Search results for: single inductor multi output (SIMO)

Authors: E. Feizi Ashtiani, S. Jamshidi, M.H Niksokhan, A. Feizi Ashtiani

Abstract:

Waste load allocation (WLA) policies may use multi-objective optimization methods to find the most appropriate and sustainable solutions. These usually intend to simultaneously minimize two criteria, total abatement costs (TC) and environmental violations (EV). If other criteria, such as inequity, need for minimization as well, it requires introducing more binary optimizations through different scenarios. In order to reduce the calculation steps, this study presents value index as an innovative decision making approach. Since the value index contains both the environmental violation and treatment costs, it can be maximized simultaneously with the equity index. It implies that the definition of different scenarios for environmental violations is no longer required. Furthermore, the solution is not necessarily the point with minimized total costs or environmental violations. This idea is testified for Haraz River, in north of Iran. Here, the dissolved oxygen (DO) level of river is simulated by Streeter-Phelps equation in MATLAB software. The WLA is determined for fish farms using multi-objective particle swarm optimization (MOPSO) in two scenarios. At first, the trade-off curves of TC-EV and TC-Inequity are plotted separately as the conventional approach. In the second, the Value-Equity curve is derived. The comparative results show that the solutions are in a similar range of inequity with lower total costs. This is due to the freedom of environmental violation attained in value index. As a result, the conventional approach can well be replaced by the value index particularly for problems optimizing these objectives. This reduces the process to achieve the best solutions and may find better classification for scenario definition. It is also concluded that decision makers are better to focus on value index and weighting its contents to find the most sustainable alternatives based on their requirements.

Keywords: waste load allocation (WLA), value index, multi objective particle swarm optimization (MOPSO), Haraz River, equity

Procedia PDF Downloads 425

Authors: Minjin Kim, Jihwan Kim, Bongsoo Kim, Junho Suh

Abstract:

We demonstrate nanomechanical resonators constructed with synthetic nanowires (NWs) and study their electro-mechanical properties at millikelvin temperatures. Nanomechanical resonators are fabricated using single-crystalline Au NWs and InAs NWs. The mechanical resonance signals are acquired by either magnetomotive or capacitive detection methods. The Au NWs are synthesized by chemical vapor transport method at 1100 °C, and they exhibit clean surface and single-crystallinity with little defects. Due to pristine surface quality, these Au NW mechanical resonators could provide an ideal model system for studying surface-related effects on the mechanical systems. The InAs NWs are synthesized by molecular beam epitaxy or metal organic chemical vapor deposition method. The InAs NWs show electronic conductance modulation resembling Coulomb blockade, which also manifests in the mechanical resonance signals in the form of damping and resonance frequency shift. Our result provides an evidence of strong electro-mechanical coupling in synthetic NW nanomechanical resonators.

Keywords: Au nanowire, InAs nanowire, nanomechanical resonator, synthetic nanowires

Procedia PDF Downloads 210

Authors: Saumya Srivastava, Rina Maiti

Abstract:

In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

Procedia PDF Downloads 124

Authors: Rosana Goncalves Das Dores, Maira Fonseca, Fernando Finger, Vicente Casali

Abstract:

Equisetum hyemale L. (horsetail, Equisetaceae) is a medicinal plant used and commercialized in simple paper bags or non-ecological packaging in Brazil. The aim of this work was to evaluate the relation between the bioactive compounds of horsetail stems stored in ecological packages (multi-ply paper sacks) at room temperature. Stems in primary and secondary stage were harvested from an organic estate, on December 2016, selected, measured (length from the soil to the apex (cm), stem diameter at ground level (DGL mm) and breast height (DBH mm) and cut into 10 cm. For the post-harvest evaluations, stems were stored in multi-ply paper sacks and evaluated daily to the respiratory rate, fresh weight loss, pH, presence of fungi / mold, phenolic compounds and antioxidant activity. The analyses were done with four replicates, over time (regression) and compared at 1% significance (Tukey test). The measured heights were 103.7 cm and 143.5 cm, DGL was 2.5mm and 8.4 mm and DBH of 2.59 and 6.15 mm, respectively for primary and secondary stems stage. At both stages of development, in storage in multi-ply paper sacks, the greatest mass loss occurred at 48 h, decaying up to 120 hours, stabilizing at 192 hours. The peak respiratory rate increase occurred in 24 hours, coinciding with a change in pH (temperature and mean humidity was 23.5°C and 55%). No fungi or mold were detected, however, there was loss of color of the stems. The average yields of ethanolic extracts were equivalent (approximately 30%). Phenolic compounds and antioxidant activity were higher in secondary stems stage in up to 120 hours (AATt0 = 20%, AATt30 = 45%), decreasing at the end of the experiment (240 hours). The packaging used allows the commercialization of fresh stems of Equisetum for up to five days.

