Search results for: time prediction algorithms

Authors: B. Mack, K. Tampe-Mai, E. Diesch

Abstract:

Findings of an ongoing discrete choice study of future transport mode choice will be presented. Many urban centers face the triple challenge of having to cope with ever increasing traffic congestion, environmental pollution, and greenhouse gas emission brought about by private car use. In principle, private car use may be diminished by extending public transport systems like bus lines, trams, tubes, and trains. However, there are limits to increasing the (perceived) spatial and temporal flexibility and reducing peak-time crowding of classical public transport systems. An emerging new type of system, publicly or privately operated on-demand shuttle bus services, seem suitable to ameliorate the situation. A fleet of on-demand shuttle busses operates without fixed stops and schedules. It may be deployed efficiently in that each bus picks up passengers whose itineraries may be combined into an optimized route. Crowding may be minimized by limiting the number of seats and the inter-seat distance for each bus. The study is conducted as a discrete choice experiment. The choice between private car, public transport, and shuttle service is registered as a function of several push and pull factors (financial costs, travel time, walking distances, mobility tax/congestion charge, and waiting time/parking space search time). After the completion of the discrete choice items, the study participant is asked to rate the three modes of transport with regard to the pull factors of comfort, safety, privacy, and opportunity to engage in activities like reading or surfing the internet. These ratings are entered as additional predictors into the discrete choice experiment regression model. The study is conducted in the region of Stuttgart in southern Germany. N=1000 participants are being recruited. Participants are between 18 and 69 years of age, hold a driver’s license, and live in the city or the surrounding region of Stuttgart. In the discrete choice experiment, participants are asked to assume they lived within the Stuttgart region, but outside of the city, and were planning the journey from their apartment to their place of work, training, or education during the peak traffic time in the morning. Then, for each item of the discrete choice experiment, they are asked to choose between the transport modes of private car, public transport, and on-demand shuttle in the light of particular values of the push and pull factors studied. The study will provide valuable information on the potential of switching from private car use to the use of on-demand shuttles, but also on the less desirable potential of switching from public transport to on-demand shuttle services. Furthermore, information will be provided on the modulation of these switching potentials by pull and push factors.

Keywords: determinants of travel mode choice, on-demand shuttle services, private car use, public transport

Procedia PDF Downloads 163

Authors: Ali Naeemy, Mir Ghasem Hoseini

Abstract:

In this study, for the first time, an analytical method developed and validated by combining electrochemical impedance spectroscopy and electromembrane extraction (EIS-EME) by Vulcan/poly pyrrole nanocomposite modified screen-printed electrode (PPY–VU/SPE) for accurately quantifying zolpidem. EME parameters optimized, including solvent composition, voltage, pH adjustments and extraction time. Zolpidem was transferred from a donor solution (pH 5) to an acceptor solution (pH 13) using a hollow fiber in 1-octanol as a membrane, driven by a 60 V voltage for 25 minutes, ensuring precise and selective extraction. In comparison with SPE, VU/SPE and PPY/SPE, the PPY–VU/SPE was much more efficient for ZP oxidation. Calibration curves with good linearity were obtained in the concentration range of 2-75 µmol L-1 using the EIS-EME with the detection limit of 0.5 µmol L-1 . Finally, the EIS-EME by using the PPY– VU/SPE was successfully used to determine ZP in tablet dosage form, urine and plasma samples. Keywords: Electrochemical impedance spectroscopy, Electromembrane extraction, Zolpidem, Vulcan, poly pyrrole, Screen printed electrode

Keywords: electrochemical impedance spectroscopy, electromembrane extraction, screen printed electrode, zolpidem

Procedia PDF Downloads 20

Authors: Shafait Hussain Ali

Abstract:

