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5th International Conference on computer engineering and Intelligent Control, ICCEIC 2024

作者： Xu, Fangqin Jiang, Yue Yang, Shun Liu, Yixuan Shanghai Publishing and Printing College Department of Information and Intelligent Engineering Shanghai China College of Communication and Art Design University of Shanghai for Science and Technology Shanghai China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798331507992

With the advancements in IoT technology, cloud computing, and big data technology, smart living has become an integral part of people's lives. However, traditional intelligent data computing relies heavily on cloud services, which often suffer from issues such as slow response times and high energy consumption. To address these issues, this study introduces an edge computing architecture centered on gateways, serving as an intermediate layer between user devices and cloud servers. This architecture enables high-speed data processing, low-latency responses, and rapid end-to-end connections, making it suitable for most small-scale and personal smart home systems. © 2024 IEEE.

关键词： Smart homes

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A Multi-Task Learning Framework for Joint Sub-Nyquist Wideband Spectrum Sensing and Modulation Recognition

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China Communications 2025年第1期22卷 128-138页

作者： Dong Xin Stefanos Bakirtzis Zhang Jiliang Zhang Jie Department of Electronic and Electrical Engineering University of SheffieldSheffield S102TNUnited Kingdom Department of Computer Science and Technology University of CambridgeCambridge CB21TNUnited Kingdom College of Information Science and Engineering Northeastern UniversityShenyang 110819China Ranplan Wireless Network Design Ltd. Cambridge CB233UYUnited Kingdom

The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication ***,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high *** paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this *** this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband *** demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband ***,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking.

关键词： automated modulation classification cognitive radio convolutional neural networks deep learning spectrum sensing sub-Nyquist sampling

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Scar-Aware Late Mechanical Activation Detection Network for Optimal Cardiac Resynchronization Therapy Planning 22

Scar-Aware Late Mechanical Activation Detection Network for ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Xing, Jiarui Wang, Shuo Petal, Amit R. Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao School of Medicine University of Virginia Health System United States University of Virginia Health System Department of Biomedical Engineering United States University of Virginia Department of Electrical and Computer Engineering United States University of Virginia Department of Computer Science United States

ISBN: (纸本)9798331520526

Accurate identification of late mechanical activation (LMA) regions is crucial for optimal cardiac resynchronization therapy (CRT) lead implantation. However, existing approaches using cardiac magnetic resonance (CMR) imaging often over-look myocardial scar information, which may be mistakenly identified as delayed activation regions. To address this issue, we propose a scar-aware LMA detection network that simultaneously detects myocardial scar and prevents LMA localization in these scarred regions. More specifically, our model integrates a pre-trained scar segmentation network using late gadolinium enhancement (LGE) CMRs into a LMA detection network based on highly accurate strain derived from displacement encoding with stimulated echoes (DENSE) CMRs. We introduce a novel scar-aware loss function that utilizes the segmented scar information to discourage false-positive detections of late activated areas. Our model can be trained with or without paired LGE data. During inference, our model does not require the input of LGE images, leveraging learned patterns from strain data alone to mitigate false-positive LMA detection in potential scar regions. We evaluate our model on subjects with and without myocardial scar, demonstrating significantly improved LMA detection accuracy in both scenarios. Our work paves the way for improved CRT planning, potentially leading to better patient outcomes. © 2025 IEEE.

关键词： Cardiac resynchronization therapy

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Video Anomaly Latent Training GAN (VALT GAN): Enhancing Anomaly Detection Through Latent Space Mining

Video Anomaly Latent Training GAN (VALT GAN): Enhancing Anom...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Sethi, Anikeit Saini, Krishanu Singh, Rituraj Saurav, Sumeet Tiwari, Aruna Singh, Sanjay Chauhan, Vikas Indian Institute of Technology Indore Computer Science and Engineering Indore India Intelligent System Groups CSIR-CEERI Pilani India National Taipei University of Technology Electrical and Computer Science Taipei City106 Taiwan

ISBN: (纸本)9781665430654

Anomaly detection in video data plays a crucial role in numerous applications, such as industrial monitoring and automated surveillance. This paper presents a novel method for video anomaly detection (VAD) using Generative Adversarial Networks (GANs). The proposed method called VALT-GAN combines two separate branches, one for spatial information and the other for temporal information, to capture relevant features from video data. The framework is utilized to learn the normal features from the training video dataset, enabling the generator to produce realistic samples. However, existing GAN-based methods face challenges in detecting subtle or unseen anomalies. To address this, we introduce latent mining for adversarial training which allows us to train a robust GAN model with high anomaly detection (AD) capability. We exploit the latent space following the continuous nature of the generator using the Iterative Fast Gradient Signed Method (IFGSM) which improves the quality of the generated images. Experimental evaluations show the effectiveness of VALT-GAN as compared to traditional methods on UCSD (University of California, San Diego) Peds2, CUHK (Chinese University of Hong Kong) Avenue, and ShanghaiTech datasets. © 2023 IEEE.

关键词： Anomaly Detection Generative Adversarial Networks Latent Space Mining

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Dispersion engineering of Crystalline Microresonators for Optical Frequency Comb Generation beyond C-Band 16

Dispersion Engineering of Crystalline Microresonators for Op...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Takabayashi, Ryomei Kodama, Hikaru Tanikawa, Koya Kakinuma, Yasuhiro Tanabe, Takasumi Fujii, Shun Keio University Faculty of Science and Technology Department of Electronics and Electrical Engineering Yokohama223-8522 Japan Keio University Faculty of Science and Technology Department of System Design Engineering Yokohama223-8522 Japan Keio University Faculty of Science and Technology Department of Physics Yokohama223-8522 Japan

We detail a dispersion engineering strategy for microresonators allowing us to generate microcombs in the near-infrared and mid-infrared regimes, and identifying optimal shapes for achieving anomalous dispersion suite... 详细信息

ISBN: (纸本)9798350372076

关键词： Infrared devices

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Cross-modal change detection flood extraction based on self-supervised contrastive pre-training

Cross-modal change detection flood extraction based on self-...

