, mass customization is not effectively feasible without integrating the concept of agility. To gain the customers’ satisfaction, the companies need to be quick in responding to their customers’ demands, thus highlighting the significance of agility. This research offers a different method that successfully integrates mass customization and fast production in manufacturing industries. This research is built upon the hypothesis that the success key to being agile in mass customization is to forecast demand, cooperate with suppliers, and control inventory. Therefore, the significance of the supply chain (SC) is more pertinent when it comes to this stage. Since SC behavior is dynamic and its behavior changes constantly, companies have to apply one of the predicting techniques to identify the changes associated with SC behavior to be able to respond properly to any unwelcome events. System dynamics utilized in this research is a simulation approach to provide a mathematical model among different variables to understand, control, and forecast SC behavior. The final stage is delayed differentiation, the production strategy considered in this research. In this approach, the main platform of products is produced and stocked and when the company receives an order from a customer, a specific customized feature is assigned to this platform and the customized products will be created. The main research question is to what extent applying system dynamics for the prediction of SC behavior improves the agility of mass customization. This research is built upon a qualitative approach to bring about richer, deeper, and more revealing results. The data is collected through interviews and is analyzed through NVivo software. This proposed model offers numerous benefits such as reduction in the number of product inventories and their storage costs, improvement in the resilience of companies’ responses to their clients’ needs and tastes, the increase of profits, and the optimization of productivity with the minimum level of lost sales.

Keywords: agility, manufacturing, resilience, supply chain

Procedia PDF Downloads 79

Authors: Md Tanvir Rahman, Mahbube Subhani, Mahmud Ashraf, Paul Kremer

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Cross-laminated timber (CLT) is made from layers of timber boards orthogonally oriented in the thickness direction, and due to this, CLT can withstand bi-axial bending in contrast with most other engineered wood products such as laminated veneer lumber (LVL) and glued laminated timber (GLT). Wood is cylindrically anisotropic in nature and is characterized by significantly lower elastic modulus and shear modulus in the planes perpendicular to the fibre direction, and is therefore classified as orthotropic material and is thus characterized by 9 elastic constants which are three elastic modulus in longitudinal direction, tangential direction and radial direction, three shear modulus in longitudinal tangential plane, longitudinal radial plane and radial tangential plane and three Poisson’s ratio. For simplification, timber materials are generally assumed to be transversely isotropic, reducing the number of elastic properties characterizing it to 5, where the longitudinal plane and radial planes are assumed to be planes of symmetry. The validity of this assumption was investigated through numerical modelling of CLT with both orthotropic mechanical properties and transversely isotropic material properties for three softwood species, which are Norway spruce, Douglas fir, Radiata pine, and three hardwood species, namely Victorian ash, Beech wood, and Aspen subjected to uniformly distributed loading under simply supported boundary condition. It was concluded that assuming the timber to be transversely isotropic results in a negligible error in the order of 1 percent. It was also observed that along with longitudinal elastic modulus, ratio of longitudinal shear modulus (GL) and rolling shear modulus (GR) has a significant effect on a deflection for CLT panels of lower span to depth ratio. For softwoods such as Norway spruce and Radiata pine, the ratio of longitudinal shear modulus, GL to rolling shear modulus GR is reported to be in the order of 12 to 15 times in literature. This results in shear flexibility in transverse layers leading to increased deflection under out-of-plane loading. The rolling shear modulus of hardwoods has been found to be significantly higher than those of softwoods, where the ratio between longitudinal shear modulus to rolling shear modulus as low as 4. This has resulted in a significant rise in research into the manufacturing of CLT from entirely from hardwood, as well as from a combination of softwood and hardwoods. The commonly used beam theory to analyze the performance of CLT panels under out-of-plane loads are the Shear analogy method, Gamma method, and k-method. The shear analogy method has been found to be the most effective method where shear deformation is significant. The effect of the ratio of longitudinal shear modulus and rolling shear modulus of cross-layer on the deflection of CLT under uniformly distributed load with respect to its length to depth ratio was investigated using shear analogy method. It was observed that shear deflection is reduced significantly as the ratio of the shear modulus of the longitudinal layer and rolling shear modulus of cross-layer decreases. This indicates that there is significant room for improvement of the bending performance of CLT through developing hybrid CLT from a mix of softwood and hardwood.

Keywords: rolling shear modulus, shear deflection, ratio of shear modulus and rolling shear modulus, timber

Procedia PDF Downloads 109

Authors: Praveena Sinha

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Context: Diabetes is a global public health burden, particularly affecting postmenopausal women. Insulin resistance (IR) is prevalent in this population, and it is associated with an increased risk of developing type 2 diabetes. Yoga therapy is gaining attention as a complementary intervention for diabetes due to its potential to address stress psychophysiology. This study focuses on the efficacy of a 12-week yoga practice in attenuating insulin resistance in early postmenopausal women. Research Aim: The aim of this research is to investigate the effect of a 3-month long yoga practice on insulin resistance in early postmenopausal women. Methodology: The study conducted a prospective longitudinal design with 67 women within five years of menopause. Participants were divided into two groups based on their willingness to join yoga. The Yoga group (n = 37) received routine gynecological management along with an integrated yoga module, while the Non-Yoga group (n = 30) received only routine management. Insulin resistance was measured using the homeostasis model assessment of insulin resistance (HOMA-IR) method before and after the intervention. Statistical analysis was performed using GraphPad Prism Version 5 software, with statistical significance set at P < 0.05. Findings: The results indicate a significant decrease in serum fasting insulin levels and HOMA-IR measurements in the Yoga group, although the decrease did not reach statistical significance. In contrast, the Non-Yoga group showed a significant rise in serum fasting insulin levels and HOMA-IR measurements after 3 months, suggesting a detrimental effect on insulin resistance in these postmenopausal women. Theoretical Importance: This study provides evidence that a 12-week yoga practice can attenuate the increase in insulin resistance in early postmenopausal women. It highlights the potential of yoga as a preventive measure against the early onset of insulin resistance and the development of type 2 diabetes mellitus. Regular yoga practice can be a valuable tool in addressing hormonal imbalances associated with early postmenopause, leading to a decrease in morbidity and mortality related to insulin resistance and type 2 diabetes mellitus in this population. Data Collection and Analysis Procedures: Data collection involved measuring serum fasting insulin levels and calculating HOMA-IR. Statistical analysis was performed using GraphPad Prism Version 5 software, and mean values with standard error of the mean were reported. The significance level was set at P < 0.05. Question Addressed: The study aimed to address whether a 3-month long yoga practice could attenuate insulin resistance in early postmenopausal women. Conclusion: The research findings support the efficacy of a 12-week yoga practice in attenuating insulin resistance in early postmenopausal women. Regular yoga practice has the potential to prevent the early onset of insulin resistance and the development of type 2 diabetes mellitus in this population. By addressing the hormonal imbalances associated with early post menopause, yoga could significantly decrease morbidity and mortality related to insulin resistance and type 2 diabetes mellitus in these subjects.

