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Adaptive Stabilization Control by Deep Reinforcement Learning for Hovering Drone Surveillance

IEEE Transactions on Mobile Computing 2025年

作者： Lee, Chao-Yang Tsai, Ang-Hsun Wang, Li-Chun National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Taiwan Feng Chia University Department of Communications Engineering Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Taiwan

This paper proposes an adaptive stabilization control mechanism by using deep reinforcement learning (DRL) for hovering drones that have to execute a surveillance task for a long time. For long-endurance flights, we design and implement a buoyancy-aided unmanned aerial vehicle (UAV) that can use buoyancy lift to decrease the weight and increase the battery capacity so that the flight time can be significantly extended. However, the balloons of the buoyancy-aided UAV can cause "an inverted pendulum effect" and an instability issue on the drone attitude because the increased surface is easily affected by the gusty wind. We propose a buoyancy-aided adaptive stabilization control (BAASC) method with the DRL to stabilize the attitude and extend the flight time of the quadrotor-based buoyancy-aided UAV. This proposed model can immediately control the speeds of all rotors to balance the attitude based on the current state of the drone. Therefore, the degree of swing can be stabilized, and the inverted pendulum effect can be eliminated. The experimental results reveal that the designed buoyancy-aided UAV with the proposed BAASC scheme can effectively stabilize the attitude to extend the flight time by 112.8% compared with a nonbuoyancy-aided UAV under a gusty wind disturbance. 2002-2012 IEEE.

关键词： Buoyancy

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Glacier: Glass-Box Transformer for Interpretable Dynamic Neuroimaging 48

Glacier: Glass-Box Transformer for Interpretable Dynamic Neu...

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

作者： Mahmood, Usman Fu, Zening Calhoun, Vince Plis, Sergey Emory University AtlantaGA United States Georgia State University Department of Computer Science AtlantaGA United States Georgia Institute of Technology Department of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9781728163277

Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a 'glass-box' deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain's intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson's correlation coefficients. © 2023 IEEE.

关键词： Neuroimaging

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Design and Analysis of Printed Conformal Antenna System for Inter and Intra Vehicular (V2V) Communication Utilizations

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Progress In Electromagnetics Research C 2024年 142卷 143-150页

作者： Raju, Padmanabha Rao, Bathula Sadasiva Jackson, Beulah Islam, Tanvir Madhav, Boddapati Taraka Phani Das, Sudipta Devi, Yalavarthi Usha Department of ECE Sri Vishnu Engineering College for Women AP Vishnupur Bhimavaram India Department of ECE Swarnandhra College of Engineering and Technology Seetarampuram AP Narsapur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Antennas and Liquid Crystal Research Center Department of ECE Koneru Lakshmaiah Education Foundation AP India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

A multiple antenna placement system analysis for the improvement of efficiency and capacity for inter and intra vehicular communication is proposed in this article. Four antennas are placed in the four locations of the vehicular body which includes roof, side mirror, rear screen, and dashboard. The constituted antenna occupying the dimension of 40×38.5×0.2 mm3 on flexible substrate material of photo paper and the bending analysis of the model as per the conformal nature on vehicular body is also analyzed and presented in this work. The received power from each receiving antenna response with respect to the transmitter has been analyzed. The channel capacity with respect to the antenna position for V2V communication is analyzed in different areas and in different environmental conditions. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Receiving antennas

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REVBiT: REVerse engineering of BiTstream for LUT Extraction & Logic Identification

REVBiT: REVerse Engineering of BiTstream for LUT Extraction ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Anmol Singh Narwariya Chetan Talele Pabitra Das Amit Acharyya Department of Electrical Engineering Indian Institute of Technology Hyderabad India Department of Computer Science and Engineering Vellore Institute of Technology India