Since its outbreak in China, the Covid_19 19 disease has been caused by the corona virus SARS N coyote 2. Italy was the first Western country to be severely affected, and the first country to take drastic measures to control the disease. In start of December 2019, the sudden outbreaks of the Coronary Virus Disease was caused by a new Corona 2 virus (SARS-CO2) of acute respiratory syndrome in china city Wuhan. The World Health Organization declared the epidemic a public health emergency of international concern on January 30, 2020,. On February 14, 2020, 49,053 laboratory-confirmed deaths and 1481 deaths have been reported worldwide. The threat of the disease has forced most of the governments to implement various control measures. Therefore it becomes necessary to analyze the Italian data very carefully, in particular to investigates and to find out the present condition and the number of infected persons in the form of positive cases, death, hospitalized or some other features of infected persons will clear in simple form. So used such a model that will clearly shows the real facts and figures and also understandable to every readable person which can get some real benefit after reading it. The model used must includes(total positive cases, current positive cases, hospitalized patients, death, recovered peoples frequency rates ) all features that explains and clear the wide range facts in very simple form and helpful to administration of that country.

Keywords: machine learning tools and techniques, rapid miner tool, Naive-Bayes algorithm, predictions

Procedia PDF Downloads 92

Authors: Sikander Nawaz Khan

Abstract:

Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information of natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provide precise and accurate locations and other related information like speed, track, direction and distance of target object to emergency responders. Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing vital role in disaster management. Early on Geographic Information System (GIS) was used to collect, arrange, and map the spatial information but now it has capability to analyze spatial data. This analytical ability of GIS is the main cause of its adaption by different emergency services providers like police and ambulance service. Full potential of these so called 3S technologies cannot be used in alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

Keywords: disaster mitigation, GIS, GPS, remote sensing

Procedia PDF Downloads 457

Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

Abstract:

Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

Procedia PDF Downloads 28

Authors: Zakir H. Shaik, Punam L. Vartak

Abstract:

Management By Objectives (MBO) is a management technique in which objectives of an organisation are conveyed to the employees to establish the individual goals. These objectives and goals are then monitored and assessed jointly by management and the employee time to time. This tool can be used for planning, monitoring as well as for performance appraisal. The success of an organisation is largely dependent on its’s Vision. Thus, it is of paramount importance to achieve the realm of vision through a mission which is well crafted within the organisation to address the objectives. The success of the mission depends upon how realistic and action oriented philosophical approach, an organisation caters to; and how the individual goals are set to track and meet the objectives. Thus, focused and passionate efforts of the team, assigned for the mission, are an absolute obligation for achieving the vision of any organisation. Any construction site is generally a controlled disorder having huge investments, resources and logistics involved. The Construction progression is time-consuming with many isolated as well as interconnected activities. Traditional MBO approach can be unsuccessful if planning and control is non-realistic and inflexible. Moreover, the Construction Industry is far behind understanding these concepts. It is important to address the employee engagement in defining and creating awareness to achieve the targets. Besides, current economic environment and competitive world demands refined management tools to achieve profit, growth and survival of the business. Therefore, the necessity of rational MBO becomes vital part towards the success of an organisation. This paper details about the philosophical assumptions to develop the grounded theory in lieu of achieving objectives through RATIONAL MBO approach in Construction Contracting Organisations. The goals and objectives of the Construction Contracting Organisations can be achieved efficiently by adopting this RATIONAL MBO approach, as those are based on realistic, logical and balanced assumptions.

Keywords: growth, leadership, management by objectives, Management By Objectives (MBO), profit, rational

Procedia PDF Downloads 138

Authors: Ke Lin, Steve Wu Qing Yang, Zhang Nan

Abstract:

The medicine and drugs has in the past been manufactured to the final products and then used laboratory analysis to verify their quality. However the industry needs crucially a monitoring technique for the final batch to batch quality check. The introduction of process analytical technology (PAT) provides an incentive to obtain real-time information about drugs on the production line, with the following optical techniques being considered: near-infrared (NIR) spectroscopy, Raman spectroscopy and imaging, mid-infrared spectroscopy with the use of chemometric techniques to quantify the final product. However, presents problems in that the spectra obtained will consist of many combination and overtone bands of the fundamental vibrations observed, making analysis difficult. In this work, we describe a non-destructive system and method for capsule and liquid medicine identification, more particularly, using terahertz time-domain spectroscopy and/or designed terahertz portable system for identifying different types of medicine in the package of capsule or in liquid medicine bottles. The target medicine can be detected directly, non-destructively and non-invasively.

