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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ding, Qun Xu, Xiaolong Gui, Wenming Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China

ISBN: (纸本)9798350331684

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs. © 2023 IEEE.

关键词： Reinforcement learning

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Semi-global Stereo Matching Algorithm by Superpixel Guided Cost Aggregation 23

Semi-global Stereo Matching Algorithm by Superpixel Guided C...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Jiang, Puzhao Ma, Xing Mu, Chunyang Ping, Lujing College of Electronic and Information Engineering North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing North Minzu University Yinchuan China College of Electronic and Information Engineering North Minzu University Yinchuan China

ISBN: (纸本)9798400708237

Aiming at the problem of disparity calculation in small occluded areas by using path aggregation to fuse adjacent pixel features in traditional semi-global stereo matching algorithms, a semi-global stereo matching algorithm by superpixel guided cost aggregation is proposed. The algorithm begins by fusing local feature information of the image with superpixel blocks during the first aggregation step, and then combines the path aggregation to complete the entire cost aggregation step. The disparity of 4 groups of classic Middlebury image pairs is calculated experimentally, and the average mismatch rate of all regions and non-occluded regions is 4.69%, more than 0.36% higher than the comparison algorithm, and the mismatch rate of non-occlusion regions is 3.13%, more than 1.12% higher than the comparison algorithm. Experiments show that the proposed algorithm can effectively optimize the disparity of partially occluded pixels and improve the effect of non-occluded areas. © 2023 ACM.

关键词： Superpixels

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DomainVoyager: Embracing The Unknown Domain by Prompting for Automatic Augmentation

DomainVoyager: Embracing The Unknown Domain by Prompting for...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chuang Liu Haogang Zhu Xiu Su State Key Laboratory of Software Development Environment Lab Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Zhongfa Aviation Institute Beihang University Hangzhou P.R.China Zhongguancun Laboratory Beijing China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

For medical image analysis, domain generalization (DG) faces significant challenges due to variances in data across medical imaging devices. Addressing this, we introduce Prompt Guided Domain Aligning Augmentation (PGDAA), a novel approach that harnesses Large Language Models (LLMs) to iteratively refine data augmentation sequences in DG for medical segmentations. Specifically, by harnessing the LLM’s advanced capabilities in interpreting and responding to specific prompts, PGDAA iteratively voyages the parameter searching space for data augmentation, identifying optimal augmentation sequences. In each searching round, the LLM leverages provided prior data augmentation methods, their parameters, and corresponding evaluation results to acquire a sufficient performance memory bank, thereby proposing more effective augmentation sequences to significantly narrowing inter-domain gaps. Notably, this integration of LLMs in DG represents a pioneering application in the field, which adds minor training parameters and can be easily combined with other DG benchmarks for further improvements. Comprehensive experiments reveal our method outperforms the baseline by 4.47% and 5.08% on Fundus and Prostate datasets, achieving 90.10% and 89.28% accuracy, respectively.

关键词： Training Image segmentation Image analysis Accuracy Large language models Benchmark testing data augmentation

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A Point Cloud Completion Method for Industrial Component Based on Multiple Encoders

A Point Cloud Completion Method for Industrial Component Bas...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lei Chen, Ran Huang, Wei Chen, Yiping Yin, Jun School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9798350303759

In real-world scenarios, laser-scanned point clouds often suffer from missing data points due to occlusion and other factors, significantly impacting subsequent tasks. We propose a novel point cloud completion method based on self-attention mechanisms and multi-layer perceptrons to address this issue, building upon the PCN model. Our experimental evaluations on the PCN dataset demonstrate notable enhancements in the completion effectiveness and evaluation metrics compared to the baseline model. Additionally, focusing on the point cloud completion context for industrial components, we introduce an approach for constructing an industrial component point cloud dataset. This method leverages 3D mesh models to swiftly generate complete point clouds with uniform distribution and devoid of noise while simulating the incomplete point clouds obtained from real-world scanning scenarios. The final results fully exhibit the feasibility of this scheme, which is a strong candidate for future industrial component point cloud completion. © 2023 IEEE.

关键词： Signal encoding

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Mining Top-k Frequent Patterns over Streaming Graphs 28th

Mining Top-k Frequent Patterns over Streaming Graphs

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Xi Zhang, Qianzhen Guo, Deke Zhao, Xiang Institute for Quantum Information and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Mining top-k frequent patterns is an important operation on graphs, which is defined as finding k interesting subgraphs with the highest frequency. Most existing work assumes a static graph. However, graphs are dynamic in nature, which is described as streaming graphs. Mining top-k frequent patterns in streaming graphs is challenging due to the streaming nature of the input and the exponential time complexity of the problem. A naive solution is to calculate approximations of the frequent patterns in the streaming graph and then find the top-k answers, which is a memory- and time-consuming method. In this paper, we design a novel auxiliary data structure, FPC, to detect valid subgraph patterns and their frequency in real-time. We first convert each newly produced subgraph into a sequence and then map it into corresponding tracks in FPC based on hash functions. We theoretically prove that FPC can provide unbiased estimation and then give an error bound of our algorithm. In addition, we propose a vertical hashing and candidate buckets sampling technique to further improve FPC with higher space utilization and higher accuracy. Extensive experiments confirm that our approach generates high-quality results compared to the baseline method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Li, Yangding Fu, Shaobin Feng, Hao Zeng, Yangyang Wang, Jinghao Jiang, Zhihao Zhang, Lvyun Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University School of Information Science and Engineering Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China Hechi University School of Big Data and Computer Science Guangxi China

ISBN: (纸本)9798350312492

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive. © 2023 IEEE.