Keywords: post menopause, insulin resistance, HOMA-IR, yoga, type 2 diabetes mellitus

Procedia PDF Downloads 54

Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

Procedia PDF Downloads 287

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

Procedia PDF Downloads 64

Authors: Jelena Filipovic, Marija Bodroza-Solarov, Milenko Kosutic, Nebojsa Novkovic, Vladimir Filipovic, Vesna Vucurovic

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Spelt wheat is suitable raw material for extruded products such as pasta, special types of bread and other products of altered nutritional characteristics compared to conventional wheat products. During the process of extrusion, spelt is exposed to high temperature and high pressure, during which raw material is also mechanically treated by shear forces. Spelt wheat is growing without the use of pesticides in harsh ecological conditions and in marginal areas of cultivation. So it can be used for organic and health safe food. Pasta is the most popular foodstuff; its consumption has been observed to rise. Pasta quality depends mainly on the properties of flour raw materials, especially protein content and its quality but starch properties are of a lesser importance. Pasta is characterized by significant amounts of complex carbohydrates, low sodium, total fat fiber, minerals, and essential fatty acids and its nutritional value can be improved with additional functional component. Over the past few decades, wheat pasta has been successfully formulated using different ingredients in pasta to cater health-conscious consumers who prefer having a product rich in protein, healthy lipids and other health benefits. Flaxseed flour is used in the production of bakery and pasta products that have properties of functional foods. However, it should be taken into account that food products retain the technological and sensory quality despite the added flax seed. Flaxseed contains important substances in its composition such as vitamins and minerals elements, and it is also an excellent source of fiber and one of the best sources of ω-3 fatty acids and lignin. In this paper, the quality and identification of spelt extruded product with the addition of flax seed, which is positively contributing to the nutritive and technology changes of the product, is investigated. ω-3 fatty acids are polyunsaturated essential fatty acids, and they must be taken with food to satisfy the recommended daily intake. Flaxseed flour is added in the quantity of 10/100 g of sample and 20/100 g of sample on farina. It is shown that the presence of ω-3 fatty acids in pasta can be clearly distinguished from other fatty acids by gas chromatography with mass spectrometry. Addition of flax seed flour influence chemical content of pasta. The addition of flax seed flour in spelt pasta in the quantities of 20g/100 g significantly increases the share of ω-3 fatty acids, which results in improved ratio of ω-6/ω-3 1:2.4 and completely satisfies minimum daily needs of ω-3 essential fatty acids (3.8 g/100 g) recommended by FDA. Flex flour influenced the pasta quality by increasing of hardness (2377.8 ± 13.3; 2874.5 ± 7.4; 3076.3 ± 5.9) and work of shear (102.6 ± 11.4; 150.8 ± 11.3; 165.0 ± 18.9) and increasing of adhesiveness (11.8 ± 20.6; 9.,98 ± 0.12; 7.1 ± 12.5) of the final product. Presented data point at good indicators of technological quality of spelt pasta with flax seed and that GC-MS analysis can be used in the quality control for flax seed identification. Acknowledgment: The research was financed by the Ministry of Education and Science of the Republic of Serbia (Project No. III 46005).

Keywords: GC-MS analysis, ω-3 fatty acids, flex seed, spelt wheat, daily needs

Procedia PDF Downloads 146

Authors: Manushi Srivastava

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Introduction: Doctor’s role, and relation with respect to patient care is at the core of medical ethics. The rapid pace of medical advances along with increasing consumer awareness about their rights and hike in cost of effective health care demand a robust, transparent and patient-friendly medical care system. However, doctors’ role performance is still in the frame of activity-passivity model of Doctor-Patient Relationship (DPR) where doctors act as parent and use to instruct their patients, without their consensus that is not going to help in the 21st century. Thus the current situation is a new challenge for traditional doctor-patient relationship after the introduction of Consumer Protection Act (CPA) in medical profession and the same is evidenced by increasing cases of medical litigation. To strengthen this system of medical services, the doctor plays a vital role, and the same should be reviewed in the present context. Objective: To understand the opinion of consultants regarding medical negligence and effect of Consumer Protection Act in terms of current practices of patient care. Method: This is a cross-sectional study in which both quantitative and qualitative methods are applied. Total 69 consultants were selected from multi-specialty hospitals of densely populated Varanasi city catering a population of about 1.8 million. Two-stage sampling was used for selection of respondents. At the first stage, selection of major wards (Medicine, Surgery, Ophthalmology, Gynaecology, Orthopaedics, and Paediatrics) was carried out, which are more susceptible to medical negligence. At the second stage, selection of consultants from the respective wards was carried out. In-depth Interviews were conducted with the help of semi-structured schedule. Two case studies of medical negligence were also carried out as part of the qualitative study. Analysis: Data were analyzed with the help of SPSS software (21.0 trial version). Semi-structured research tool was used to know consultant’s opinion about the pattern of medical negligence cases, litigations and claims made by patient community and inclusion of government medical services in CPA. Statistical analysis was done to describe data, and non-parametric test was used to observe the association between the variables. Analysis of Verbatim was used in case-study. Findings and Conclusion: Majority (92.8%) of consultants felt changes in the behaviour of community (patient) after implementation of CPA, as it had increased awareness about their rights. Less than half of the consultants opined that Medical Negligence is an Unintentional act of doctors and generally occurs due to communication gap and behavioural problem between doctor and patients. Experienced consultants ( > 10 years) pointed out that unethical practice by doctors and mal-intention of patient to harass doctors were additional reasons of Medical Negligence. In-depth interview revealed that now patients’ community expects more transparency and hence they demand cafeteria approach in diagnosis and management of cases. Thus as study results, we propose ‘Agreement Model’ of DPR to re-ensure ethical practice in medical profession.