Keywords: terahertz, non-destructive, non-invasive, chemical identification

Procedia PDF Downloads 116

Authors: Hyun-Woo Cho

Abstract:

The real-time fault monitoring and diagnosis of large scale production processes is helpful and necessary in order to operate industrial process safely and efficiently producing good final product quality. Unusual and abnormal events of the process may have a serious impact on the process such as malfunctions or breakdowns. This work try to utilize process measurement data obtained in an on-line basis for the safe and some fault-free operation of industrial processes. To this end, this work evaluated the proposed intelligent process data monitoring framework based on a simulation process. The monitoring scheme extracts the fault pattern in the reduced space for the reliable data representation. Moreover, this work shows the results of using linear and nonlinear techniques for the monitoring purpose. It has shown that the nonlinear technique produced more reliable monitoring results and outperforms linear methods. The adoption of the qualitative monitoring model helps to reduce the sensitivity of the fault pattern to noise.

Keywords: process data, data mining, process operation, real-time monitoring

Procedia PDF Downloads 622

Authors: Kelemewerk Destalem, Joongjae Cho, Jaeseong Lee, Ju H. Park, Joonhyuk Yoo

Abstract:

Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method.

Keywords: background subtraction, background updating, real time, light weight algorithm, temporal difference

Procedia PDF Downloads 324

Authors: Delele Worku Ayele, Bing-Joe Hwang

Abstract:

A method that does not employ hot injection techniques has been developed for the size-tunable synthesis of high-quality CdSe quantum dots (QDs) with a zinc blende structure. In this environmentally benign synthetic route, which uses relatively less toxic precursors, solvents, and capping ligands, CdSe QDs that absorb visible light are obtained. The size of the as-prepared CdSe QDs and, thus, their optical properties can be manipulated by changing the microwave reaction conditions. The QDs are characterized by XRD, TEM, UV-vis, FTIR, time-resolved fluorescence spectroscopy, and fluorescence spectrophotometry. In this approach, the reaction is conducted in open air and at a much lower temperature than in hot injection techniques. The use of microwaves in this process allows for a highly reproducible and effective synthesis protocol that is fully adaptable for mass production and can be easily employed to synthesize a variety of semiconductor QDs with the desired properties. The possible application of the as-prepared CdSe QDs has been also assessed using deposition on TiO2 films.

Keywords: average life time, CdSe QDs, microwave (MW), mass production oleic acid, Na2SeSO3

Procedia PDF Downloads 302

Authors: Debabrata Das

Abstract:

This paper explains how to design a regulation loop for a power supply circuit. It also discusses the need of a regulation loop and the improvement of a circuit with regulation loop. A sample design is used to demonstrate how to use PSpice to design feedback loop to control output voltage of a power supply and how to check if the power supply is stable or oscillatory. A sample design is made using a specific Integrated Circuit (IC) available in the PSpice library. A designer can experiment feedback loop design using Cadence Pspice tool. PSpice is easy to use, reliable, and convenient. To test a feedback loop, generally, engineers use trial and error method with the hardware which takes a lot of time and manpower. Moreover, it is expensive because component and Printed Circuit Board (PCB) may go bad. PSpice can be used by designers to test their loop designs without using hardware circuits. A designer can save time, cost, manpower and simulate his/her power supply circuit accurately before making a real hardware using this software package.