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Field-Programmable Gate Arrays (FPGAs) are widely used in various applications due to their flexibility and reconfigurability, and they store the functionality of digital design in the form of configuration frames within the bitstream. In the earlier studies, state-of-art methodologies, such as BIL and RapidSmith reverse engineer the bitstream to identify the boolean logic of LUTs using the Xilinx ISE tool, which provides bitstream and textual information of placed design in the form of a Xilinx Design Language (XDL) file. However, the more recent tool, Xilinx Vivado, does not include XDL support or text-based hardware adjustments. To resolve the above problem, here we introduce a methodology called REVBiT for LUT extraction and boolean logic identification that offers the potential to verify functionality against a trusted reference or rectify corrupted bitstream data by correcting it. Also, our propose methodology verified on AMD Xilinx 7-Series, Ultrascale and Ultrascale+ device families FPGAs using the Xilinx Vivado tool and does not rely on additional information besides the bitstream. We achieved 100% accuracy for the LUT extraction and 93.86%, 96.26%, and 95.16% accuracy for the boolean function identification for 7-Series, Ultrascale and Ultrascale+ device families, respectively.

关键词： Accuracy Dictionaries Reverse engineering Predictive models Hardware Table lookup Logic

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Battery Refurbishment Technologies in Industry 4.0: A Review 6

Battery Refurbishment Technologies in Industry 4.0: A Review

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6th International Conference on electrical, Control and Instrumentation engineering, ICECIE 2024

作者： Nguyen-Quang, Nam Nguyen-Vinh, Khuong Gono, Radomir Ho Chi Minh City University of Technology Vietnam National University Ho Chi Minh City Ho Chi Minh City Viet Nam School of Science Engineering and Technology RMIT University Ho Chi Minh City Viet Nam VSB Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic

ISBN: (纸本)9798350380040

The increasing demand for batteries in various industries has resulted in the problem of battery waste. An industry 4.0 will need to tackle this kind of environmental problem. With the need for a more sustainable solution, battery refurbishment technologies have emerged as a way to reduce the environmental impact of battery waste, conserve resources, and save costs. In this review, we will discuss the existing battery refurbishment technologies in the world, including their advantages and disadvantages. The authors also discussed about their current battery refurbishment project with the help of artificial intelligence. © 2024 IEEE.

关键词： Battery Refurbishment Environmental Impact Industry 4.0 Sustainable

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SHAP zero Explains Genomic Models with Near-zero Marginal Cost for Future Queried Sequences

arXiv

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arXiv 2024年

作者： Tsui, Darin Musharaf, Aryan Erginbas, Yigit Efe Kang, Justin Singh Aghazadeh, Amirali School of Electrical and Computer Engineering Georgia Institute of Technology United States School of Computer Science Georgia Institute of Technology United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

With the rapid growth of large-scale machine learning models in genomics, Shapley values have emerged as a popular method for model explanations due to their theoretical guarantees. While Shapley values explain model predictions locally for an individual input query sequence, extracting biological knowledge requires global explanation across thousands of input sequences. This demands exponential model evaluations per sequence, resulting in significant computational cost and carbon footprint. Herein, we develop SHAP zero, a method that estimates Shapley values and interactions with a near-zero marginal cost for future queried sequences after paying a one-time fee for model sketching. SHAP zero achieves this by establishing a surprisingly underexplored connection between the Shapley values and interactions and the Fourier transform of the model. Explaining two genomic models, one trained to predict guide RNA binding and the other to predict DNA repair outcome, we demonstrate that SHAP zero achieves orders of magnitude reduction in amortized computational cost compared to state-of-the-art algorithms, revealing almost all predictive motifs—a finding previously inaccessible due to the combinatorial space of possible interactions. © 2024, CC BY-NC-SA.

关键词： Carbon footprint

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BatteryCDE: A Transferable Future Capacity Estimation Method for Battery Degradation with Irregular Sampling and Missing Data

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IEEE Transactions on Transportation Electrification 2025年

作者： Wang, Tianjing Ren, Chao Xiong, Houbo Song, Qun Dong, ZhaoYang City University of Hong Kong Department of Electrical Engineering Hong Kong KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Zhejiang University School of Electrical Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

