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Journal of Network Intelligence 2023年第2期8卷 347-363页

作者： Liao, Lyuchao Chen, Ruixiang Shi, Jinjin Zheng, Qi Xiong, Rong Zeng, Jiemao Fujian Provincial Universities Key Laboratory of Industrial Control and Data Analysis Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology Fujian University of Technology Fuzhou350118 China Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology Fujian University of Technology Fuzhou350118 China

Object identification is a significant computer vision issue with a growing number of applications in the area of autonomous driving. In order to solve the problem of difficult detection of small objects and dense objects in the target detection algorithm in the autonomous driving scenarios,we propose an object detection method with a hybrid domain attention mechanism. In the model, the Neck network is integrated with a parallel mixed-domain attention mechanism to increase the usable features in the feature map from both channel and spatial aspects. Then, we improve the Spatial Pyramid Pooling (SPP) module with dilated convolutions to limit the loss of important information in the feature map after max pooling, which further optimizes the detector performance. Finally, a fusion approach is proposed to improve the detection layer and enhance the capability of detecting tiny objects. Extensive experiments on the BDD100k dataset show that the proposed model outperforms the state-of-the-art YOLOv5s model with an average accuracy increase of 6.22%(mAP @0.5). © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolution

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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Information Freshness in Coordinate Decision-Making Communication Systems

Information Freshness in Coordinate Decision-Making Communic...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhou, Siyuan Wang, Ziqing Dong, Yunquan Tang, Bin Tan, Guoping Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Hohai University College of Information Science and Engineering Nanjing China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing China

ISBN: (纸本)9798350351255

The promptness of decision-making is of paramount importance in coordinate decision-making communication systems. In this paper, we envision a system in which a sender transmits information to a receiver for decision-making. The decision-making process encompasses the extraction of decision-related information followed by subsequent computation. To assess the impact of communication delays, randomness of decision-making and computational time on the immediacy of decisions, we introduce a novel performance metric, namely the age of decision (AoD). Specifically, AoD is defined as the time elapsed since the generation of the update upon which the most recent decision is based. Considering the stochastic nature of decision-making, we analyze the average peak AoD in systems where the arrival and service processes follow general processes or Poisson processes. To facilitate our derivations, we introduce the concept of effective decisions to denote those decisions that can lead to the system's peak AoD. Simulation results confirm the validity of our derivations and illustrate that appropriately increasing the rate of decision-making can significantly reduce the system's average peak AoD. © 2024 IEEE.

关键词： Stochastic systems

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Meta-path Enhanced knowledge Graph Convolutional Network for Recommender Systems 12

Meta-path Enhanced Knowledge Graph Convolutional Network for...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Wang, Ru Wu, Meng Ji, Shengwei Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Ministry of Education School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665438582

knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1) the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG;2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively. © 2021 IEEE.

关键词： knowledge graph

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LuoJia-Explorer: Unmanned Collaborative Localization and Mapping System 1

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Wu, Weitong Chen, Chi Yang, Bisheng State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Engineering Research Center of Space-Time Data Capturing and Smart Application The Ministry of Education of P.R.C. Wuhan430079 China

ISBN: (数字)9789819710997

ISBN: (纸本)9789819710980

Over the past two decades, numerous single-robot SLAM systems have been developed and proposed. However, the problem of collaborative localization and mapping with a team of robots remains an open challenge. Collaborative localization and mapping plays a crucial role in enhancing mapping efficiency and has a wide application in search and rescue, infrastructure monitoring, extraterrestrial planetary cave exploration, etc. In this paper, we propose LuoJia-Explorer, an unmanned collaborative localization and mapping system for ground platforms. The proposed system is centralized with a main control center. Each ground platform is equipped with a multi-beam LiDAR and IMU to perform LiDAR-inertial odometry in its own coordinate system. Through mesh networking communication technology, the main control center receives the mapping result and trajectory from each platform. At the control center, global SLAM is performed, which includes loop detection, relative pose estimation and global pose graph optimization, to obtain a globally consistent mapping result. Two ground platforms were built to verify the proposed system. Field experiments have preliminarily verified the effectiveness of this system. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Mapping

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Research on recognition algorithm for gesture page turning based on wireless sensing

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Intelligent and Converged Networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

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DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution 43

DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framew...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Mei Jin, Ye Zhu, Jinhui South China University of Technology School of Automation Science and Engineering Guangzhou510641 China South China University of Technology School of Software Engineering Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581581

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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Rare earth element Ce enables high thermoelectric performance in n-type SnSe polycrystals

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Journal of Materials Science & Technology 2023年第12期143卷 234-241页

作者： Shan Li Li Yin Yijie Liu Xiaodong Wang Chen Chen Qian Zhang School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

SnSe exhibits excellent thermoelectric performance in both n-and p-type single crystals,but its n-type polycrystals are restricted because of the lower electrical ***,we dually introduced rare earth element Ce and PbTe to optimize the thermoelectric properties of n-type SnSe *** is demonstrated that Ce is an effective cationic dopant to convert SnSe from p-to n-type conductor,and an enhanced peak zT value of∼0.9 at 823 K was obtained in Sn 0.97 Ce 0.03 Se due to the improved power ***,PbTe alloying not only reduced the band gap to increase the carrier concentration,but also enhanced the density-of-states effective mass,and hence further increased the power factor in the whole measured temperature ***,the lattice thermal conductivity was significantly reduced owing to the enhanced phonon scattering by the mass and strain *** a result,the peak zT value was increased to∼1.3 for Sn 0.9 Pb 0.07 Ce 0.03 Se 0.93 Te 0.07 together with a high average zT value of∼0.52 in the temperature range of 300 to 823 K.

关键词： Thermoelectric N-type SnSe Ce doping PbTe alloying

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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multilevel Feature Interaction module (TML-FI), and a Multilevel Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multilevel features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. Finally, ML-FFD integrates the multilevel features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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Research on access control decision model of Internet of Things based on attribute exploration

Research on access control decision model of Internet of Thi...

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2021 International Conference on Signal Processing and Communication Technology, SPCT 2021

作者： Shen, XiaJiong Zhang, Peng Zhang, YanFeng Henan Key Laboratory of Big Data Analysis and Processing Henan Univ. HA Kaifeng475004 China School of Computer and Information Engineering Henan Univ. HA Kaifeng475004 China Institute of Data and Knowledge Engineering Henan Univ. HA Kaifeng475004 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

With the rapid development of the Internet of things, the problem of access control has become increasingly prominent. In order to solve the problems of high redundancy and low integrity caused by the traditional Role-Based Access Control model, this paper proposes an access control model of Internet of things based on attribute exploration. Firstly, the decision model is established based on attribute exploration algorithm, and finally merged into a complete decision model. The model has the function of dynamically modifying access rights in the process of building decision model. It is a new exploration in the field of access control model. © 2022 SPIE.

关键词： Internet of things

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