Search results for: distributed network

Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

Procedia PDF Downloads 337

Authors: Rodrigo Calili, Anna Carolina Sermarini, João Henrique Azevedo, Vanessa Cardoso de Albuquerque, Felipe Gonçalves, Gilberto Jannuzzi

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The diffusion of Distributed Energy Resources (DER) has caused structural changes in the relationship between consumers and electrical systems. The Photovoltaic Distributed Generation (PVDG), in particular, is an essential strategy for achieving the 2030 Agenda goals, especially SDG 7 and SDG 13. However, it is observed that most projects involving this technology in Brazil are restricted to the wealthiest classes of society, not yet reaching the low-income population, aligned with theories of energy justice. Considering the research for energy equality, one of the policies adopted by governments is the social electricity tariff (SET), which provides discounts on energy tariffs/bills. However, just granting this benefit may not be effective, and it is possible to merge it with DER technologies, such as the PVDG. Thus, this work aims to evaluate the economic viability of the policy to replace the social electricity tariff (the current policy aimed at the low-income population in Brazil) by PVDG projects. To this end, a proprietary methodology was developed that included: mapping the stakeholders, identifying critical variables, simulating policy options, and carrying out an analysis in the Brazilian context. The simulation answered two key questions: in which municipalities low-income consumers would have lower bills with PVDG compared to SET; which consumers in a given city would have increased subsidies, which are now provided for solar energy in Brazil and for the social tariff. An economic model was created for verifying the feasibility of the proposed policy in each municipality in the country, considering geographic issues (tariff of a particular distribution utility, radiation from a specific location, etc.). To validate these results, four sensitivity analyzes were performed: variation of the simultaneity factor between generation and consumption, variation of the tariff readjustment rate, zeroing CAPEX, and exemption from state tax. The behind-the-meter modality of generation proved to be more promising than the construction of a shared plant. However, although the behind-the-meter modality presents better results than the shared plant, there is a greater complexity in adopting this modality due to issues related to the infrastructure of the most vulnerable communities (e.g., precarious electrical networks, need to reinforce roofs). Considering the shared power plant modality, many opportunities are still envisaged since the risk of investing in such a policy can be mitigated. Furthermore, this modality can be an alternative due to the mitigation of the risk of default, as it allows greater control of users and facilitates the process of operation and maintenance. Finally, it was also found, that in some regions of Brazil, the continuity of the SET presents more economic benefits than its replacement by PVDG. However, the proposed policy offers many opportunities. For future works, the model may include other parameters, such as cost with low-income populations’ engagement, and business risk. In addition, other renewable sources of distributed generation can be studied for this purpose.

Keywords: low income, subsidy policy, distributed energy resources, energy justice

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Ojin Kwon, Yong-Jin Yoon, Liu Xin, Zhang Hongbao

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Wireless Body Area Network (WBAN) is a short-range wireless communication around human body for various applications such as wearable devices, entertainment, military, and especially medical devices. WBAN attracts the attention of continuous health monitoring system including diagnostic procedure, early detection of abnormal conditions, and prevention of emergency situations. Compared to cellular network, WBAN system is more difficult to control inter- and inner-cell interference due to the limited power, limited calculation capability, mobility of patient, and non-cooperation among WBANs. In this paper, we compare the performance of resource allocation scheme based on several Pseudo Orthogonal Codewords (POCs) to mitigate inter-WBAN interference. Previously, the POCs are widely exploited for a protocol sequence and optical orthogonal code. Each POCs have different properties of auto- and cross-correlation and spectral efficiency according to its construction of POCs. To identify different WBANs, several different pseudo orthogonal patterns based on POCs exploits for resource allocation of WBANs. By simulating these pseudo orthogonal resource allocations of WBANs on MATLAB, we obtain the performance of WBANs according to different POCs and can analyze and evaluate the suitability of POCs for the resource allocation in the WBANs system.

Keywords: wireless body area network, body sensor network, resource allocation without feedback, interference mitigation, pseudo orthogonal pattern

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Authors: Fadhl Alakwaa

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One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.