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2024 ISPRS Technical Commission I Mid-term Symposium on Intelligent Sensing and Remote Sensing Application

作者： Feng, Wenqing Guan, Fangli Sun, Chenhao Xu, Wei School of Computer Science Hangzhou Dianzi University Hangzhou China Electrical & Information Engineering School Changsha University of Science & Technology Changsha China Information System and Management College National University of Defense Technology Changsha China

Flood extraction is a critical issue in remote sensing analysis. Accurate flood extraction faces challenges such as complex scenes, image differences across modalities, and a shortage of labeled samples. Traditional supervised deep learning algorithms demonstrate promising prospects in flood extraction. They mostly rely on abundant labeled data. However, in practical applications, there is a scarcity of available labeled samples for flood change regions, leading to an expensive acquisition of such data for flood extraction. In contrast, there is a wealth of unlabeled data in remote sensing images. Self-supervised contrastive learning (SSCL) provides a solution, allowing learning from unlabeled data without explicit labels. Inspired by SSCL, we utilized the open-source CAU-Flood dataset and developed a framework for cross-modal change detection in flood extraction (CMCDFE). We employed the Barlow Twin (BT) SSCL algorithm to learn effective visual feature representations of flood change regions from unlabeled cross-modal bi-temporal remote sensing data. Subsequently, these well-initialized weight parameters were transferred to the task of flood extraction, achieving optimal accuracy. We introduced the improved CS-DeepLabV3+ network for extracting flood change regions from cross-modal bi-temporal remote sensing data, incorporating the CBAM dual attention mechanism. By demonstrating on the CAU-Flood dataset, we proved that fine-tuning with only a pre-trained encoder can surpass widely used ImageNet pre-training methods without additional data. This approach effectively addresses downstream cross-modal change detection flood extraction tasks. © 2024 Wenqing Feng et al.

关键词： Extraction

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Optimization Scheduling of Multi-Energy Microgrid Based on the QUBO Quantum Computing Model 19th

Optimization Scheduling of Multi-Energy Microgrid Based on t...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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An RFID-Based Battery-Less Vibration Monitoring system for electrical Appliances

An RFID-Based Battery-Less Vibration Monitoring System for E...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Song, Zequn Sun, Ran Rahmadya, Budi Takeda, Shigeki Engineering Graduate School Of Science And Engineering Ibaraki University Department Of Electrical And Electronic System Hitachi Japan Engineering Ibaraki University Department Of Electrical And Electronic System Hitachi Japan Andalas University Computer System Department Padang Indonesia

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

This paper presents a vibration monitoring system for electrical appliances. This system is based on RFID sensors and edge processing technologies. For long-term monitoring, two different operation modes referred to as standby and active modes are introduced. The difference between the two modes is radio wave radiation times. The standby mode is useful to reduce energy consumption and temperature increase of an RFID reader, and amount of data uploaded to a network. This mode also detects a beginning of a vibration event caused by the motor of an electrical appliance. The standby mode subsequently triggers the active mode. The active mode accurately monitors the vibration event and keeps the measured data only for the active mode. Experiments for monitoring a refrigerator demonstrate that the proposed modes enable efficient vibration detections. This system can prevent unintended COVID-19 vaccine disposals caused by the problematic operation and management of refrigerators. © 2022 IEEE.

关键词： COVID-19

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Research on Sensorless Control of Double High Frequency Injection SynRM Based on Static Shaft Torque Ripple Suppression 2

Research on Sensorless Control of Double High Frequency Inje...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Wang, Jianyuan Jing, Hanghui Li, Yingjie Wang, Haoyu School of Electrical Engineering Xi'an University of Technology Shaanxi Xi'an China School of System Design and Intelligent Manufacturing South University of Science and Technology of China Guangdong Shenzhen China

ISBN: (纸本)9798350313765

When the synchronous reluctance motor runs without position sensor control, there is a strong coupling nonlinear problem of quadrature axis inductance, which affects the precise of rotor position detect and cannot ensure the stability of the motor at low speed range. Control in the low speed range of the motor, a sensorless control method of double high-frequency injection based on static axis torque ripple suppression is proposed. By establishing a nonlinear function model of flux and current of the quadrature axis, the Angle error caused by the cross-coupling of the inductance of the quadrature axis is reduced. The high frequency signal is injected into the d-q axis at the same time to reduce the high frequency torque ripple contained in the high frequency amplitude in the traditional high frequency injection, and the rotor position estimation accuracy and the transient performance of the motor are improved. Finally, the effectiveness of the control scheme is verified by experiments on a 1.5kW synchronous reluctance motor pair drag and load experimental platform. © 2023 IEEE.

关键词： cross saturation double high frequency square wave injection high frequency torque ripple off-line parameter identification synchronous reluctance motor

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Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture

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Journal of Reliable Intelligent Environments 2023年第3期9卷 303-317页

作者： De Donato, Lorenzo Dirnfeld, Ruth Somma, Alessandra De Benedictis, Alessandra Flammini, Francesco Marrone, Stefano Saman Azari, Mehdi Vittorini, Valeria Department of Electrical Engineering and Information Technology University of Naples Federico II Via Claudio 21 Naples80125 Italy Department of Computer Science and Media Technology Linnaeus University Växjö Sweden School of Innovation Design and Engineering Mälardalen University Eskilstuna Sweden

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow. © 2023, The Author(s).

关键词： Cyber Physical system

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