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IEEE International Conference on Systems, Man and Cybernetics

作者： Hengshan Zhang Di Wu Zhongmin Wang Yanping Chen School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China

ISBN: (纸本)9781665442084

With the large-scale application of lithium-ion batteries (LIB), using deep neural networks to predict the remaining useful life (RUL) of LIB has gradually become a hotshot in recent years. RUL prediction method based on deep neural network can avoid studying electrochemical phenomena and manual extracting the features in battery. But single neural network has the different prediction accuracy and features extraction on different dataset. In this study, an ensemble method for the heterogeneous neural network is proposed, which integrates the prediction results of multiple heterogeneous neural networks with the adaptive weight. The weight of the neural network is higher with the closer correlation to the majority prediction results, vice versa. Furthermore, the weight of the neural network is adjusted via the predicting results for neural network on the different dataset, so that the computed weight of the neural network is adapted to the various dataset, and the effects of poor predictions of certain neural networks can be reduced sufficiently. The effectiveness of the ensemble method is verified on MIT-Stanford LIB degradation dataset, and the results show that the proposed method has higher accuracy than the existing ensemble methods for neural network.

关键词： Deep learning Lithium-ion batteries Degradation Adaptation models Correlation Neural networks Manuals

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Software Module Clustering Using the Hierarchical Clustering Combination Method 7

Software Module Clustering Using the Hierarchical Clustering...

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7th International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2022

作者： Xia, Hong Zhang, Yongkang Chen, Yanping Zhang, Hengshan Wang, Zhongmin Wang, Fengwei School of Computer Science and Technology Xi'An Univ. of P. and Telecom. Shaanxi Key Lab. of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications School of Computer Science and Technology Xi'an China ZTECorporation Shenzhen China

ISBN: (纸本)9781665487115

The scale of software applications has increased dramatically. Hierarchical clustering is a good method for modular recovery of software architecture. Because the different evaluation criteria of software types and clustering results, a single hierarchical clustering algorithm cannot integrate the number of clusters, arbitrary decision-making, recovery quality and other indicators on different evaluation standards, there is no comprehensive clustering algorithm selection method. We propose a hierarchical clustering combination algorithm and use principal component analysis to combine the results of multiple hierarchical clusters, the combined result retain the basic information of each clustering algorithm as much as possible. The experimental results show that compared with the single software hierarchical clustering method and other combined methods, the result of our method is closer to expert decomposition, and it has good performance on a variety of indicators. © 2022 IEEE.

关键词： Cloud computing Software architecture Clustering methods Software algorithms Decision making Clustering algorithms Big data

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FF-KGAT: Feature Fusion Based Knowledge Graph Attention network for Recommendation 5

FF-KGAT: Feature Fusion Based Knowledge Graph Attention Netw...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tao, Li Xiaoge, Li Chaodong, Wang Xianliang, Li Shuai, Gao Dan, Han Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China Inspur Software Technology Co Ltd Software Development Project Management China Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Communication and Information Engineering Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (纸本)9781450396899

It is commonly agreed that a recommender system based on knowledge graph (KG) should not only use user-item interactions, but also take side information into account to deal with the problem of data sparsity. However, existing KG-based models present unique challenges that have the shortcoming of cold start and redundant iterations. To address the issue, we propose a Feature Fusion-based Knowledge Graph Attention network (FF-KGAT) for the recommendation. FF-KGAT handles the cold-start problem through introducing user characteristics to fully explore the information of users. Additionally, FF-KGAT introduces the feature fusion graph, which removes slight nodes to obtain fewer iterations. Experimental results show that the proposed model significantly outperforms baseline methods. Particularly, the ndcg is increased by 9.83% and 10.25% on two public datasets, compared with the best performance of existing methods. © 2022 ACM.

关键词： Recommender systems

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International Conference on networking and network Applications (NaNA)

作者： Zhongmin Wang Rui Zhou Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an Shaanxi China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

The information interaction when the brain processes emotional activities is intricate. Therefore, it is very necessary for us to explore the mechanisms of the functional coordination of various brain regions. Recent studies have demonstrated that there are hub regions in the brain that are highly connected and highly central, and are responsible for coordinating dynamic interactions between brain regions. This study aims to construct the dynamic brain networks related to emotions and examine the structure of the hubs of these networks that reflect functional connectivity. In this paper, we demonstrate that a combination of network hubs set a closely connected collective called “rich-club”,which can be used as the core network architecture for identifying different emotions. According to the phase synchronization relationship of the Electroencephalogram (EEG) in three time periods to construct the corresponding brain networks in different sub-states, we found that the dynamic connection patterns produced by different emotions are different, which is manifested in the different rich-club organization. The discovery of rich-club structure related to emotions in this study is of great significance for studying the brain mechanisms behind human emotional processes.

关键词： Emotion recognition Organizations Color network architecture Electroencephalography Synchronization

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A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

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Journal of Southeast University(English Edition) 2018年第1期34卷 28-35页

作者： Sun Jiaze Wang Gang School of Computer Science and Technology Xi’anUniversity of Posts and TelecommunicatioosXi’an 710121China Shaanxi Key Laboratory of Network Data Intelligent Processing X’an University of Posts and TelecommunicationsX’an 710121China

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is ***,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea ***,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test *** typical open source projects are selected as benchmark *** and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.

关键词： test case prioritization integer linear programming(I LP) genetic algorithm time constraint

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BRA-LADH: Bi-Level Routing Attention and Asymmetric Dual-Head Based Framework for Intestinal Polyp Detection

BRA-LADH: Bi-Level Routing Attention and Asymmetric Dual-Hea...