Keywords: doctors, communication, consumer protection act (CPA), medical error

Procedia PDF Downloads 150

Authors: Mostafa Zandi, Ramin Mansouri

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Spillways are one of the most important hydraulic structures of dams that provide the stability of the dam and downstream areas at the time of flood. Morning-Glory spillway is one of the common spillways for discharging the overflow water behind dams, these kinds of spillways are constructed in dams with small reservoirs. In this research, the hydraulic flow characteristics of a morning-glory spillways are investigated with CFD model. Two dimensional unsteady RANS equations were solved numerically using Finite Volume Method. The PISO scheme was applied for the velocity-pressure coupling. The mostly used two-equation turbulence models, k- and k-, were chosen to model Reynolds shear stress term. The power law scheme was used for discretization of momentum, k , and  equations. The VOF method (geometrically reconstruction algorithm) was adopted for interface simulation. The results show that the fine computational grid, the input speed condition for the flow input boundary, and the output pressure for the boundaries that are in contact with the air provide the best possible results. Also, the standard wall function is chosen for the effect of the wall function, and the turbulent model k -ε (Standard) has the most consistent results with experimental results. When the jet is getting closer to end of basin, the computational results increase with the numerical results of their differences. The lower profile of the water jet has less sensitivity to the hydraulic jet profile than the hydraulic jet profile. In the pressure test, it was also found that the results show that the numerical values of the pressure in the lower landing number differ greatly in experimental results. The characteristics of the complex flows over a Morning-Glory spillway were studied numerically using a RANS solver. Grid study showed that numerical results of a 57512-node grid had the best agreement with the experimental values. The desired downstream channel length was preferred to be 1.5 meter, and the standard k-ε turbulence model produced the best results in Morning-Glory spillway. The numerical free-surface profiles followed the theoretical equations very well.

Keywords: morning-glory spillway, CFD model, hydraulic characteristics, wall function

Procedia PDF Downloads 62

Authors: S. Lahijanian, M. Mobli, B. Baninasab, N. Etemadi

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Low temperature extremes are amongst the major stresses that adversely affect the plant growth and productivity. Cold stress causes oxidative stress, physiological, morphological and biochemical changes in plant cells. Generally, low temperatures similar to salinity and drought exert their negative effects mainly by disrupting the ionic and osmotic equilibrium of the plant cells. Changes in climatic condition leading to more frequent extreme conditions will require adapted crop species on a larger scale in order to sustain agricultural production. Eucalyptus is a diverse genus of flowering trees (and a few shrubs) in the myrtle family, Myrtaceae. Members of this genus dominate the tree flora of Australia. The eucalyptus genus contains more than 580 species and large number of cultivars, which are native to Australia. Large distribution and diversity of compatible eucalyptus cultivars reflect the fact of ecological flexibility of eucalyptus. Some eucalyptus cultivars can sustain hard environmental conditions like high and low temperature, salinity, high level of PH, drought, chilling and freezing which are intensively effective on crops with tropical and subtropical origin. In this study, we tried to evaluate freezing tolerance of 12 eucalyptus genotypes by means of four different morphological and physiological methods: Chlorophyll fluorescence, electrolyte leakage, proline and stomatal density. The studied cultivars include Eucalyptus camaldulensis, E. coccifera, E. darlympleana, E. erythrocorys, E. glaucescens, E. globulus, E. gunnii, E. macrocorpa, E. microtheca, E. rubida, E. tereticornis, and E. urnigera. Except for stomatal density recording, in other methods, plants were exposed to five gradual temperature drops: zero, -5, -10, -15 and -20 degree of centigrade and they remained in these temperatures for at least one hour. Experiment for measuring chlorophyll fluorescence showed that genotypes E. erythrocorys and E. camaldulensis were the most resistant genotypes and E. gunnii and E.coccifera were more sensitive than other genotypes to freezing stress effects. In electrolyte leakage experiment with regard to significant interaction between cultivar and temperature, genotypes E. erythrocorys and E.macrocorpa were shown to be the most tolerant genotypes and E. gunnii, E. urnigera, E. microtheca and E. tereticornis with the more ionic leakage percentage showed to be more sensitive to low temperatures. Results of Proline experiment approved that the most resistant genotype to freezing stress is E. erythrocorys. In the stomatal density experiment, the numbers of stomata under microscopic field were totally counted and the results showed that the E. erythrocorys and E. macrocorpa genotypes had the maximum and E. coccifera and E. darlympleana genotypes had minimum number of stomata under microscopic field (0.0605 mm2). In conclusion, E. erythrocorys identified as the most tolerant genotype; meanwhile E. gunnii classified as the most freezing susceptible genotype in this investigation. Further, remarkable correlation was not obtained between the stomatal density and other cold stress measures.

Keywords: chlorophyll fluorescence, cold stress, ionic leakage, proline, stomatal density

Procedia PDF Downloads 247

Authors: Mahyar MehrAfarin

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The Sistan region in eastern Iran is a significant archaeological area in Iran and the Middle East, encompassing 10,000 square kilometers. Previous archeological field surveys have identified 1662 ancient sites dating from prehistoric periods to the Islamic period. Research Aim: This article aims to explore the utilization of modern technologies and computers in archaeological field surveys in Sistan, Iran, and the benefits derived from their implementation. Methodology: The research employs a descriptive-analytical approach combined with field methods. New technologies and software, such as GPS, drones, magnetometers, equipped cameras, satellite images, and software programs like GIS, Map source, and Excel, were utilized to collect information and analyze data. Findings: The use of modern technologies and computers in archaeological field surveys proved to be essential. Traditional archaeological activities, such as excavation and field surveys, are time-consuming and costly. Employing modern technologies helps in preserving ancient sites, accurately recording archaeological data, reducing errors and mistakes, and facilitating correct and accurate analysis. Creating a comprehensive and accessible database, generating statistics, and producing graphic designs and diagrams are additional advantages derived from the use of efficient technologies in archaeology. Theoretical Importance: The integration of computers and modern technologies in archaeology contributes to interdisciplinary collaborations and facilitates the involvement of specialists from various fields, such as geography, history, art history, anthropology, laboratory sciences, and computer engineering. The utilization of computers in archaeology spanned across diverse areas, including database creation, statistical analysis, graphics implementation, laboratory and engineering applications, and even artificial intelligence, which remains an unexplored area in Iranian archaeology. Data Collection and Analysis Procedures: Information was collected using modern technologies and software, capturing geographic coordinates, aerial images, archeogeophysical data, and satellite images. This data was then inputted into various software programs for analysis, including GIS, Map source, and Excel. The research employed both descriptive and analytical methods to present findings effectively. Question Addressed: The primary question addressed in this research is how the use of modern technologies and computers in archeological field surveys in Sistan, Iran, can enhance archaeological data collection, preservation, analysis, and accessibility. Conclusion: The utilization of modern technologies and computers in archaeological field surveys in Sistan, Iran, has proven to be necessary and beneficial. These technologies aid in preserving ancient sites, accurately recording archaeological data, reducing errors, and facilitating comprehensive analysis. The creation of accessible databases, statistics generation, graphic designs, and interdisciplinary collaborations are further advantages observed. It is recommended to explore the potential of artificial intelligence in Iranian archaeology as an unexplored area. The research has implications for cultural heritage organizations, archaeology students, and universities involved in archaeological field surveys in Sistan and Baluchistan province. Additionally, it contributes to enhancing the understanding and preservation of Iran's archaeological heritage.

