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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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Open Radio Access Network RIC Empowered Reconfigurable Intelligent Surface: A Physical-Layer Security Perspective

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 615-625页

作者： Lien, Shao-Yu Tseng, Chih-Cheng Hung, Wei-Cheng Tsai, Cheng-You Liu, Ting-Yu Deng, Der-Jiunn Lin, Yuan-Chun Lin, Shih-Cheng Chang, Chia-Chan Chang, Sheng-Fuh Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan711010 Taiwan Department of Electrical Engineering National Ilan University Ilan260007 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua50074 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621301 Taiwan

The reconfigurable intelligent surface (RIS) steering reflective beam directions toward a target mobile user equipment (UE) has been a promising technology for coverage enhancement and physical-layer (PHY) security to avoid signal eavesdroppers and jammers. However, tracking and predicting the moving trajectory of UE demand significant computing burdens unaffordable to the RIS. Performing beam steering computations on the radio access network (RAN) intelligent controller in the open RAN (O-RAN) architecture therefore emerges as a revolutionary paradigm. In this paper, we propose a PHY-secured beam steering design based on a pre-processing-based Kalman filter algorithm. Through a pre-processing phase to derive the radiation feature, the proposed design accurately tracks the position of the mobile UE and quickly recovers the position search while position tracking fails. Through practical field-try experiments, we justify the capability of O-RAN RIC-empowered RIS for PHY security. © 2013 IEEE.

关键词： Radio access networks

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On the Attractive and Repulsive Forces of Generalized Stochastic Neighbor Embedding With Alpha-Divergence

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IEEE Access 2024年 12卷 90380-90394页

作者： Lin, Hsin-Yi Tseng, Huan-Hsin Chien, Jen-Tzung Seton Hall University Department Of Mathematics And Computer Science South OrangeNJ07079 United States Brookhaven National Laboratory Computational Science Initiative UptonNY11973 United States Institute Of Electrical And Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Stochastic neighbor embedding (SNE) performs nonlinear transformation from high-dimensional observation space to low-dimensional latent space which preserves neighbor affinities. Data pairs in latent space tend to be crowded due to the dimensionality reduction. To mitigate the crowding problem, certain characteristics are favorable in the design of the SNE setting. This study presents a fundamental analysis of SNE that not only generalizes the previous SNEs but also provides a systematic way to understand the intrinsic properties. From the perspective of theoretical connection, we are able to conceive a new generalized SNE (g-SNE) by introducing a regularized power-law distribution with the α-divergence for manifold learning. The proposed method generalizes and incorporates various favorable features for the clustering process. In addition, the proposed method provides high flexibility, admitting tailored realizations to properly reflect the similarity between original and dimension-reduced samples. Experiments are performed to analyze the proposed method, and its effectiveness is demonstrated with several learning tasks. © 2024 The Authors.

关键词： Data visualization

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NS3-Based Communication Network Modeling for Multi-Microgrid systems: Design and Evaluation

NS3-Based Communication Network Modeling for Multi-Microgrid...

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2025 IEEE Green Technologies Conference, GreenTech 2025

作者： Ali, Ola Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States

ISBN: (纸本)9798331520182

This paper presents a communication emulation model for enhancing the efficiency of multi-microgrid (MMG) networks, employing Network Simulator 3 (ns3). MMG systems require robust communication frameworks for coordination, particularly under varying traffic conditions. The introduced model accommodates multiple local area networks (LANs) that support efficient communication across multiple autonomous AC microgrids. The developed model facilitates seamless bidirectional information exchange between the microgrids' local controllers and the MMG control center (MMGCC), employing TCP/IP protocol to ensure high data transmission integrity and low latency. A detailed design in C + is demonstrated, including the configuration of the communication nodes, topology, communication links, and quality of service (QoS) analysis. The network performance is rigorously assessed across low and high traffic conditions, with performance metrics such as throughput, delay, and percentage packet loss at each node. Results confirm the effectiveness of the introduced model in sustaining minimal packet loss, high throughput, and low delays under dynamic operating scenarios. These findings establish the developed model as a viable solution for meeting the real-time communication requirements of MMG energy management systems. © 2025 IEEE.

关键词： Transmission control protocol

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis 20

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nikoupour, Maryam Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran Iranian Research Organization for Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository's epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods. © 2024 IEEE.