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Natural Language processing (ICNLP), International Conference on

作者： Xiaoying Pan Yaya Mu Sun Jia Han Chen Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Colorectal cancer is a disease with a very high mortality rate, making timely diagnosis and prevention a crucial task. In clinical practice, the detection of intestinal polyps presents challenges due to their small size, complex background, and large span in target scale, making it difficult to accurately detect small targets. Existing detectors struggle to adaptively extract discriminative features, and their detection heads lack specificity, leading to difficulties in accurately identifying polyps against similar intestinal wall backgrounds. To address this issue, we propose a Bi-level Routing Attention and Asymmetric Dual-Head based framework for intestinal polyp detection(BRA-LADH). This approach enhances the detection capabilities of the branches through strategies such as global information fusion and adaptive sample selection. It dynamically sets thresholds for each target, ultimately solving the problems of missed and false detections caused by high similarity between the foreground and background and large variations in target scale, thus improving detection accuracy. Qualitative and quantitative experiments on three datasets demonstrate that this method outperforms existing ones, showing strong stability and generalization performance.

关键词： Training Head Prevention and mitigation Detectors Routing Feature extraction Stability analysis Magnetic heads Natural language processing Lesions

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Padding Adversarial Training: Enhancing Robustness with Adversarial Space Hypothesis

SSRN

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SSRN 2022年

作者： Sun, Jiaze Wen, Sulei School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi’an710127 China

Adversarial training, represented by the projected gradient descent(PGD) adversarial training, effectively improves adversarial robustness within the upper bound of perturbation to the deep neural network(DNN) by applying adversarial examples to model retraining. When the perturbation is beyond the pale, though, the defense it provides to the model is invalid. Furthermore, it is also vulnerable to adaptive attacks and novel attacks uninvolved in adversarial training. To solve these problems, we propose the adversarial space hypothesis based on the manifold theory to explain the existence of adversarial examples from a feature space perspective. We further introduce padding adversarial training, a technique enhancing adversarial training with the adversarial space hypothesis. Padding adversarial training improves the generalization of the standard adversarial training’s robustness by 20% and provides an additional detection defense for adversarial examples with high perturbation. Moreover, we propose trap smoothing, which optimizes the target manifold’s distribution and establishes the inducing relationship between the target manifold and the feature space labelled by the padding classes. On MNIST and CIFAR-10, padding adversarial training is robust to white, black and adaptive attacks in low perturbation conditions and provides the 80%~98% detection rate of adversarial examples in high perturbation conditions. The code is available at:https://***/Ccsk-Xian/Padding-adversarial-training. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Shadow Removal from a Single Image Using an Enhanced Generative Adversarial network

Shadow Removal from a Single Image Using an Enhanced Generat...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Jing Zhang Weiwei Kong College of Computer Science Xi’an University of Posts and Telecommunications Xi’an China the Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

Image shadow removal is challenging due to the diversity of shadows and the dependence on the background of the area covered by shadows. In this paper, we design an enhanced generative adversarial network (EGAN) to handle the shadow removal task. EGAN is a complicated generative adversarial network that may exploit previous info, like shadow masks and lightness estimation, for improving recovery capability. In addition, EGAN can effectively utilize the details of shadow regions and eliminate artifacts. EGAN owns three main advantages. First, EGAN employs stricter rules for discriminators, which can upgrade the instability of traditional GAN. Second, EGAN involves a completely unique enhanced attention module, which may efficiently pick up different types of previous info to accelerate the learning efficiency of image reconstruction. Finally, EGAN is essentially an original cascaded network, that is, DRDU, which is beneficial for efficiently detecting hierarchical features of image recovery. Simulation experiments are conducted to verify the effectiveness of the proposed method on two benchmark datasets including SRD and ISTD, the strengths of our proposed method are indicated over the current representative ones according to subjective performance and objective estimation indexes.

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LFD-RSNet: Lightweight Face Detection network for Rainy Weather Scenes

LFD-RSNet: Lightweight Face Detection Network for Rainy Weat...

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Natural Language processing (ICNLP), International Conference on

作者： Xiaoying Pan Yamie Ding Nijuan Zhang Yaya Mu Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In response to the problem of low detection accuracy of lightweight models for face detection in complex rainy scenes, this paper proposes a face detection network FDL-RSNet for complex rainy scenes. This network integrates an adaptive image denoising module, an optimizer module, an HCA attention module, and a balanced branching strategy. The FDL-RSNet network can effectively address various challenges in complex rainy backgrounds, such as raindrop noise, differences in image lighting and contrast, and face detection challenges caused by low-quality images with dense targets. Experimental results on the UFDD Rain datasets show that FDL-RSNet has superior face detection capabilities in real-world unconstrained complex rainy scenes, Proved its effectiveness and reliability under such highly challenging conditions.

关键词： Adaptation models Rain Adaptive systems Noise Lighting Natural language processing Face detection Reliability Meteorology Image denoising

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Improved Siamese network for Video-Based Person Re-identification

Improved Siamese Network for Video-Based Person Re-identific...

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International Conference on networking and network Applications (NaNA)

作者： Zhongmin Wang Lanlan Cai Na Duan Lin Fan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

In the field of human-computer interaction and visual surveillance, person re-identification is undoubtedly a key task. In this paper, we propose improved Siamese network for video-based person re-identification, which combines the verification model and the classification model, in a way that all the supervision information of the samples is fully utilized. The key frame extraction is able to select informative frames over the sequence, which removes occluded frames according to optical flow vector and measures the similarity of two adjacent frames' contents in terms of the block mutual information entropy. A convolutional network and long short-term memory joint training is used as a feature extractor to re-identify using the improved Siamese network framework. Experimental data on the iLIDS-VID and PRID-2011 datasets show that this approach is superior to existing video-based re-identification methods.

关键词： Feature extraction Optical flow Mutual information data mining Entropy Logic gates Cameras

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