Keywords: gene regulatory network, biclustering, denoising, system biology

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Authors: Ashish Payal, C. S. Rai, B. V. R. Reddy

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With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m² grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

Keywords: localization, wireless sensor networks, artificial neural network, radial basis function, multi-layer perceptron, backpropagation, RSSI, GPS

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Authors: Reza Moslemi, Sebastien Perrier

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Water infrastructure deterioration can result in increased operational costs owing to increased repair needs and non-revenue water and consequently cause a reduced level of service and customer service satisfaction. Various water main condition assessment technologies have been introduced to the market in order to evaluate the level of pipe deterioration and to develop appropriate asset management and pipe renewal plans. One of the challenges for any condition assessment and inspection program is to determine the percentage of the water network and the combination of pipe segments to be inspected in order to obtain a meaningful representation of the status of the entire water network with a desirable level of accuracy. Traditionally, condition assessment has been conducted by selecting pipes based on age or location. However, this may not necessarily offer the best approach, and it is believed that by using a smart sampling methodology, a better and more reliable estimate of the condition of a water network can be achieved. This research investigates three different sampling methodologies, including random, stratified, and systematic. It is demonstrated that selecting pipes based on the proposed clustering and sampling scheme can considerably improve the ability of the inspected subset to represent the condition of a wider network. With a smart sampling methodology, a smaller data sample can provide the same insight as a larger sample. This methodology offers increased efficiency and cost savings for condition assessment processes and projects.

Keywords: condition assessment, pipe degradation, sampling, water main

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Authors: Haoqi Gao, Koichi Ogawara

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Retinal vascular segmentation of color fundus is the basis of ophthalmic computer-aided diagnosis and large-scale disease screening systems. Early screening of fundus diseases has great value for clinical medical diagnosis. The traditional methods depend on the experience of the doctor, which is time-consuming, labor-intensive, and inefficient. Furthermore, medical images are scarce and fraught with legal concerns regarding patient privacy. In this paper, we propose a new Generative Adversarial Network based on CycleGAN for retinal fundus images. This method can generate not only synthetic fundus images but also generate corresponding segmentation masks, which has certain application value and challenge in computer vision and computer graphics. In the results, we evaluate our proposed method from both quantitative and qualitative. For generated segmented images, our method achieves dice coefficient of 0.81 and PR of 0.89 on DRIVE dataset. For generated synthetic fundus images, we use ”Toy Experiment” to verify the state-of-the-art performance of our method.

Keywords: retinal vascular segmentations, generative ad-versarial network, cyclegan, fundus images

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Authors: Li Guo, Jianhong Wang, Dian Huang, Shengzhong Feng

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Computing networks as a new network architecture has shown great promise in boosting the utilization of different resources, such as computing, caching, and communications. To maximise the efficiency of resource orchestration in computing network control systems (CNCSs), this work proposes a dynamic orchestration strategy of a different resource based on task requirements from computing power requestors (CPRs). Specifically, computing power providers (CPPs) in CNCSs could share information with each other through communication channels on the basis of blockchain technology, especially their current idle resources. This dynamic process is modeled as a cooperative game in which CPPs have the same target of maximising long-term rewards by improving the resource utilization ratio. Meanwhile, the task requirements from CPRs, including size, deadline, and calculation, are simultaneously considered in this paper. According to task requirements, the proposed orchestration strategy could schedule the best-fitting resource in CNCSs, achieving the maximum long-term rewards of CPPs and the best quality of experience (QoE) of CRRs at the same time. Based on the EdgeCloudSim simulation platform, the efficiency of the proposed strategy is achieved from both sides of CPRs and CPPs. Besides, experimental results show that the proposed strategy outperforms the other comparisons in all cases.

Keywords: computing network control systems, resource orchestration, dynamic scheduling, blockchain, cooperative game

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Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

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Authors: Abhishek Jain, Kavish Kejriwal, V. Balaji Rao, Abhigna Chavda

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Increasing environmental and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a supply chain. A challenging task in today’s food industry is the distribution of high-quality food items throughout the food supply chain. Improper storage and unwanted transportation are the major hurdles in food supply chain and can be tackled by making dynamic storage facility location decisions with the distribution network. Since food supply chain in India is one of the biggest supply chains in the world, the companies should also consider environmental impact caused by the supply chain. This project proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a food supply chain network (SCN). A Multi-Objective Mixed-Integer Linear Programming (MOMILP) model between overall cost and environmental impact caused by the SCN is formulated for the problem. The goal of MOMILP is to determine the pareto solutions for overall cost and environmental impact caused by the supply chain. This is solved by using GAMS with CPLEX as third party solver. The outcomes of the project are pareto solutions for overall cost and environmental impact, facilities to be operated and the amount to be transferred to each warehouse during the time horizon.

