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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Zhu, Chen Huang, Jie Research Center for Optical Fiber Sensing Zhejiang Laboratory Hangzhou311100 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

The possibility of employing a light source with a small wavelength bandwidth (35 nm) and a coarsely resolved spectrometer (~166 pm) for the interrogation of a Vernier effect-based high-sensitivity optical fiber senso... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical fibers

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Physics-Driven Deep Learning Reconstruction of Frequency-Modulated Rabi-Encoded Echoes for Faster Accessible MRI 46

Physics-Driven Deep Learning Reconstruction of Frequency-Mod...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Saberi, Mahdi Jenkins, Parker Garwood, Michael Akcakaya, Mehmet University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN United States University of Minnesota Center for Magnetic Resonance Research MinneapolisMN United States University of Minnesota Department of Biomedical Engineering MinneapolisMN United States

ISBN: (纸本)9798350371499

Magnetic resonance imaging (MRI) is a powerful imaging modality with exceptional soft tissue contrast capabilities, but it is estimated to only serve 10% of the world's population reliably. This lack of access is largely due to the multi-million cost of initial investment, as well as recurring expenses. Radiofrequency (RF) imaging methods present an opportunity to reduce MRI costs by replacing expensive B0 gradients with less expensive RF B1+ field gradients for spatial encoding. Frequency-modulated Rabi encoded echoes (FREE) is one such technique that has demonstrated robust phase-encoded imaging capabilities over large inhomogeneities. However, conventional reconstruction of such acquisitions lead to distortions due to nonlinear phase accrual, and imaging speed is slow when B1+ phase encoding is employed in two spatial dimensions. In this work, we propose a physics-driven deep learning (PD-DL) reconstruction approach to resolve distortion artifacts of FREE acquisitions, while enabling higher acceleration rates. We map out the forward operator for FREE encoding, and devise an unrolled network that utilizes this operator. Results on sequential gradient superposition (SGS) FREE sequence indicates feasibility of up to 4-fold acceleration with a single receive-coil. © 2024 IEEE.

关键词： Frequency modulation

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Robust Deep Transfer Learning Based Object Detection and Tracking Approach

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Intelligent automation & Soft Computing 2023年第3期35卷 3613-3626页

作者： C.Narmadha T.Kavitha R.Poonguzhali V.Hamsadhwani Ranjan walia Monia B.Jegajothi Electronics and Communication Engineering Periyar Maniammai Institute of Science&Technology(PMIST)Thanjavur613403India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Electrical Engineering Department Model Institute of Engineering&Technology(Autonomous)Jammu181122India Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatra182320India Center for Energy Research Chennai Institute of TechnologyChennai600069India

At present days,object detection and tracking concepts have gained more importance among researchers and business ***,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking *** paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)*** AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT *** AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast *** RPN is a full convolution network that concurrently predicts the bounding box and score of different *** RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection ***,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature *** performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight ***,softmax layer is applied to classify the *** performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes *** outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

关键词： Object detection tracking deep learning deep transfer learning image annotation

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CLIPREC: Graph-Based Domain Adaptive Network for Zero-Shot Referring Expression Comprehension

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IEEE Transactions on Multimedia 2024年 26卷 2480-2492页

作者： Ke, Jingcheng Wang, Jia Chen, Jun-Cheng Jhuo, I-Hong Lin, Chia-Wen Lin, Yen-Yu National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering Hsinchu300093 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115201 Taiwan Microsoft SeattleWA98052 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan

Referring expression comprehension (REC) is a cross-modal matching task that aims to localize the target object in an image specified by a text description. Most existing approaches for this task focus on identifying only objects whose categories are covered by training data. This restricts their generalization to unseen categories and practical usage. To address this issue, we propose a domain adaptive network called CLIPREC for zero-shot REC, which integrates the Contrastive Language-Image Pretraining (CLIP) model for graph-based REC. The proposed CLIPREC is composed of a graph collaborative attention module with two directed graphs: one for objects in an image and the other for their corresponding categorical labels. To carry out zero-shot REC, we leverage the strong common image-text feature space from the CLIP model to correlate the two graphs. Furthermore, a multilayer perceptron is introduced to enable feature alignment so that the CLIP model is adapted to the expression representation from the language parser, resulting in effective reasoning from expressions involving both seen and unseen object categories. Extensive experimental and ablation results on several widely-adopted benchmarks show that the proposed approach performs favorably against state-of-the-art approaches for zero-shot REC. © 1999-2012 IEEE.

