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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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Torque Ripple Reduction by Injecting q-axis Suppression Current for Half-wave Rectified Brushless Synchronous Motors

Torque Ripple Reduction by Injecting q-axis Suppression Curr...

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International Conference on electrical Machines and Systems (ICEMS)

作者： Kohei Kanaida Tetsuji Daido Shin-Ichi Hamasaki Takashi Abe Division of Electrical Engineering and Computer Science Nagasaki University Japan

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

In recent years, electric vehicles have become widespread from the viewpoint of environmental issues. Variable magnetic field motors have been proposed as a solution to environmental issues. The half-wave rectified variable field brushless synchronous motor uses a high-frequency current that varies the field magnetic flux by superimposing it on the stator current. One of the problems of this motor is that torque pulsation occurs by the high-frequency exciting current. In this study, we propose a method to suppress torque pulsation. The q-axis compensation current for torque pulsation is derived by a mathematical model, and its effectiveness is verified through a prototype machine.

关键词： Magnetic flux Torque Brushless motors Prototypes Stators Synchronous motors Mathematical models

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Operation Optimization of Laser-Powered Aerial Data Harvesting for Passive IoT Networks

Operation Optimization of Laser-Powered Aerial Data Harvesti...

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IEEE Conference on Wireless Communications and Networking

作者： Amr M. Abdelhady Abdulkadir Çelik Carles Diaz-Vilor Hamid Jafarkhani Ahmed M. Eltawil Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah Province Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Center for Pervasive Communications and Computing University of California Irvine CA USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper investigates the maximization of har-vested data in a laser-powered uncrewed aerial vehicle (UAV) supporting Internet of Things (IoT) deployment. The system enables battery-free IoT devices to establish communication links with the UAV via bistatic backscattering with the aid of a power beacon source. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of mixed convex/concave positive functions. Finally, the simulations conducted show that the proposed algorithm provides 90% increase in collected data under different operation conditions.

关键词： Wireless communication Heuristic algorithms Power lasers Data collection Autonomous aerial vehicles Trajectory Internet of Things

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Towards Detection of Hirschsprung's Disease using Dielectric Properties: Feasibility in thin mice colon

Towards Detection of Hirschsprung's Disease using Dielectric...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Samaddar, Poulami Gaddam, Sunil Gopalakrishnan, Keerthy Cherukuri, Akhila Modi, Vaishnavi Dey, Shuvashis Roy, Sayan Mitra, Dipankar Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Dept. of Medicine RochesterMN United States North Dakota State University Dept. of Electrical and Computer Engineering FargoND United States South Dakota Mines Dept. of Electrical Engineering and Computer Science Rapid CitySD United States University of Wisconsin-La Crosse Dept. of Computer Science La CrosseWI United States Mayo Clinic Department of Radiology RochesterMN United States

ISBN: (纸本)9781665442282

Hirschsprung disease is a congenital disability due to the defect in migration of colonic ganglion. Definitive diagnosis is based on the findings of histopathological evaluation of rectal biopsies which can be highly variable. Post-op perforations and bleeding are major complications of rectal biopsy. Difficulty in establishing a definitive diagnosis leads to severe complications. Research studies on the myoelectrical activity in Hirschsprung's disease have shown disorganized electrical activities and variations in spontaneous membrane activity in the affected colon. electrical properties can be measured as complex permittivity using dielectric spectroscopy as an attempt to diagnose Hirschsprung's disease. Dielectric spectroscopy measurements on thin biological samples offer challenges due to the requirements of sample size and probe positioning. In this work, the feasibility of dielectric property measurement of thin colon of early postnatal mice using the coaxial probe method is investigated. Complex permittivity of colon tissue was calculated from simulated return loss using a previously published mathematical model and compared with measured results. The results showed very good agreement from 500 MHz to 8 GHz range and demonstrate the potential for using dielectric properties as a biomarker for Hirschsprung disease. © 2023 IEEE.

关键词： Electromagnetic simulation

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Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching

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IEEE Transactions on Communications 2025年

作者： Tharakan, Krishnendu S. Dahrouj, Hayssam Kouzayha, Nour Elsawy, Hesham Al-Naffouri, Tareq Y. Queen's University School of Computing Kingston Canada University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955-6900 Saudi Arabia

Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of O(1/√T) is obtained for the proposed algorithm, where T is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms. © 1972-2012 IEEE.

