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ADVANCED INTELLIGENT SYSTEMS 2022年第7期4卷

作者： Poddar, Swapnadeep Chen, Zhesi Ma, Zichao Zhang, Yuting Chan, Chak Lam Jonathan Ren, Beitao Zhang, Qianpeng Zhang, Daquan Shen, Guozhen Zeng, Haibo Fan, Zhiyong Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China State Key Laboratory for Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences & Center of Materials Science and Optoelectronic Engineering University of Chinese Academy of Sciences Beijing China MIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics & Nanomaterials School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing China

The Gestalt principles of perceptual learning elucidate how the human brain categorizes and comprehends a set of visual elements grouped together. One of the principles of Gestalt perceptual learning is the law of closure which propounds that human perception has the proclivity to visualize a fragmented object as a preknown whole by bridging the missing gaps. Herein, a letter recognition scheme emulating the Gestalt closure principle is demonstrated, utilizing artificial synapses made of 3D integrated MA(3)Bi(2)I(9) (MBI) perovskite nanowire (NW) array. The artificial synapses exhibit short-term plasticity (STP) and long-term potentiation (LTP) and a transition from STP to LTP with increasing number of input electrical pulses. Initiatory ab initio molecular dynamics (AIMD) simulations attribute the conductance change in the MBI NW artificial synapses to the rotation of MA(+) clusters, culminating in charge exchange between MA(+) and Bi2I93-. Each device yields 40 conductance states with excellent retention >10(5) s, minimal variation (2 sigma/mean) <10%, and endurance of approximate to 10(5) cycles. MBI NW-based artificial neural network (ANN) is constructed to recognize fragmented letters alike their distinction in unabridged form and also the gradual withering of synaptic connectivity with engendered missing fragments is demonstrated, thereby successfully implementing Gestalt closure principle.

关键词： artificial synapses Gestalt principle of closure letter recognition perovskite nanowires

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NeuroFusion-Trans: A Novel Transformer-Based EEG-EMG Fusion Model for Assistive Robotics

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IEEE Access 2025年 13卷 103245-103263页

作者： Tipu Sultan Guangping Liu Pascal Sikorski Samah Alshathri Walid El-Shafai Madi Babaiasl Aerospace and Mechanical Engineering Department Saint Louis University St. Louis MO USA Computer Science Department Saint Louis University St. Louis MO USA Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia Computer Science Department Automated Systems and Computing Laboratory (ASCL) Prince Sultan University Riyadh Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt

User intent recognition from multimodal neurophysiological signals, particularly electroencephalography (EEG) and electromyography (EMG), is critical for enhancing human-machine interaction in assistive robotics. Recent advances in neurophysiological signal processing have enabled enhanced user intent recognition for assistive robotics and human-machine interfaces. However, achieving high accuracy and real-time adaptability in electromyography (EMG) and electroencephalography (EEG)-based gesture recognition remains challenging due to temporal misalignment, weak cross-modality fusion, and lack of adaptive learning. This paper proposes NeuroFusion-Trans, a novel transformer-based framework that improves EEG-EMG gesture recognition by improving temporal resolution, using cross-modality attention, and integrating adaptive online learning. Temporal resolution enhancement ensures dynamic EEG-EMG synchronization for improved signal alignment. The cross-modality attention mechanism captures interdependencies between EEG and EMG signals, leading to more accurate intent classification. Adaptive online learning enables real-time personalization by dynamically adjusting to user-specific variations. The model is evaluated on two publicly available EEG-EMG upper-limb gesture datasets: Dataset 1 (5,296 for training, 1,324 for validation) and Dataset 2 (5,276 for training, 1,304 for validation). NeuroFusion-Trans achieves state-of-the-art performance, with an accuracy of 97% and 96% and Cohen’s Kappa of 0.97 and 0.95 after online adaptation, significantly outperforming baseline models such as CNN-LSTM, GRU, and LSTMNet. Ablation studies reveal that removing the cross-modality attention mechanism reduces accuracy by 6.1%, underscoring its importance in exploiting the EEG-EMG dependencies. Turning off synchronization leads to a 6.7% performance drop, demonstrating the necessity of real-time learning for robust intent recognition. Furthermore, NeuroFusion-Trans enhances EEG-EMG synchr

关键词： Brain modeling Electromyography Electroencephalography Adaptation models Real-time systems Synchronization Accuracy Transformers Robots Gesture recognition

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Image Segmentation via Fischer-Burmeister Total Variation and Thresholding

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Advances in Applied Mathematics and Mechanics 2022年第4期14卷 960-988页

作者： TingtingWu Yichen Zhao Zhihui Mao Li Shi Zhi Li Yonghua Zeng School of Science Nanjing University of Posts and TelecommunicationsNanjingJiangsu 210023China The Department of Computer Science and Technology Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghai 200241China College of Field Engineering PLA Army Engineering UniversityNanjingJianhsu 210007China

Image segmentation is a significant problem in image *** this paper,we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation(FBTV).The first stage of our method is to calculate a smooth solution from the FBTV Mumford-Shah ***,we design a new difference of convex algorithm(DCA)with the semi-proximal alternating direction method of multipliers(sPADMM)*** the second stage,we make use of the smooth solution and the K-means method to obtain the segmentation *** simulate images more accurately,a useful operator is introduced,which enables the proposed model to segment not only the noisy or blurry images but the images with missing pixels *** demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods,especially on the images with missing pixels.