Keywords: Iran, sistan, archaeological surveys, computer use, modern technologies

Procedia PDF Downloads 65

Authors: Evgeny V. Blagin, Aleksandr I. Dovgjallo, Dmitry A. Uglanov

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This article deals with research of the working process in the thermocompressor which operates on cryogenic working fluid. Thermocompressor is device suited for the conversation of heat energy directly to the potential energy of pressure. Suggested thermocompressor is suited for operation during liquid natural gas (LNG) re-gasification and is placed after evaporator. Such application of thermocompressor allows using of the LNG cold energy for rising of working fluid pressure, which then can be used for electricity generation or another purpose. Thermocompressor consists of two chambers divided by the regenerative heat exchanger. Calculation algorithm for unsteady calculation of thermocompressor working process was suggested. The results of this investigation are to change of thermocompressor’s chambers temperature and pressure during the working cycle. These distributions help to find out the parameters, which significantly influence thermocompressor efficiency. These parameters include regenerative heat exchanger coefficient of the performance (COP) dead volume of the chambers, working frequency of the thermocompressor etc. Exergy analysis was performed to estimate thermocompressor efficiency. Cryogenic thermocompressor operated on nitrogen working fluid was chosen as a prototype. Calculation of the temperature and pressure change was performed with taking into account heat fluxes through regenerator and thermocompressor walls. Temperature of the cold chamber significantly differs from the results of steady calculation, which is caused by friction of the working fluid in regenerator and heat fluxes from the hot chamber. The rise of the cold chamber temperature leads to decreasing of thermocompressor delivery volume. Temperature of hot chamber differs negligibly because losses due to heat fluxes to a cold chamber are compensated by the friction of the working fluid in the regenerator. Optimal working frequency was selected. Main results of the investigation: -theoretical confirmation of thermocompressor operation capability on the cryogenic working fluid; -optimal working frequency was found; -value of the cold chamber temperature differs from the starting value much more than the temperature of the hot chamber; -main parameters which influence thermocompressor performance are regenerative heat exchanger COP and heat fluxes through regenerator and thermocompressor walls.

Keywords: cold energy, liquid natural gas, thermocompressor, regenerative heat exchanger

Procedia PDF Downloads 572

Authors: Evren Isen

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In grid-connected renewable energy systems, input power is controlled by AC/DC converter or/and DC/DC converter depending on output voltage of input source. The power is injected to DC-link, and DC-link voltage is regulated by inverter controlling the grid current. Inverter performance is considerable in grid-connected renewable energy systems to meet the utility standards. In this paper, modelling and simulation of hysteresis current controlled single-phase grid-connected inverter that is utilized in renewable energy systems, such as wind and solar systems, are presented. 2 kW single-phase grid-connected inverter is simulated in Simulink and modeled in Matlab-m-file. The grid current synchronization is obtained by phase locked loop (PLL) technique in dq synchronous rotating frame. Although dq-PLL can be easily implemented in three-phase systems, there is difficulty to generate β component of grid voltage in single-phase system because single-phase grid voltage exists. Inverse-Park PLL with low-pass filter is used to generate β component for grid angle determination. As grid current is controlled by constant bandwidth hysteresis current control (HCC) technique, average switching frequency and variation of switching frequency in a fundamental period are considered. 3.56% total harmonic distortion value of grid current is achieved with 0.5 A bandwidth. Average value of switching frequency and total harmonic distortion curves for different hysteresis bandwidth are obtained from model in m-file. Average switching frequency is 25.6 kHz while switching frequency varies between 14 kHz-38 kHz in a fundamental period. The average and maximum frequency difference should be considered for selection of solid state switching device, and designing driver circuit. Steady-state and dynamic response performances of the inverter depending on the input power are presented with waveforms. The control algorithm regulates the DC-link voltage by adjusting the output power.

Keywords: grid-connected inverter, hysteresis current control, inverter modelling, single-phase inverter

Procedia PDF Downloads 467

Authors: Andrew Gray, Zhibing Zhang

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The ability for consumer products to deliver a sustained perfume response can be a key driver for a variety of applications. Many compounds in perfume oils are highly volatile, meaning they readily evaporate once the product is applied, and the longevity of the scent is poor. Perfume capsules have been introduced as a means of abating this evaporation once the product has been delivered. The impermeable capsules are aimed to be stable within the formulation, and remain intact during delivery to the desired substrate, only rupturing to release the core perfume oil through application of mechanical force applied by the consumer. This opens up the possibility of obtaining an olfactive response hours, weeks or even months after delivery, depending on the nature of the desired application. Tailoring the properties of the polymeric capsules to better address the needs of the application is not a trivial challenge and currently design of capsules is largely done by trial and error. The aim of this work is to have more predictive methods for capsule design depending on the consumer application. This means refining formulations such that they rupture at the right time for the specific consumer application, not too early, not too late. Finding the right balance between these extremes is essential if a benefit is sought with respect to neat addition of perfume to formulations. It is important to understand the forces that influence capsule rupture, first, by quantifying the magnitude of these different forces, and then by assessing bulk rupture in real-world applications to understand how capsules actually respond. Samples were provided by an industrial partner and the mechanical properties of individual capsules within the samples were characterized via a micromanipulation technique, developed by Professor Zhang at the University of Birmingham. The capsules were synthesized such as to change one particular physicochemical property at a time, such as core: wall material ratio, and the average size of capsules. Analysis of shell thickness via Transmission Electron Microscopy, size distribution via the use of a Mastersizer, as well as a variety of other techniques confirmed that only one particular physicochemical property was altered for each sample. The mechanical analysis was subsequently undertaken, showing the effect that changing certain capsule properties had on the response under compression. It was, however, important to link this fundamental mechanical response to capsule performance in real-world applications. As such, the capsule samples were introduced to a formulation and exposed to full scale stresses. GC-MS headspace analysis of the perfume oil released from broken capsules enabled quantification of what the relative strengths of capsules truly means for product performance. Correlations have been found between the mechanical strength of capsule samples and performance in terms of perfume release in consumer applications. Having a better understanding of the key parameters that drive performance benefits the design of future formulations by offering better guidelines on the parameters that can be adjusted without worrying about the performance effects, and singles out those parameters that are essential in finding the sweet spot for capsule performance.

