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Directional relation reasoning based on boundary sensitive model in three-dimensional space

Journal of Computational Information systems 2015年第17期11卷 6345-6354页

作者： Kong, Dehan Liu, Yongshan Ma, Junlei Wang, Yuzheng Han, Ameng Department of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China Telecommunications Engineering with Management Beijing University of Posts and Telecommunications Beijing China Information Technology Office of Yanshan University Qinhuangdao China

In three-dimension space, with the minimum bounding rectangular (MBR) of any type of object's projections in xoy and xoz planes, there are four possible types of projection: minimum bounding rectangular, horizontal line, vertical line and point, then a boundary sensitive directional relationship matrix model (BSDRM) is proposed, the model can unified representation direction relations between objects of the four different types of projection, further, the three dimensional direction relation is inferred by combining the two direction matrixes in plane xoy and plane xoz. Based on this model, single direction relation reasoning table and multi direction relation reasoning method are proposed. Finally, direction relation reasoning arithmetic is given and proved to be true. © 2015, by Binary Information Press

关键词： Boundary Sensitive Combinative Reasoning Table Directional Relationship Matrix Model Relationship Reasoning Three-dimension Directional

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The analysis to gamma oscillations induced in high dose DHPG and spike-gamma oscillation relationship

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International Journal of Advancements in Computing technology 2012年第20期4卷 553-560页

作者： Wen, Dong Lu, Chengbiao Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University QinhuangdaoHebei 066004 China School of Information Science and Engineering Yanshan University QinhuangdaoHebei 066004 China Institue of Electrical Engineering Yanshan University Qinhuangdao Hebei 066004 China

Neuronal oscillations in the gamma frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of high dose DHPG in the generation of oscillatory field activity at gamma frequency in the medical septal diagonal band of Broca (MSDB) slice prepared from rat brain, and to determine the phase relationship between spikes and gamma oscillations. The high dose DHPG induced large amplitude field population spiking activity, which is correlated linearly with the field gamma oscillations. The relationship between timing of spikes and the phase of gamma oscillation was determined by an analysis of circular statistics. Further analysis with Rayleigh test revealed that the relationship of phase-locking between spikes and gamma rhythm is of statistical significance. These data demonstrate that DHPG-sensitive neuronal networks within medial septum are able to generate gamma oscillations which are modulated by large amplitude population spikes and phase-locking of spikes to the gamma rhythm suggest the role of memory enhancement in the presence of mGluR1 activation.

关键词： Locks (fasteners)

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Improved BP classifier via distance for sample reduction

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Journal of computers (Taiwan) 2019年第2期30卷 1-13页

作者： Wen, Jia Deng, Jia Liu, Peng-Fei Wang, Hong-Jun Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China State Key Laboratory of Software Engineering of Hebei Province Yanshan University Qinhuangdao China

The BP neural network algorithm is a commonly used technique in the classification field of pattern recognition, which iterates training samples continuously to obtain the decision boundary, until the error reaches the convergence precision. However, due to the extensive redundancy of training samples, this carpet-like training method can easily lead to slow network convergence and low classification performance. In this paper, from the view of sample, a novel BP training method called improved BP Classifier algorithm using Distance for Sample Reduction (DRBP) is proposed. Through the sample weight to get the new training sample, which are the near of the classification interface like support vector, and then iterative training BP classifier to solve the classification issue. The main function of this method is to keep the original sample information, realize the sample reduction, and improve the generalization ability of the BP neural network. First, point sets is used to visualize the training process. And then use 10 sets of the University of California Irvine (UCI) to verify the effectiveness and superiority of our experiments. Finally through Cross-validation, the classification error rate of 10 sets of UCI datasets tested by DRBP training method is lower than that of standard BP neural network (STBP). So the result of the experiment showed that the method of the combined distance sample reduce with BP network can significantly enhance the generalization ability of BP network. © 2019 computer Society of the Republic of China. All rights reserved.

关键词： Classification (of information)

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Redundant filter-based efficient Minkwoski sum computation of polyhedra

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ICIC Express Letters 2014年第10期8卷 2939-2944页

作者： Zhang, Jianfei Guo, Xijuan Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province College of Information Science and Engineering Yanshan University No. 438 West Section of Hebei Avenue Haigang District Qinhuangdao 066004 China College of Computer and Control Engineering Qiqihar University No. 42 Wenhua Street Qiqihar 161006 China

On the basis of the introduction to basic theory of Minkowski sum, we analyze the procedure of constructing the boundary of Minkowski sum and present a new method that can compute Minkowski sum of the polyhedra based on redundant information filtering. The method can filter the edges and facets that can be processed in the next computational stage, and reduce the number of the edges and facets that must be computed to improve the computing performance. The experiments show that the presented method has better performance including computing efficiency, accuracy and degeneration problems and fits for application domains that require high efficient and accurate computation. © 2014 ICIC International.