Keywords: paper sacks, phenolic content, antioxidant activity, medicinal plants, post-harvest, ecological packages, Equisetum

Procedia PDF Downloads 167

Authors: Maciej Jedrzejczyk, Karolina Marzantowicz

Abstract:

Traditional utility value chain is being transformed during last few years into unbundled markets. Increased distributed generation of energy is one of considerable challenges faced by Smart Grid networks. New sources of energy introduce volatile demand response which has a considerable impact on traditional middlemen in E&U market. The purpose of this research is to search for ways to allow near-real-time electricity markets to transact with surplus energy based on accurate time synchronous measurements. A proposed framework evaluates the use of secure peer-2-peer (P2P) communication and distributed transaction ledgers to provide flat hierarchy, and allow real-time insights into present and forecasted grid operations, as well as state and health of the network. An objective is to achieve dynamic grid operations with more efficient resource usage, higher security of supply and longer grid infrastructure life cycle. Methods used for this study are based on comparative analysis of different distributed ledger technologies in terms of scalability, transaction performance, pluggability with external data sources, data transparency, privacy, end-to-end security and adaptability to various market topologies. An intended output of this research is a design of a framework for safer, more efficient and scalable Smart Grid network which is bridging a gap between traditional components of the energy network and individual energy producers. Results of this study are ready for detailed measurement testing, a likely follow-up in separate studies. New platforms for Smart Grid achieving measurable efficiencies will allow for development of new types of Grid KPI, multi-smart grid branches, markets, and businesses.

Keywords: autonomous agents, Distributed computing, distributed ledger technologies, large scale systems, micro grids, peer-to-peer networks, Self-organization, self-stabilization, smart grids

Procedia PDF Downloads 303

Authors: Shorena Pharjiani

Abstract:

The Present empirical paper investigates the relationship between FDI and economic growth by 10 selected industries in 10 Central and Eastern European countries from the period 1995 to 2012. Different estimation approaches were used to explore the connection between FDI and economic growth, for example OLS, RE, FE with and without time dummies. Obtained empirical results leads to some main consequences: First, the Central and East European countries (CEEC) attracted foreign direct investment, which raised the productivity of industries they entered in. It should be concluded that the linkage between FDI and output growth by industries is positive and significant enough to suggest that foreign firm’s participation enhanced the productivity of the industries they occupied. There had been an endogeneity problem in the regression and fixed effects estimation approach was used which partially corrected the regression analysis in order to make the results less biased. Second, it should be stressed that the results show that time has an important role in making FDI operational for enhancing output growth by industries via total factor productivity. Third, R&D positively affected economic growth and at the same time, it should take some time for research and development to influence economic growth. Fourth, the general trends masked crucial differences at the country level: over the last 20 years, the analysis of the tables and figures at the country level show that the main recipients of FDI of the 11 Central and Eastern European countries were Hungary, Poland and the Czech Republic. The main reason was that these countries had more open door policies for attracting the FDI. Fifth, according to the graphical analysis, while Hungary had the highest FDI inflow in this region, it was not reflected in the GDP growth as much as in other Central and Eastern European countries.

Keywords: central and East European countries (CEEC), economic growth, FDI, panel data

Procedia PDF Downloads 237

Authors: Keunhong Chae, Seokho Yoon

Abstract:

Recently, the global navigation satellite system (GNSS) such as Galileo and GPS is employing more satellites to provide a higher degree of accuracy for the location service, thus calling for a more efficient signaling scheme among the satellites used in the overall GNSS network. In that the network throughput is improved, the spatial diversity can be one of the efficient signaling schemes; however, it requires multiple antenna that could cause a significant increase in the complexity of the GNSS. Thus, a diversity scheme called the cooperative signaling was proposed, where the virtual multiple-input multiple-output (MIMO) signaling is realized with using only a single antenna in the transmit satellite of interest and with modeling the neighboring satellites as relay nodes. The main drawback of the cooperative signaling is that the relay nodes receive the transmitted signal at different time instants, i.e., they operate in an asynchronous way, and thus, the overall performance of the GNSS network could degrade severely. To tackle the problem, several modified cooperative signaling schemes were proposed; however, all of them are difficult to implement due to a signal decoding at the relay nodes. Although the implementation at the relay nodes could be simpler to some degree by employing the time-reversal and conjugation operations instead of the signal decoding, it would be more efficient if we could implement the operations of the relay nodes at the source node having more resources than the relay nodes. So, in this paper, we propose a novel cooperative signaling scheme, where the data signals are combined in a unique way at the source node, thus obviating the need of the complex operations such as signal decoding, time-reversal and conjugation at the relay nodes. The numerical results confirm that the proposed scheme provides the same performance in the cooperative diversity and the bit error rate (BER) as the conventional scheme, while reducing the complexity at the relay nodes significantly. Acknowledgment: This work was supported by the National GNSS Research Center program of Defense Acquisition Program Administration and Agency for Defense Development.

