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Zero-shot image segmentation for scene objects based on the L0 gradient minimization and adaptive superpixel method

Neural Computing and Applications 2025年第16期37卷 10141-10161页

作者： Yan, Hailong Huang, Junjian Zheng, Mao Tang, Yijie School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China College of Electronic and Information Engineering Southwest University Chongqing China School of Microelectronics and Communication Engineering Chongqing University Chongqing China

This paper explores using convolutional neural networks (CNNs) for unsupervised image segmentation. The method enhances pixel labeling accuracy through superpixel and propagates back strategies. Leveraging CNN’s feature extraction capabilities, pixels are assigned labels without training data or prior knowledge. In an unsupervised context, a single image is the network’s input, and parameters are updated via gradient descent. A preprocessing module applies image smoothing before network input to improve segmentation performance. The convolutional kernels alternate between 3×3 and 1×1 sizes, and the activation functions, including ReLU and Batch Normalization, are reordered. Additionally, the Gray-Level Co-occurrence Matrix is integrated, and an adaptive superpixel method is introduced. Experimental results show significant improvements in Precision, Recall, and F1-score across corresponding datasets and superior performance in statistical indices like ARE, DH, AMI, and FMI. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Nonlinear convergence factor-based manta ray foraging optimization algorithm for combined economic emission dispatch problem

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Soft Computing 2025年第2期29卷 1053-1089页

作者： Zhang, Xing-Yue Wang, Jie-Sheng Zhu, Jun-Hua Bao, Yin-Yin Zheng, Yue Hao, Wen-Kuo School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Province Anshan City China

The optimization objective of Combined Economic Emissions Dispatch (CEED) must simultaneously ensure that full generation costs and pollutant emissions are minimized to meet load demands and constraints. Though the CEED problem itself has multi-objective to be optimized (e.g., fuel cost and emissions), it can be converted into a single objective with a price penalty factor. A nonlinear convergence factor-based manta ray foraging optimization algorithm was proposed for solving the CEED problems. Six nonlinear convergence functions, including the Gaussian function, sine function, cosine function, tangent function, power function and exponential function (named S1MRFO ~ S6MRFO), are introduced in the spiral foraging behavior stage based on the hypotrochoid manta ray foraging optimization algorithm (HYMRFO). These modifications enhance the MRFO algorithm's search capabilities and avoid getting trapped in local minima. The CEC-BC-2017 benchmark functions were used to examine the performance of six revised mathematical spiral feeding strategies for MRFO algorithm, and the S6MRFO algorithm with the best results was chosen. Combining the S6MRFO algorithm with other intelligent optimization algorithms, such as the Grey Wolf Optimizer (GWO), Arithmetic Optimization Algorithm (AOA), Multi-Verse Optimizer (MVO), Harris Hawk Optimization (HHO), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA) and Ant Lion Optimizer (ALO) are compared together for optimal performance. Finally, the CEED cases with twenty units of 2500 MW and six units with four different loads (150 MW, 175 MW, 200 MW, and 225 MW) were solved by MRFO algorithm based on nonlinear convergence factors. The simulation results show that in all four test cases, the suggested strategy has the lowest fuel cost and the fewest hazardous emissions, where total costs are about 3% less than PSO and emissions are about 5.6% less. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Spr

关键词： Exponential functions

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Modeling contextual goals and detecting context conflicts from BDD-based user stories

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International Journal of Computers and Applications 2024年第12期46卷 1057-1068页

作者： Zheng, Liwei Wang, Yan Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： BDD-based user stories contextual goal model context conflict detection

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A novel active-passive protection strategy endows carbonyl iron with excellent intelligent anti-corrosion and microwave absorption dual functional characteristics

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Nano Research 2025年第1期18卷 487-500页

作者： Wei Tian Quanyuan Feng Linbo Zhang Yinfan Liu Xian Jian Longjiang Deng Institute of Microelectronics School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 611756China National Engineering Researching Centre of Electromagnetic Radiation Control Materials Key Laboratory of Multi-Spectral Absorbing Materials and Structures of Ministry of EducationState Key Laboratory of Electronic Thin Films and Integrated DevicesSchool of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 611731China The Yangtze Delta Region Institute(Huzhou)&School of Electronic Science and Engineering University of Electronic Science and Technology of ChinaHuzhou 313001China

