Search results for: wireless sensor network

Authors: Easa Ali Abbasi, Akbar Allahverdizadeh, Reza Jahangiri, Behnam Dadashzadeh

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Electrical current measurement is a suitable method for the performance determination of electrical devices. There are two contact and noncontact methods in this measuring process. Contact method has some disadvantages like having direct connection with wire which may endamage the system. Thus, in this paper, a bimorph piezoelectric cantilever beam which has a permanent magnet on its free end is used to measure electrical current in a noncontact way. In mathematical modeling, based on Galerkin method, the governing equation of the cantilever beam is solved, and the equation presenting the relation between applied force and beam’s output voltage is presented. Magnetic force resulting from current carrying wire is considered as the external excitation force of the system. The results are compared with other references in order to demonstrate the accuracy of the mathematical model. Finally, the effects of geometric parameters on the output voltage and natural frequency are presented.

Keywords: cantilever beam, electrical current measurement, forced excitation, piezoelectric

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Authors: Sema Kayapinar, Ismail Karaoglan, Turan Paksoy, Hadi Gokcen

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Green Supply Chain Management is gaining growing interest among researchers and supply chain management. The concept of Green Supply Chain Management is to integrate environmental thinking into the Supply Chain Management. It is the systematic concept emphasis on environmental problems such as reduction of greenhouse gas emissions, energy efficiency, recycling end of life products, generation of solid and hazardous waste. This study is to present a green supply chain network model integrated Internet of Things applications. Internet of Things provides to get precise and accurate information of end-of-life product with sensors and systems devices. The forward direction consists of suppliers, plants, distributions centres and sales and collect centres while, the reverse flow includes the sales and collects centres, disassembled centre, recycling and disposal centre. The sales and collection centre sells the new products are transhipped from factory via distribution centre and also receive the end-of life product according their value level. We describe green logistics activities by presenting specific examples including “recycling of the returned products and “reduction of CO2 gas emissions”. The different transportation choices are illustrated between echelons according to their CO2 gas emissions. This problem is formulated as a mixed integer linear programming model to solve the green supply chain problems which are emerged from the environmental awareness and responsibilities. This model is solved by using Gams package program. Numerical examples are suggested to illustrate the efficiency of the proposed model.

Keywords: green supply chain optimization, internet of things, greenhouse gas emission, recycling

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: A. Boonpratatong, J. Pantong, S. Kiattisaksophon, W. Senavongse

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In this study, an integrated sensing and modeling system for manual wheelchair propulsion measurement and propulsion efficiency calculation was used to indicate the level of overuse. Seven subjects participated in the measurement. On the level surface, the propulsion efficiencies were not different significantly as the riding speed increased. By contrast, the propulsion efficiencies on the 15-degree incline were restricted to around 0.5. The results are supported by previously reported wheeling resistance and propulsion torque relationships implying margin of the overuse. Upper limb musculoskeletal injuries and syndromes in manual wheelchair riders are common, chronic, and may be caused at different levels by the overuse i.e. repetitive riding on steep incline. The qualitative analysis such as the mechanical effectiveness on manual wheeling to establish the relationship between the riding difficulties, mechanical efforts and propulsion outputs is scarce, possibly due to the challenge of simultaneous measurement of those factors in conventional manual wheelchairs and everyday environments. In this study, the integrated sensing and modeling system were used to measure manual wheelchair propulsion efficiency in conventional manual wheelchairs and everyday environments. The sensing unit is comprised of the contact pressure and inertia sensors which are portable and universal. Four healthy male and three healthy female subjects participated in the measurement on level and 15-degree incline surface. Subjects were asked to perform manual wheelchair ridings with three different self-selected speeds on level surface and only preferred speed on the 15-degree incline. Five trials were performed in each condition. The kinematic data of the subject’s dominant hand and a spoke and the trunk of the wheelchair were collected through the inertia sensors. The compression force applied from the thumb of the dominant hand to the push rim was collected through the contact pressure sensors. The signals from all sensors were recorded synchronously. The subject-selected speeds for slow, preferred and fast riding on level surface and subject-preferred speed on 15-degree incline were recorded. The propulsion efficiency as a ratio between the pushing force in tangential direction to the push rim and the net force as a result of the three-dimensional riding motion were derived by inverse dynamic problem solving in the modeling unit. The intra-subject variability of the riding speed was not different significantly as the self-selected speed increased on the level surface. Since the riding speed on the 15-degree incline was difficult to regulate, the intra-subject variability was not applied. On the level surface, the propulsion efficiencies were not different significantly as the riding speed increased. However, the propulsion efficiencies on the 15-degree incline were restricted to around 0.5 for all subjects on their preferred speed. The results are supported by the previously reported relationship between the wheeling resistance and propulsion torque in which the wheelchair axle torque increased but the muscle activities were not increased when the resistance is high. This implies the margin of dynamic efforts on the relatively high resistance being similar to the margin of the overuse indicated by the restricted propulsion efficiency on the 15-degree incline.