Keywords: global navigation satellite network, cooperative signaling, data combining, nodes

Procedia PDF Downloads 282

Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

Abstract:

Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

Procedia PDF Downloads 13

Authors: Pooja S. Billade, Sanjay S. Chopade

Abstract:

This paper focuses on the design of three phase induction motor control using single phase input and GSM.The controller used in this work is a wireless speed control using a GSM technique that proves to be very efficient and reliable in applications.The most common principle is the constant V/Hz principle which requires that the magnitude and frequency of the voltage applied to the stator of a motor maintain a constant ratio. By doing this, the magnitude of the magnetic field in the stator is kept at an approximately constant level throughout the operating range. Thus, maximum constant torque producing capability is maintained. The energy that a switching power converter delivers to a motor is controlled by Pulse Width Modulated signals applied to the gates of the power transistors in H-bridge configuration. PWM signals are pulse trains with fixed frequency and magnitude and variable pulse width. When a PWM signal is applied to the gate of a power transistor, it causes the turn on and turns off intervals of the transistor to change from one PWM period.

Keywords: index terms— PIC, GSM (global system for mobile), LCD (Liquid Crystal Display), IM (Induction Motor)

Procedia PDF Downloads 449

Authors: Shalini TV, Gangadharan GG, Sriranjini S Jaideep, ASN Seshasayee, Awadhesh Pandit

Abstract:

Introduction: Prakriti (body-mind constitution of an individual) is a conventional, customized and unique understanding of which is essential for the personalized medicine described in Ayurveda, Indian System of Medicine. Based on the Doshas( functional, bio humoral unit in the body), individuals are categorized into three major Prakriti- Vata, Pitta, and Kapha. The human gut microbiome hosts plenty of highly diverse and metabolically active microorganisms, mainly dominated by the bacteria, which are known to influence the physiology of an individual. Few researches have shown the correlation between the Prakriti and the biochemical parameters. In this study, an attempt was made to explore any correlation between the Prakriti (phenotype of an individual) with the Genetic makeup of the gut microbiome in healthy individuals. Materials and methods: 270 multi-ethnic, healthy volunteers of both sex with the age group between 18 to 40 years, with no history of antibiotics in the last 6 months were recruited into three groups of Vata, Pitta, and Kapha. The Prakriti of the individual was determined using Ayusoft, a software designed by CDAC, Pune, India. The volunteers were subjected to initial screening for the assessment of their height, weight, Body Mass Index, Vital signs and Blood investigations to ensure they are healthy. The stool and saliva samples of the recruited volunteers were collected as per the standard operating procedure developed, and the bacterial DNA was isolated using Qiagen kits. The extracted DNA was subjected to 16s rRNA sequencing using the Illumina kits. The sequencing libraries are targeting the variable V3 and V4 regions of the 16s rRNA gene. Paired sequencing was done on the MiSeq system and data were analyzed using the CLC Genomics workbench 11. Results: The 16s rRNA sequencing of the V3 and V4 regions showed a diverse pattern in both the oral and stool microbial DNA. The study did not reveal any specific pattern of bacterial flora amongst the Prakriti. All the p-values were more than the effective alpha values for all OTUs in both the buccal cavity and stool samples. Therefore, there was no observed significant enrichment of an OTU in the patient samples from either the buccal cavity or stool samples. Conclusion: In healthy volunteers of multi-ethnicity, due to the influence of the various factors, the correlation between the Prakriti and the gut microbiome was not seen.

Keywords: gut microbiome, ayurveda Prakriti, sequencing, multi-ethnic urban population

Procedia PDF Downloads 137

Authors: Gunasekaran Raja, Ramkumar Jayaraman, Rajakumar Arul, Kottilingam Kottursamy

Abstract:

Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.

Keywords: encoding and decoding, buffer, network coding, degree distribution, broadband wireless networks, multicast

Procedia PDF Downloads 412

Authors: Paul R Armstrong

Abstract:

Doubled haploids (DHs) have become an important breeding tool for creating maize inbred lines, although several bottlenecks in the DH production process limit wider development, application, and adoption of the technique. DH kernels are typically sorted manually and represent about 10% of the seeds in a much larger pool where the remaining 90% are hybrid siblings. This introduces time constraints on DH production and manual sorting is often not accurate. Automated sorting based on the chemical composition of the kernel can be effective, but devices, namely NMR, have not achieved the sorting speed to be a cost-effective replacement to manual sorting. This study evaluated a single kernel near-infrared reflectance spectroscopy (skNIR) platform to accurately identify DH kernels based on oil content. The skNIR platform is a higher-throughput device, approximately 3 seeds/s, that uses spectra to predict oil content of each kernel from maize crosses intentionally developed to create larger than normal oil differences, 1.5%-2%, between DH and hybrid kernels. Spectra from the skNIR were used to construct a partial least squares regression (PLS) model for oil and for a categorical reference model of 1 (DH kernel) or 2 (hybrid kernel) and then used to sort several crosses to evaluate performance. Two approaches were used for sorting. The first used a general PLS model developed from all crosses to predict oil content and then used for sorting each induction cross, the second was the development of a specific model from a single induction cross where approximately fifty DH and one hundred hybrid kernels used. This second approach used a categorical reference value of 1 and 2, instead of oil content, for the PLS model and kernels selected for the calibration set were manually referenced based on traditional commercial methods using coloration of the tip cap and germ areas. The generalized PLS oil model statistics were R2 = 0.94 and RMSE = .93% for kernels spanning an oil content of 2.7% to 19.3%. Sorting by this model resulted in extracting 55% to 85% of haploid kernels from the four induction crosses. Using the second method of generating a model for each cross yielded model statistics ranging from R2s = 0.96 to 0.98 and RMSEs from 0.08 to 0.10. Sorting in this case resulted in 100% correct classification but required models that were cross. In summary, the first generalized model oil method could be used to sort a significant number of kernels from a kernel pool but was not close to the accuracy of developing a sorting model from a single cross. The penalty for the second method is that a PLS model would need to be developed for each individual cross. In conclusion both methods could find useful application in the sorting of DH from hybrid kernels.