关键词： Electroencephalography

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Deep Reinforcement Learning Enabled Bi-level Robust Parameter Optimization of Hydropower-dominated systems for Damping Ultra-low Frequency Oscillation

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Journal of Modern Power systems and Clean Energy 2023年第6期11卷 1770-1783页

作者： Guozhou Zhang Junbo Zhao Weihao Hu Di Cao Nan Duan Zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 610054China IEEE Department of Electrical and Computer Engineering University of ConnecticutStorrsCT06269USA Lawrence Livermore National Laboratory LivermoreUSA Department of Energy Technology Aalborg University9220 AalborgDenmark

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

关键词： Bi-level robust parameter optimization deep reinforcement learning deep deterministic policy gradient ultralow frequency oscillation damping control stability

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Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

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IEEE Robotics and Automation Letters 2024年第4期9卷 3878-3885页

作者： Tsai, Tsung-Han Yan, Bing Luh, Peter B. Yang, Haw-Ching Bragin, Mikhail A. Cheng, Fan-Tien National Cheng Kung University Institute of Manufacturing Information and Systems Tainan701401 Taiwan Rochester Institute of Technology Department of Electrical and Microelectronic Engineering RochesterNY14623 United States University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States National Taiwan University Department of Electrical Engineering Taipei106329 Taiwan National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung824005 Taiwan University of California Department of Electrical and Computer Engineering RiversideCA92507 United States

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult to predict without sufficient historical data. The purpose of this letter is to develop a framework for near-optimal scheduling of IC packaging considering processing time variations and factory practices. Using an orthogonal greedy algorithm and recurrent neural network, this framework extracts key features and predicts the processing times of trial run lots and production lots for each operation using these algorithms. After formulating the scheduling problem in an integer linear programming form, an ordinal optimization method is embedded within the decomposition and coordination framework to obtain dynamic and near-optimal schedules in a computationally efficient manner. This approach can improve the efficiency of IC packaging and potentially other operations by optimizing lot scheduling, reducing production tardiness, and increasing productivity in the factory, paving the way for self-optimizing factories in the future. © 2016 IEEE.

关键词： Integer programming

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An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

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computers, Materials & Continua 2022年第2期70卷 2923-2936页

作者： Abdul Kadir Hamid Fahd N.Al-Wesabi Nadhem Nemri Ammar Zahary Imran Khan Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySanaaYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPaki

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

关键词： 5G device-to-device communication wireless networks spectrum

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A Wireless and Battery-Free Wearable Pressure Sensing System for Human-Machine Interaction and Health Monitoring

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第6期2卷 439-447页

作者： Lee, Sang Won Pei, Xiaochang Rajendran, Jerome Esfandyarpour, Rahim University of California at Irvine Henry Samueli School of Engineering Department of Electrical Engineering and Computer Science Laboratory for Integrated Nano Bio Electronics Innovation IrvineCA92697 United States

Wearable devices have received tremendous interest in healthcare monitoring due to their significant advantages in terms of flexibility, noninvasiveness, wireless capability, and portability. However, they still face challenges, including the need for external power sources, bulky measuring instruments, and a lack of a wireless data transport system. Herein, we propose a PEDOT:PSS/MXene pressure sensing platform based on 2-D e-textile and screen-printed interdigitated electrodes (IDEs). Both materials improve the electrical conductivity, sensitivity, stability, flexibility, and mechanical durability of the pressure sensor. A near-field communication (NFC) readout system provides real-time wireless monitoring by transmitting signals from the sensor to a smartphone app, without external batteries. The pressure sensor has sensitivity, a wide detection range, excellent repeatability and stability, fast response/recovery time, and long-term durability. Long-term usability was verified through the bending test up to 21400 cycles. The flexible pressure sensor successfully monitored wrist pulse, physical movements, and various muscle movements in the body. Repeatable, stable, reliable, and regular signals were obtained wirelessly through the NFC readout system. Our sensing platform could be a promising solution for healthcare monitoring, rehabilitation therapy, and early medical diagnosis. In addition, it suggests a new platform for helping people with disabilities to communicate with others through human-machine interaction. © 2022 IEEE.

关键词： Pressure sensors

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Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

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computers, Materials & Continua 2022年第1期70卷 1177-1193页

作者： Murad A.Almekhlafi Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Khalid Mahmood Hany Mahgoub Noha Negm Amin M.El-Kustaban Ammar Zahary Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarP.O.B 814Pakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Computer Science Department King Khaled UniversityKSA&Faculty of Computers and InformationComputer Science DepartmentMenoufia UniversityEgypt Computer Science Department King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt Electronics Department Faculty of EngineeringUSTSana’aYemen Faculty of Computer and IT Sana’a UniversityYemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of *** the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra *** was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the *** average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently *** results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the *** average wind speed in the five years was 7.95 m/*** average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the *** study provides useful information for developing wind energy and an efficient wind approach.

关键词： Energy potential wind characteristics Weibull distribution wind power density Socotra island

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