关键词： Image segmentation Fischer-Burmeister total variation difference of convex algorithm sPADMM K-means method.

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Quantum K-Means Model based on Optimization Framework

Quantum K-Means Model based on Optimization Framework

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Shao, Zexiang Zhang, Shibin Kumar, Sachin School of Cyberspace Security Chengdu University of Information Technology Chengdu610225 China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu610225 China Ajay Kumar Garg Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9781665457033

The Quantum K-means clustering algorithm offers the advantage of quantum parallel computing, but suffers from issues related to cluster center initialization and sensitivity to noisy data due to its similarity with the K-means clustering algorithm. To address these challenges, we propose a quantum K-means model based on an optimization framework. Specifi-cally, we use a quantum genetic algorithm with pin optimization to adjust the fitness function for the Quantum K-means clustering algorithm. The optimally adapted quantum genetic algorithm reduces the sensitivity of cluster center initialization and improves the noise robustness of the Quantum K-means clus-tering algorithm using a noise reduction self-encoder. As a result, the proposed model achieves better clustering performance even in the presence of noise. Experimental results demonstrate that our proposed quantum K-means model has fewer clustering iterations, lower probability of falling into local optima, and improved noise robustness after optimizing the clustering cen-ters. The model's effectiveness in clustering is also demon-strated experimentally. © 2023 IEEE.

关键词： Noise abatement

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Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Covering

arXiv

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arXiv 2023年

作者： Hou, Fei Chen, Xuhui Wang, Wencheng Qin, Hong He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Department of Computer Science Stony Brook University New York United States School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user-specified parameter r (a small positive real number) as input and extracts an iso-surface with an iso-value r using the conventional marching cubes algorithm. We show that the computed iso-surface is the boundary of the r-offset volume of the target zero level-set S, which is an orientable manifold, regardless of the topology of S. Next, the algorithm computes a covering map to project the boundary mesh onto S, preserving the mesh's topology and avoiding folding. If S is an orientable manifold surface, our algorithm separates the double-layered mesh into a single layer using a robust minimum-cut post-processing step. Otherwise, it keeps the double-layered mesh as the output. We validate our algorithm by reconstructing 3D surfaces of open models and demonstrate its efficacy and effectiveness on synthetic models and benchmark datasets. Our experimental results confirm that our method is robust and produces meshes with better quality in terms of both visual evaluation and quantitative measures than existing UDF-based methods. The source code is available at https://***/jjjkkyz/DCUDF. © 2023, CC BY-NC-ND.

关键词： Topology

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When are ensembles really effective? 23

When are ensembles really effective?

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Ryan Theisen Hyunsuk Kim Yaoqing Yang Liam Hodgkinson Michael W. Mahoney Department of Statistics University of California Berkeley Department of Computer Science Dartmouth College School of Mathematics and Statistics University of Melbourne Australia International Computer Science Institute Lawrence Berkeley National Laboratory and Department of Statistics University of California Berkeley

Ensembling has a long history in statistical data analysis, with many impactful applications. However, in many modern machine learning settings, the benefits of ensembling are less ubiquitous and less obvious. We study, both theoretically and empirically, the fundamental question of when ensembling yields significant performance improvements in classification tasks. Theoretically, we prove new results relating the ensemble improvement rate (a measure of how much ensembling decreases the error rate versus a single model, on a relative scale) to the disagreement-error ratio. We show that ensembling improves performance significantly whenever the disagreement rate is large relative to the average error rate; and that, conversely, one classifier is often enough whenever the disagreement rate is low relative to the average error rate. On the way to proving these results, we derive, under a mild condition called competence, improved upper and lower bounds on the average test error rate of the majority vote classifier. To complement this theory, we study ensembling empirically in a variety of settings, verifying the predictions made by our theory, and identifying practical scenarios where ensembling does and does not result in large performance improvements. Perhaps most notably, we demonstrate a distinct difference in behavior between interpolating models (popular in current practice) and non-interpolating models (such as tree-based methods, where ensembling is popular), demonstrating that ensembling helps considerably more in the latter case than in the former.