Keywords: consumer products, mechanical and physicochemical properties, perfume capsules, rupture behaviour

Procedia PDF Downloads 125

Authors: Thiwanka Weerakkody, Lakmali Handagiripathira, Poshitha Dabare, Thisari Guruge

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The determination of gross alpha and beta activity in water is important in a wide array of environmental studies and these parameters are considered in international legislations on the quality of water. This technique is commonly applied as screening method in radioecology, environmental monitoring, industrial applications, etc. Measuring of Gross Alpha and Beta emitters by using iSolo alpha beta counting system is an adequate nuclear technique to assess radioactivity levels in natural and waste water samples due to its simplicity and low cost compared with the other methods. Twelve water samples (Six samples of commercially available bottled drinking water and six samples of industrial waste water) were measured by standard method EPA 900.0 consisting of the gas-less, firm wear based, single sample, manual iSolo alpha beta counter (Model: SOLO300G) with solid state silicon PIPS detector. Am-241 and Sr90/ Y90 calibration standards were used to calibrate the detector. The minimum detectable activities are 2.32mBq/L and 406mBq/L, for alpha and beta activity, respectively. Each of the 2L water samples was evaporated (at low heat) to a small volume and transferred into 50mm stainless steel counting planchet evenly (for homogenization) and heated by IR lamp and the constant weighted residue was obtained. Then the samples were counted for gross alpha and beta. Sample density on the planchet area was maintained below 5mg/cm. Large quantities of solid wastes sludges and waste water are generated every year due to various industries. This water can be reused for different applications. Therefore implementation of water treatment plants and measuring water quality parameters in industrial waste water discharge is very important before releasing them into the environment. This waste may contain different types of pollutants, including radioactive substances. All these measured waste water samples having gross alpha and beta activities, lower than the maximum tolerance limits for industrial waste water discharge of industrial waste in to inland surface water, that is 10-9µCi/mL and 10-8µCi/mL for gross alpha and beta respectively (National Environmental Act, No. 47 of 1980). This is according to extraordinary gazette of the democratic socialist republic of Sri Lanka in February 2008. The measured water samples were below the recommended radioactivity levels and do not pose any radiological hazard when releasing the environment. Drinking water is an essential requirement of life. All the drinking water samples were below the permissible levels of 0.5Bq/L for gross alpha activity and 1Bq/L for gross beta activity. The values have been proposed by World Health Organization in 2011; therefore the water is acceptable for consumption of humans without any further clarification with respect to their radioactivity. As these screening levels are very low, the individual dose criterion (IDC) would usually not be exceeded (0.1mSv y⁻¹). IDC is a criterion for evaluating health risks from long term exposure to radionuclides in drinking water. Recommended level of 0.1mSv/y expressed a very low level of health risk. This monitoring work will be continued further for environmental protection purposes.

Keywords: drinking water, gross alpha, gross beta, waste water

Procedia PDF Downloads 183

Authors: Komal Rathore, Kiesha Pierre, Kyle Cogswell, Aaron Driscoll, Andres Tejada Martinez, Gita Iranipour, Luke Mulford, Aydin Sunol

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Modeling of Biological Wastewater Treatment Plants requires several parameters for kinetic rate expressions, thermo-physical properties, and hydrodynamic behavior. The kinetics and associated mechanisms become complex due to several biological processes taking place in wastewater treatment plants at varying times and spatial scales. A dynamic process model that incorporated the complex model for activated sludge kinetics was developed using the BioWin software platform for an Advanced Wastewater Treatment Plant in Valrico, Florida. Due to the extensive number of tunable parameters, an experimental design was employed for judicious selection of the most influential parameter sets and their bounds. The model was tuned using both the influent and effluent plant data to reconcile and rectify the forecasted results from the BioWin Model. Amount of mixed liquor suspended solids in the oxidation ditch, aeration rates and recycle rates were adjusted accordingly. The experimental analysis and plant SCADA data were used to predict influent wastewater rates and composition profiles as a function of time for extended periods. The lumped dynamic model development process was coupled with Computational Fluid Dynamics (CFD) modeling of the key units such as oxidation ditches in the plant. Several CFD models that incorporate the nitrification-denitrification kinetics, as well as, hydrodynamics was developed and being tested using ANSYS Fluent software platform. These realistic and verified models developed using BioWin and ANSYS were used to plan beforehand the operating policies and control strategies for the biological wastewater plant accordingly that further allows regulatory compliance at minimum operational cost. These models, with a little bit of tuning, can be used for other biological wastewater treatment plants as well. The BioWin model mimics the existing performance of the Valrico Plant which allowed the operators and engineers to predict effluent behavior and take control actions to meet the discharge limits of the plant. Also, with the help of this model, we were able to find out the key kinetic and stoichiometric parameters which are significantly more important for modeling of biological wastewater treatment plants. One of the other important findings from this model were the effects of mixed liquor suspended solids and recycle ratios on the effluent concentration of various parameters such as total nitrogen, ammonia, nitrate, nitrite, etc. The ANSYS model allowed the abstraction of information such as the formation of dead zones increases through the length of the oxidation ditches as compared to near the aerators. These profiles were also very useful in studying the behavior of mixing patterns, effect of aerator speed, and use of baffles which in turn helps in optimizing the plant performance.

Keywords: computational fluid dynamics, flow-sheet simulation, kinetic modeling, process dynamics

Procedia PDF Downloads 196

Authors: Berfin Yildiz

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These days, increasing global warming effects, limited amount of energy resources, etc., necessitates the awareness that must be present in every profession group. The architecture and construction sectors are responsible for both the embodied and operational energy of the materials. This responsibility has led designers to seek alternative solutions for energy-efficient material selection. The choice of energy-efficient material requires consideration of the entire life cycle, including the building's production, use, and disposal energy. The aim of this study is to investigate the method of material selection of external thermal insulation composite systems (ETICS). Embodied and in-use energy values of material alternatives were used for the evaluation in this study. The operational energy is calculated according to the u-value calculation method defined in the TS 825 (Thermal Insulation Requirements) standard for Turkey, and the embodied energy is calculated based on the manufacturer's Energy Performance Declaration (EPD). ETICS consists of a wall, adhesive, insulation, lining, mechanical, mesh, and exterior finishing materials. In this study, lining, mechanical, and mesh materials were ignored because EPD documents could not be obtained. The material selection problem is designed as a hypothetical volume area (5x5x3m) and defined as a multi-objective discrete optimization problem for external thermal insulation composite systems. Defining the problem as a discrete optimization problem is important in order to choose between materials of various thicknesses and sizes. Since production and use energy values, which are determined as optimization objectives in the study, are often conflicting values, material selection is defined as a multi-objective optimization problem, and it is aimed to obtain many solution alternatives by using Hypervolume (HypE) algorithm. The enrollment process started with 100 individuals and continued for 50 generations. According to the obtained results, it was observed that autoclaved aerated concrete and Ponce block as wall material, glass wool, as insulation material gave better results.

