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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mingdong Li Shuhang Chen Zhijia Zhao Yiwen Wang Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology School of Mechanical and Electrical Engineering Guangzhou University Department of Electronic and Computer Engineering The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology

Neural connectivity describes how neuron populations coordinate and create cognitive and behavioral functions. Neural connectivity performs dynamics where its population spiking responses to stimuli or intention change over time. Brain-machine interface (BMI) provides a framework for studying dynamical neural connectivity. In BMI, point process is a powerful technique in analyzing the single neuronal tuning. And generalized linear mode (GLM) as an encoding model can incorporate the tuning in kinematics and the neural connectivity. Quantification and tracking of dynamic neural connectivity can contribute to the elucidation of the generation of brain functions in a computational way. However, most of the previous work focused on single neuronal adaptation to kinematics. When a neuron is significantly modulated by some other neurons in some tasks, the shape of the log likelihood function for single neuronal observations can be narrowed in some dimensions. And the existing gradient-based methods are not able to reach the optimum in a fast and adaptive searching way. In this work, to maximize the likelihood of observations and obtain the dynamic neural connectivity tuning parameters, we proposed a conjugate gradient-based encoding model (CGE). We illustrate CGE for likelihood function using the real experimental data under manual control and brain control. The results show that the proposed CGE has better performance in tracking the dynamic neural connectivity tuning parameters and modeling neural *** Relevance— Not directly related.

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Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

arXiv

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arXiv 2023年

作者： Khanal, Saroj Graf, Christoph Liang, Zhirui Dvorkin, Yury Ünel, Burçin The Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States The Departments of Electrical and Computer Engineering Department and Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218 United States The Institute for Policy Integrity New York University School of Law New YorkNY10012 United States

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios resulting from offshore wind integration. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment. Copyright © 2023, The Authors. All rights reserved.

关键词： Investments

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Architecture for Compact Photonic Downconversion of Broadband RF Signals

Architecture for Compact Photonic Downconversion of Broadban...

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CLEO: Science and Innovations, S and I 2022

作者： O'Malley, Nathan P. McKinzie, Keith A. Alshaykh, Mohammed S. Liu, Junqiu Leaird, Daniel E. Kippenberg, Tobias J. McKinney, Jason D. Weiner, Andrew M. School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Electrical Engineering Department King Saud University Riyadh11421 Saudi Arabia LausanneCH-1015 Switzerland U.S. Naval Research Laboratory WashingtonDC20375 United States

We demonstrate the use of a dual comb photonic system for downconversion and disambiguation of RF signals ranging from 4.3 GHz to 17.3 GHz. Our system has future potential for miniaturization, a key for deployment in ...

ISBN: (纸本)9781557528209

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Barrier Certificate based Safe Control for LiDAR-based systems under Sensor Faults and Attacks

Barrier Certificate based Safe Control for LiDAR-based Syste...

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IEEE Conference on Decision and Control

作者： Hongchao Zhang Shiyu Cheng Luyao Niu Andrew Clark Electrical and Systems Engineering Department McKelvey School of Engineering Washington University in St. Louis St. Louis Department of Electrical and Computer Engineering University of Washington Seattle

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Autonomous Cyber-Physical systems (CPS) fuse proprioceptive sensors such as GPS and exteroceptive sensors including Light Detection and Ranging (LiDAR) and cameras for state estimation and environmental observation. It has been shown that both types of sensors can be compromised by malicious attacks, leading to unacceptable safety violations. We study the problem of safety-critical control of a LiDAR-based system under sensor faults and attacks. We propose a framework consisting of fault tolerant estimation and fault tolerant control. The former reconstructs a LiDAR scan with state estimations, and excludes the possible faulty estimations that are not aligned with LiDAR measurements. We also verify the correctness of LiDAR scans by comparing them with the reconstructed ones and removing the possibly compromised sector in the scan. Fault tolerant control computes a control signal with the remaining estimations at each time step. We prove that the synthesized control input guarantees system safety using control barrier certificates. We validate our proposed framework using a UAV delivery system in an urban environment. We show that our proposed approach guarantees safety for the UAV whereas a baseline fails.