Keywords: power electronics, feedback loop, regulation, stability, pole, zero, oscillation

Procedia PDF Downloads 335

Authors: Ekin Nurbaş

Abstract:

One of the essential challenges in array signal processing, which has drawn enormous research interest over the past several decades, is estimating the direction of arrival (DOA) of plane waves impinging on an array of sensors. In recent years, the Compressive Sensing based DoA estimation methods have been proposed by researchers, and it has been discovered that the Compressive Sensing (CS)-based algorithms achieved significant performances for DoA estimation even in scenarios where there are multiple coherent sources. On the other hand, the Genetic Algorithm, which is a method that provides a solution strategy inspired by natural selection, has been used in sparse representation problems in recent years and provides significant improvements in performance. With all of those in consideration, in this paper, a method that combines the Genetic Algorithm (GA) and the Multi-Criteria Decision Making (MCDM) approaches for Direction of Arrival (DoA) estimation in the Compressive Sensing (CS) framework is proposed. In this method, we generate a multi-objective optimization problem by splitting the norm minimization and reconstruction loss minimization parts of the Compressive Sensing algorithm. With the help of the Genetic Algorithm, multiple non-dominated solutions are achieved for the defined multi-objective optimization problem. Among the pareto-frontier solutions, the final solution is obtained with the multiple MCDM methods. Moreover, the performance of the proposed method is compared with the CS-based methods in the literature.

Keywords: genetic algorithm, direction of arrival esitmation, multi criteria decision making, compressive sensing

Procedia PDF Downloads 135

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

Abstract:

Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

Procedia PDF Downloads 78

Authors: Pravina Gurjar, Bothiraja Pour, Vijay Kumbhar, Ganesh Dama

Abstract:

Andrographolide (AG), a bioactive phytoconstituent, has a wider range of pharmacological action. However, due to the intestinal degradation, shows low oral bioavailability. The aim of the present work was to develop Floating In-situ gelling Gastro retentive System (FISGS) for AG in order to enhance its site specific absorption and minimize pH dependent hydrolysis in alkaline environment. Further to increase its therapeutic efficacy for peptic ulcer disease caused by H. pyroli. Gellan based floating in situ gelling system of AG were prepared by using sodium citrate and calcium carbonate. The 32 factorial designs was used to study the effect of gellan and calcium carbonate concentration (independent variables) on dependent variable such as viscosity, floating lag time and drug release. Developed system was evaluated for drug content, floating lag time, viscosity, and drug release studies. Drug content, viscosity, and floating lag time was found to be 81-99%, 67-117 Cps, and 3-5 sec, respectively. The obtained system showed good in vitro floating ability for more than 12 h using 0.1 N HCl as dissolution medium with initial burst release followed by the controlled zero order drug release up to 24 hrs. In vivo testing of FISGS of AG to rats demonstrated significant antiulcer activity that were evaluated by various parameters like pH, volume, total acidity, millimole equivalent of H+ ions/30 min, and protein content of gastric content. The densities of all the formulation batches were found to be near about 0.9 and floating duration above 12 hr. It was observed that with the increase in conc. of gellan there was increase in the viscosity of formulation but all formulations were in optimum range. The drug content of optimized batch was found to be 99.23. In histopathology study of stomach, the villi at the mucosal surface, the intercellular junction, the intestinal lumen were intact; no destruction of the epithelium, and submucosal gland in formulation treated and control group animals as compared to pure drug AG and standard ranitidine. Gellan-based in situ gastro retentive floating system could be advantageous in terms of increased bioavailability of AG to maintain an effective drug conc. in gastric fluid as well as in serum for longer period of time.

