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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

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Energy Engineering 2025年第1期122卷 243-264页

作者： Liyao Yang HongyanMa Yingda Zhang Wei He School of Electrical and information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Institute of Distributed Energy Storage Safety Big Data Beijing100044China Beijing Key laboratory of Intelligent processing for Building Big Data Beijing100044China

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management ***,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is *** address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and ***,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance ***,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution *** Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction ***,the proposed models are validated using NASA and CALCE lithium-ion battery *** results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

关键词： State of health remaining useful life variational modal decomposition random forest twin support vector machine convolutional optimization algorithm

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

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International Journal of Automation and computing 2017年第1期14卷 57-67页

作者： S. Nagaraju Manish Kashyap Mahua Bhattachraya visual information processing lab Indian Institute of Information Technology and Management Gwalior India

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.

关键词： Density based clustering neighborhood difference density-based spatial clustering of applications with noise (DBSCAN),space density indexing (SDI) core object.

Video Saliency Detection via Pairwise Interaction

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Chinese Journal of Electronics 2020年第3期29卷 427-436页

作者： QIU Wenliang GAO Xinbo HAN Bing visual information processing laboratory School of Electronic Engineering Xidian University

We propose a novel video saliency detection method based on pairwise interaction learning in this paper. Different from the traditional video saliency detection methods, which mostly combine spatial and temporal features, we adopt Least squares Conditional random field(LS-CRF) to capture the interaction information of regions within a frame or between video ***, dual graph-connection models are built on superpixels structure of each frame for training and testing, respectively. In order to extract the essential scene structure from video sequences, LS-CRF is introduced to learn the background texture, object components and the various relationships between foreground and background regions through the training set, and each region will be distributed an inferred saliency value in testing *** from the learned diverse relations among scene regions, the proposed approach achieves reliable results especially on multiple objects scenes or under highly complicated scenes. Further, we substitute weak saliency maps for pixel-wise annotations in training phase to verify the expansibility and practicability of the proposed method. Extensive quantitative and qualitative experiments on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms.

关键词： Video saliency detection Conditional random fields Dual graph-connection model Pairwise interaction

DKP-SLAM:A visual SLAM for Dynamic Indoor Scenes Based on Object Detection and Region Probability

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Computers, Materials & Continua 2025年第1期82卷 1329-1347页

作者： Menglin Yin Yong Qin Jiansheng Peng College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi546300China Key laboratory of AI and information processing Education Department of Guangxi Zhuang Autonomous RegionHechi546300China Guangxi Key laboratory of Sericulture Ecology and Applied Intelligent Technology School of Chemistry and BioengineeringHechi UniversityHechi546300China

In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and *** paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic ***,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed ***,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points *** process divides the image into low dynamic,suspicious dynamic,and high dynamic *** the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these *** with geometric methods,it detects and removes the dynamic *** final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.

关键词： visual SLAM dynamic scene YOLOX K-means++clustering dynamic probability

Neural Wave Representation in Early Vision

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Science China Chemistry 1993年第6期36卷 677-684页

作者：汪云九齐翔林姚国正王孟 visual information processing laboratory Institute of Biophysics Academia Sinica Beijing 100101 PRC

The neural wave representation which describes spatiotemporal information in early vision is proposed in terms of extended Gabor functions in this paper. Its reasonability, completeness and uncertainty in spatiotemporal domains and their frequency domains were investigated. The ranges of these parameters in this representation were estimated on electrophysiological and psychophysical data. We derived the partial differential equations which the neural wave satisfied. The general significance of 3D neural wave representation has been explored.

关键词： early vision 3D Gabor function neural wave representation.