Keywords: multi-objective mixed linear programming, food supply chain network, GAMS, multi-product, multi-period, environment

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

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Authors: Go Woon Kwak, Siyoung Jun, Soyun Maeng, Haeyoung Lee

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Entry is an education platform used in South Korea, created to help students learn to program, in which they can learn to code while playing. Using the online version of the entry, teachers can easily assign programming homework to the student and the students can make programs simply by linking programming blocks. However, the programs may be made by others, so that the authors of the programs should be identified. In this paper, as the first step toward author identification of entry programs, we present an artificial neural network based classification approach to identify genders of authors of a program written in an entry. A neural network has been trained from labeled training data that we have collected. Our result in progress, although preliminary, shows that the proposed approach could be feasible to be applied to the online version of entry for gender identification of authors. As future work, we will first use a machine learning technique for age identification of entry programs, which would be the second step toward the author identification.

Keywords: artificial intelligence, author identification, deep neural network, gender identification, machine learning

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Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

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Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Chia-Chi Chang

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Most battery-driven wireless sensor devices will enter in sleep mode as soon as possible to extend the overall lifetime of a sensor network. It is necessary to turn off unnecessary radio and peripheral functions, especially the radio unit always consumes more energy than other components during wireless communication. The microcontroller is the most important part of the wireless sensor device. It is responsible for the manipulation of sensing data and communication protocols. The microcontroller always has different sleep modes, each with a different level of energy usage. The deeper the sleep, the lower the energy consumption. Most wireless sensor devices can only enter the sleep mode: the external low-frequency oscillator is still running to wake up the sleeping microcontroller when the sleep timer expires. In this paper, our sensor device can enter the extended sleep mode: none of the oscillator is running and the wireless sensor device has the nanoampere consumption and self-awaking ability. Finally, these wireless sensor devices were deployed in a smart home security network.

Keywords: wireless sensor network, battery-driven, sleep mode, home security

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Authors: Fiseha M. Guangul, Annissa Muhammed, Aja O. Chikere

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Group task is one method to create the conducive environment for the active teaching-learning process. Performing group task with active involvement of students will benefit the students in many ways. However, in most cases all students do not participate actively in the group task, and hence the intended benefits are not acquired. This paper presents the improvements of students’ participation in the group task and learning from the group task by introducing different techniques to enhance students’ participation. For the purpose of this research Carpentry and Joinery II (WT-392) course from Wood Technology Department at Adama Science and Technology University was selected, and five groups were formed. Ten group tasks were prepared and the first five group tasks were distributed to the five groups in the first day without introducing the techniques that are used to enhance participation of students in the group task. On another day, the other five group tasks were distributed to the same groups and various techniques were introduced to enhance students’ participation in the group task. The improvements of students’ learning from the group task after the implementation of the techniques. After implementing the techniques the evaluation showed that significant improvements were obtained in the students’ participation and learning from the group task.

Keywords: group task, students participation, active learning, the evaluation method

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

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Authors: Maximilian Richter

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Implementing shared autonomous electric vehicles (SAEVs) has many advantages. The main advantages are achieved when SAEVs are offered as on-demand services by a fleet operator. However, autonomous mobility on demand (AMoD) will be distributed nationwide only if a fleet operation is economically profitable for the operator. This paper proposes a microscopic approach to modeling two implementation scenarios of an AMoD fleet. The city of Zurich is used as a case study, with the results and findings being generalizable to other similar European and North American cities. The data are based on the traffic model of the canton of Zurich (Gesamtverkehrsmodell des Kantons Zürich (GVM-ZH)). To determine financial profitability, demand is based on the simulation results and combined with analyzing the costs of a SAEV per kilometer. The results demonstrate that depending on the scenario; journeys can be offered profitably to customers for CHF 0.3 up to CHF 0.4 per kilometer. While larger fleets allowed for lower price levels and increased profits in the long term, smaller fleets exhibit elevated efficiency levels and profit opportunities per day. The paper concludes with recommendations for how fleet operators can prepare themselves to maximize profit in the autonomous future.