Keywords: contact pressure sensor, inertia sensor, integrating sensing and modeling system, manual wheelchair propulsion efficiency, manual wheelchair propulsion measurement, tangential force, resultant force, three-dimensional riding motion

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Jennifer A. Kowalkowski

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Production agriculture (farming) is a physically, emotionally, and cognitively stressful occupation, where workers have little control over the stressors that impact both their work and their lives. In an occupation already rife with hazards, these occupational-related stressors have been shown to increase farm workers’ risks for illness, injury, disability, and death associated with their work. Despite efforts to mitigate the negative health effects from occupational-related stress (ORS) and to promote health and well-being (HWB) among farmers in the US, marked improvements have not been attained. Social support accessed through social networks has been shown to buffer against the negative health effects from stress, yet no studies have directly examined these relationships among farmers. The purpose of this study was to use social network analysis to explore the social networks of farm owner-operators and the social supports available to them for mitigating the negative health effects of ORS. A convenience sample of 71 farm owner-operators from a Midwestern County in the US completed and returned a mailed survey (55.5% response rate) that solicited information about their social networks related to ORS. Farmers reported an average of 2.4 individuals in their personal networks and higher levels of comfort discussing ORS with female network members. Farmers also identified few connections (3.4% density) and indicated low comfort with members of affiliation networks specific to ORS. Findings from this study highlighted that farmers accessed different social networks and resources for their personal HWB than for issues related to occupational(farm-related) health and safety. In addition, farmers’ social networks for personal HWB were smaller, with different relational characteristics than reported in studies of farmers’ social networks related to occupational health and safety. Collectively, these findings suggest that farmers conceptualize personal HWB differently than farm health and safety. Therefore, the same research approaches and targets that guide occupational health and safety research may not be appropriate for personal HWB for farmers. Interventions and programming targeting ORS and HWB have largely been offered through the same platforms or mechanisms as occupational health and safety programs. This may be attributed to the significant overlap between the farm as a family business and place of residence, or that ORS stems from farm-related issues. However, these assumptions translated to health research of farmers and farm families from the occupational health and safety literature have not been directly studied or challenged. Thismay explain why past interventions have not been effective at improving health outcomes for farmers and farm families. A close examination of findings from this study raises important questions for researchers who study agricultural health. Findings from this study have significant implications for future research agendas focused on addressing ORS, HWB, and health disparities for farmersand farm families.

Keywords: agricultural health, occupational-related stress, social networks, well-being

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Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

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This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

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Authors: Mohammed Alonazi, Natalia Beloff, Martin White

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This paper presents a proposal for a mobile government adoption and utilization model (MGAUM), which is a framework designed to increase the adoption rate of m-government services in Saudi Arabia. Recent advances in mobile technologies such are Mobile compatibilities, The development of wireless communication, mobile applications and devices are enabling governments to deliver services in new ways to citizens more efficiently and economically. In the last decade, many governments around the globe are utilizing these advances effectively to develop their next generation of e-government services. However, a low adoption rate of m-government services by citizens is a common problem in Arabian countries, including Saudi Arabia. Yet, to our knowledge, very little research has been conducted focused on understanding the factors that influence citizen adoption of these m-government services in this part of the world. A set of social, cultural and technological factors have been identified in the literature, which has led to the formulation of associated research questions and hypotheses. These hypotheses will be tested on Saudi citizens using questionnaires and interview methods based around the technology acceptance model. A key objective of the MGAUM framework is to investigate and understand Saudi citizens perception towards adoption and utilization of m-government services.

