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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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A Robust Feature Extraction Method for Heterogenous Remote Sensing Images Based on Feature Transfer 5

A Robust Feature Extraction Method for Heterogenous Remote S...

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

作者： Qiao, Pingan Hua, Zhifan Yang, Dongfang Miao, Xin Xiao, Zhao Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China School of Computer Science Xi'an University of Posts and Telecommunications Xi'an710121 China Graduate School Rocket Force University of Engineering Xi'an710025 China School of Automation Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

A robust feature extraction method based on feature transfer for heterogeneous remote sensing images is proposed to address the problem of insufficient generalization ability of traditional edge feature extraction methods applied to heterogeneous remote sensing images. Firstly, the heterogeneous remote sensing images are transferred based on CycleGAN to reduce the impact of heterogeneous remote sensing images on the edge feature extraction results due to the cross-data domain. Secondly, dilated convolution is introduced in the edge feature extraction model DexiNed, which makes the optimized network model pay more attention to the structured features of the image. The experimental results show that the proposed method improves the structured feature extraction accuracy of heterogenous remote sensing images, which has better robust feature extraction effect compared with DexiNed. © 2022 ACM.

关键词： Feature extraction

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A New Visual & Inertial Based Satellite Quality Evaluation Method 5

A New Visual & Inertial Based Satellite Quality Evaluation M...

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

作者： Qiao, Pingan Wu, Ruichen Sun, Lei Yang, Dongfang Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China School of Computer Science Xi'an University of Posts and Telecommunications Xi'an710121 China School of Automation Xi'an University of Posts and Telecommunications Xi'an710121 China The Rocket Force University of Engineering Graduate School Xi'an710025 China

ISBN: (纸本)9781450396899

Visual and inertial navigation have obvious complementarity in navigation accuracy, and the combined navigation of the two has excellent anti-interference ability. In this paper, a visual-inertial-based satellite quality evaluation method is proposed. This method can judge whether the GPS (Global Positioning System) is interfered by comparing the visual-inertial navigation information with the GPS data information in the geographic coordinate system. This method enables the UAV to quickly switch the navigation mode under the condition of being disturbed, which ensures that the navigation function is accurate and free from interference, and provides a more solid foundation for the navigation of the UAV and other platforms. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Object Detection Algorithm for Improving Non-Maximum Suppression Using GIoU

Object Detection Algorithm for Improving Non-Maximum Suppres...

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作者： Zhiqiang Hou Xiaoyi Liu Lilin Chen College of Computer Science and Technology Xian University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications

To address the problem of object miss detection and object false detection in single threshold-Non-Maximum Suppression algorithm,this paper proposed a GDT-NMS(Generalized Intersection over Union Dual Threshold NMS,GDT-NMS)) algorithm which using GIoU(Generalized Intersection over Union).Using the GIoU indicator computing the similarity between objects,can better describe the relative position and overlap between the *** we proposed the dual-threshold NMS algorithm,which can balance the relationship between the object missed detection problem and the object false detection problem,reduce "false positive example" *** nonlinearly processing the weight function,the object is better *** algorithm uses Faster R-CNN as the *** experimental results show that the improved algorithm has outstanding performance.

关键词： GDT NMS Object Detection Algorithm for Improving Non-Maximum Suppression Using GIoU

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An Ensemble Method for the Heterogeneous Neural network to Predict the Remaining Useful Life of Lithium-ion Battery

An Ensemble Method for the Heterogeneous Neural Network to P...

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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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