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An End-to-End Classification Network Model Based on Hybrid Supervision for Industrial Surface Defect Detection

International Journal of Pattern Recognition and Artificial Intelligence 2025年第5期39卷

作者： Qin, Runbing Chen, Ningjiang Gan, Shukun Guangxi University School of Computer Electronics and Information Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning530004 China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning530004 China

Industrial part surface defect detection aims to precisely locate defects in images, which is crucial for quality control in manufacturing. The traditional method needs to be designed in advance, but it has shortcomings in terms of generalization ability. Deep neural network-based defect detection faces issues such as low resolution, data scarcity, labeling costs, and high computation. Therefore, an improved solution is necessary to enhance industrial product quality control efficiency and accuracy. A new method is proposed that utilizes end-to-end training of a two-stage neural network based on segmentation with an extended training process. The gradient flow from the classification to the segmentation network is adjusted to prevent unstable features from interfering with learning. To address the problem of image oversampling and undersampling during training, a frequency sampling scheme for negative samples is introduced. Additionally, positive pixels in the region-based segmentation mask are weighted using the distance transform algorithm so that regions with a high probability of defects can be detected without detailed annotation. Experiments are conducted across three distinct defect datasets by applying hybrid supervision encompassing diverse conditions. In the optimal case, the AP rate of the proposed model on the DAGM and KolektorSDD datasets reaches 100%, and the AP rate on the Severstal Steel dataset reaches 98.99%. The experiments’ results show that the detection accuracy has improved greatly, showing how well the proposed method works in the industry. © 2025 World Scientific Publishing Company.

关键词： Deep neural networks

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RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories

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Frontiers of Information Technology & Electronic Engineering 2019年第2期20卷 222-237页

作者： Cheng YANG Qiang FAN Tao WANG Gang YIN Xun-hui ZHANG Yue YU Hua-min WANG Key Laboratory of Parallel and Distributed Computing National University of Defense Technology

With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness,a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called "RepoLike," to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.

关键词： Social coding Open-source software Personal recommendation GitHub

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Social media in Git Hub: the role of @-mention in assisting software development

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Science China(Information Sciences) 2017年第3期60卷 50-67页

作者： Yang ZHANG Huaimin WANG Gang YIN Tao WANG Yue YU Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology

Recently, many researches propose that social media tools can promote the collaboration among developers, which are beneficial to the software development. Nevertheless, there is little empirical evidence to confirm that using @-mention has indeed a beneficial impact on the issues in Git Hub. In order to begin investigating such claim, we examine data from two large and successful projects hosted on Git Hub, the Ruby on Rails and the Angular JS. By using qualitative and quantitative analysis, we give an in-depth understanding on how @-mention is used in the issues and the role of @-mention in assisting software development. Our statistical results indicate that, @-mention attracts more participants and tends to be used in the difficult issues. @-mention favors the solving process of issues by enlarging the visibility of issues and facilitating the developers' collaboration. Our study also build an @-network based on the @-mention database we *** the @-network, we investigate its evolution over time and prove that we certainly have the potential to mine the relationships and characteristics of developers by exploiting the knowledge from the @-network.

关键词： issues social media @-mention Git Hub software development

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Why API documentation is insufficient for developers: an empirical study

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Science China(Information Sciences) 2021年第1期64卷 248-250页

作者： Qiang FAN Yue YU Tao WANG Gang YIN Huaimin WANG Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology

Dear editor,Application programming interface (API) documentation plays an important role in software development and reuse [1] for both API maintainers and API *** documentation helps developers understand and reuse codes effectively [2] and focus their time on desired interfaces and functions instead of the entire system [3].Most high-quality open source projects maintain complete and informative official *** documentation typically conveys detailed specifications,such as class/inter face hierarchies and method descriptions,which can be of great benefit to developers [4].However,despite its authoritativeness and thoroughness,single-sourced official documentation does not always meet the developers'requirements [5].

关键词： Information Systems and Communication Service

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PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning

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Ad Hoc Networks 2025年 176卷

作者： Ou, Pingjie Chen, Ningjiang Yang, Long School of Computer and Electronic Information Guangxi University Nanning Guangxi530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Nanning Guangxi530004 China The Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning Guangxi530004 China

In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data's inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions. © 2025

关键词： Cache memory

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Bipartite-Oriented distributed Graph Partitioning for Big Learning

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Journal of Computer Science & Technology 2015年第1期30卷 20-29页

作者：陈榕施佳鑫陈海波臧斌宇 Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Shanghai 200240 China