Keywords: e-government, m-government, citizen services quality, technology acceptance model, Saudi Arabia, adoption framework.

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Authors: Sofia Mylona, Dale Hume

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The main scope of this work is to compare the experimental thermal conductivity results of fluids between devices using transient techniques. A range of different liquids within a range of viscosities was measured with two or more devices, and the results were compared between the different methods and the reference equations wherever it was available. The liquids selected are the most commonly used in academic or industrial laboratories to calibrate their thermal conductivity instruments having a variety of thermal conductivity, viscosity, and density. Three transient methods (Transient Hot Wire, Transient Plane Source, and Transient Line Source) were compared for the thermal conductivity measurements taken by using them. These methods have been chosen as the most accurate and because they all follow the same idea; as a function of the logarithm of time, the thermal conductivity is calculated from the slope of a plot of sensor temperature rise. For all measurements, the selected temperature range was at the atmospheric level from 10 to 40 ° C. Our results are coming with an agreement with the objections of several scientists over the reliability of the results of a few popular devices. The observation was surprising that the device used in many laboratories for fast measurements of liquid thermal conductivity display deviations of 500 percent which can be very poorly reproduced.

Keywords: accurate data, liquids, thermal conductivity, transient methods.

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Authors: Nibal Al-Batsh, Issam A. Al-Khatib, Subha Ghannam

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Yatta is a town in the Governorate of Hebron, located 9 km south of Hebron City in the West Bank. The town houses over 100,000 people, 49% of which are females; a population that doubles every 15 years. Yatta has been connected to a water network since 1974 serving nearly 85% of the households. The water network is old and inadequate to meet the needs of the population. The water supply made available to the area is also very limited, estimated to be around 20 l/c/d. Residents are thus forced to rely on water vendors which supply water with a lower quality compared to municipal water while being 400% more expensive. As a cheaper and more reliable alternative, rainwater harvesting is a common practice in the area, with the majority of the households owning at least one cistern. Rainwater harvesting is of great socioeconomic importance in areas where water sources are scarce or polluted. In this research, the quality of harvested rainwater used for drinking and domestic purposes in the Yatta area was assessed throughout a year. A total of 100 samples, were collected from (cisterns) with an average capacity of 69 m3, which are adjacent to cement-roof catchment areas with an average area of 145 m2. Samples were analyzed for a number of parameters including: pH, alkalinity, hardness, turbidity, Total Dissolved Solids (TDS), NO3, NH4, chloride and salinity. Biological and microbiological contents such as Total Coliforms (TCC) and Fecal Coliforms (FC) bacteria were also tested. Results showed that most of the rainwater samples were within WHO and EPA guidelines set for chemical parameters. The research also addressed the impact of different socioeconomic attributes on rainwater harvesting through questionnaire that was pre-tested before the actual statically sample is collected.

Keywords: rainwater, harvesting, water quality, socio-economic aspects

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: Nur Bella Octoria Bella, Ayomi Dita Rarasati