关键词： Information filtering

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Physically based simulation of solid objects' burning

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7145 LNCS卷 110-120页

作者： Liu, Shiguang An, Tai Gong, Zheng Hagiwara, Ichiro School of Computer Science and Technology Tianjin University Tianjin 300072 China State Key Lab of Virtual Reality Technology and System Beihang University Beijing 100191 China Department of Mechanical Science and Engineering Tokyo Institute of Technology Tokyo 152-8550 Japan

ISBN: (纸本)9783642290497

This paper presents a novel method for realistic simulation of solid objects' burning. The temperature field is first constructed based on combustion theories. Then, a temperature field motivated interaction model (TFMI) is proposed to simulate the interactions between the fire and the objects during burning. In TFMI, the decomposition of the objects is modeled by improving the level set method and the spreading of fire is calculated using the updated temperature field at each time step. Our method can deal with varied topologies of different objects during burning. The fire is simulated by adopting stable fluid method and integrated into the whole burning scenes. Finally, various solid objects' burning scenes are rendered automatically using the above model. The experiment results show the validity of our method. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Temperature

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A new estimator analysis method to synchronization among multivariate neural series of mild cognitive impairment

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第23期6卷 868-874页

作者： Wen, Dong Guan, Xinyong Chen, Ruihong The Key Laboratory for Computer Virtual Technology System Integration of HeBei Province Yanshan University Qinhuangdao Hebei 066004 China Liren College Yanshan University Qinhuangdao Hebei 066004 China Division of General Internal Medicine Xiantao First People's Hospital Xiantao Hubei 433000 China

Synchronization decay of resting state EEG has shown that cognitive dysfunction in mild cognitive impairment (MCI) was relevant to a loss of functional connectivity in intermediate frequency bands. A new estimator analysis method called new S estimator (NSE) proposed recently by us quantifies synchronization between neuronal signals at multiple *** paper meant to explore the behavior of synchronization of multichannel EEG in MCI and healthy normal controls at rest, and preliminarily make clear the clinical significance of the NSE in MCI *** (EEG) were recorded from 10 MCI patients and 12 age-matched healthy normal controls (NC). NSE values were computed both across the all frequency band and separately in the delta, theta, alpha, beta (including beta1, beta2 and beta3), and gamma bands. The Montreal Cognitive Assessment (MoCA) was used to assess the symptom severity of MCI patients and *** values in the alpha and beta1 bands were significantly lower in MCI patients than in NC. NSE values in the alpha and beta1 bands were positively correlated with the MoCA scores in all participants (MCI and NC). In MCI patients, NSE values in the alpha and beta1 bands were also positively correlated with MoCA *** results suggest that NSE values are a useful correlate of EEG synchronization in MCI patients.

关键词： Synchronization

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An algorithm based on bit vector decomposition and hash linklist for mining frequent patterns in data streams

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第22期6卷 570-579页

作者： Ren, Jiadong Zhang, Yuanyuan Yu, Shiying College of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China Hebei Provincial S and T Management Information Center 159 Dongfeng Road Shijiazhuang 050021 China

Most of algorithms based on bit vector for mining frequent pattern always produce candidate itemsets and scan the same data repeatedly;this increases the running time and the space consumption. In this paper, an algorithm BVDAHL based on bit vector decomposition and hash linklist for mining frequent patterns on data streams which solves the above issues, is proposed. The itemsets (transactions) whose corresponding number of 1 is k, denoted as k-one itemsets (k-one transactions). The arrival transactions are converted into bit vectors, and permutations and combinations are used to decompose converted results, then the decomposed itemsets are stored in the hash linklist. In hash function, according to the position of 1 of bit vector to compute the address, if the transaction has same address and same content that already exists in data domain, then the value adds one in count domain, otherwise the pointer in pointer domain points to the next node directly, it is no need to deal with conflict. Afterwards the property of anti-monotonic will be used to prune the k+1-one transactions in bit vector table if there exists infrequent k-one itemsets that decomposed by such k+1-one transactions. Then the algorithm repeats the above processes until the transaction in bit vector table can not be decomposed. Finally we will receive the frequent itemsets by comparing the obtained value of count domain in hash linklist with minSup. Experiment results show that BVDAHL is very efficiency and scalable.

关键词： Hash functions

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A hierarchical residual network with compact triplet-center loss for sketch recognition

arXiv

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arXiv 2021年

作者： Wang, Lei Zhang, Shihui He, Huan Zhang, Xiaoxiao Sang, Yu School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Qinhuangdao066004 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Qinhuangdao066004 China

With the widespread use of touch-screen devices, it is more and more convenient for people to draw sketches on screen. This results in the demand for automatically understanding the sketches. Thus, the sketch recognition task becomes more significant than before. To accomplish this task, it is necessary to solve the critical issue of improving the distinction of the sketch features. To this end, we have made efforts in three aspects. First, a novel multi-scale residual block is designed. Compared with the conventional basic residual block, it can better perceive multi-scale information and reduce the number of parameters during training. Second, a hierarchical residual structure is built by stacking multi-scale residual blocks in a specific way. In contrast with the single-level residual structure, the learned features from this structure are more sufficient. Last but not least, the compact triplet-center loss is proposed specifically for the sketch recognition task. It can solve the problem that the triplet-center loss does not fully consider too large intra-class space and too small inter-class space in sketch field. By studying the above modules, a hierarchical residual network as a whole is proposed for sketch recognition and evaluated on Tu-Berlin benchmark thoroughly. The experimental results show that the proposed network outperforms most of baseline methods and it is excellent among non-sequential models at present. Copyright © 2021, The Authors. All rights reserved.

关键词： Touch screens

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

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12th International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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