Keywords: autonomous vehicle, mobility on demand, traffic simulation, fleet provider

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Authors: Li Yongyi

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In order to explore the distribution of geological disasters within the expressway area of Shaanxi Province, the Analytic Hierarchy Process theory is applied based on the geographic information system technology platform, and the ground elevation, rainfall, vegetation coverage and other indicators are selected for analysis, and the expressway area is sensitive Sexual evaluation. The results show that the highway area disasters in Shaanxi Province are mainly distributed in the southern mountainous areas and are dominated by landslides; the disaster area ratio basically increases with the increase in ground elevation, surface slope, surface undulation, rainfall, and vegetation coverage. The increase in the distance from the river shows a decreasing trend; after grading the disaster sensitivity within 5km of the expressway, the extremely sensitive area, the highly sensitive area, the medium sensitive area, the low sensitive area, and the extremely low sensitive area respectively account for 8.17%、15.80%、22.99%、26.22%、26.82%. Highly sensitive road areas are mainly distributed in southern Shaanxi.

Keywords: highway engineering, sensitivity, analytic hierarchy process, geological hazard, road area

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Authors: Ather Saeed, Arif Khan, Jeffrey Gosper

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Sensor devices are prone to errors and sudden node failures, which are difficult to detect in a timely manner when deployed in real-time, hazardous, large-scale harsh environments and in medical emergencies. Therefore, the loss of data can be life-threatening when the sensed phenomenon is not disseminated due to sudden node failure, battery depletion or temporary malfunctioning. We introduce a set of partial differential equations for localizing faults, similar to Green’s and Maxwell’s equations used in Electrostatics and Electromagnetism. We introduce a node organization and clustering scheme for self-stabilizing sensor networks. Green’s theorem is applied to regions where the curve is closed and continuously differentiable to ensure network connectivity. Experimental results show that the proposed GTFD (Green’s Theorem fault-detection and Self-stabilization) protocol not only detects faulty nodes but also accurately generates network stability graphs where urgent intervention is required for dynamically self-stabilizing the network.

Keywords: Green’s Theorem, self-stabilization, fault-localization, RSSI, WSN, clustering

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Authors: Jared Oluoch

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Connected vehicles are equipped with wireless sensors that aid in Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication. These vehicles will in the near future provide road safety, improve transport efficiency, and reduce traffic congestion. One of the challenges for connected vehicles is how to ensure that information sent across the network is secure. If security of the network is not guaranteed, several attacks can occur, thereby compromising the robustness, reliability, and efficiency of the network. This paper discusses existing security mechanisms and unique properties of connected vehicles. The methodology employed in this work is exploratory. The paper reviews existing security solutions for connected vehicles. More concretely, it discusses various cryptographic mechanisms available, and suggests areas of improvement. The study proposes a combination of symmetric key encryption and public key cryptography to improve security. The study further proposes message aggregation as a technique to overcome message redundancy. This paper offers a comprehensive overview of connected vehicles technology, its applications, its security mechanisms, open challenges, and potential areas of future research.

Keywords: VANET, connected vehicles, 802.11p, WAVE, DSRC, trust, security, cryptography

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Authors: Ahmed Tarek, Ahmed Alveed

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Vehicle routing problem is a well-known re-search avenue in computing. Modern vehicle routing is more focused with the GPS-based coordinate system, as the state-of-the-art vehicle, and trucking systems are equipped with digital navigation. In this paper, a new two dimensional coordinate-based algorithm for addressing the vehicle routing problem for a supply chain network is proposed and explored, and the algorithm is compared with other available, and recently devised heuristics. For the algorithms discussed, which includes the pro-posed coordinate-based search heuristic as well, the advantages and the disadvantages associated with the heuristics are explored. The proposed algorithm is studied from the stand point of a small supermarket chain delivery network that supplies to its stores in four different states around the East Coast area, and is trying to optimize its trucking delivery cost. Minimizing the delivery cost for the supply network of a supermarket chain is important to ensure its business success.