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DKI Jakarta is Indonesia's capital city with the 10th highest congestion rate in the world based on the 2019 traffic index. Other than that based on World Air Quality Report in 2019 showed DKI Jakarta's air pollutant concentrate 49.4 µg and the 5th highest air pollutant in the world. In the urban city nowadays, the mobility rate is high enough and the efficiency for sustainability assessment in transport infrastructure development is needed. This efficiency is the important key for sustainable infrastructure development. DKI Jakarta is nowadays in the process of constructing the railway infrastructure to support the transportation system. The problems appearing are the railway infrastructure networks and the service in DKI Jakarta already planned based on sustainability factors or not. Therefore, the aim of this research is to make the evaluation of railways infrastructure networks performance and services in DKI Jakarta regards on the railway sustainability key factors. Further, this evaluation will be used to make the railway sustainability assessment framework and to offer some of the alternative solutions to improve railway transportation sustainability in DKI Jakarta. Firstly a very detailed literature review of papers that have focused on railway sustainability factors and their improvements of railway sustainability, published in the scientific journal in the period 2011 until 2021. Regarding the sustainability factors from the literature review, further, it is used to assess the current condition of railway infrastructure in DKI Jakarta. The evaluation will be using a Likert rate questionnaire and directed to the transportation railway expert and the passenger. Furthermore, the mapping and evaluation rate based on the sustainability factors will be compared to the effect factors using the Analytical Hierarchical Process (AHP). This research offers the network's performance and service rate impact on the sustainability aspect and the passenger willingness for using the rail public transportation in DKI Jakarta.

Keywords: transportation sustainability, railway transportation, sustainability, DKI Jakarta

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Simon Bambey, Edward Lim, Kai Corley-Jory, Pooya Taheri

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The main goal of this paper is to propose a simple and low-cost microcontroller-based laser communication link. To demonstrate that laser communication is a viable and efficient means for transmitting data, a transceiver capable of transfer rates of approximately 0.7 kB/s is prototyped. The hardware used for the transceiver consists of Commercial Off-The-Shelf (COTS) lasers, photodiodes, and the Arduino Mega 2560 which is an open-source and easy-to-use microcontroller-based platform intended for making interactive projects. A graphic user interface utilizing the Meteor framework is developed to facilitate the communication between the user and transceiver. The developed transceiver prototype is capable of receiving and transmitting data at significant ranges with no loss of information. Furthermore, stable and secure communication is achieved through several mechanisms developed to manage simultaneous sending and receiving, in addition to detecting physical interruptions during transmission. The design setup is scalable and with further development can be transformed into a fiber-optic transmission system. Due to its nature, laser communication is very secure and can provide a safe and private communication link. Overall, this paper demonstrates how laser communication can be an economical, durable, and effective means of information transfer.

Keywords: Arduino microcontrollers, laser applications, user interfaces, wireless communication

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Authors: P. Saravanakumar, N. Raja, M. Rameshkumar, D. Mohankumar, R. Sateeshkumar, B. Maheshwari

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This research work is designed using Microsoft VB. Net as front end and MySQL as back end. The project deals with secure the user transaction in the ATM system. This application contains the option for sending the failed transaction details to the particular customer by using the SMS. When the customer withdraws the amount from the Bank ATM system, sometimes the amount will not be dispatched but the amount will be debited to the particular account. This application is used to avoid this type of problems in the ATM system. In this proposed system using IR technique to detect the dispatched amount. IR Transmitter and IR Receiver are placed in the path of cash dispatch. It is connected each other through the IR signal. When the customers withdraw the amount in the ATM system then the amount will be dispatched or not is monitored by IR Receiver. If the amount will be dispatched then the signal will be interrupted between the IR Receiver and the IR Transmitter. At that time, the monitoring system will be reduced their particular withdraw amount on their account. If the cash will not be dispatched, the signal will not be interrupted, at that time the particular withdraw amount will not be reduced their account. If the transaction completed successfully, the transaction details such as withdraw amount and current balance can be sent to the customer via the SMS. If the transaction fails, the transaction failed message can be send to the customer.