Keywords: NIR, haploids, maize, sorting

Procedia PDF Downloads 303

Authors: Keyao Li, Sai On Cheung

Abstract:

Incorporation of alternative dispute resolution (ADR) mechanism has been the main feature of current trend of construction project dispute resolution (CPDR). ADR approaches have been identified as efficient mechanisms and are suitable alternatives to litigation and arbitration. Moreover, the use of ADR in this multi-tiered dispute resolution process often leads to repeated evaluations of a same dispute. Multi-tiered CPDR may become a breeding ground for cognitive biases. When completed knowledge is not available at the early tier of construction dispute resolution, disputing parties may form preconception of the dispute matter or the counterpart. This preconception would influence their information processing in the subsequent tier. Disputing parties tend to search and interpret further information in a self-defensive way to confirm their early positions. Their imbalanced information collection would boost their confidence in the held assessments. Their attitudes would be hardened and difficult to compromise. The occurrence of cognitive bias, therefore, impedes efficient dispute settlement. This study aims to explore ways to minimize bias in CPDR. Based on a comprehensive literature review, three types of bias minimizing approaches were collected: strategy-based, attitude-based and process-based. These approaches were further operationalized into bias minimizing measures. To verify the usefulness and practicability of these bias minimizing measures, semi-structured interviews were conducted with ten CPDR third party neutral professionals. All of the interviewees have at least twenty years of experience in facilitating settlement of construction dispute. The usefulness, as well as the implications of the bias minimizing measures, were validated and suggested by these experts. There are few studies on cognitive bias in construction management in general and in CPDR in particular. This study would be the first of its type to enhance the efficiency of construction dispute resolution by highlighting strategies to minimize the biases therein.

Keywords: bias, construction project dispute resolution, minimization, multi-tiered, semi-structured interview

Procedia PDF Downloads 186

Authors: Hee Sun (Sunny) Choi, Gerhard Bruyns, Wang Zhang, Sky Cheng, Saijal Sharma

Abstract:

This research addresses the need for a comprehensive framework that can guide the design and assessment of multi-level public spaces and public realms and their impact on the built environment. The study aims to understand and measure the neural mechanisms involved in this process. By doing so, it can lay the foundation for vertical and volumetric urbanism and ensure consistency and excellence in the field while also supporting scientific research methods for urban design with cognitive neuroscientists. To investigate these aspects, the paper focuses on the neighborhood scale in Hong Kong, specifically examining multi-level public spaces and quasi-public spaces within both commercial and residential complexes. The researchers use predictive Artificial Intelligence (AI) as a methodology to assess and comprehend the applicability of the urban design framework for vertical and volumetric urbanism. The findings aim to identify the factors that contribute to successful public spaces within a vertical living environment, thus introducing a new typology of public spaces.

Keywords: vertical urbanism, scientific research methods, spatial cognition, urban design guideline

Procedia PDF Downloads 86

Authors: R. Ahmadi, A. Khodadadi, M. Abdollahi

Abstract:

The removal of copper from a dilute synthetic wastewater (10 mg/L) was studied by ion flotation at laboratory scale. Anionic sodium dodecyl sulfate (SDS) was used as a collector and ethanol as a frother. Different parameters such as pH, collector and frother concentrations, foam height and bubble size distribution (multi bubble ion flotation) were tested to determine the optimum flotation conditions in a Denver type flotation machine. To see into the effect of bubbles size distribution in this paper, a nano-micro bubble generator was designed. The nano and microbubbles that are generated in this way were combined with normal size bubbles generated mechanically. Under the optimum conditions (concentration of SDS: 192mg/l, ethanol: 0.5%v/v, pH value: 4 and froth height=12.5 cm) the best removal obtained for the system Cu/SDS with a dry foam (water recovery: 15.5%) was 85.6%. Coalescence of nano-microbubbles with bubbles of normal size belonging to mechanical flotation cell improved the removal of Cu to a maximum floatability of 92.8% and reduced the water recovery to a 13.1%.The flotation time decreased considerably at 37.5% when the multi bubble ion flotation was used.