关键词： Object detection

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Optimization of Intelligent Classroom based on CAD Multi-Valued Color Image Segmentation Algorithm

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computer-Aided Design and Applications 2023年第S3期20卷 16-28页

作者： Wang, Nanlan Li, Jianqi Balaji, Prasanalakshmi Vaiyapuri, Thavavel Verma, Amit School of Computer and Electrical Engineering Hunan university of Arts and Science Hunan Changde415000 China International Education Center Philippine Christian University Manila1772 Philippines Department of Computer Science King Khalid University Saudi Arabia Department of Computer Sciences College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Saudi Arabia Department of CSE University Centre for Research & Development Chandigarh University Punjab Mohali140413 India

In order to solve the problem that the efficiency of smart classroom is not high enough, a multi valued color image segmentation algorithm based on CAD is proposed to optimize the smart classroom. This paper explores the notion of smart education as well as a conceptual framework to enable children to receive excellent education and to spark their interest through creative learning. Firstly, CAD software is used to improve software design efficiency and optimize product technology;Secondly, the core technology of the smart classroom is improved by using the national value color image segmentation algorithm, and compared with the smart classroom without the multi value color image segmentation algorithm;Finally, the utilization efficiency of the improved smart classroom is counted. It is proved that the smart classroom based on CAD multi valued color image segmentation algorithm can give greater play to the role of smart classroom, reduce the teaching pressure of teachers by 20% and improve the learning efficiency of students by 20%. The intelligent classroom has a higher degree of informatization and more powerful functions, which is of great help to promote classroom communication and improve communication efficiency. The smart classroom is easier to run and adapt thanks to the optimization of the CAD multi threshold color picture segmentation algorithm, which considerably improves the system's work efficiency and capability. However, the smart classroom also has its own shortcomings, which are mainly reflected in the high investment cost, the complex design of the system and the high maintenance cost in the later stage. With the optimization of CAD multi valued color image segmentation algorithm, the smart classroom is easier to operate and adjust, which greatly enhances the work efficiency and ability of the system. © 2023 CAD Solutions, LLC.

关键词： computer aided design

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D-SVM over networked systems with non-ideal linking conditions

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Iran Journal of computer Science 2023年第4期6卷 375-386页

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Zarrabi, Houman Faculty of Mechanical Engineering Semnan University Semnan Iran Electrical Engineering and Computer Science Department Oregon State University Corvallis OR United States Iran Telecom Research Center (ITRC) Tehran Iran

This paper considers distributed optimization algorithms, with application in binary classification via distributed support vector machines (D-SVM) over multi-agent networks subject to some link nonlinearities. The agents solve a consensus-constraint distributed optimization cooperatively via continuous-time dynamics, while the links are subject to strongly sign-preserving odd nonlinear conditions. Logarithmic quantization and clipping (saturation) are two examples of such nonlinearities. In contrast to existing literature that mostly considers ideal links and perfect information exchange over linear channels, we show how general sector-bounded models affect the convergence to the optimizer (i.e. the SVM classifier) over dynamic balanced directed networks. In general, any odd sector-bounded nonlinear mapping can be applied to our dynamics. The main challenge is to show that the proposed system dynamics always have one zero eigenvalue (associated with the consensus) and the other eigenvalues all have negative real parts. This is done by recalling arguments from matrix perturbation theory. Then, the solution is shown to converge to the agreement state under certain conditions. For example, the gradient tracking (GT) step size is tighter than the linear case by factors related to the upper/lower sector bounds. To the best of our knowledge, no existing work in distributed optimization and learning literature considers non-ideal link conditions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Clipping Distributed optimization Log quantization Matrix perturbation theory SVM

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Decision-Making in Robotic Grasping with Large Language Models 16th

Decision-Making in Robotic Grasping with Large Language Mo...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhang, Haoyang Qian, Haofu Meng, Qiwei Sun, Yinan Sun, Yao Song, Wei Zhu, Shiqiang Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

Recent advances in large language models have highlighted their potential to encode massive amounts of semantic knowledge for long-term autonomous decision-making, positioning them as a promising solution for powering the cognitive capabilities of future home-assistant robots. However, while large language models can provide high-level decision, there is still no unified paradigm for integrating them with robots’ perception and low-level action. In this paper, we propose a framework centered around a large language model, integrated with visual perception and motion planning modules, to investigate the robotic grasping task. Unlike traditional methods that only focus on generating stable grasps, our proposed approach can handle personalized user instructions and perform tasks more effectively in home scenarios. Our approach integrates existing state-of-the-art models in a simple and effective way, without requiring any fine-tuning, which makes it low-cost and easy to deploy. Experiments on a physical robot system demonstrate the feasibility of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Decision making