关键词： Laser radar Fault tolerant systems Urban areas Propioception Fault tolerant control Sensor fusion Sensor systems

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Adaptive Softmax Regression for Credit Scoring 16th

Adaptive Softmax Regression for Credit Scoring

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Munkhdalai, Lkhagvadorj Davagdorj, Khishigsuren Pham, Van-Huy Ryu, Keun Ho Database/Bioinformatics Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789813367562

Credit scoring is a classification task from the machine learning perspective. Efficiently classifying bad borrowers is the main aim of building a credit scoring model. This work proposes a novel adaptive softmax regression method for credit scoring. We augment a simple softmax regression by deep neural networks to make its estimated probabilities as an adaptive for each observation. Our experimental result on public benchmark datasets shows that adaptive softmax regression outperformed the machine learning baselines in terms of Brier score, area under the curve (AUC) and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Decision making

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Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

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computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

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Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-based models and Granular Neural Networks

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IEEE Transactions on Fuzzy systems 2024年

作者： Wang, Dan Richter, Monika Zhu, Xiubin Pedrycz, Witold Gacek, Adam Sobotnicki, Aleksander Li, Zhiwu Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710054 China Center for Biomedical Engineering Lukasiewicz Research Network-Krakow Institute of Technology Zabrze41-800 Poland Xidian University School of Mechano-Electronic Engineering Xi'an710071 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey Macau University of Science and Technology Institute of Systems Engineering Taipa China

In this study, we propose a novel methodology for determining accurate positions of characteristic points encountered in the analysis of bioimpedance signals. The proposed approach fully utilizes two fundamental modeling pursuits based on fuzzy rule-based models and neural networks. We take advantages of the unique capabilities of fuzzy rule-based models to characterize the nonlinear relationship between the acquired bioimpedance signals and its temporal coordinate. The fuzzy modeling approach is used to approximate the process that generates the bioimpedance signals through a collection of rules (if-then statements). In the sequel, the parameters of the models are used as the inputs of neural network models to determine the position of characteristic points. We further augment the numeric neural network to its granular counterpart to accommodate the uncertainty in the available experimental evidence through allocating a certain level of information granularity across the parameter space. The resulting granular outputs (intervals) become reflective of the quality and a level of confidence associated with the prediction results. The quality of the prediction results is quantified in terms of the coverage and specificity criteria. The performance index is also enhanced to deal with the situation when the positions provided by experts are also information granules (intervals). The performance of the proposed approach is justified through a collection of experiments carried out on the collected real-world bioimpedance signals. Experimental results show that the proposed approach achieved higher accuracy in determining the position of characteristic points in comparison with other existing methods. © 1993-2012 IEEE.

关键词： Fuzzy models

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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电催化还原二氧化碳合成C_(3+)产物

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Science China Materials 2024年第6期67卷 1780-1790页

作者：罗雨婷胡书萁刘碧录 Department of Electrical and Computer Engineering University of TorontoTorontoOntario M5S 3G4Canada Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute&Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China

通过电催化还原反应将二氧化碳(CO_(2))转换为重要燃料和化工原料意义重大,有利于推进工业低碳化发展.如今,该技术路线可以实现C_(1)和C_(2)产物的高选择性、快速合成,但合成高值碳三加(C_(3+))产物的转化率仍较低.本文中,我们聚焦电催... 详细信息

通过电催化还原反应将二氧化碳(CO_(2))转换为重要燃料和化工原料意义重大,有利于推进工业低碳化发展.如今,该技术路线可以实现C_(1)和C_(2)产物的高选择性、快速合成,但合成高值碳三加(C_(3+))产物的转化率仍较低.本文中,我们聚焦电催化还原二氧化碳合成C_(3+)产物的进展,重点关注CO_(2)转化为C_(3+)产物的催化剂材料、电化学系统和串联反应器这三个方面.最后,本文指出了该领域面临的主要挑战和未来的研究方向.

关键词：电催化还原二氧化碳催化剂材料化工原料高选择性快速合成转化率

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A Deep Learning Approach to Recognizing New Thai Currency Using Transfer Learning for Visually Impaired Individuals

A Deep Learning Approach to Recognizing New Thai Currency Us...

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International Conference on Signal Processing and Communication systems, ICSPCS

作者： Irfan Ahmad Abubakar Sharif Aneeka Azmat Muhammad Usman Jamil Maynooth International Engineering College Faculty of Science and Engineering Maynooth University Ireland Maynooth International Engineering College Faculty of Science and Engineering Maynooth University Fuzhou James Watt School of Engineering University of Glasgow United Kingdom College of Electrical Engineering and Computer Science Institute of Information Systems and Application National Tsinghua University Hsinchu Taiwan School of Mechanical and Electrical Engineering Chuzhou University Chuzhou China

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions.

关键词： Deep learning Training Accuracy Face recognition Transfer learning Pressing Robustness Mobile applications Convolutional neural networks Currencies

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