Connection Ensemble Model of Local Neural Circuits

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Science China Chemistry 1994年第10期37卷 1198-1207页

作者：姚国正王孟陶霖密 laboratory of visual information processing Institute of BiophysicsAcademia SinicaBeijing 100101PRC

A connection ensemble model (CEM) of local neural circuits in central nervous system(CNS) is proposed in this *** interactions are investigated at two levels,*** synaptic con-nection between single neurons and the macro-connections between different neuronal *** efficacy ofmacro-connection is quantitatively determined by the efficacies of constituent synapses,and is *** model provides a reasonable description for the functional invariance of local neural circuits withstructural diversities,which is a widely observed fact in *** is expected that the works in CEMmodel would enrich the current research in neural dynamics.

关键词： local neural circuit synaptic connection macro-connection connection space connection ensemble

Neural Network Approaches to visual Motion Perception

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Science China Chemistry 1994年第2期37卷 177-189页

作者：郭爱克杨先一 laboratory of visual information processing Institute of Biophysics Academia Sinica Beijing 100101 PRC)

This paper concerns certain difficult problems in image processing and perception: neuro-computation of visual motion information. The first part of this paper deals with the spatial physiological integration by the figure-ground discrimination neural network in the visual system of the fly. We have outlined the fundamental organization and algorithms of this neural network, and mainly concentrated on the results of computer simulations of spatial physiological integration. It has been shown that the gain control mechanism , the nonlinearity of synaptic transmission characteristic , the interaction between the two eyes , and the directional selectivity of the pool cells play decisive roles in the spatial physiological integration. In the second part, we have presented a self-organizing neural network for the perception of visual motion by using a retinotopic array of Reichardt's motion detectors and Kohonen's self-organizing maps. It .has been demonstrated by computer simulations that the network is able to learn to solve the ambiguities given by local motion detection mechanism. The resultant self-organized configuration in the output layer is resembling direction selective columns which first appear in area MT of the primate visual system. It has been explored that the spatio-temporal coherences, mapping, cooperation, competition, and Hebb rule are the basic neural principles for visual motion perception.

关键词： Reichardt’s algorithm figure-ground discrimination Kohonen’s algorithm and self organization.

Postsynaptic Potentials and Morphological Features of Tectal Cells in Homing Pigeons

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Science China Chemistry 1993年第3期36卷 297-304页

作者：吴冈义王书荣颜坤 laboratory for visual information processing Institute of Biophysics Academia Sinica Beijing 100101 PRC

In this study postsynaptic potentials responding to the electrical stimulation of the optic nerve and the nucleus isthmipars parvocellularis (Ipc) were intracellularly recorded from 86 tectal cells in pigeons, 13 of which were marked with Lucifer yellow (LY). According to types of postsynaptic potentials and their succesion, the postsynaptic responses could be classified into four types: excitatory postsynaptic potential (EPSP)-inhibitory postsynaptic potential (IPSP) sequence (EI type), E, I and IE *** them, E or EI responses were mainly produced to the optic nerve stimulation, while the electrical stimulation of the lpc elicited I responses in more than half the recorded ***-marked tectal cells included five ganglionic cells, four pyramidal cells, two bipolar cells and two stellate *** ganglionic cells were mainly located in tectal layers Ⅲ and Ⅳ, whereas the others were in layer Ⅱ.It appears from both intracellular recordings and fluorescent markings that there is some correlation betwe n the morphological features and the postsynaptic response types of tectal cells.

关键词： homing pigeon nucleus isthmi tectum postsynaptie potentials electrical stimulation fluorescent marking.

Autonomous driving: cognitive construction and situation understanding

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Science China(information Sciences) 2019年第8期62卷 75-101页

作者： Shitao CHEN Zhiqiang JIAN Yuhao HUANG Yu CHEN Zhuoli ZHOU Nanning ZHENG Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Engineering laboratory for visual information processing and Applications

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency,poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes,the efficiency of event-related mechanism is more significant than single-attribute scenario perception ***, an effective self-driving method should not be confined to the traditional computing framework of‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It furth

关键词： autonomous driving event-driven mechanism cognitive construction situation understanding intuitive reasoning