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IEEE Transactions on Artificial Intelligence 2025年第06期6卷 1614-1625页

作者： Sun, Genchen Liu, Zhengkun Gan, Lin Su, Hang Li, Ting Zhao, Wenfeng Sun, Biao Tianjin University School of Electrical and Information Engineering Tianjin300072 China Northwestern Polytechnical University School of Automation Xi'an710072 China Politecnico di Milano Department of Electronics Information and Bioengineering Milan20133 Italy Chinese Academy of Medical Science Institute of Biomedical Engineering Pecking Union Medical College Tianjin300020 China Binghamton University Department of Electrical and Computer Engineering State University of New York BinghamtonNY United States

In recent years, Neural Architecture Search (NAS) has marked significant advancements, yet its efficacy is marred by the dependence on substantial computational resources. To mitigate this, the development of NAS benchmarks has emerged, offering datasets that enumerate all potential network architectures and their performances within a predefined search space. Nonetheless, these benchmarks predominantly focus on convolutional architectures, which are criticized for their limited interpretability and suboptimal hardware efficiency. Recognizing the untapped potential of Spiking Neural Networks (SNNs) - often hailed as the third generation of neural networks for their biological realism and computational thrift - this study introduces SpikeNAS-Bench. As a pioneering benchmark for SNN, SpikeNAS-Bench utilizes a cell-based search space, integrating leaky integrate-and-fire (LIF) neurons with variable thresholds as candidate operations. It encompasses 15,625 candidate architectures, rigorously evaluated on CIFAR10, CIFAR100 and Tiny-ImageNet datasets. This paper delves into the architectural nuances of SpikeNAS-Bench, leveraging various criteria to underscore the benchmark's utility and presenting insights that could steer future NAS algorithm designs. Moreover, we assess the benchmark's consistency through three distinct proxy types: zero-cost-based, early-stop-based, and predictor-based proxies. Additionally, the paper benchmarks seven contemporary NAS algorithms to attest to SpikeNAS-Bench's broad applicability. We commit to providing training logs, diagnostic data for all candidate architectures, and the promise to release all code and datasets post-acceptance, aiming to catalyze further exploration and innovation within the SNN domain. SpikeNAS-Bench is open source at https://***/XXX (hidden for double anonymous review). © 2020 IEEE.

关键词： HTTP

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Control Strategies for Electric Vehicle Drives: FOC Implementation with MTPA and Field Weakening 15

Control Strategies for Electric Vehicle Drives: FOC Implemen...

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15th International Conference Elektro, ELEKTRO 2024

作者： Sadaf, Majda Farooq, Adil Ullah, Zahid Ahsan, Muhammad School of Electrical and Electronic Engineering Newcastle University United Kingdom Maynooth University Department of Electronic Engineering Ireland Politecnico di Milano Department of Electronics Information and Bioengineering Italy University of Alabama at Birmingham Department of Computer Science United States

ISBN: (纸本)9798350372359

The reliability and performance of Electric vehicles (EVs) rely on effective control of electric drives, thereby improving the overall driving experience. To efficiently control the electric drive, this paper implemented a Field Oriented Control (FOC) scheme for controlling a permanent magnet synchronous motor (PMSM) drive designed for EVs. The FOC scheme is integrated with Maximum Torque per Ampere (MTPA) and field weakening control strategies to broaden the speed range of the drive at rated power density and maximum torque. The entire FOC control scheme and MTPA control reference function block are modeled using MATLAB/SIMULINK. The paper comprehensively reviews the mathematical equations governing FOC, field weakening control, and MTPA methods. The research also incorporates simulation results obtained under various driving conditions for EVs, demonstrating the effectiveness of the implemented control strategies. © 2024 IEEE.

关键词： Vector control (Electric machinery)

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High-yield and rapid isolation of extracellular vesicles by flocculation via orbital acoustic trapping:FLOAT

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Microsystems & Nanoengineering 2024年第1期10卷 313-323页

作者： Joseph Rufo Peiran Zhang Zeyu Wang Yuyang Gu Kaichun Yang Joseph Rich Chuyi Chen Ruoyu Zhong Ke Jin Ye He Jianping Xia Ke Li Jiarong Wu Yingshi Ouyang Yoel Sadovsky Luke P.Lee Tony Jun Huang Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNCUSA Department of Biomedical Engineering Duke UniversityDurhamNCUSA Department of Obstetrics Gynecologyand Reproductive SciencesMagee-Womens Research InstituteUniversity of PittsburghPittsburghPAUSA Department of Microbiology and Molecular Genetics School of MedicineUniversity of PittsburghPittsburghPAUSA Renal Division and Division of Engineering in Medicine Department of MedicineBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Department of Bioengineering Department of Electrical Engineering and Computer ScienceUniversity of CaliforniaBerkeleyBerkeleyCAUSA Department of Biophysics Institute of Quantum BiophysicsSungkyunkwan UniversitySuwonKorea