关键词： Recommender systems

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Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous data 41

Contamination-Resilient Anomaly Detection via Adversarial Le...

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41st International Conference on Machine Learning, ICML 2024

作者： Lv, Wenxi Su, Qinliang Wan, Hai Xu, Hongteng Xu, Wenchao School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Gaoling School of Artifical Intelligence Renmin University of China Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong

Many existing anomaly detection methods assume the availability of a large-scale normal dataset. But for many applications, limited by resources, removing all anomalous samples from a large unlabeled dataset is unrealistic, resulting in contaminated datasets. To detect anomalies accurately under such scenarios, from the probabilistic perspective, the key question becomes how to learn the normal-data distribution from a contaminated dataset. To this end, we propose to collect two additional small datasets that are comprised of partially-observed normal and anomaly samples, and then use them to help learn the distribution under an adversarial learning scheme. We prove that under some mild conditions, the proposed method is able to learn the correct normal-data distribution. Then, we consider the overfitting issue caused by the small size of the two additional datasets, and a correctness-guaranteed flipping mechanism is further developed to alleviate it. Theoretical results under incomplete observed anomaly types are also presented. Extensive experimental results demonstrate that our method outperforms representative baselines when detecting anomalies under contaminated datasets. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Sequential Three-Way Group Decision-Making for Double Hierarchy Hesitant Fuzzy Linguistic Term Set

SSRN

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SSRN 2024年

作者： Luo, Nanfang Zhang, Qinghua Xie, Qin Wang, Yutai Yin, Longjun Wang, Guoyin Chongqing Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life *** research lacks tools to fuse information quickly and interpret decision results for partially formed *** limitation is particularly noticeable when there is a need to improve the efficiency of *** address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) model is constructed via granular *** model considers the vagueness, hesitation, and variation in the double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) GDM ***, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are ***, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function at each ***, the granular structure of DHHFLTS based on sequential three-way decision (S3WD) is defined, and the decision-making strategy and interpretation of decision-level are proposed. Furthermore, the algorithm of S3W-GDM model is ***, an illustrative example are presented, and the comparative and sensitivity analyses are performed to verify the superiority of this model. © 2024, The Authors. All rights reserved.

关键词： Information fusion

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Complex Substation Scene Segmentation Method Based on Optimized Straight Line Detection

Complex Substation Scene Segmentation Method Based on Optimi...

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2023 China Automation Congress, CAC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Hao Liu, Chuang Yin, Jun School of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)9798350303759

This paper proposes a scene segmentation method based on optimized line detection to address the difficulty of segmenting spatially independent equipment in complex substation scenes. The method first employs the RANSAC method to simulate the ground plane and extract the ground point cloud. Then, it projects the 3D point cloud scene onto a 2D plane to obtain a 2D image of the substation. Straight line segments of the power line are extracted from the image using the edge detection algorithm and an improved line detection algorithm. The straight line segments that do not belong to the power line are filtered out using the surrounding features of the 3D point corresponding to the 2D point on the line. The power line's straight line segment is extended using the region-growing algorithm. Finally, the power line clustering is completed using European clustering based on the three-dimensional coordinates of the two ends of the power line, thereby completing the scene segmentation. Experimental results show that the proposed method can effectively segment substation equipment. Compared to traditional segmentation methods, this method has an average power line identification accuracy of 14% higher. © 2023 IEEE.

关键词： Edge detection

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LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micro Base Stations

LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micr...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Mingming Gao Xinyu Wang Congying Wang Zhongxiang Zhang Shiquan Shan Yonghui Song Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications College of Electronics and Information Engineering Liaoning Technicial University Huludao Liaoning China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A Doherty Power Amplifier (DPA) has been designed and optimized specifically for compact mobile base station deployment, operating within a frequency range of 3.3 GHz to 3.6 GHz. The amplifier utilizes the proprietary DS10-2092 LDMOS transistors developed by the company for both the carrier and peak power amplifiers. Physical test results indicate that the DPA achieves saturated output power meeting the specified requirements, with a maximum saturated drain efficiency of 41%. The drain efficiency remains stable between 26.3% and 30.7% over a 6 dB power back-off range, demonstrating its superior performance in high-efficiency and broadband applications.

关键词： Base stations Power measurement 5G mobile communication Power amplifiers Energy efficiency Power electronics Distance measurement Transistors Power generation Optimization

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