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16th International Conference on Probabilistic Safety Assessment and Management, PSAM 2022

作者： Zhao, Yunfei Huang, Linan Zhu, Quanyan Smidts, Carol Nuclear Engineering Program Department of Mechanical and Aerospace Engineering The Ohio State University 201 W 19th Ave ColumbusOH43210 United States Department of Electrical and Computer Engineering New York University 370 Jay Street BrooklynNY11201 United States

The trend of digitization in various industrial systems has exposed them to an increasing number of cyberattacks. Therefore, it is of vital importance to reduce the cybersecurity risk of industrial systems through cost-effective decisions on cybersecurity investment. In making such decisions, the defender is usually faced with challenges that arise from incomplete information about the attacker. In this paper, we propose a Bayesian game approach to model the optimal cybersecurity investment strategy under such situations. In this approach, the defender categorizes the attacker into a finite number of types, e.g., various levels of capability, and assigns a probability distribution over the different types of attackers. Then, the defender optimizes his/her cybersecurity investment based on risk assessment considering the possible attack efforts of these various types of attackers, with the objective of minimizing the expected cyberattack loss and the cybersecurity investment cost. The proposed method is demonstrated using a numerical example. We perform a sensitivity analysis for model parameters that can be difficult to obtain in practical applications, e.g., the defender's loss caused by a successful attack. Key observations of the example include the threshold principle (i.e., the defender should not make any investment if the loss of a successful attack is below a certain threshold) and the conservation of loss (i.e., losses for one type of attacker may correspond to gains for another type of attacker). The proposed method can be used to support cybersecurity investment decisions by industrial system owners. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.

关键词： Investments

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Implementation of LEACH Protocol for Cluster Head Selection in D2D Cooperative Communication Systems

Implementation of LEACH Protocol for Cluster Head Selection ...

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2021 International Conference on computer System, Information Technology, and electrical engineering, COSITE 2021

作者： Raziah, Isyatur Yunida, Yunida Muharar, Rusdha Away, Yuwaldi Nasaruddin, Nasaruddin Universitas Syiah Kuala Doctoral Program School of Engineering Banda Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9781665425094

Device-to-device communication (D2D) is a promising technology for future mobile networks that allow users to communicate directly without using the cellular system's infrastructure. However, since the battery power of each mobile device is limited, it is necessary to extend the network life by reducing its energy consumption. Clustering-based protocols could reduce the energy consumption by balancing all nodes into cluster heads (CH). This paper implements the recent hierarchical routing based on the Low Energy Adaptive Hierarchical Clustering (LEACH) protocol for a CH selection to increase device lifetime in cooperative D2D communication systems. First, the CH is selected based on each device's distance and the residual energy. Then, the LEACH protocol is implemented to choose CH in the D2D cooperative communication system to reduce energy consumption and increase the network life and aims to increase the reliability of the cooperative D2D communication system. Moreover, the proposed system model is provided to analyze cluster formation and energy consumption using computer simulation. The simulation results show that implementing the LEACH protocol can increase network life by knowing the number of dead devices and reducing energy consumption in cooperative D2D communication systems. © 2021 IEEE.

关键词： Energy utilization

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A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORING IL-6 BIOMARKER 25

A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORIN...

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25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2021

作者： Laliberte, Kaitlyn E. Scott, Patrick Khan, Niazul I. Mahmud, Md Shaad Song, Edward Department of Electrical and Computer Engineering University of New Hampshire United States Materials Science Program University of New Hampshire United States

ISBN: (纸本)9781733419031

Graphene field-effect transistor (GFET) is becoming an increasingly popular biosensing platform for monitoring health conditions through biomarker detection. Moreover, the graphene's 2-dimensional geometry makes it ideal for implementing flexible wearable devices. In this work, we present a printable graphene transistor-based biosensor that can be used as a wearable sensor to detect a protein biomarker Interleukin-6 (IL-6). Our device contains a voltage control, a current meter, and a power source all integrated into a single miniaturized platform making it a truly skin-mounted wearable device. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Graphene transistors

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Measurement of Muscle Stimulation and Hot Compression for Rehabilitation 3

Measurement of Muscle Stimulation and Hot Compression for Re...