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Targeted Pareto Optimization for Subset Selection With Monotone Objective Function and Cardinality Constraint

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Shang, Ke Wu, Guotong Pang, Lie Meng Ishibuchi, Hisao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Subset selection, a fundamental problem in various domains, is to choose a subset of elements from a large candidate set under a given objective or multiple objectives. Pareto optimization for subset selection (POSS) has emerged as a powerful paradigm for addressing subset selection problems. Recently, some POSS variants have been proposed to further improve its performance. In this paper, we propose a new POSS variant, named Targeted Pareto Optimization for Subset Selection (TPOSS). TPOSS differs from POSS in four aspects: problem formulation, population initialization, mutation, and environmental selection. The main idea of TPOSS is to focus the search on the target region of subset selection with respect to the subset cardinality in order to improve the search efficiency. We conduct comprehensive experiments to compare TPOSS with six state-of-the-art algorithms on three subset selection tasks (i.e., sparse regression, unsupervised feature selection, and hypervolume subset selection) where the size of the candidate sets ranges from 20 to 400. Experimental results show that with respect to the objective value of the best feasible subset, TPOSS outperforms the other algorithms on all the three tasks, which suggests the potential of TPOSS to enhance subset selection in various domains. IEEE

关键词： Linear programming

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Riemannian Complex Matrix Convolution Network for PolSAR Image Classification

arXiv

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arXiv 2023年

作者： Shi, Junfei Wang, Wei Jin, Haiyan Nie, Mengmeng Ji, Shanshan Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China The Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

Recently, deep learning methods have achieved superior performance for Polarimetric Synthetic Aperture Radar(PolSAR) image classification. Existing deep learning methods learn PolSAR data by converting the covariance matrix into a feature vector or complex-valued vector as the input. However, all these methods cannot learn the structure of complex matrix directly and destroy the channel correlation. To learn geometric structure of complex matrix, we propose a Riemannian complex matrix convolution network for PolSAR image classification in Riemannian space for the first time, which directly utilizes the complex matrix as the network input and defines the Riemannian operations to learn complex matrix’s features. The proposed Riemannian complex matrix convolution network considers PolSAR complex matrix endowed in Riemannian manifold, and defines a series of new Riemannian convolution, ReLu and LogEig operations in Riemannian space, which breaks through the Euclidean constraint of conventional networks. Then, a CNN module is appended to enhance contextual Riemannian features. Besides, a fast kernel learning method is developed for the proposed method to learn class-specific features and reduce the computation time effectively. Experiments are conducted on three sets of real PolSAR data with different bands and sensors. Experiments results demonstrates the proposed method can obtain superior performance than the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Enhancing Advanced Time-Series Forecasting of Electric Energy Consumption Based on RNN Augmented with LSTM Techniques

Enhancing Advanced Time-Series Forecasting of Electric Energ...

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International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： Benkhalfallah, Mohamed Salah Kouah, Sofia Benkhalfallah, Fateh Artificial Intelligence and Autonomous Things Laboratory Oum El Bouaghi Algeria Department of Mathematics and Computer Science University of Oum El Bouaghi Oum El Bouaghi Algeria Tebessa Algeria Faculty of Exact Sciences and Natural and Life Sciences Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (纸本)9783031714252

Forecasting energy consumption remains an important endeavor, given the indispensable role of energy in human existence and most economic activities. In recent years, the growing importance of Artificial Intelligence and Machine Learning approaches to forecasting energy consumption has become increasingly pronounced. These advanced techniques are adept at processing large datasets, enabling nuanced analyses of consumption patterns and precise rate determinations. However, energy overproduction leads to resource depletion and increased operational costs, underscoring the need for careful monitoring and accurate forecasting of energy consumption rates. Such forecasts are essential for devising efficient, safe, and sustainable energy management strategies, optimizing equipment maintenance protocols, and facilitating informed production decision-making. This paper strives to provide a predictive model for hourly energy consumption rates leveraging Recurrent Neural Network (RNN) enhanced with Long Short-Term Memory (LSTM) Deep Learning techniques. These techniques have demonstrated effective results and performance, receiving an outstanding R-squared of 99.00%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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State of Charge Management for Grid-Connected PV with Supercapacitor System Considering LVRT Operation

State of Charge Management for Grid-Connected PV with Superc...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Kritprajun, Paychuda Tolbert, Leon M. Sun, Jingjing Wang, Jingxin Praisuwanna, Nattapat Liu, Yunting Ferrari, Maximiliano The University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States The Pennsylvania State University Department of Electrical Engineering University ParkPA United States Oak Ridge National Laboratory Grid Components and Control Oak RidgeTN United States

ISBN: (纸本)9798350316445

This paper presents a state of charge (SOC) management strategy to ensure low-voltage ride-through (LVRT) services of a grid-connected photovoltaic (PV) with a supercapacitor (SC) energy storage system (PVSS). A concept of SOC reservation for LVRT operations is introduced to guarantee that the PV grid-connected system can provide LVRT services complying with grid code requirements. SOC management is derived by considering SC limitations, the current rating of the converters, and grid conditions. Therefore, safe operation of the power system and PVSS can be achieved. The study also adopts an electrothermal model of the SC to adjust the current set points during SOC management by considering the temperature-dependent SC parameters;thus, both safety and full utilization of the SC can be achieved. Consequently, SOC management can rapidly be accomplished, thereby increasing the certainty of providing LVRT services to the system. © 2023 IEEE.

关键词： Grid-connected photovoltaic system low-voltage ride-through state of charge management supercapacitor

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