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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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An in-Contact Robotic System for the Process of Desoldering PCB Components

arXiv

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arXiv 2024年

作者： Santos, Silvia Marques, Lino Neto, Pedro Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030-290 Portugal University of Coimbra CEMMPRE ARISE Department of Mechanical Engineering Coimbra3030-788 Portugal

The disposal and recycling of electronic waste (e-waste) poses a significant global challenge. The disassembly of components is a crucial step in achieving an efficient recycling process, avoiding destructive methods. While manual disassembly remains the norm due to the diversity and complexity of components, there is a growing interest in automating the process to enhance efficiency and reduce labor costs. This study endeavors to automate the desoldering process and the extraction of components from printed circuit boards (PCBs) by implementing a robotic solution. The proposed strategy encompasses multiple phases, one of which involves the precise contact of the developed robotic tool with the components on the PCB. The tool was designed to exert a controlled force on the PCB component, thereby efficiently desoldering it from the board. Results demonstrate the feasibility of achieving a high success rate in removing PCB components, including the ones from mobile phones. The desoldering success rate observed is approximately 100% for the larger components. © 2024, CC BY.

关键词： Printed circuit boards

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Wireless modulation classification based on Radon transform and convolutional neural networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6263-6272页

作者： Ghanem, Hanan S. Al-Makhlasawy, Rasha M. El-Shafai, Walid Elsabrouty, Maha Hamed, Hesham F. A. Salama, Gerges M. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia61111 Egypt Department of Computers and Systems Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions. © 2022, The Author(s).

关键词： Fading channels

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Single-Channel Super-Resolution SAR-GMTI via Atomic Norm Minimization

Single-Channel Super-Resolution SAR-GMTI via Atomic Norm Min...

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IEEE National Conference on Radar

作者： Bariscan Yonel Birsen Yazici Sean Thammakhoune Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

Synthetic aperture radar (SAR) imaging algorithms are designed to image stationary scatterers. A moving target is mis-positioned and results in artifacts in reconstructed images. In this paper, we present a novel approach to ground moving target imaging (GMTI) using a single channel SAR data based on atomic norm minimization (ANM) that is capable of localizing targets with sub-resolution cell precision. We use ANM to decompose the received SAR data into the echoes received from each scatterer in the scene, which facilitates processing echoes individually and isolating velocity related phase variations by cross-correlations across the slow-time samples. We use these estimates for motion compensation, and form the SAR image using the dual polynomial of the atomic decomposition. Motion parameters are then numerically estimated without a pre-specified grid. We derive the theoretical requirements on SAR imaging parameters, and detectable velocity limit; and demonstrate the off-grid, sub-resolution cell localization capability of our ANM-based method in SAR-GMTI setting by numerical simulations.

关键词： Location awareness Superresolution Imaging Radar imaging Minimization Radar polarimetry Motion compensation

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Pseudo-label Assisted Optimization of Multi-branch Network for Cross-domain Person Re-identification 20

Pseudo-label Assisted Optimization of Multi-branch Network f...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Wang, Zhengyang Guo, Shuxiang Shang, Xue Ye, Xiufen Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin China National University of Singapore Department of Electrical and Computer Engineering Singapore Beijing Institute of Technology School of Life Science Beijing China

ISBN: (纸本)9798350320831

Person re-identification plays an important role in the field of robot intelligence perception and public safety. Its main task is to identify person targets under cross-camera. However, the domain diversity between different datasets poses a clear challenge for adapting a model trained on one dataset to another. Currently, person re-identification methods based on domain adaptive learning and pseudo-label have made good progress on this problem. Unfortunately, inferior pseudo-labels and source domain noise affect the performance. In order to improve the quality of generated pseudo-labels and enhance the feature representation capability of the model, we propose a pseudo-label assisted optimization of a multi-branch person re-identification method. The multi-branch network is able to extract and represent more effective global and local features, and the generated pseudo-labels are optimized by using cosine similarity and DBSCAN clustering on the feature vectors, thus improving the consistency of the supervised information to enhance the cross-domain recognition performance. We also use a loss function combining cross-entropy loss and triplet loss to make the best feature learning. Experiments show that our method performs well in the Market-to-Duke and Duke-to-Market cross-domain recognition tasks. © 2023 IEEE.

关键词： Intelligent robots

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Improved Bound for Robust Causal Bandits with Linear Models

Improved Bound for Robust Causal Bandits with Linear Models

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IEEE International Symposium on Information Theory

作者： Zirui Yan Arpan Mukherjee Burak Varıcı Ali Tajer Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This paper investigates the robustness of causal bandits (CBs) in the face of temporal model fluctuations. This setting deviates from the existing literature's widely-adopted assumption of constant causal models. The focus is on causal systems with linear structural equation models (SEMs). The SEMs and the time-varying pre- and post-interventional statistical models are all unknown and subject to variations over time. The goal is to design a sequence of interventions that incur the smallest cumulative regret compared to an oracle aware of the entire causal model and its fluctuations. A robust CB algorithm is proposed, and its cumulative regret is analyzed by establishing both upper and lower bounds on the regret. It is shown that in a graph with maximum in-degree $d$ , length of the largest causal path $L$ , and an aggregate model deviation $C$ , the regret is upper bounded by $\tilde{\mathrm{O}}(d^{L-\frac{1}{2}}(\sqrt{T}+C))$ and lower bounded by $\Omega(d^{\frac{L}{2}-2}\max\{\sqrt{T}\,\ d^{2}C\})$ . The proposed algorithm achieves nearly optimal $\tilde{\mathcal{O}}(\sqrt{T})$ regret when $C$ is $o(\sqrt{T})$ , maintaining sub-linear regret for a broad range of C.