Keywords: ATM system, monitoring system, IR Transmitter, IR Receiver

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Authors: S. Łęgowik-Świącik

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This article is a contribution to the research into the determinants of value creation via digital platforms in variable operating conditions. The dynamics of the market environment caused by the COVID-19 pandemic have made enterprises built on digital platforms financially successful. While many classic companies are struggling with the uncertainty of conducting a business and difficulties in the process of value creation, digital platforms create value by modifying the existing business model to meet the changing needs of customers. Therefore, the objective of this publication is to understand and explain the relationship between value creation and the conversion of the business model built on digital platforms under pandemic conditions. The considerations relating to the conceptual framework and determining the research objective allowed for adopting the hypothesis, assuming that the processes of value creation are evolving, and the measurement of these processes allows for the protection of value created and enables its growth in changing circumstances. The research methods, such as critical literature analysis and case study, were applied to accomplish the objective pursued and verify the hypothesis formulated. The empirical research was carried out based on the data from enterprises listed on the Nasdaq Stock Exchange: Amazon, Alibaba, and Facebook. The research period was the years 2018-2021. The surveyed enterprises were chosen based on the targeted selection. The problem discussed is important and current since the lack of in-depth theoretical research results in few attempts to identify the determinants of value creation via digital platforms. The above arguments led to an attempt at theoretical analysis and empirical research to fill in the gap perceived by deepening the understanding of the process of value creation through network effects via digital platforms under pandemic conditions.

Keywords: business model, digital platforms, enterprise management, pandemic conditions, value creation process

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Ryan Chalk, G. M. Shafiullah

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Climate change is a pertinent issue facing governments and societies around the world. The industrial revolution has resulted in a steady increase in the average global temperature. The mining and energy production industries have been significant contributors to this change prompting government to intervene by promoting low emission technology within these sectors. This paper initially reviews the energy problem in Australia and the mining sector with a focus on the energy requirements and production methods utilised in Western Australia (WA). Renewable energy in the form of utility-scale solar photovoltaics (PV) provides a solution to these problems by providing emission-free energy which can be used to supplement the existing natural gas turbines in operation at the proposed site. This research presents a custom renewable solution for the mining site considering the specific township network, local weather conditions, and seasonal load profiles. A summary of the required PV output is presented to supply slightly over 50% of the towns power requirements during the peak (summer) period, resulting in close to full coverage in the trench (winter) period. Dig Silent Power Factory Software has been used to simulate the characteristics of the existing infrastructure and produces results of integrating PV. Large scale PV penetration in the network introduce technical challenges, that includes; voltage deviation, increased harmonic distortion, increased available fault current and power factor. Results also show that cloud cover has a dramatic and unpredictable effect on the output of a PV system. The preliminary analyses conclude that mitigation strategies are needed to overcome voltage deviations, unacceptable levels of harmonics, excessive fault current and low power factor. Mitigation strategies are proposed to control these issues predominantly through the use of high quality, made for purpose inverters. Results show that use of inverters with harmonic filtering reduces the level of harmonic injections to an acceptable level according to Australian standards. Furthermore, the configuration of inverters to supply active and reactive power assist in mitigating low power factor problems. Use of FACTS devices; SVC and STATCOM also reduces the harmonics and improve the power factor of the network, and finally, energy storage helps to smooth the power supply.

Keywords: climate change, mitigation strategies, photovoltaic (PV), power quality

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Authors: Farhana Tarannum

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Ambient (outdoor) air pollution is now recognized as an important problem, both nationally and worldwide. The concentrations of gaseous pollutants (SOx, NOx, CO and O3) have been determined from samples collected at Kallyanpur along Shamoli corridor in Dhaka city. Pollutants were determined in a sample collected at ground level and a roof of a 7-storied building. These pollutants are emitted largely from stationary sources like fossil fuel fired power plants, industrial plants, and manufacturing facilities as well as mobile sources. The incomplete combustion of fuel, wood and the Sulphur containing fuel used in the vehicles are one of the main causes of CO and SOx respectively in our natural environment. When the temperature of combustion in high enough and some of that nitrogen reacts with oxygen in the air, various nitrogen oxides (NOx) are then formed. The VOCs react with NOx in the presence of sunlight to form O3. UV Visible spectrophotometric method has been used for the determination of SOx, NOx and O3. The sensor type device was used for the estimation of CO. It was found that the air pollutants (CO, SOx, NOx and O3) of a sample collected at the roof of a building were lower compared to the ground level; it indicated that ground level people are mostly affected by the gaseous pollutants.