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3rd International Conference on electrical, Communication and computer engineering, ICECCE 2021

作者： Chirakanphaisarn, Neramitr Thongkanluang, Thadsanee Thammasat University Faculty of Engineering Department of Electrical and Computer Engineering Thailand Suratthani Rajabhat University Program in Materials Technology Manufacturing Faculty of Science and Technology Thailand

ISBN: (纸本)9781665438971

This research is to study the electric muscle stimulation system and hot compress. As well as focusing on building tools for applications in rehabilitation medicine and physical therapy. The neuromuscular system is an important mechanism in human life. In the event that the muscles are stressed or contracted while the subject is recovering, the need for recovery and stimulation tools is an important part of the physical therapy. We designed electrical circuits for automatic control and used an ARDUINO microcontroller to connect analog and digital ports to transmit and receive clinical parameters. The number of voluntary subjects was 20. Each subject remembered to perform 5 stimulation sessions for 10 minutes. Total period that the researcher collects data is approximately 3 months. The trials were based on ethical studies in humans, where trials and researches showed that subjects were 980% satisfied with very few side effects, and subjects had improved muscle development. © 2021 IEEE.

关键词： Muscle

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A Computational Model of Children's Learning and Use of Probabilities Across Different Ages

arXiv

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arXiv 2023年

作者： Wang, Zilong Shultz, Thomas R. Nobandegani, Ardavan S. Integrated Program in Neuroscience McGill University Canada Department of Psychology School of Computer Science McGill University Canada Department of Psychology Department of Electrical & Computer Engineering McGill University Canada

Recent empirical work has shown that human children are adept at learning and reasoning with probabilities. Here, we model a recent experiment investigating the development of school-age children's non-symbolic probability reasoning ability using the Neural Probability Learner and Sampler (NPLS) system. We demonstrate that NPLS can accurately simulate children's probability judgments at different ages, tasks and difficulty levels to discriminate two probabilistic choices through accurate probability learning and sampling. An extension of NPLS using a skewed heuristic distribution can also model children's tendency to wrongly select the outcome with more favorable items but less likely to draw the favorable ones when the probabilistic choices are similar. We discuss the roles of two model parameters that can be adjusted to simulate the probability matching versus probability maximization phenomena in children, and why frequency biases children's probabilistic judgments. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Deep Learning Networks for Handwritten Bangla Character Recognition

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IAENG International Journal of Applied Mathematics 2023年第4期53卷

作者： Begum, H. Islam, M.M. Eva, H.S. Emon, N.H. Siddique, F.A. Department of Electrical and Electronic Engineering East West University A/2 Aftabnagar Dhaka Bangladesh Department of Electrical Engineering and Computer Science South Dakota State University United States Engineering and Design Department Reverie Power and Automation Engineering Limited Bangladesh Program in Smart System Integrated Solutions Aalto University Finland

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Classification (of information)

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Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting

Hybrid Deep Learning Model for Accurate Short-Term Electrici...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Arash Moradzadeh Mostafa Mouhammadpourfard Yang Weng Suhas Pol S. M. Muyeen Department of Electrical Engineering Qatar University Doha Qatar National Wind Institute Texas Tech University Lubbock TX USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Renewable Energy Program Texas Tech University Lubbock TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced techniques, including data preprocessing and denoising through a Stacked Denoising Autoencoder (SDAE), along with enhanced temporal modeling via Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks. By capturing the complex dynamics inherent in electricity pricing data, the proposed hybrid model significantly enhances forecasting accuracy. Trained on data from 2017 and 2018, with 2019 used for testing, the model achieves a strong correlation coefficient (R = 99.86%) and substantially lowers forecasting errors. Comparative evaluations against established forecasting methods highlight the model's superior performance. This work demonstrates the practical value of deep learning techniques in the energy sector, particularly in responding to the volatility of demand and supply in real-time electricity markets.