Carbonyl iron absorbers(CI)face significant challenges in practical applications,such as corrosion,interface bonding failure,detachment,and high maintenance ***,we have developed intelligent self-healing technology based on proactive/passive mechanisms via in situ synthesis of self-healing factors(polydopamine/benzotriazole(PDA/BTA))and physical barrier layers(SiO_(2)/1,1,1,3,3,3-hexamethyl disilazane,SiO_(2)/HMDS)to enhance corrosion resistance,while also being compatible with efficient microwave absorption *** unique multiscale structure gives full play to the utilization of the roles of each functional layer,including the intelligent self-healing features of PDA/BTA,physical shielding and spatial confinement characteristics of SiO_(2)/HMDS,and magnetic-dielectric synergistic mechanism resulted from the good impedance matching characteristics,the conduction loss,the interfacial polarization loss and natural *** asfabricated composites achieved an exceptional minimum reflection loss value of-55.4 dB at 10.7 GHz and the effective absorption band of 7.6 ***,it still exhibits obvious self-healing and corrosion resistance characteristics after 360 h corrosion treatment,ascribed to the self-healing mechanism of PDA/BTA and the blocking intervention effect of SiO_(2)/*** work is considered to pave the way for the synthesis of high-performance magnetic absorbers,especially in enhancing their intelligent self-healing ability in corrosive environments.

关键词： microwave absorption intelligent self-healing technology carbonyl iron multiscale structure magneticdielectric synergistic mechanism

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Binary Program Vulnerability Mining Based on Neural Network

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Computers, Materials & Continua 2024年第2期78卷 1861-1879页

作者： Zhenhui Li Shuangping Xing Lin Yu Huiping Li Fan Zhou Guangqiang Yin Xikai Tang Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Electrical and Computer Engineering University ofWaterlooWaterlooN2L 3G1Canada

software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining ***,these tools suffer from some *** terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search ***,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information *** this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation *** leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion *** combination allows for the unified handling of binary programs across various architectures,compilers,and compilation ***,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)***,the graph embedding network is utilized to evaluate the similarity of program *** on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target *** solved content serves as the initial seed for targeted *** binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity *** approach facilitates

关键词： Vulnerability mining de-obfuscation neural network graph embedding network symbolic execution

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security software defined network Lifecycle management

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Competition for gradient-free tuning of large language models:approaches, results, current challenges and future directions

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National science Review 2023年第6期10卷 16-19页

作者： Tingfeng Cao Liang Chen Dixiang Zhang Tianxiang Sun Zhengfu He Xipeng Qiu Xing Xu Hai Zhang School of Software Engineering South China University of Technology School of Computer Science Fudan University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural language processing tasks,but also captured widespread attention from the public due to their great potential in a variety of real-world applications (***,search engines,writing assistants,etc.)through providing general-purpose intelligent services.A few of the LLMs are becoming foundation models,an analogy to infrastructure,that empower hundreds of downstream applications.

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

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Computer Modeling in engineering & sciences 2024年第5期139卷 1239-1261页

作者： Yujie Duan Jianguo Liang Jianglin Liu Haifeng Gao Yinhui Li Jinzhu Zhang Xinyu Wen College of Mechanical and Vehicle Engineering Taiyuan University of TechnologyTaiyuan030024China College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan030024China School of Electronic Information Engineering Taiyuan University of Science and TechnologyTaiyuan030000China

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension *** order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward *** addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter ***,the fluctuation signal is reconstructed by a third-order auxiliary *** estimated signal feedforward compensates for the actual tension ***,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the ***,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source *** stability of the method is analyzed by using the Lyapunov ***,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

关键词： Constant tension control anti-fluctuation strategy tension fluctuation observer time-varying parameters fractional-order PID controller feedforward compensate

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RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

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Journal of Systems engineering and electronics 2024年第3期35卷 558-574,F0002页

作者： FAN Rong SI Chengke HAN Yi WAN Qun School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Institute of Electronic and Electrical Engineering Civil Aviation Flight University of ChinaGuanghan 618307China

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of *** this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,***,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train *** introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is *** improves physical interpretability of ***,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of ***,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is ***,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction *** results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

关键词： specific emitter identification(SEI) deep learning(DL) radio frequency fingerprint(RFF) multidimensional feature extraction(MFE) variational mode decomposition(VMD)

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