Keywords: andrographolide, floating drug delivery, in situ gelling system, gastroretentive system

Procedia PDF Downloads 348

Authors: Sang Hun Park, Moung Young Lee, Su Min Yu, Hyun Sang Jo, Ji Hyeon Kim, Chul Gyu Song

Abstract:

A near-infrared light source used as a light source in the fluorescence imaging system is suitable for use in real-time during the operation since it has no interference in surgical vision. However, fluorescence images do not have depth information. In this paper, we configured the device with the research on molecular imaging systems for monitoring thrombus imaging using fluorescence and photoacoustic. Fluorescence imaging was performed using a phantom experiment in order to search the exact location, and the Photoacoustic image was in order to detect the depth. Fluorescence image obtained when evaluated through current phantom experiments when the concentration of the contrast agent is 25μg / ml, it was confirmed that it looked sharper. The phantom experiment is has shown the possibility with the fluorescence image and photoacoustic image using an indocyanine green contrast agent. For early diagnosis of cardiovascular diseases, more active research with the fusion of different molecular imaging devices is required.

Keywords: fluorescence, photoacoustic, indocyanine green, carotid artery

Procedia PDF Downloads 582

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

Procedia PDF Downloads 396

Authors: Ahmad Idris, Bishir Kado, Murtala Umar, Armaya`u Suleiman Labo

Abstract:

Superpave is the short form of Superior Performing Asphalt Pavement and represents a basis for specifying component materials, asphalt mixture design and analysis, and pavement performance prediction. This new technology is the result of long research projects conducted by the strategic Highway Research program (SHRP) of the Federal Highway Administration. This research was aimed at examining the suitability of Aggregates found in Kano for used in Superpave design method. Aggregates samples were collected from different sources in Kano Nigeria and their Engineering properties, as they relate to the SUPERPAVE design requirements were determined. The average result of Coarse Aggregate Angularity in Kano was found to be 87% and 86% of one fractured face and two or more fractured faces respectively with a standard of 80% and 85% respectively. Fine Aggregate Angularity average result was found to be 47% with a requirement of 45% minimum. A flat and elongated particle which was found to be 10% has a maximum criterion of 10%. Sand equivalent was found to be 51% with the criteria of 45% minimum. Strength tests were also carried out, and the results reflect the requirements of the standards. The tests include Impact value test, Aggregate crushing value, and Aggregate Abrasion tests and the results are 27.5%, 26.7%, and 13%, respectively, with the maximum criteria of 30%. Specific gravity was also carried out and the result was found to have an average value of 2.52 with a criterion of 2.6 to 2.9 and Water absorption was found to be 1.41% with maximum criteria of 0.6%. From the study, the result of the tests indicated that the aggregates properties has met the requirements of Superpave design method based on the specifications of ASTMD 5821, ASTM D 4791, AASHTO T176, AASHTO T33 and BS815.

Keywords: Superpave, aggregates, asphalt mix, Kano

Procedia PDF Downloads 381

Authors: Somayyeh Naeemi, Ezhar Tamam

Abstract:

In most of the countries, Facebook allocated high rank of usage among other social network sites. Several studies have examined the effect of Facebook intensity on individuals’ psychological well-being. However, few studies have investigated its effect on eudaimonic well-being. The current study explored how emotional dependency to Facebook relates to psychological well-being in terms of eudaimonic well-being. The number of 402 adolescents 13-16 years old who studied in upper secondary school in Malaysia participated in this study. It was expected to find out a negative association between emotional dependency to Facebook and time spent on Facebook and psychological well-being. It also was examined the moderation effects of self-efficacy on psychological well-being. The results by Structural Equation Modeling revealed that emotional dependency to Facebook has a negative effect on adolescents’ psychological well-being. Surprisingly self-efficacy did not have moderation effect on the relationship between emotional dependency to Facebook and psychological well-being. Lastly, the emotional dependency to Facebook and not the time spent on Facebook lessen adolescents’ psychological well-being, suggesting the value of investigating Facebook usage among college students in future studies.