Keywords: gaseous pollutants, UV-visible spectrophotometry, ambient air quality, Dhaka city

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Authors: Alcardo Alex Barakabitze, Tan Xiaoheng

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Orthogonal Frequency Division Multiplexing (OFDM) is a special case of Multi-Carrier Modulation (MCM) technique which transmits a stream of data over a number of lower data rate subcarriers. OFDM splits the total transmission bandwidth into a number of orthogonal and non-overlapping subcarriers and transmit the collection of bits called symbols in parallel using these subcarriers. This paper explores the Peak to Average Power Reduction (PAPR) using the Partial Transmit Sequence technique. We provide the distribution analysis and the basics of OFDM signals and then show how the PAPR increases as the number of subcarriers increases. We provide the performance analysis of CCDF and PAPR expressed in decibels through MATLAB simulations. The simulation results show that, in PTS technique, the performance of PAPR reduction in OFDM systems improves significantly as the number of sub-blocks increases. However, by keeping the same number of sub-blocks variation, oversampling factor and the number of OFDM blocks’ iteration for generating the CCDF, the OFDM systems with 128 subcarriers have an improved performance in PAPR reduction compared to OFDM systems with 256, 512 or >512 subcarriers.

Keywords: OFDM, peak to average power reduction (PAPR), bit error rate (BER), subcarriers, wireless communications

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Authors: Arfanara Najnin, Michael W. Roach, Jr., Dr. Jianhong Cecilia Xia

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The SkyCar Rapid Transit System (SRT) is an innovative intelligent transport system for the sustainable urban transport system. This system will increase the urban area network connectivity and decrease urban area traffic congestion. The SRT system is designed as a suspended Personal Rapid Transit (PRT) system that travels under a guideway 5m above the ground. A driver-less passenger is via pod-cars that hang from slender beams supported by columns that replace existing lamp posts. The beams are setup in a series of interconnecting loops providing non-stop travel from beginning to end to assure journey time. The SRT forward movement is effected by magnetic motors built into the guideway. Passenger stops are at either at line level 5m above the ground or ground level via a spur guideway that curves off the main thoroughfare. The main objective of this paper is to propose an integrated Automated Transit Network (ATN) technology for the future intelligent transport system in the urban built environment. To fulfil the objective a 4D simulated model in the urban built environment has been proposed by using the concept of SRT-ATN system. The methodology for the design, construction and testing parameters of a Technology Demonstrator (TD) for proof of concept and a Simulator (S) has been demonstrated. The completed TD and S will provide an excellent proving ground for the next development stage, the SRT Prototype (PT) and Pilot System (PS). This paper covered by a 4D simulated model in the virtual built environment is to effectively show how the SRT-ATN system works. OpenSim software has been used to develop the model in a virtual environment, and the scenario has been simulated to understand and visualize the proposed SkyCar Rapid Transit Network model. The SkyCar system will be fabricated in a modular form which is easily transported. The system would be installed in increasingly congested city centers throughout the world, as well as in airports, tourist resorts, race tracks and other special purpose for the urban community. This paper shares the lessons learnt from the proposed innovation and provides recommendations on how to improve the future transport system in urban built environment. Safety and security of passengers are prime factors to be considered for this transit system. Design requirements to meet the safety needs to be part of the research and development phase of the project. Operational safety aspects would also be developed during this period. The vehicles, the track and beam systems and stations are the main components that need to be examined in detail for safety and security of patrons. Measures will also be required to protect columns adjoining intersections from errant vehicles in vehicular traffic collisions. The SkyCar Rapid Transit takes advantage of all current disruptive technologies; batteries, sensors and 4G/5G communication and solar energy technologies which will continue to reduce the costs and make the systems more profitable. SkyCar's energy consumption is extremely low compared to other transport systems.