Keywords: embodied energy, multi-objective discrete optimization, performative design, thermal insulation

Procedia PDF Downloads 123

Authors: Deb Kumar Shah, M. Shaheer Akhtar, Ha Ryeon Lee, O-Bong Yang, Chong Yeal Kim

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The crystalline silicon (Si) solar cells are highly renowned photovoltaic technology and well-established as the commercial solar technology. Most of the solar panels are globally installed with the crystalline Si solar modules. At the present scenario, the major photovoltaic (PV) market is shared by c-Si solar cells, but the cost of c-Si panels are still very high as compared with the other PV technology. In order to reduce the cost of Si solar panels, few necessary steps such as low-cost Si manufacturing, cheap antireflection coating materials, inexpensive solar panel manufacturing are to be considered. It is known that the antireflection (AR) layer in c-Si solar cell is an important component to reduce Fresnel reflection for improving the overall conversion efficiency. Generally, Si wafer exhibits the 30% reflection because it normally poses the two major intrinsic drawbacks such as; the spectral mismatch loss and the high Fresnel reflection loss due to the high contrast of refractive indices between air and silicon wafer. In recent years, researchers and scientists are highly devoted to a lot of researches in the field of searching effective and low-cost AR materials. Silicon nitride (SiNx) is well-known AR materials in commercial c-Si solar cells due to its good deposition and interaction with passivated Si surfaces. However, the deposition of SiNx AR is usually performed by expensive plasma enhanced chemical vapor deposition (PECVD) process which could have several demerits like difficult handling and damaging the Si substrate by plasma when secondary electrons collide with the wafer surface for AR coating. It is very important to explore new, low cost and effective AR deposition process to cut the manufacturing cost of c-Si solar cells. One can also be realized that a nano-texturing process like the growth of nanowires, nanorods, nanopyramids, nanopillars, etc. on Si wafer can provide a low reflection on the surface of Si wafer based solar cells. The above nanostructures might be enhanced the antireflection property which provides the larger surface area and effective light trapping. In this work, we report on the development of crystalline Si solar cells without using the AR layer. The Silicon wafer was modified by growing nanowires like Si nanostructures using the wet controlled etching method and directly used for the fabrication of Si solar cell without AR. The nanostructures over Si wafer were optimized in terms of sizes, lengths, and densities by changing the etching conditions. Well-defined and aligned wires like structures were achieved when the etching time is 20 to 30 min. The prepared Si nanostructured displayed the minimum reflectance ~1.64% at 850 nm with the average reflectance of ~2.25% in the wavelength range from 400-1000 nm. The nanostructured Si wafer based solar cells achieved the comparable power conversion efficiency in comparison with c-Si solar cells with SiNx AR layer. From this study, it is confirmed that the reported method (controlled wet etching) is an easy, facile method for preparation of nanostructured like wires on Si wafer with low reflectance in the whole visible region, which has greater prospects in developing c-Si solar cells without AR layer at low cost.

Keywords: chemical etching, conversion efficiency, silicon nanostructures, silicon solar cells, surface modification

Procedia PDF Downloads 115

Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

Procedia PDF Downloads 60

Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

Procedia PDF Downloads 39

Authors: Naser Kazemi Eilaki, Carolyn Ahmer, Ilona Heldal, Bjarne Christian Hagen

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Older people and people with disabilities are recognized as at-risk groups when it comes to egress and travel from hazard zone to safe places. One's disability can negatively influence her or his escape time, and this becomes even more important when people from this target group live alone. This research deals with the fire safety of mentioned people's buildings by means of probabilistic methods. For this purpose, fire safety is addressed by modeling the egress of our target group from a hazardous zone to a safe zone. A common type of detached house with a prevalent plan has been chosen for safety analysis, and a limit state function has been developed according to the time-line evacuation model, which is based on a two-zone and smoke development model. An analytical computer model (B-Risk) is used to consider smoke development. Since most of the involved parameters in the fire development model pose uncertainty, an appropriate probability distribution function has been considered for each one of the variables with indeterministic nature. To achieve safety and reliability for the at-risk groups, the fire safety index method has been chosen to define the probability of failure (causalities) and safety index (beta index). An improved harmony search meta-heuristic optimization algorithm has been used to define the beta index. Sensitivity analysis has been done to define the most important and effective parameters for the fire safety of the at-risk group. Results showed an area of openings and intervals to egress exits are more important in buildings, and the safety of people would improve with increasing dimensions of occupant space (building). Fire growth is more critical compared to other parameters in the home without a detector and fire distinguishing system, but in a home equipped with these facilities, it is less important. Type of disabilities has a great effect on the safety level of people who live in the same home layout, and people with visual impairment encounter more risk of capturing compared to visual and movement disabilities.

Keywords: fire safety, at-risk groups, zone model, egress time, uncertainty

Procedia PDF Downloads 90

Authors: Khadija Refouh

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Culture bound-expressions have been a bottleneck for Natural Language Processing (NLP) and comprehension, especially in the case of machine translation (MT). In the last decade, the field of machine translation has greatly advanced. Neural machine translation NMT has recently achieved considerable development in the quality of translation that outperformed previous traditional translation systems in many language pairs. Neural machine translation NMT is an Artificial Intelligence AI and deep neural networks applied to language processing. Despite this development, there remain some serious challenges that face neural machine translation NMT when translating culture bounded-expressions, especially for low resources language pairs such as Arabic-English and Arabic-French, which is not the case with well-established language pairs such as English-French. Machine translation of opaque idioms from English into French are likely to be more accurate than translating them from English into Arabic. For example, Google Translate Application translated the sentence “What a bad weather! It runs cats and dogs.” to “يا له من طقس سيء! تمطر القطط والكلاب” into the target language Arabic which is an inaccurate literal translation. The translation of the same sentence into the target language French was “Quel mauvais temps! Il pleut des cordes.” where Google Translate Application used the accurate French corresponding idioms. This paper aims to perform NMT experiments towards better translation of opaque idioms using high quality clean multilingual corpus. This Corpus will be collected analytically from human generated idiom translation. AutoML translation, a Google Neural Machine Translation Platform, is used as a custom translation model to improve the translation of opaque idioms. The automatic evaluation of the custom model will be compared to the Google NMT using Bilingual Evaluation Understudy Score BLEU. BLEU is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Human evaluation is integrated to test the reliability of the Blue Score. The researcher will examine syntactical, lexical, and semantic features using Halliday's functional theory.