关键词： Federated learning

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Prediction and Electricity Forecasting on the Individual Household Level based on PSO-LSSVM Approach 7

Prediction and Electricity Forecasting on the Individual Hou...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Padmaja, Voleti Tamizhselvi, A. Kathir, I. Mohan, Arvind Pal, Subharun Rastogi, Ravi Geethanjali College of Engineering and Technology Electrical and Electronics Engineering Hyderabad India St. Joseph's College of Engineering Department of Information Technology Chennai India V.S.B. Engineering College Department of Electrical & Electronics Engineering Karur India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir Jagti India NIELIT Electronics Division Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350340600

When smart metering systems are used to encourage energy conservation at the residential level, new research challenges arise in the areas of monitoring usage and providing accurate load forecasts. Intelligent smart meters rely on accurate predictions of future electricity use. Many energy and operational improvements, such as more efficient appliances, renewable energy sources, and advanced metering systems, are included into micro-grids. Here is an alternative approach to estimating annual energy use in single-family homes. Incorporating the effects of residents' daily activities and appliance usages on overall home power consumption improves the accuracy of the forecasting model. Data preparation, feature selection, and model training are the three phases of the suggested approach. Data preprocessing usually entails cleaning the data and preparing the features to be used. When it comes to feature selection, the K-means clustering approach is frequently utilized. After using information gain for feature selection, the models are trained using PSO-LSSVM. The proposed method outperforms both GRU and LSSVM, two well-established competitors. © 2023 IEEE.

关键词： K-means clustering

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Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization

Optimizing Privacy and Utility Tradeoffs for Group Interests...

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International Joint Conference on Neural Networks (IJCNN)

作者： Bishwas Mandal George Amariucai Shuangqing Wei Department of Computer Science Kansas State University Manhattan KS USA Division of Electrical & Computer Engineering Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

We propose a novel problem formulation to address the privacy-utility tradeoff, specifically when dealing with two distinct user groups characterized by unique sets of private and utility attributes. Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes and often rely on auxiliary datasets or manual annotations, we introduce a collaborative data-sharing mechanism between two user groups through a trusted third party. This third party uses adversarial privacy techniques with our proposed data-sharing mechanism to internally sanitize data for both groups and eliminates the need for manual annotation or auxiliary datasets. Our methodology ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features. Importantly, even if analysts or adversaries possess auxiliary datasets containing raw data, they are unable to accurately deduce private features. Additionally, our data-sharing mechanism is compatible with various existing adversarially trained privacy techniques. We empirically demonstrate the effectiveness of our approach using synthetic and real-world datasets, showcasing its ability to balance the conflicting goals of privacy and utility.

关键词： Data privacy Privacy Accuracy Annotations Neural networks Collaboration Manuals

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Study on ESS maximum capacity calculation and operation algorithm based on load pattern of substations in urban railway

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Transactions of the Korean Institute of electrical Engineers 2021年第9期70卷 1405-1412页

作者： Yun, Chi-Myeong Cho, Gyu-Jung Kim, Hyungchul Kim, Chul-Hwan Jung, Hosung Smart Electrical & Signaling Division Korea Railroad Research Institute Korea Republic of Dept. of Transportation Engineering Korea University of Science and Technology Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In this paper, we introduce the method of reducing only the morning peak of the ESS and the method of calculating the maximum ESS capacity according to the method of reducing the entire day by using the characteristic that the load of the urban railway substation shows a different load pattern depending on weekdays, weekends, and holidays. Furthermore, an operation method suitable for urban railroad is introduced using the operation algorithm for each of the five points of time of the ESS according to the load pattern. To verify the introduced maximum capacity calculation method and ESS operation algorithm, power analysis program, PSCAD/EMTDC was used. Copyright © The Korean Institute of electrical Engineers.

关键词： Cost reduction

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On biased compression for distributed learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 12974-13023页

作者： Aleksandr Beznosikov Samuel Horváth Peter Richtárik Mher Safaryan Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA and Skolkovo Institute of Science and Technology Moscow Russia and School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Russia Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA

In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased compressors often show superior performance in practice when compared to the much more studied and understood unbiased compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate $O\left( \delta L \exp[-\frac{\mu K}{\delta L}] + \frac{(C + \delta D)}{K\mu}\right)$, where δ ≥1 is a compression parameter which grows when more compression is applied, L and µ are the smoothness and strong convexity constants, C captures stochastic gradient noise (C = 0 if full gradients are computed on each node) and D captures the variance of the gradients at the optimum (D = 0 for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance.

关键词： compression operators biased compressors distributed learning linear convergence error feedback

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Probabilistic Tube-based Control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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IEEE Conference on Decision and Control

作者： Eleftherios E. Vlahakis Lars Lindemann Pantelis Sopasakis Dimos V. Dimarogonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles CA USA School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Northern Ireland UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical.

关键词： Control design Scalability Linearity Probabilistic logic Scheduling Optimization Multi-agent systems

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