Extracellular vesicles(EVs)have been identified as promising biomarkers for the noninvasive diagnosis of various ***,challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low ***,we report a high-yield(>90%)and rapid(<10 min)EV isolation method called FLocculation via Orbital Acoustic Trapping(FLOAT).The FLOAT approach utilizes an acoustofluidic droplet centrifuge to rotate and controllably heat liquid *** adding a thermoresponsive polymer flocculant,nanoparticles as small as 20 nm can be rapidly and selectively concentrated at the center of the *** demonstrate the ability of FLOAT to separate urinary EVs from the highly abundant Tamm-Horsfall protein,addressing a significant obstacle in the development of EV-based liquid *** to its high-yield nature,FLOAT reduces biofluid starting volume requirements by a factor of 100(from 20 mL to 200µL),demonstrating its promising potential in point-of-care diagnostics.

关键词： trapping utilize separating

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Boost DC-DC Converter with Non-dissipative Snubber

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IEEJ Transactions on electrical and Electronic engineering 2024年第11期19卷 1858-1878页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung No.57 Sec. 2 Zhongshan Rd. Taiping Dist Taichung41170 Taiwan

To provide a hold-up time function in DC-DC supplies for cell site stations or data centers, using a boost converter with a bulk output capacitor as a front-end converter stage is a simple and highly cost-effective solution. However, conventional boost converters with a hard-switching operation result in low conversion efficiency and increased electromagnetic interference emissions. In this paper, a boost converter with a new non-dissipative snubber is proposed that only requires two additional diodes, an inductor, and a multilayer ceramic capacitor. The main feature of the proposed snubber is that the components are not located on the main power processing path. This means they only require low ratings, improving cost-effectiveness. A prototype converter is analyzed, realized, and applied to a 300 W redundant DC-DC power supply module for a 5G cell base station. © 2024 The Author(s). IEEJ Transactions on electrical and Electronic engineering published by Institute of electrical Engineers of Japan and Wiley Periodicals LLC. © 2024 The Author(s). IEEJ Transactions on electrical and Electronic engineering published by Institute of electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： Boost converter

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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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IEEE Access 2025年 13卷 87753-87776页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu880011 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior;however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%. © 2013 IEEE.

关键词： Convolutional neural networks

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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Automating life science labs at the single-cell level through precise ultrasonic liquid sample ejection:PULSE

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Microsystems & Nanoengineering 2024年第6期10卷 173-188页

作者： Peiran Zhang Zhenhua Tian Ke Jin Kaichun Yang Wesley Collyer Joseph Rufo Neil Upreti Xianjun Dong Luke P.Lee Tony Jun Huang Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Department of Mechanical Engineering Virginia Polytechnic Institute and State UniversityBlacksburgVA 24061USA Genomics and Bioinformatics Hub Department of NeurologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Department of Medicine Brigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Department of Bioengineering Department of Electrical Engineering and Computer ScienceUniversity of California at BerkeleyBerkeleyCAUSA Institute of Quantum Biophysics Department of BiophysicsSungkyunkwan UniversitySuwonKorea Department of Chemistry&Nanoscience Ewha Womans UniversitySeoulKorea

Laboratory automation technologies have revolutionized biomedical ***,the availability of automation solutions at the single-cell level remains scarce,primarily owing to the inherent challenges of handling cells with such small dimensions in a precise,biocompatible ***,we present a single-cell-level laboratory automation solution that configures various experiments onto standardized,microscale test-tube matrices via our precise ultrasonic liquid sample ejection technology,known as *** enables the transformation of titer plates into microdroplet arrays by printing nanodrops and single cells acoustically in a programmable,scalable,and biocompatible *** pipetting robots,PULSE enables researchers to conduct biological experiments using single cells as anchoring points(e.g.,1 cell vs.1000 cells per“tube”),achieving higher resolution and potentially more relevant data for modeling and downstream *** demonstrate the ability of PULSE to perform biofabrication,precision gating,and deterministic array barcoding via preallocated droplet-addressable *** cells can be gently printed at a speed range of 5–20 cell⋅s−1 with an accuracy of 90.5–97.7%,which can then adhere to the substrate and grow for up to 72 h while preserving cell *** the deterministic barcoding experiment,95.6%barcoding accuracy and 2.7%barcode hopping were observed by comparing the phenotypic data with known genotypic data from two types of single *** PULSE platform allows for precise and dynamic analyses by automating experiments at the single-cell level,offering researchers a powerful tool in biomedical research.

关键词： precise liquid offering

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN computer science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids 48

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Davis, Katherine Serpedin, Erchin Texas A and M University Department of Electrical and Computer Engineering College StationTX United States Texas A and M University at Qatar Electrical and Computer Engineering Program Doha Qatar Tennessee Technological University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9781728163277

False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-Task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5-100% and 99-100%, respectively, presenting improvements of 5-30% compared to benchmarks. © 2023 IEEE.

关键词： Smart power grids

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

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