Keywords: emotional dependency to facebook, psychological well-being, eudaimonic well-being, self-efficacy, adolescent

Procedia PDF Downloads 500

Authors: Adesuyi Ayodeji Steve, Zahn Munch

Abstract:

This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

Procedia PDF Downloads 384

Authors: DucTho Le, Duy Kien Dao, Quoc Tinh Bui, Haidang Phan

Abstract:

The article is concerned with the motion of ultrasonic guided waves in a unidirectional fiber-reinforced composite plate under acoustic sources. Such unidirectional composite material has orthotropic elastic properties as it is very stiff along the fibers and rather compliant across the fibers. The dispersion equations of free Lamb waves propagating in an orthotropic layer are derived that results in the dispersion curves. The connection of these equations to the Rayleigh-Lamb frequency relations of isotropic plates is discussed. By the use of reciprocity in elastodynamics, closed-form solutions of elastic wave motions subjected to time-harmonic loads in the layer are computed in a simple manner. We also consider the problem of Lamb waves generated by a set of time-harmonic sources. The obtained computations can be very useful for developing ultrasound-based methods for nondestructive evaluation of composite structures.

Keywords: lamb waves, fiber-reinforced composite plates, dispersion equations, nondestructive evaluation, reciprocity theorems

Procedia PDF Downloads 137

Authors: Soumya Sajjan

Abstract:

Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

Procedia PDF Downloads 393

Authors: Kamyar Maleki Bagherabadi, Torstein Aarseth Bø, Truls Flatberg, Olve Mo

Abstract:

Hydrogen fuel cells and batteries are one of the promising solutions aligned with carbon emission reduction goals for the marine sector. However, the higher installation and operation costs of hydrogen-based systems compared to conventional diesel gensets raise questions about the appropriate hydrogen tank size, energy, and fuel consumption estimations. Ship designers need methodologies and tools to calculate energy and fuel consumption for different component sizes to facilitate decision-making regarding feasibility and performance for retrofits and design cases. The aim of this work is to compare three alternative modeling approaches for the estimation of energy and fuel consumption with various hydrogen tank sizes, battery capacities, and load-sharing strategies. A fishery vessel is selected as an example, using logged load demand data over a year of operations. The modeled power system consists of a PEM fuel cell, a diesel genset, and a battery. The methodologies used are: first, an energy-based model; second, considering load variations during the time domain with a rule-based Power Management System (PMS); and third, a load variations model and dynamic PMS strategy based on optimization with perfect foresight. The errors and potentials of the methods are discussed, and design sensitivity studies for this case are conducted. The results show that the energy-based method can estimate fuel and energy consumption with acceptable accuracy. However, models that consider time variation of the load provide more realistic estimations of energy and fuel consumption regarding hydrogen tank and battery size, still within low computational time.

Keywords: fuel cell, battery, hydrogen, hybrid power system, power management system

Procedia PDF Downloads 13

Authors: Kwok W. Mui, Ling T. Wong, Chi T. Cheung

Abstract:

Cooling energy consumption in the residential sector, different from shopping mall, office or commercial buildings, is significantly subject to occupant decisions where in-depth investigations are found limited. It shows that energy consumptions could be associated with housing types. Surveys have been conducted in existing Hong Kong public housings to understand the housing characteristics, apartment electricity demands, occupant’s thermal expectations, and air–conditioning usage patterns for further cooling energy-saving assessments. The aim of this study is to develop a hybrid cooling energy prediction model, which integrated by EnergyPlus (EP) and artificial neural network (ANN) to estimate cooling energy consumption in public residential sector. Sensitivity tests are conducted to find out the energy impacts with changing building parameters regarding to external wall and window material selection, window size reduction, shading extension, building orientation and apartment size control respectively. Assessments are performed to investigate the relationships between cooling demands and occupant behavior on thermal environment criteria and air-conditioning operation patterns. The results are summarized into a cooling energy calculator for layman use to enhance the cooling energy saving awareness in their own living environment. The findings can be used as a directory framework for future cooling energy evaluation in residential buildings, especially focus on the occupant behavioral air–conditioning operation and criteria of energy-saving incentives.