Keywords: SkyCar, rapid transit, Intelligent Transport System (ITS), Automated Transit Network (ATN), urban built environment, 4D Visualization, smart city

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Authors: Samah Jawad Jabari

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Water is the most important and valuable resource not only for human life but also for all living things on the planet. The water supply utilities should fulfill the water requirement quantitatively and qualitatively. Drinking water systems are exposed to both natural (hurricanes and flood) and manmade hazards (risks) that are common in Palestine. Non-Revenue Water (NRW) is a manmade risk which remains a major concern in Palestine, as the NRW levels are estimated to be at a high level. In this research, Hebron city water distribution network was taken as a case study to estimate and audit the NRW levels. The research also investigated the state of the existing water distribution system in the study area by investigating the water losses and obtained more information on NRW prevention and management practices. Data and information have been collected from the Palestinian Water Authority (PWA) and Hebron Municipality (HM) archive. In addition to that, a questionnaire has been designed and administered by the researcher in order to collect the necessary data for water auditing. The questionnaire also assessed the views of stakeholder in PWA and HM (staff) on the current status of the NRW in the Hebron water distribution system. The important result obtained by this research shows that NRW in Hebron city was high and in excess of 30%. The main factors that contribute to NRW were the inaccuracies in billing volumes, unauthorized consumption, and the method of estimating consumptions through faulty meters. Policy for NRW reduction is available in Palestine; however, it is clear that the number of qualified staff available to carry out the activities related to leak detection is low, and that there is a lack of appropriate technologies to reduce water losses and undertake sufficient system maintenance, which needs to be improved to enhance the performance of the network and decrease the level of NRW losses.

Keywords: non-revenue water, water auditing, leak detection, water meters

Procedia PDF Downloads 300

Authors: Abdel-Monem Sayed Mohamed

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Lake Aswan is one of the largest man-made reservoirs in the world. The reservoir began to fill in 1964 and the level rose gradually, with annual irrigation cycles, until it reached a maximum water level of 181.5 m in November 1999, with a capacity of 160 km3. The filling of such large reservoir changes the stress system either through increasing vertical compressional stress by loading and/or increased pore pressure through the decrease of the effective normal stress. The resulted effect on fault zones changes stability depending strongly on the orientation of pre-existing stress and geometry of the reservoir/fault system. The main earthquake occurred on November 14, 1981, with magnitude 5.5. This event occurred after 17 years of the reservoir began to fill, along the active part of the Kalabsha fault and located not far from the High Dam. Numerous of small earthquakes follow this earthquake and continue till now. For this reason, 13 seismograph stations (radio-telemetry network short-period seismometers) were installed around the northern part of Lake Aswan. The main purpose of the network is to monitor the earthquake activity continuously within Aswan region. The data described here are obtained from the continuous record of earthquake activity and lake-water level variation through the period from 1982 to 2015. The seismicity is concentrated in the Kalabsha area, where there is an intersection of the easterly trending Kalabsha fault with the northerly trending faults. The earthquake foci are distributed in two seismic zones, shallow and deep in the crust. Shallow events have focal depths of less than 12 km while deep events extend from 12 to 28 km. Correlation between the seismicity and the water level variation in the lake provides great suggestion to distinguish the micro-earthquakes, particularly, those in shallow seismic zone in the reservoir–triggered seismicity category. The water loading is one factor from several factors, as an activating medium in triggering earthquakes. The common factors for all cases of induced seismicity seem to be the presence of specific geological conditions, the tectonic setting and water loading. The role of the water loading is as a supplementary source of earthquake events. So, the earthquake activity in the area originated tectonically (ML ≥ 4) and the water factor works as an activating medium in triggering small earthquakes (ML ≤ 3). Study of the inducing seismicity from the water level variation in Aswan Lake is of great importance and play great roles necessity for the safety of the High Dam body and its economic resources.

Keywords: Aswan lake, Aswan seismic network, seismicity, water level variation

Procedia PDF Downloads 373

Authors: Urvee B. Trivedi, U. D. Dalal

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As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.

Keywords: cognitive radio (CR), probability of detection (PD), probability of false alarm (PF), primary user (PU), secondary user (SU), fast Fourier transform (FFT), signal to noise ratio (SNR)

Procedia PDF Downloads 346

Authors: Alessandro Andre Leme

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To analyze three Brazilian think tanks: a) Fernando Henrique Foundation; b) Celso Furtado International Center; c) Millennium Institute and how they dispute interpretations about the type of development and State that should be adopted in Brazil. We will make use of Network and content analysis of the sites. The analyzes show a dispute that goes from a defense of ultraliberalism to developmentalism, going through a hybrid between State and Market voiced in each of the Think Tanks.