Keywords: froth flotation, copper, water treatment, optimization, recycling

Procedia PDF Downloads 504

Authors: Abdullah Al Farwan, Ya Zhang

Abstract:

In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

Procedia PDF Downloads 169

Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

Abstract:

In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

Procedia PDF Downloads 242

Authors: Yun-Tsuen Chen, Shih-Ting Huang, Pi-Fen Cheng, Heng-Hua Wang, Hui-Zhu Chen

Abstract:

This paper discusses the experience of caring for a patient diagnosed with breast cancer and later received total mastectomy during a 2nd trimester pregnancy. She was hospitalized from January 31 to February 4, 2018. Using 'Gordon’s 11 Functional Health Patterns' through physical exams and interviews, the researcher assessed the patient’s physical and mental health and determined the patient to have anxiety, acute pain, and body image disturbance. After establishing a strong relationship with the patient, the researcher helped the patient express her anxiety and personal feelings. A multi-specialist team was formed to evaluate both the patient and her unborn child, before, during, and after surgery. This individualized care allowed the patient and her child to optimize the post-operative results. Aside from medication, the patient also received non-medicinal treatment, including improvement of sleep quality with body positioning, diaphragmatic breathing exercises for pain and stress relief after surgery. Throughout hospitalization, the patient’s physical and emotional needs were addressed daily with listening sessions and empathy. The patient’s husband was also incorporated in the patient’s recovery by teaching both he and the patient how to change the sterile wound dressing, which may have the added benefit of improving marital relationships through shared activities of nurturing. The patient was also given advice about how to improve self-confidence through clothing. Lastly, the patient was encouraged to join a support group for breast cancer patients. Through the sharing of experience in groups and within the family, the patient was helped to adapt to the change of her appearance and re-establish her self-confidence. This level of care expedited the patient’s return to her family life and role of being a mother.

Keywords: anxiety, body image disturbance, breast cancer during pregnancy, multi-specialist team

Procedia PDF Downloads 100

Authors: Farah Ali, Tehreem Fayyaz, Musadiq Idris

Abstract:

The present work was undertaken to investigate effects of various extracts of withania somnifera for anti-diabetic activity in alloxan induced diabetic rabbits. Rabbits were acclimatized for a week to standard laboratory temperature. Animals were fed according to a strict schedule (8 am, 3 pm and 10 pm) with green fodder (Medicago sativa) and tap water ad libitum. Animals were divided into nine groups of six rabbits each in a random manner. Body weights and physical activities of all rabbits were recorded before start of experiments. The animals of group 1 and 2 were given lactose (250 mg/kg, p.o) and Withania somniferaroot powder (100 mg/kg, p.o) respectively daily from day 1-20. Animals of group 3 were given alloxan (100 mg/kg, i.v) as a single dose on day 1. Powdered root of Withania somnifera in the doses of 100, 150, 200 mg/kg and its aqueous and ethanol extracts (equivalent to 200 mg/kg of crude drug) were given to the treated animals (groups 4-8), respectively by oral route for three weeks (day 1-20o.d), along with alloxan (100 mg/kg, i.v) as a single dose on day 1. Group 9 was treated with metformin (200 mg/kg, p.o) daily from day 1-20, along with a single dose of alloxan (100 mg/ kg, i.v) on day 1. Fasting serum glucose concentration in groups 3-9 was increased significantly (p<0.05) on day 3, with a maximum increase (215.3 mg/dl) in animals of toxic control (TC) group (3) on day 21 of the experiment as compared to normal control (NC) group (1). Effects of different doses (100, 150, 200 mg/kg, p.o) of W. somnifera root powder (WS) decreased the fasting serum glucose concentration as compared to toxic control group, with a maximum decrease (88.3 mg/dl) in group 2 (treated control) on day 21 of the experiment. Metformin (200 mg/kg, p.o) (reference control), aqueous extract (AWS) and ethanol extract (EWS) of W. somnifera (equivalent to 100 mg/kg W.somnifera root, p.o) antagonized the effects of alloxan as compared to toxic control group. These results indicate that the W. somnifera possess significant anti–diabetic activity.

Keywords: diabetes, serum, glucose, blood, sugar, rabbits

Procedia PDF Downloads 524

Authors: Alex B. Cusick

Abstract:

The objective of this study is to improve upon the current ²³⁸PuO₂ fuel technology for space and defense applications. Modern RTGs (radioisotope thermoelectric generators) utilize the heat generated from the radioactive decay of ²³⁸Pu to create heat and electricity for long term and remote missions. Application of RTG technology is limited by the scarcity and expense of producing the isotope, as well as the power output which is limited to only a few hundred watts. The scarcity and expense make the efficient use of ²³⁸Pu absolutely necessary. By utilizing the decay of ²³⁸Pu, not only to produce heat directly but to also indirectly induce fission in ²³⁹Pu (which is already present within currently used fuel), it is possible to see large increases in temperature which allows for a more efficient conversion to electricity and a higher power-to-weight ratio. This concept can reduce the quantity of ²³⁸Pu necessary for these missions, potentially saving millions on investment, while yielding higher power output. Current work investigating radioisotope power systems have focused on improving efficiency of the thermoelectric components and replacing systems which produce heat by virtue of natural decay with fission reactors. The technical feasibility of utilizing (α,n) reactions to induce fission within current radioisotopic fuels has not been investigated in any appreciable detail, and our study aims to thoroughly investigate the performance of many such designs, develop those with highest capabilities, and facilitate experimental testing of these designs. In order to determine the specific design parameters that maximize power output and the efficient use of ²³⁸Pu for future RTG units, MCNP6 simulations have been used to characterize the effects of modifying fuel composition, geometry, and porosity, as well as introducing neutron moderating, reflecting, and shielding materials to the system. Although this project is currently in the preliminary stages, the final deliverables will include sophisticated designs and simulation models that define all characteristics of multiple novel RTG fuels, detailed enough to allow immediate fabrication and testing. Preliminary work has consisted of developing a benchmark model to accurately represent the ²³⁸PuO₂ pellets currently in use by NASA; this model utilizes the alpha transport capabilities of MCNP6 and agrees well with experimental data. In addition, several models have been developed by varying specific parameters to investigate their effect on (α,n) and (n,fi ssion) reaction rates. Current practices in fuel processing are to exchange out the small portion of naturally occurring ¹⁸O and ¹⁷O to limit (α,n) reactions and avoid unnecessary neutron production. However, we have shown that enriching the oxide in ¹⁸O introduces a sufficient (α,n) reaction rate to support significant fission rates. For example, subcritical fission rates above 10⁸ f/cm³-s are easily achievable in cylindrical ²³⁸PuO₂ fuel pellets with a ¹⁸O enrichment of 100%, given an increase in size and a ⁹Be clad. Many viable designs exist and our intent is to discuss current results and future endeavors on this project.

Keywords: radioisotope thermoelectric generators (RTG), Pu-238, subcritical reactors, (alpha, n) reactions

Procedia PDF Downloads 173

Authors: Biruhi Tesfaye, Avinash M. Potdar

Abstract:

The laboratory test process to determine the California bearing ratio (CBR) of black cotton soils is not only overpriced but also time-consuming as well. Hence advanced prediction of CBR plays a significant role as it is applicable In pavement design. The prediction of CBR of treated soil was executed by Artificial Neural Networks (ANNs) which is a Computational tool based on the properties of the biological neural system. To observe CBR values, combined eggshell and waste glass was added to soil as 4, 8, 12, and 16 % of the weights of the soil samples. Accordingly, the laboratory related tests were conducted to get the required best model. The maximum CBR value found at 5.8 at 8 % of eggshell waste glass powder addition. The model was developed using CBR as an output layer variable. CBR was considered as a function of the joint effect of liquid limit, plastic limit, and plastic index, optimum moisture content and maximum dry density. The best model that has been found was ANN with 5, 6 and 1 neurons in the input, hidden and output layer correspondingly. The performance of selected ANN has been 0.99996, 4.44E-05, 0.00353 and 0.0067 which are correlation coefficient (R), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) respectively. The research presented or summarized above throws light on future scope on stabilization with waste glass combined with different percentages of eggshell that leads to the economical design of CBR acceptable to pavement sub-base or base, as desired.

Keywords: CBR, artificial neural network, liquid limit, plastic limit, maximum dry density, OMC

Procedia PDF Downloads 193

Authors: Grzegorz Sierpiński, Ireneusz Celiński, Marcin Staniek

Abstract:

The article discusses multi modal mobility in contemporary societies as a main planning and organization issue in the functioning of administrative bodies, a problem which really exists in the space of contemporary cities in terms of shaping modern transport systems. The article presents classification of available resources and initiatives undertaken for developing multi modal mobility. Solutions can be divided into three groups of measures–physical measures in the form of changes of the transport network infrastructure, organizational ones (including transport policy) and information measures. The latter ones include in particular direct support for people travelling in the transport network by providing information about ways of using available means of transport. A special measure contributing to this end is a trip planner. The article compares several selected planners. It includes a short description of the Green Travelling Project, which aims at developing a planner supporting environmentally friendly solutions in terms of transport network operation. The article summarizes preliminary findings of the project.