关键词： Deep learning Accuracy Electricity Noise reduction Bidirectional long short term memory Predictive models Data models Stakeholders Forecasting Testing

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Assessment of ATH-Filled Silicone Rubber Under DC Dry Band Arcing 24

Assessment of ATH-Filled Silicone Rubber Under DC Dry Band A...

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24th International Middle East Power System Conference, MEPCON 2023

作者： Mehmood, Basharat Ghunem, Refat El-Hag, Ayman Hassan, Mohammad K. Al-Sulaiti, Leena Abdala, Ahmed University of Waterloo Electrical & Computer Engineering Department 200 University Avenue W WaterlooONN2L 3G1 Canada OttawaONK1A0R6 Canada Qatar University Center for Advanced Materials Qatar Texas A&m University at Qatar Chemical Engineering Program Doha Qatar

ISBN: (纸本)9798350358469

This paper investigates the tracking and erosion performance of silicone rubber filled with alumina trihydrate under DC dry band arcing in the inclined plane tracking and erosion test (IPT). Alumina trihydrate is incorporated into silicone rubber at 50 wt.% loading level for preparing silicone rubber composites, which are exposed to DC dry band arcing under mild and severe voltage levels in the inclined plane tracking and erosion test. Test outcomes indicate that the mechanism of silicone rubber deterioration changes with respect to the severity of the DC dry band arcing. It is found that dry band arcing tends to stimulate surface tracking under mild voltage levels and erosion due to thermal degradation under relatively higher voltage levels (critical). Moreover, test results indicate that silicone rubber erosion is not solely influenced by the dry band arcing power derived from the leakage current, but also dependent on additional contributing mechanisms leading to elevated surface temperatures and severe erosion. © 2023 IEEE.

关键词： Erosion

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Effect of Parasitic Leakage Currents Associated with the Regrown Aperture of AlGaN/GaN Current Aperture Vertical Electron Transistors (CAVETs)

Effect of Parasitic Leakage Currents Associated with the Reg...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sara Pouladi Jae Hyun Ryou Department of Mechanical Engineering Texas Center for Superconductivity at UH Advanced Manufacturing Institute University of Houston USA Department of Electrical and Computer Engineering Materials Science and Engineering Program University of Houston USA

We present a numerical simulation on the effect of leakage paths in the regrown GaN layer in the aperture and above the current blocking layer (CBL) of current aperture vertical electron transistor (CAVET) devices. Here, a 2D TCAD modeling is employed to simulate a CAVET device structure considering two main origins of parasitic leakage current from CBL/regrown-GaN interface and gate/regrown-GaN bulk and their degree of detrimental effect on the characteristics of AlGaN/GaN CAVETs.

关键词： Performance evaluation Apertures Logic gates Numerical simulation Wide band gap semiconductors Numerical models Leakage currents

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Short Wave Infrared Upconversion Imager with Electron Blocking Layer to Screen Electron Injection into Photosensitive Layer

Short Wave Infrared Upconversion Imager with Electron Blocki...

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IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS)

作者： Chanho Shin Ning Li Tse Nga Ng Material Science and Engineering Program University of California San Diego La Jolla USA Department of Electrical and Computer Engineering University of California San Diego La Jolla USA

In this work, we demonstrated upconversion imagers integrated with shortwave infrared photodetectors paired with an electron blocking layer. The use of electron blocking layer screened charge injection to prevent recombination in photosensitive layer. The characteristics of each electron blocking layer were analyzed in aspects of noise and detectivity. For the optimized device, the parasitic luminance in the dark was efficiently suppressed, and the photon-to-photon efficiency was increased. The electron blocking layer used in this work is generally applicable for upconversion imagers using different absorption and emitting materials.

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