Keywords: artificial neural network, cooling energy, occupant behavior, residential buildings, thermal environment

Procedia PDF Downloads 154

Authors: Mesut BALIBEY, Serpil TÜRKYILMAZ

Abstract:

A popular topic in the econometrics and time series area is the cointegrating relationships among the components of a nonstationary time series. Engle and Granger’s least squares method and Johansen’s conditional maximum likelihood method are the most widely-used methods to determine the relationships among variables. Furthermore, a method proposed to test a unit root based on the periodogram ordinates has certain advantages over conventional tests. Periodograms can be calculated without any model specification and the exact distribution under the assumption of a unit root is obtained. For higher order processes the distribution remains the same asymptotically. In this study, in order to indicate advantages over conventional test of periodograms, we are going to examine a possible relationship between tourism and economic growth during the period 1999:01-2010:12 for Turkey by using periodogram method, Johansen’s conditional maximum likelihood method, Engle and Granger’s ordinary least square method.

Keywords: cointegration, economic growth, periodogram ordinate, tourism

Procedia PDF Downloads 253

Authors: Miroslav Popelka, Jakub Nozicka

Abstract:

The whole work is based on possibility to use Lego Mindstorms robotics systems to reduce costs. Lego Mindstorms consists of a wide variety of hardware components necessary to simulate, programme and test of robotics systems in practice. To programme algorithm, which simulates space using the ultrasonic sensor, was used development environment supplied with kit. Software Matlab was used to render values afterwards they were measured by ultrasonic sensor. The algorithm created for this paper uses theoretical knowledge from area of signal processing. Data being processed by algorithm are collected by ultrasonic sensor that scans 2D space in front of it. Ultrasonic sensor is placed on moving arm of robot which provides horizontal moving of sensor. Vertical movement of sensor is provided by wheel drive. The robot follows map in order to get correct positioning of measured data. Based on discovered facts it is possible to consider Lego Mindstorm for low-cost and capable kit for real-time modelling.

Keywords: LEGO Mindstorms, ultrasonic sensor, real-time modeling, 2D object, low-cost robotics systems, sensors, Matlab, EV3 Home Edition Software

Procedia PDF Downloads 455

Authors: Boutemak Khalida, Dahmani Siham

Abstract:

Garden cress (Lepidium Sativum L.) belonging to the family Brassicaceae, is edible growing annual herb. Its various parts (roots, leaves and seeds) have been used to treat various human ailments. Its seed extracts have been screened for various biological activities like hypotensive, antimicrobial, bronchodilator, hypoglycaemic and antianemic. The aim of the present study is to optimize the process parameters (cellulase concentration and incubation time) of enzymatic pre-treatment of the garden cress seeds and to evaluate the effect of cellulase pre-treatment of the crushed seeds on the oil yield, physico-chemical properties and antibacterial activity and comparing to non-enzymatic method. The optimum parameters of cellulase pre-treatment were as follows: cellulase of 0,1% w/w and incubation time of 2h. After enzymatic pre-treatment, the oil was extracted by n-hexane for 1.5 h, the oil yield was 4,01% for cellulase pre-treatment as against 10,99% in the control sample. The decrease in yield might be caused a result of mucilage. Garden cress seeds are covered with a layer of mucilage which gels on contact with water. At the same time, the antibacterial activity was carried out using agar diffusion method against 4 food-borne pathogens (Escherichia coli, Salmonella typhi,Staphylococcus aureus, Bacillus subtilis). The results showed that bacterial strains are very sensitive to the oil with cellulase pre-treatment. Staphylococcus aureus is extremely sensitive with the largest zone of inhibition (40 mm), Escherichia coli and salmonella typhi had a very sensitive to the oil with a zone of inhibition (26 mm). Bacillus subtilizes is averagely sensitive which gave an inhibition of 16 mm. But it does not exhibit sensivity to the oil without enzymatic pre-treatment with a zone inhibition (< 8 mm). Enzymatic pre-treatment could be useful for antimicrobial activity of the oil, and hold a good potential for use in food and pharmaceutical industries.