Keywords: sociopolitical and economic thinking, development, strategies, intellectuals, state

Procedia PDF Downloads 153

Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

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Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

Procedia PDF Downloads 147

Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 132

Authors: Yuning Guan

Abstract:

Under the background of rapid urbanization, mountainous counties need to break through mountain barriers for urban expansion due to undulating topography, resulting in ecological problems such as landscape fragmentation and reduced biodiversity. Green infrastructure networks are constructed to alleviate the contradiction between urban expansion and ecological protection, promoting the healthy and sustainable development of urban ecosystems. This study applies the MSPA model, the MCR model and Linkage Mapper Tools to identify eco-sources and eco-corridors in the Shapingba District of Chongqing and combined with landscape connectivity assessment and circuit theory to delineate the importance levels to extract ecological pinch point areas on the corridors. The results show that: (1) 20 ecological sources are identified, with a total area of 126.47 km², accounting for 31.88% of the study area, and showing a pattern of ‘one core, three corridors, multi-point distribution’. (2) 37 ecological corridors are formed in the area, with a total length of 62.52km, with a ‘more in the west, less in the east’ pattern. (3) 42 ecological pinch points are extracted, accounting for 25.85% of the length of the corridors, which are mainly distributed in the eastern new area. Accordingly, this study proposes optimization strategies for sub-area protection of ecological sources, grade-level construction of ecological corridors, and precise restoration of ecological pinch points.

Keywords: green infrastructure network, morphological spatial pattern, minimal cumulative resistance, mountainous counties, circuit theory, shapingba district

Procedia PDF Downloads 47

Authors: R. Sri Karthi Krishna, A. Saravana Kumar, Kesava Brahmaji, V. S. Vinoj

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The lunar surface may contain rough and non-uniform terrain with dips and peaks. Soft-landing is a method of landing the lander on the lunar surface without any damage to the vehicle. This project focuses on finding a safe landing site for the vehicle by developing a method for the lateral velocity determination of the lunar lander. This is done by processing the real time images obtained by means of an on-board vision sensor. The hazard avoidance phase of the soft-landing starts when the vehicle is about 200 m above the lunar surface. Here, the lander has a very low velocity of about 10 cm/s:vertical and 5 m/s:horizontal. On the detection of a hazard the lander is navigated by controlling the vertical and lateral velocity. In order to find an appropriate landing site and to accordingly navigate, the lander image processing is performed continuously. The images are taken continuously until the landing site is determined, and the lander safely lands on the lunar surface. By integrating this vision-based navigation with the INS a better accuracy for the soft-landing of the lunar lander can be obtained.

Keywords: vision aided INS, image processing, lateral velocity estimation, materials engineering

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Authors: Wambugu Naftaly, Ruisheng Wang, Zhijun Wang

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Models based on convolutional neural networks (CNNs), in conjunction with Transformer, have excelled in semantic segmentation, a fundamental task for intelligent Earth observation using remote sensing (RS) imagery. Nonetheless, tokenization in the Transformer model undermines object structures and neglects inner-patch local information, whereas CNNs are unable to simulate global semantics due to limitations inherent in their convolutional local properties. The integration of the two methodologies facilitates effective global-local feature aggregation and interactions, potentially enhancing segmentation results. Inspired by the merits of CNNs and Transformers, we introduce a layer-level feature aggregation network (LLFA-Net) to address semantic segmentation of fine-resolution remote sensing (FRRS) images for land cover classification. The simple yet efficient system employs a transposed unit that hierarchically utilizes dense high-level semantics and sufficient spatial information from various encoder layers through a layer-level feature aggregation module (LLFAM) and models global contexts using structured Transformer blocks. Furthermore, the decoder aggregates resultant features to generate rich semantic representation. Extensive experiments on two public land cover datasets demonstrate that our proposed framework exhibits competitive performance relative to the most recent frameworks in semantic segmentation.

Keywords: land cover mapping, semantic segmentation, remote sensing, vision transformer networks, deep learning

Procedia PDF Downloads 13