关键词： Fluctuations Upper bound Numerical analysis Aggregates Mathematical models Robustness Faces

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Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces 24

Exploring the trade-off between deep-learning and explainabl...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Luis H. Cubillos Guy Revach Matthew J. Mender Joseph T. Costello Hisham Temmar Aren Hite Diksha Zutshi Dylan M. Wallace Xiaoyong Ni Madison M. Kelberman Matthew S. Willsey Ruud J.G. van Sloun Nir Shlezinger Parag Patil Anne Draelos Cynthia A. Chestek Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan Department of Information Technology and Electrical Engineering ETH Zürich Switzerland Departments of Electrical & Computer Engineering Biomedical Engineering Robotics Computational Medicine & Bioinformatics and Neurosurgery University of Michigan and Biointerfaces Institute and Neuroscience Institute University of Michigan Department of Electrical Engineering Eindhoven University of Technology Netherlands School of Electrical and Computer Engineering Ben-Gurion University Israel

ISBN: (纸本)9798331314385

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the 'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on Kalman-Net, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n =13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite th

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TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition

TReR: A Lightweight Transformer Re-Ranking Approach for 3D L...

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International Conference on Intelligent Transportation

作者： Tiago Barros Luís Garrote Martin Aleksandrov Cristiano Premebida Urbano J. Nunes Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Portugal Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Berlin

Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-based place recognition to find revisited places efficiently. However, when deployed in challenging real-world scenarios, the place recognition models become more complex, which comes at the cost of high computational demand. This work tackles this problem from an information-retrieval perspective, adopting a first-retrieve-then-re-ranking paradigm, where an initial loop candidate ranking, generated from a 3D place recognition model, is re-ordered by a proposed lightweight transformer-based re-ranking approach (TReR). The proposed approach relies on global descriptors only, being agnostic to the place recognition model. The experimental evaluation, conducted on the KITTI Odometry dataset, where we compared TReR with s.o.t.a. re-ranking approaches such as αQE and SGV, indicate the robustness and efficiency when compared to αQE while offering a good trade-off between robustness and efficiency when compared to SGV.

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BAI in Exponential Family: Efficiency and Optimality

BAI in Exponential Family: Efficiency and Optimality

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IEEE International Symposium on Information Theory

作者： Arpan Mukherjee Ali Tajer Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This paper investigates a hitherto unaddressed as-pect of best arm identification (BAI) in stochastic multi-armed bandits in the fixed -confidence setting. Two essential metrics for assessing bandit algorithms are computational efficiency and performance optimality (e.g., in sample complexity). In stochastic BAI literature, there have been advances in designing algorithms to achieve optimal performance, but they are generally computationally expensive to implement (e.g., optimization-based methods). There also exist approaches that have high computationally efficiency but do not achieve the optimal performance (e.g., UCB-based methods) or achieve it up to a gap (e.g., the $\beta-$ optimal approaches in top-two methods). This paper introduces a framework and an algorithm for BAI that achieves optimal performance with a computationally efficient set of decision rules. The central process that facilitates this is a routine for sequentially estimating the optimal allocations up to sufficient fidelity. Specifically, these estimates are accurate enough for identifying the best arm (hence, achieving optimality) but not excessively accurate to an unnecessary extent (hence, maintaining efficiency). Numerical evaluations are provided to (i) establish the optimality and efficiency of the algorithm, (ii) showcase the implicit estimation property of the proposed allocation rules, and (ii) demonstrate the superior performance of the proposed algorithms compared to the existing ones.

关键词： Measurement Accuracy Costs Estimation Rendering (computer graphics) Computational efficiency Complexity theory

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Toward a personalized autonomous transportation system:Vision,challenges,and solutions

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The Innovation 2024年第6期5卷 47-49页

作者： Linlin You Mai Hao Jian Sun Yunpeng Wang Chunming Rong Chau Yuen Paolo Santi Carlo Ratti School of Intelligent Systems Engineering Sun Yat-Sen UniversityShenzhen 518107China Department of Traffic Engineering Tongji UniversityShanghai 200092China School of Transportation Science and Engineering Beihang UniversityBeijing 100083China Department of Electrical Engineering and Computer Science University of Stavanger4036 StavangerNorway School of Electrical and Electronics Engineering Nanyang Technological UniversitySingapore 639798Singapore Senseable City Lab Massachusetts Institute of TechnologyCambridgeMA 02139USA

The fragmented design of intelligent transportation systems creates isolated intelligent *** competition and information gaps are fierce and widespread,worsening traffic issues and degrading overall service ***,empowered by advanced technologies,an evolution toward an autonomous transportation system(ATS)is *** evolution aims to develop a collaborative and sustainable ecosystem,prompting interoperability within the cloud-edge-device continuum.

关键词： autonomous system prompt

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