Keywords: Lepidium sativum L., cellulase, enzymatic pretreatment, antibacterial activity.

Procedia PDF Downloads 440

Authors: Zulkifli Zainal Abidin, Ahmad Shahril Mohd Ghani

Abstract:

Agent-based systems technology has been addressed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated systems that act autonomously across open and distributed environments in solving problems. Nevertheless, it is impractical to rely on a single agent to do all computing processes in solving complex problems. An increasing number of applications lately require multiple agents to work together. A multi-agent system (MAS) is a loosely coupled network of agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver. However, the network of MAS still requires a main system to govern or oversees the operation of the agents in order to achieve a unified goal. We had developed a fleet management system (FMS) in order to manage the fleet of agents, plan route for the agents, perform real-time data processing and analysis, and issue sets of general and specific instructions to the agents. This FMS should be able to perform real-time data processing, communicate with the autonomous surface vehicle (ASV) agents and generate bathymetric map according to the data received from each ASV unit. The first algorithm is developed to communicate with the ASV via radio communication using standard National Marine Electronics Association (NMEA) protocol sentences. Next, the second algorithm will take care of the path planning, formation and pattern generation is tested using various sample data. Lastly, the bathymetry map generation algorithm will make use of data collected by the agents to create bathymetry map in real-time. The outcome of this research is expected can be applied on various other multi-agent systems.

Keywords: autonomous surface vehicle, fleet management system, multi agent system, bathymetry

Procedia PDF Downloads 258

Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

Abstract:

The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.

Keywords: neural network computing, continuous functions generating the input-output mapping, decreasing the training time, machines with big memories

Procedia PDF Downloads 267

Authors: N. Azouaou, H. Mokaddem, D. Senadjki, K. Kedjit, Z. Sadaoui

Abstract:

Introduction: Water contamination caused by dye industries, including food, leather, textile, plastic, cosmetics, paper-making, printing and dye synthesis, has caused more and more attention, since most dyes are harmful to human being and environments. Untreated coffee grounds were used as a high-efficiency adsorbent for the removal of a cationic dye (methylene blue, MB) from aqueous solution. Characterization of the adsorbent was performed using several techniques such as SEM, surface area (BET), FTIR and pH zero charge. The effects of contact time, adsorbent dose, initial solution pH and initial concentration were systematically investigated. Results showed the adsorption kinetics followed the pseudo-second-order kinetic model. Langmuir isotherm model is in good agreement with the experimental data as compared to Freundlich and D–R models. The maximum adsorption capacity was found equal to 52.63mg/g. In addition, the possible adsorption mechanism was also proposed based on the experimental results. Experimental: The adsorption experiments were carried out in batch at room temperature. A given mass of adsorbent was added to methylene blue (MB) solution and the entirety was agitated during a certain time. The samples were carried out at quite time intervals. The concentrations of MB left in supernatant solutions after different time intervals were determined using a UV–vis spectrophotometer. The amount of MB adsorbed per unit mass of coffee grounds (qt) and the dye removal efficiency (R %) were evaluated. Results and Discussion: Some chemical and physical characteristics of coffee grounds are presented and the morphological analysis of the adsorbent was also studied. Conclusions: The good capacity of untreated coffee grounds to remove MB from aqueous solution was demonstrated in this study, highlighting its potential for effluent treatment processes. The kinetic experiments show that the adsorption is rapid and maximum adsorption capacities qmax= 52.63mg/g achieved in 30min. The adsorption process is a function of the adsorbent concentration, pH and metal ion concentration. The optimal parameters found are adsorbent dose m=5g, pH=5 and ambient temperature. FTIR spectra showed that the principal functional sites taking part in the sorption process included carboxyl and hydroxyl groups.

Keywords: adsorption, methylene blue, coffee grounds, kinetic study

Procedia PDF Downloads 214

Authors: Hichem Sassi

Abstract:

Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

Procedia PDF Downloads 41