Many machine learning and data mining （MLDM] problems like recommendation, topic modeling, and medical diagnosis can be modeled as computing on bipartite graphs. However, inost distributed graph-parallel systems are oblivious to the unique characteristics in such graphs and existing online graph partitioning algorithms usually cause excessive repli- cation of vertices as well as significant pressure on network communication. This article identifies the challenges and oppor- tunities of partitioning bipartite graphs for distributed MLDM processing and proposes BiGraph, a set of bipartite-oriented graph partitioning algorithms. BiGraph leverages observations such as the skewed distribution of vertices, discriminated computation load and imbalanced data sizes between the two subsets of vertices to derive a set of optimal graph partition- ing algorithms that result in minimal vertex replication and network communication. BiGraph has been implemented on PowerGraph and is shown to have a performance boost up to 17.75X （from 1.16X） for four typical MLDM algorithnls, due to reducing up to 80% vertex replication, and up to 96% network traffic.

关键词： bipartite graph graph partitioning graph-parallel system

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Auxo: an architecture-centric framework supporting the online tuning of software adaptivity

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Science China(Information Sciences) 2015年第9期58卷 31-45页

作者： WANG HuaiMin DING Bo SHI DianXi CAO JianNong Alvin T.S.Chan National Key Laboratory of Parallel and Distributed Processing College of ComputerNational University of Defense Technology Department of Computing Hong Kong Polytechnic University

Adaptivity is the capacity of software to adjust itself to changes in its environment. A common approach to achieving adaptivity is to introduce dedicated code during software development stage. However,since those code fragments are designed a priori, self-adaptive software cannot handle situations adequately when the contextual changes go beyond those that are originally anticipated. In this case, the original builtin adaptivity should be tuned. For example, new code should be added to provide the capacity to sense the unexpected environment or to replace outdated adaptation decision logic. The technical challenges in this process, especially that of tuning software adaptivity at runtime, cannot be understated. In this paper,we propose an architecture-centric application framework for self-adaptive software named Auxo. Similar to existing work, our framework supports the development and running of self-adaptive software. Furthermore,our framework supports the tuning of software adaptivity without requiring the running self-adaptive software to be terminated. In short, the architecture style that we are introducing can encapsulate not only general functional logic but also the concerns in the self-adaptation loop(such as sensing, decision, and execution)as architecture elements. As a result, a third party, potentially the operator or an augmented software entity equipped with explicit domain knowledge, is able to dynamically and flexibly adjust the self-adaptation concerns through modifying the runtime software architecture. To truly exercise, validate, and evaluate our approach,we describe a self-adaptive application that was deployed on the framework, and conducted several experiments involving self-adaptation and the online tuning of software adaptivity.

关键词： software architecture self-adaptive software architecture style application framework software adaptation

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A novel approach for recommending semantically linkable issues in GitHub projects

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Science China(Information Sciences) 2019年第9期62卷 202-204页

作者： Yang ZHANG Yiwen WU Tao WANG Huaimin WANG Key Laboratory of Parallel and Distributed Computing National University of Defense Technology College of Computer National University of Defense Technology

Dear editor,GitHub1)is a web-based project hosting platform which was launched in 2008 and has become one of the premier open-source development sites [1]. During the software development process of GitHub projects, issue reports, as an important development knowledge, are likely to be related as they contain relevant information. One

关键词： GitHub A novel approach for recommending semantically linkable issues in GitHub projects

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Recovering Performance for Vector-based Machine Learning on Managed Runtime

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ACM SIGPLAN Notices 2017年第8期52卷 457-458页

作者： Wu, Mingyu Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

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Unique sets oriented partitioning of nested loops with non-uniform dependences 25

Unique sets oriented partitioning of nested loops with non-u...

引用

25th International Conference on parallel Processing, ICPP 1996

作者： Ju, Jialin Chaudhary, V. Parallel and Distributed Computing Laboratory Wayne State University DetroitMI United States

ISBN: (纸本)081867623X

Although many methods exist for nested loop partitioning, most of them perform poorly when parallelizing loops with non-uniform dependences. This paper addresses the issue of parallelizing nested loops with non-uniform dependences. The approach is based on convex hull theory, which has adequate information to handle non-uniform dependences. We introduce the concept of Complete Dependence Convex Hull, unique head and tail sets and abstract the dependence information into these sets. These sets form the basis of the iteration space partitions. The properties of the unique head and tail sets are derived using convex hull theory. Depending on the relative placement of these unique sets, the partitioning problem is grouped into several cases. Several partitioning schemes are also suggested for implementing the technique. Preliminary implementation results of the scheme on the Cray J916 and comparison with other schemes show a dramatic improvement in performance. © 1996 IEEE.

关键词： Computational geometry

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