Keywords: mobility, modal split, multimodal trip, multimodal platforms, sustainable transport

Procedia PDF Downloads 412

Authors: Ann D. Christy, Beenish Saba

Abstract:

The Microbial fuel Cell (MFC) is a successful integrated technology for power production and wastewater treatment. MFCs are recognized for their dual function, but research in this field is still ongoing to increase efficiency and power output. One such effort is successful integration of phototrophic and autotrophic microorganisms to create bacterio-algal MFCs for sustainable electricity production along with wastewater treatment and algal biomass production. An MFC is typically configured with an anaerobic anodic chamber containing exoelectrogenic microorganisms separated by a cation exchange membrane from an adjacent aerobic cathodic chamber. The two electrodes are connected by an external circuit. This conventional MFC can be converted into a phototrophic MFC by introducing photosynthetic microorganisms into the cathode chamber. This study examines adding a third desalination chamber to a two-chamber bacterio-algal MFC. Successful results have been observed from these three-chamber MFCs demonstrating wastewater treatment in the anodic chamber, phototrophic algal growth in the cathodic chamber, and desalination in the middle chamber. The present article will summarize successful results of the bacterio-algal fuel cells and offer insights about the mechanisms involved. Tables summarizing the input substrate along with optimized operational conditions and output performance in terms of power production and efficiencies of water and wastewater treatment will be presented. The negative impacts and challenges will be discussed, along with possible future research directions. Results suggest that the three chamber bacterio-algal desalination cell has potential as a feasible technology for power production, wastewater treatment and desalination, but it needs further investigation under optimized conditions.

Keywords: bacterio-algal MFC, three chamber, microbial fuel cell, wastewater treatment and desalination

Procedia PDF Downloads 363

Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho

Abstract:

Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.

Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning

Procedia PDF Downloads 100

Authors: Sireesha Tamada, Debraj Bhattacharjee, Pranab K. Dan, Prabha Bhola

Abstract:

The most important component in an automotive transmission system is the gearbox which controls the speed of the vehicle. In an automatic transmission, the right positioning of actuators ensures efficient transmission mechanism embodiment, wherein the challenge lies in formulating the number of actuators associated with modelling a gearbox. Data with respect to actuation and gear shifting sequence has been retrieved from the available literature, including patent documents, and has been used in this proposed heuristics based methodology for modelling actuation sequence in a gear box. This paper presents a methodological approach in designing a gearbox for the purpose of obtaining an optimal shifting sequence. The computational model considers factors namely, the number of stages and gear teeth as input parameters since these two are the determinants of the gear ratios in an epicyclic gear train. The proposed transmission schematic or stick diagram aids in developing the gearbox layout design. The number of iterations and development time required to design a gearbox layout is reduced by using this approach.

Keywords: automatic transmission, gear-shifting, multi-stage planetary gearbox, rank ordered clustering

Procedia PDF Downloads 326

Authors: A. M. Nasr, W. R. Azzam, K. E. Ebeed

Abstract:

For bored piles, careful cleaning must be used to reduce the amount of material trapped in the drilled hole; otherwise, the debris' presence might cause the soft toe effect, which would affect the axial resistance. There isn't much comprehensive research on bored piles with debris. In order to investigate the behavior of a single pile, a pile composite foundation, a two pile group, a three pile group and a four pile group investigation conducts, forty-eight numerical tests in which the debris is simulated using foam rubber.1m pile diameter and 10m length with spacing 3D and depth of foundation 1m used in this study. It is found that the existence of debris causes a reduction of bearing capacity by 64.58% and 33.23% for single pile and pile composite foundation, respectively, 23.27% and 24.24% for the number of defective piles / total number of pile =1/2 and 1 respectively for two group pile, 10.23%, 19.42% and 28.47% for the number of defective piles / total number of pile =1/3,2/3 and 1 respectively for three group pile and, this reduction increase with the increase in a number of defective piles / a total number of piles and 7.1%, 13.32%,19.02% and 26.36 for the number of defective piles / total number of pile =1/4,2/4,3/4 and 1 respectively for four group pile and decreases with an increase of number of pile duo to interaction effect.

Keywords: debris, Foundation, defective, interaction, board pile

Procedia PDF Downloads 99

Authors: Hafez Abdo, Rob Ackrill

Abstract:

Renewable energy (RE) promotion in the UK relies on multiple policy instruments, which are required to overcome the path dependency pressures favouring fossil fuels. These instruments include targeted funding schemes and economy-wide instruments embedded in the tax code. The resulting complexity of incentives raises important questions around the coherence and effectiveness of these instruments for RE generation. This complexity is exacerbated by UK RE policy being nested within EU policy in a multi-level governance (MLG) setting. To gain analytical traction on such complexity, this study will analyse policies promoting the on-farm generation of energy for heat and power, from farm and food waste, via anaerobic digestion. Utilising both primary and secondary data, it seeks to address a particular lacuna in the academic literature. Via a localised, in-depth investigation into the complexity of policy instruments promoting RE, this study will help our theoretical understanding of the challenges that MLG and path dependency pressures present to policymakers of multi-dimensional policies.