To overcome critical limitations in existing methods for battery capacity estimation, including long-term estimation, handling of irregular sampling and missing data, and transferability across different battery types, this study proposes a novel estimation method, termed batteryCDE, by integrating neural controlled differential equations (CDEs) with attention mechanisms. It first constructs a continuous data path using cubic spline interpolation, followed by neural CDEs that generate feature-wise and cycle-wise attention features to capture essential battery characteristics. These attention-weighted features are processed by another neural CDE to model differential relationships, leading to precise capacity estimations. This study also provides a theoretical analysis of the advantages of batteryCDE over conventional discrete models in terms of long-term estimation, handling of irregular sampling and missing data, and transferability. Extensive experiments evaluate batteryCDE’s performance, utilizing three scenarios: varying estimation horizons, missing data, and cross-battery transfer. Results show that batteryCDE outperforms traditional models like LSTM, Transformer and neural CDE networks. Even with 50% missing data, an estimation horizon of 100 cycles, and application to different batteries, batteryCDE maintains an estimation error below 0.05. Compared to other methods, batteryCDE reduces estimation errors by 6.59% for long-term predictions, 15.09% for handling missing data, and 17.01% for cross-battery transferability. © 2025 IEEE. All rights reserved.

关键词： Battery degradation capacity irregular and missing data neural controlled differential equations transferability

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Integration of Industrial Automation and IoT Devices on the Example of the Assembly Station with the Astorino Educational Robot 4th

Integration of Industrial Automation and IoT Devices on the ...

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4th International Conference on Intelligent Systems in Production engineering and Maintenance, ISPEM 2023

作者： Ćwikla, Grzegorz Kalinowski, Krzysztof Kciuk, Marek Gold, Michal Waluśkiewicz, Marek Sladek, Michal Tlolka, Jaroslaw Woźnica, Robert Wlazlo, Aleksander Faculty of Mechanical Engineering Silesian University of Technology Konarskiego 18A Gliwice44-100 Poland Faculty of Electrical Engineering Silesian University of Technology Akademicka 10A Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783031442810

The article presents an automated assembly station designed by students as part of Project Based Learning. The task of the project was to familiarize with the issues of integration of automation and control elements operating in various standards in order to implement the idea of Industry 4.0. The main elements of the station are the Astorino educational robot, gripper, servo drive and other professional elements of industrial automation and IoT systems. Raspberry PI, microcontrollers, etc. are also used. The assembly process of the product in the configuration in accordance with the user’s order is carried out at the station, quality control is carried out during the process using a vision system and various sensors. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data acquisition

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Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation

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IEEE Transactions on Cybernetics 2024年第8期54卷 4643-4651页

作者： Jaw, Da-Wei Huang, Shih-Chia Lu, Zhi-Hui Fung, Benjamin C. M. Kuo, Sy-Yen National Taiwan University Department of Electrical Engineering Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan Fudan University School of Computer Science The Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China McGill University School of Information Studies MontrealQCH3A 1X1 Canada

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low- and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low- and sufficient-luminance domains. © 2013 IEEE.

关键词： Feature extraction

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Metal carbonyl loaded nanoparticles for breast cancer biomarker detection 21

Metal carbonyl loaded nanoparticles for breast cancer biomar...

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Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XXI 2024

作者： Richard, Craig Hsieh, Pei-Hsuan Confer, Matthew Bhargava, Rohit Department of Bioengineering University of Illinois at Urbana-Champaign United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign United States Departments of Electrical and Computer Engineering Mechanical Science and Engineering Chemical and Biomolecular Engineering Chemistry United States Cancer Center at Illinois University of Illinois at UrbanaChampaign Urbana United States

ISBN: (纸本)9781510669758

The potential advantages of infrared spectroscopic imaging for examination of cells and tissue biopsies for cancer histopathology is high, however there are fundamental limitations of FTIR that must first be addressed. One limitation is that Infrared spectroscopy lacks the sensitivity to detect biomarkers of disease directly. With cancer being one of the leading causes of death and its increasing prevalence, it is important to develop tools that can rapidly screen and enable rapid and simultaneous fingerprinting of biologic content and metabolic state. To address these limitations, probed Infrared spectroscopy is being explored. We report the successful synthesis, characterization, and application of metal carbonyl loaded nanoparticle probes for the detection of breast cancer biomarkers. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Biomarkers

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