Keywords: coordinate-based optimal routing, Hamiltonian Circuit, heuristic algorithm, traveling salesman problem, vehicle routing problem

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Authors: Vanessa Cho, Janet Hsiao, Nigel King, Robert Anthonappa

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Background: The personal protective equipment (PPE) requirements for health care workers (HCWs) have changed significantly during the COVID-19 pandemic. Aim: To ascertain, using eye-tracking technology, what children notice the most when seeing HCWs in various PPE. Design: A Tobii nano pro-eye-tracking camera tracked 156 children's visual attention while they viewed photographs of HCWs in various PPEs. Eye Movement analysis with Hidden Markov Models (EMHMM) was employed to analyse 624 recordings using two approaches, namely (i) data-driven where children's fixation determined the regions of interest (ROIs), and (ii) fixed ROIs where the investigators predefined the ROIs. Results: Two significant eye movement patterns, namely distributed(85.2%) and selective(14.7%), were identified(P<0.05). Most children fixated primarily on the face regardless of the different PPEs. Children fixated equally on all PPE images in the distributed pattern, while a strong preference for unmasked faces was evident in the selective pattern (P<0.01). Conclusion: Children as young as 2.5 years used a top-down visual search behaviour and demonstrated their face processing ability. Most children did not show a strong visual preference for a specific PPE, while a minority preferred PPE with distinct facial features, namely without masks and loupes.

Keywords: COVID-19, PPE, dentistry, pediatric

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Authors: Meron Okbandrias

Abstract:

The South African formal economy is rule-based economy, unlike most African and Asian markets. It has a highly developed financial market. In such a market, foreign migrants have dominated the small or spaza shops that service the poor. They are highly competitive and capture significant market share in South Africa. This paper analyses the factors that assisted the foreign migrants in having a competitive age. It does that by interviewing Somali, Bangladesh, and Ethiopian shop owners in Cape Town analysing the data through a narrative analysis. The paper also analyses the 2019 South African consumer report. The three migrant nationalities mentioned above dominate the spaza shop business and have significant distribution networks. The findings of the paper indicate that family, ethnic, and nationality based network, in that order of importance, form bases for a relationship-based business network that has trust as its mainstay. Therefore, this network ensures the pooling of resources and abiding by certain principles outside the South African rule-based system. The research identified practises like bulk buying within a community of traders, sharing information, buying from a within community distribution business, community based transportation system and providing seed capital for people from the community to start a business is all based on that relationship-based system. The consequences of not abiding by the rules of these networks are social and economic exclusion. In addition, these networks have their own commercial and social conflict resolution mechanisms aside from the South African justice system. Network theory and relationship based systems theory form the theoretical foundations of this paper.

Keywords: migrant, spaza shops, relationship-based system, South Africa

Procedia PDF Downloads 121

Authors: Rejab Hajlaoui, Tarek Moulahi, Hervé Guyennet

Abstract:

In a highway scenario, the vehicle speed can exceed 120 kmph. Therefore, any vehicle can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To ensure an effective stability in vehicular ad hoc networks with minimum broadcasting storm, we have developed a weighted algorithm based on the k-medoids clustering algorithm (WKCA). Indeed, the number of clusters and the initial cluster heads will not be selected randomly as usual, but considering the available transmission range and the environment size. Then, to ensure optimal assignment of nodes to clusters in both k-medoids phases, the combined weight of any node will be computed according to additional metrics including direction, relative speed and proximity. Empirical results prove that in addition to the convergence speed that characterizes the k-medoids algorithm, our proposed model performs well both AODV-Clustering and OLSR-Clustering protocols under different densities and velocities in term of end-to-end delay, packet delivery ratio, and throughput.

Keywords: communication, clustering algorithm, k-medoids, sensor, vehicular ad hoc network

Procedia PDF Downloads 230

Authors: Qaiser Munir, Hamid Hussain

Abstract:

Oil prices are believed to have a major impact on several economic indicators, leading to several instances where a comparison between oil prices and a trade deficit of oil-importing countries have been carried out. Building upon the narrative, this paper sheds light on the ongoing debate by inquiring upon the possibility of asymmetric linkages between oil prices, industrial production, exchange rate, whole price index, and trade deficit. The analytical tool used to further understand the complexities of a recent approach called nonlinear auto-regressive distributed lag model (NARDL) is utilised. Our results suggest that there are significant asymmetric effects among the main variables of interest. Further, our findings indicate that any variation in oil prices, industrial production, exchange rate, and whole price index on trade deficit tend to fluctuate in the long run. Moreover, the long-run picture denotes that increased oil price leads to a negative impact on the trade deficit, which, in its true essence, is a disproportionate impact. In addition to this, the Wald test simultaneously conducted concludes the absence of any significant evidence of the asymmetry in the oil prices impact on the trade balance in the short-run.

Keywords: trade deficit, oil prices, developing economy, NARDL

Procedia PDF Downloads 127