Keywords: anaerobic digestion, energy, green, policy, renewable, tax, UK

Procedia PDF Downloads 371

Authors: Hong Zhang

Abstract:

The main purpose of this study is to show that machine translation (MT) post-editing (PE) training can help our Chinese students learn Spanish as a second language. Our hypothesis is that they might make better use of it by learning PE skills specific for foreign language learning. We have developed PE training materials based on the data collected in a previous study. Training material included the special error types of the output of MT and the error types that our Chinese students studying Spanish could not detect in the experiment last year. This year we performed a pilot study in order to evaluate the PE training materials effectiveness and to what extent PE training helps Chinese students who study the Spanish language. We used screen recording to record these moments and made note of every action done by the students. Participants were speakers of Chinese with intermediate knowledge of Spanish. They were divided into two groups: Group A performed PE training and Group B did not. We prepared a Chinese text for both groups, and participants translated it by themselves (human translation), and then used Google Translate to translate the text and asked them to post-edit the raw MT output. Comparing the results of PE test, Group A could identify and correct the errors faster than Group B students, Group A did especially better in omission, word order, part of speech, terminology, mistranslation, official names, and formal register. From the results of this study, we can see that PE training can help Chinese students learn Spanish as a second language. In the future, we could focus on the students’ struggles during their Spanish studies and complete the PE training materials to teach Chinese students learning Spanish with machine translation.

Keywords: machine translation, post-editing, post-editing training, Chinese, Spanish, foreign language learning

Procedia PDF Downloads 144

Authors: E. T. El Shenawy, O. N. A. Esmail, Adel A. Elbaset, Hesham F. A. Hamed

Abstract:

Generation of the electrical energy based on photovoltaic (PV) technology has been increased over the world due to either the continuous reduction in the traditional energy sources in addition to the pollution problems related to their usage, or the clean nature and safe usage of the PV technology. Also, PV systems can generate clean electricity in the site of use without any transmission, which can be considered cost effective than other generation systems. The performance of the PV system is highly affected by the amount of solar radiation incident on it. Completely or partially shaded PV systems can affect its output. The PV system can be shaded by trees, buildings, dust, incorrect system configuration, or other obstacles. The present paper studies the effect of the partial shading on the performance of a thin film PV module under climatic conditions of Cairo, Egypt. This effect was measured and evaluated according to practical measurement of the characteristic curves such as current-voltage and power-voltage for two identical PV modules (with and without shading) placed at the same time on one mechanical structure for comparison. The measurements have been carried out for the following shading patterns; half cell (bottom, middle, and top of the PV module); complete cell; and two adjacent cells. The results showed that partially shading the PV module changes the shapes of the I-V and P-V curves and produces more than one maximum power point, that can disturb the traditional maximum power point trackers. Also, the output power from the module decreased according to the incomplete solar radiation reaching the PV module due to shadow patterns. The power loss due shading was 7%, 22%, and 41% for shading of half-cell, one cell, and two adjacent cells of the PV module, respectively.

Keywords: I-V measurements, PV module characteristics, PV module power loss, PV module shading

Procedia PDF Downloads 138

Authors: Wisam H. Benamer, Ehab A. Elfallah, Mohamed A. Elshaari, Farag A. Elshaari

Abstract:

A challenge for laboratories is to provide bacterial identification and antibiotic sensitivity results within a short time. Hence, advancement in the required technology is desirable to improve timing, accuracy and quality. Even with the current advances in methods used for both phenotypic and genotypic identification of bacteria the need is there to develop method(s) that enhance the outcome of bacteriology laboratories in accuracy and time. The hypothesis introduced here is based on the assumption that the chromosome of any bacteria contains unique sequences that can be used for its identification and typing. The outcome of a pilot study designed to test this hypothesis is reported in this manuscript. Methods: The complete chromosome sequences of several bacterial species were downloaded to use as search targets for unique sequences. Visual basic and SQL server (2014) were used to generate a complete set of 18-base long primers, a process started with reverse translation of randomly chosen 6 amino acids to limit the number of the generated primers. In addition, the software used to scan the downloaded chromosomes using the generated primers for similarities was designed, and the resulting hits were classified according to the number of similar chromosomal sequences, i.e., unique or otherwise. Results: All primers that had identical/similar sequences in the selected genome sequence(s) were classified according to the number of hits in the chromosomes search. Those that were identical to a single site on a single bacterial chromosome were referred to as unique. On the other hand, most generated primers sequences were identical to multiple sites on a single or multiple chromosomes. Following scanning, the generated primers were classified based on ability to differentiate between medically important bacterial and the initial results looks promising. Conclusion: A simple strategy that started by generating primers was introduced; the primers were used to screen bacterial genomes for match. Primer(s) that were uniquely identical to specific DNA sequence on a specific bacterial chromosome were selected. The identified unique sequence can be used in different molecular diagnostic techniques, possibly to identify bacteria. In addition, a single primer that can identify multiple sites in a single chromosome can be exploited for region or genome identification. Although genomes sequences draft of isolates of organism DNA enable high throughput primer design using alignment strategy, and this enhances diagnostic performance in comparison to traditional molecular assays. In this method the generated primers can be used to identify an organism before the draft sequence is completed. In addition, the generated primers can be used to build a bank for easy access of the primers that can be used to identify bacteria.

Keywords: bacteria chromosome, bacterial identification, sequence, primer generation

Procedia PDF Downloads 194