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Solar Panel Dust Detection Using Deep Learning Model

Solar Panel Dust Detection Using Deep Learning Model

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Munira, Sidratum Ahshan, Razzaqul Abid, Md. Shadman Al-Abri, Mohammed Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electrical and Computer Engineering Sultan Qaboos University Al-Khoud Oman Department of Petroleum and Chemical Engineering Sultan Qaboos University Al-Khoud123 Oman Nanotechnology Research Center Sultan Qaboos University Al-Khoud123 Oman

ISBN: (纸本)9798350395075

Accurate detection of dust particles on solar panels is essential for guaranteeing their maximum efficiency and longevity. Dust deposition can significantly impede the amount of radiation accessing the solar cells, leading to a decrease in total energy production. The diminution in production additionally carries financial implications, particularly in solar power plants, where minimal decreases in efficiency can contribute to significant declines in revenue. Therefore, efficient dust detection is vital for optimizing the performance of solar panels, which in turn maximizes the return on investments and enhances the long-term viability of renewable energy systems. Recent studies have shown that deep learning models, trained adequately on extensive sets of labeled data, can efficiently acquire the ability to identify the presence of dust accumulation on solar panels. In addition, recent studies have shown that using metaheuristic methods for hyperparameter tuning can significantly boost the effectiveness of deep learning models. In this context, this paper aims to construct an effective convolutional neural network (CNN) model with hyperparameter tuning using the Equilibrium optimizer (EO) for accurately recognizing dust on solar panels. The obtained findings show that the suggested model provides high accuracy and achieves early convergence. © 2024 IEEE.

关键词： Convolutional neural networks

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Self-Supervise Reinforcement Learning Method for Vacant Parking Space Detection Based on Task Consistency and Corrupted Rewards

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IEEE Transactions on Intelligent Transportation Systems 2024年第2期25卷 1346-1363页

作者： Nguyen, Manh-Hung Chao, Tzu-Yin Hsiao, Ching-Chun Li, Yung-Hui Huang, Ching-Chun Hcm University of Technology and Education Faculty of Electrical Electronic Engineering Ho Chi Minh City700000 Viet Nam National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan Hon Hai Research Institute Ai Research Center New Taipei City114699 Taiwan

This paper proposes a novel task-consistency learning method that enables us to train a vacant space detection network (target task) based on the logic consistency with the semantic outcomes from a flow-based motion behavior classifier (source task) in a parking lot. Note that the source task can introduce false detection during task-consistency learning, which implies noisy rewards or supervision. The target network can be trained in a reinforcement learning setting by appropriately designing the reward mechanism upon semantic consistency. We also introduce a novel symmetric constraint to detect corrupted samples and reduce the effect of noisy rewards. Unlike conventional corrupted learning methods that use only training losses to identify corrupted samples, our symmetric constraint also explores the relationship among training samples to improve performance. Compared with conventional supervised detection methods, the main contribution of our work is the ability to learn a vacant space detector via semantic consistency rather than supervised labels. The dynamic learning property allows the proposed detector to be easily deployed and updated in various lots without heavy human loads. Experiments demonstrate that our noisy task consistency mechanism can be successfully applied to train a vacant space detector from scratch. © 2000-2011 IEEE.

关键词： Semantics

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Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1574-1589页

作者： Guoqiang Zhu Haoqi Li Xiuyu Zhang Chenliang Wang Chun-Yi Su Jiangping Hu the School of Automation Engineering Northeast Electric Power UniversityJilin 132012 the Jilin Province International Research Center of Precision Drive and Intelligent Control Jilin 132012China the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the Department of Mechanical and Industrial Engineering Concordia UniversityQCMontrealH3B 1R6Canada the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China(

For a class of high-order nonlinear multi-agent systems with input hysteresis,an adaptive consensus output-feedback quantized control scheme with full state constraints is *** major properties of the proposed control scheme are:1)According to the different hysteresis input characteristics of each agent in the multi-agent system,a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value.2)A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent *** constructing state constraint control strategy for the hysteretic multi-agent system,it ensures that all the states of the system are always maintained within a predetermined range.3)The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance,and ensures that the input signal transmitted between agents is the quantization value,and the introduced quantizer is implemented under the condition that only its sector bound property is *** stability analysis has proved that all signals of the closed-loop are semi-globally uniformly *** Star Sim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.

关键词： Adaptive quantized control barrier Lyapunov function input hysteresis multi-agent systems

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