Keywords: multilingual corpora, natural language processing (NLP), neural machine translation (NMT), opaque idioms

Procedia PDF Downloads 133

Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

Procedia PDF Downloads 115

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

Procedia PDF Downloads 64

Authors: B. K. Kanungo, Monika Thakur, Minati Baral

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8-hydroxyquinoline, (8HQ), experiences a renaissance due to its utility as a building block in metallosupramolecular chemistry and its versatile use of its derivatives in various fields of analytical chemistry, materials science, and pharmaceutics. It forms stable complexes with a variety of metal ions. Assembly of more than one such unit to form a polydentate chelator enhances its coordinating ability and the related properties due to the chelate effect resulting in high stability constant. Keeping in view the above, a nonadentate chelator N-[3,5-bis(8-hydroxyquinoline-2-amido)cyclohexyl]-8-hydroxyquinoline-2-carboxamide, (TACH2OX), containing a central cis,cis-1,3,5-triaminocyclohexane appended to three 8-hydroxyquinoline at 2-position through amide linkage is developed, and its solution thermodynamics, photophysical and Density Functional Theory (DFT) studies were undertaken. The synthesis of TACH2OX was carried out by condensation of cis,cis-1,3,5-triaminocyclohexane, (TACH) with 8‐hydroxyquinoline‐2‐carboxylic acid. The brown colored solid has been fully characterized through melting point, infrared, nuclear magnetic resonance, electrospray ionization mass and electronic spectroscopy. In solution, TACH2OX forms protonated complexes below pH 3.4, which consecutively deprotonates to generate trinegative ion with the rise of pH. Nine protonation constants for the ligand were obtained that ranges between 2.26 to 7.28. The interaction of the chelator with two trivalent metal ion Fe3+ and Al3+ were studied in aqueous solution at 298 K. The metal-ligand formation constants (ML) obtained by potentiometric and spectrophotometric method agree with each other. The protonated and hydrolyzed species were also detected in the system. The in-silico studies of the ligand, as well as the complexes including their protonated and deprotonated species assessed by density functional theory technique, gave an accurate correlation with each observed properties such as the protonation constants, stability constants, infra-red, nmr, electronic absorption and emission spectral bands. The nature of electronic and emission spectral bands in terms of number and type were ascertained from time-dependent density functional theory study and the natural transition orbitals (NTO). The global reactivity indices parameters were used for comparison of the reactivity of the ligand and the complex molecules. The natural bonding orbital (NBO) analysis could successfully describe the structure and bonding of the metal-ligand complexes specifying the percentage of contribution in atomic orbitals in the creation of molecular orbitals. The obtained high value of metal-ligand formation constants indicates that the newly synthesized chelator is a very powerful synthetic chelator. The minimum energy molecular modeling structure of the ligand suggests that the ligand, TACH2OX, in a tripodal fashion firmly coordinates to the metal ion as hexa-coordinated chelate displaying distorted octahedral geometry by binding through three sets of N, O- donor atoms, present in each pendant arm of the central tris-cyclohexaneamine tripod.

Keywords: complexes, DFT, formation constant, TACH2OX

Procedia PDF Downloads 137

Authors: George Zhou, Yunchan Chen, Candace Chien

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Kidney replacement therapy is the current standard of care for end-stage renal diseases. In-center or home hemodialysis remains an integral component of the therapeutic regimen. Arteriovenous fistulas (AVF) make up the vascular circuit through which blood is filtered and returned. Naturally, AVF patency determines whether adequate clearance and filtration can be achieved and directly influences clinical outcomes. Our aim was to build a deep learning model for automated AVF stenosis screening based on the sound of blood flow through the AVF. A total of 311 patients with AVF were enrolled in this study. Blood flow sounds were collected using a digital stethoscope. For each patient, blood flow sounds were collected at 6 different locations along the patient’s AVF. The 6 locations are artery, anastomosis, distal vein, middle vein, proximal vein, and venous arch. A total of 1866 sounds were collected. The blood flow sounds are labeled as “patent” (normal) or “stenotic” (abnormal). The labels are validated from concurrent ultrasound. Our dataset included 1527 “patent” and 339 “stenotic” sounds. We show that blood flow sounds vary significantly along the AVF. For example, the blood flow sound is loudest at the anastomosis site and softest at the cephalic arch. Contextualizing the sound with location metadata significantly improves classification performance. How to encode and incorporate categorical metadata is an active area of research1. Herein, we study ordinal (i.e., integer) encoding schemes. The numerical representation is concatenated to the flattened feature vector. We train a vision transformer (ViT) on spectrogram image representations of the sound and demonstrate that using scalar multiples of our integer encodings improves classification performance. Models are evaluated using a 10-fold cross-validation procedure. The baseline performance of our ViT without any location metadata achieves an AuROC and AuPRC of 0.68 ± 0.05 and 0.28 ± 0.09, respectively. Using the following encodings of Artery:0; Arch: 1; Proximal: 2; Middle: 3; Distal 4: Anastomosis: 5, the ViT achieves an AuROC and AuPRC of 0.69 ± 0.06 and 0.30 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 10; Proximal: 20; Middle: 30; Distal 40: Anastomosis: 50, the ViT achieves an AuROC and AuPRC of 0.74 ± 0.06 and 0.38 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 100; Proximal: 200; Middle: 300; Distal 400: Anastomosis: 500, the ViT achieves an AuROC and AuPRC of 0.78 ± 0.06 and 0.43 ± 0.11. respectively. Interestingly, we see that using increasing scalar multiples of our integer encoding scheme (i.e., encoding “venous arch” as 1,10,100) results in progressively improved performance. In theory, the integer values do not matter since we are optimizing the same loss function; the model can learn to increase or decrease the weights associated with location encodings and converge on the same solution. However, in the setting of limited data and computation resources, increasing the importance at initialization either leads to faster convergence or helps the model escape a local minimum.

Keywords: arteriovenous fistula, blood flow sounds, metadata encoding, deep learning

Procedia PDF Downloads 71

Authors: Tingting Huang, Sha Li, Geoffrey Griffiths, Martin Lukac, Jianning Zhu

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Territorial landscapes have experienced a gradual loss of their typical characteristics during long-term human activities. In order to protect the integrity of regional landscapes, it is necessary to characterize, evaluate and protect them in a graded manner. The study takes Fujian, China, as an example and classifies the landscape characters of the site at the regional scale, middle scale, and detailed scale. A multi-scale approach combining parametric and holistic approaches is used to classify and partition the landscape character types (LCTs) and landscape character areas (LCAs) at different scales, and a multi-element landscape assessment approach is adopted to explore the conservation strategies of the landscape character. Firstly, multiple fields and multiple elements of geography, nature and humanities were selected as the basis of assessment according to the scales. Secondly, the study takes a parametric approach to the classification and partitioning of landscape character, Principal Component Analysis, and two-stage cluster analysis (K-means and GMM) in MATLAB software to obtain LCTs, combines with Canny Operator Edge Detection Algorithm to obtain landscape character contours and corrects LCTs and LCAs by field survey and manual identification methods. Finally, the study adopts the Landscape Sensitivity Assessment method to perform landscape character conservation analysis and formulates five strategies for different LCAs: conservation, enhancement, restoration, creation, and combination. This multi-scale identification approach can efficiently integrate multiple types of landscape character elements, reduce the difficulty of broad-scale operations in the process of landscape character conservation, and provide a basis for landscape character conservation strategies. Based on the natural background and the restoration of regional characteristics, the results of landscape character assessment are scientific and objective and can provide a strong reference in regional and national scale territorial spatial planning.

Keywords: parameterization, multi-scale, landscape character identify, landscape character assessment

Procedia PDF Downloads 80

Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

Procedia PDF Downloads 171

Authors: Mamoon Masud, Suleman Mazhar

Abstract:

Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

Procedia PDF Downloads 131

Authors: Raja Umer Sajjad, Chang Hee Lee

Abstract:

Stormwater runoff is the leading contributor to pollution of receiving waters. In response, an efficient stormwater monitoring program is required to quantify and eventually reduce stormwater pollution. The overall goals of stormwater monitoring programs primarily include the identification of high-risk dischargers and the development of total maximum daily loads (TMDLs). The challenge in developing better monitoring program is to reduce the variability in flux estimates due to sampling errors; however, the success of monitoring program mainly depends on the accuracy of the estimates. Apart from sampling errors, manpower and budgetary constraints also influence the quality of the estimates. This study attempted to develop optimum stormwater monitoring design considering both cost and the quality of the estimated pollutants flux. Three years stormwater monitoring data (2012 – 2014) from a mix land use located within Geumhak watershed South Korea was evaluated. The regional climate is humid and precipitation is usually well distributed through the year. The investigation of a large number of water quality parameters is time-consuming and resource intensive. In order to identify a suite of easy-to-measure parameters to act as a surrogate, Principal Component Analysis (PCA) was applied. Means, standard deviations, coefficient of variation (CV) and other simple statistics were performed using multivariate statistical analysis software SPSS 22.0. The implication of sampling time on monitoring results, number of samples required during the storm event and impact of seasonal first flush were also identified. Based on the observations derived from the PCA biplot and the correlation matrix, total suspended solids (TSS) was identified as a potential surrogate for turbidity, total phosphorus and for heavy metals like lead, chromium, and copper whereas, Chemical Oxygen Demand (COD) was identified as surrogate for organic matter. The CV among different monitored water quality parameters were found higher (ranged from 3.8 to 15.5). It suggests that use of grab sampling design to estimate the mass emission rates in the study area can lead to errors due to large variability. TSS discharge load calculation error was found only 2 % with two different sample size approaches; i.e. 17 samples per storm event and equally distributed 6 samples per storm event. Both seasonal first flush and event first flush phenomena for most water quality parameters were observed in the study area. Samples taken at the initial stage of storm event generally overestimate the mass emissions; however, it was found that collecting a grab sample after initial hour of storm event more closely approximates the mean concentration of the event. It was concluded that site and regional climate specific interventions can be made to optimize the stormwater monitoring program in order to make it more effective and economical.

Keywords: first flush, pollutant load, stormwater monitoring, surrogate parameters

Procedia PDF Downloads 231

Authors: Muhammad Tariq, Muhammad Waseem, Muhammad Hidayat Rasool

Abstract:

Isolation and characterization of chromium tolerant Staphylococcus aureus from industrial wastewater and their potential use to bioremediate environmental chromium. Objectives: Chromium with its great economic importance in industrial use is major metal pollutant of the environment. Chromium are used in different industries for various applications such as textile, dyeing and pigmentation, wood preservation, manufacturing pulp and paper, chrome plating, steel and tanning. The release of untreated chromium in industrial effluents causes serious threat to environment and human health, therefore, the current study designed to isolate chromium tolerant Staphylococcus aureus for removal of chromium prior to their final discharge into the environment due to its cost effective and beneficial advantage over physical and chemical methods. Methods: Wastewater samples were collected from discharge point of different industries. Heavy metal analysis by atomic absorption spectrophotometer and microbiological analysis such as total viable count, total coliform, fecal coliform and Escherichia coli were conducted. Staphylococcus aureus was identified through gram’s staining, biomeriux vitek 2 microbial identification system and 16S rRNA gene amplification by polymerase chain reaction. Optimum growth conditions with respect to temperature, pH, salt concentrations and effect of chromium on the growth of bacteria, resistance to other heavy metal ions, minimum inhibitory concentration and chromium uptake ability of Staphylococcus aureus strain K1 was determined by spectrophotometer. Antibiotic sensitivity pattern was also determined by disc diffusion method. Furthermore, chromium uptake ability was confirmed by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope equipped with Oxford Energy Dipersive X-ray (EDX) micro analysis system. Results: The results presented that optimum temperature was 35ᵒC, pH was 8.0 and salt concentration was 0.5% for growth of Staphylococcus aureus K1. The maximum uptake ability of chromium by bacteria was 20mM than other heavy metal ions. The antibiotic sensitivity pattern revealed that Staphylococcus aureus was vancomycin and methicillin sensitive. Non hemolytic activity on blood agar and negative coagulase reaction showed that it was non-pathogenic. Furthermore, the growth of bacteria decreases in the presence of chromium and maximum chromium uptake by bacteria observed at optimum growth conditions. Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and Energy dispersive X-ray (EDX) analysis confirmed the presence of chromium uptake by Staphylococcus aureus K1. Conclusion: The study revealed that Staphylococcus aureus K1 have the potential to bio-remediate chromium toxicity from wastewater. Gradually, this biological treatment becomes more important due to its advantage over physical and chemical methods to protect environment and human health.

Keywords: wastewater, staphylococcus, chromium, bioremediation

Procedia PDF Downloads 156