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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhenkun Jin Shuai Huang Xin Nie Xinlei Zhou Yuanyuan Yi Gefei Zhou Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models.

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Ground-to-air Defense Firepower Assignment Decision Based on Non-cooperative Game

Ground-to-air Defense Firepower Assignment Decision Based on...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Cui, Mengya Zhang, Yong Sun, Wenjuan School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

The key problem of ground-to-air defense is how to make accurate and effective firepower assignment decision for attacking targets. A multi-objective optimization model of ground-to-air defense firepower assignment is established with the aim of maximizing the damage probability and minimizing the weapon consumption, and non-cooperative game theory is used to solve the problem. A non-cooperative game model is established by mapping the two objectives to players, and weapon assignment schemes to strategy profiles. An improved tabu genetic algorithm based on Nash equilibrium is designed to get the assignment scheme. Experimental results show that the proposed method has better convergence and stability than the improved genetic algorithm based on tabu search for multi-objective optimization model. © 2021 IEEE

关键词： Genetic algorithms

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Weapon Target Assignment for Surface-to-air Defense with an Improved Reinforcement Genetic Algorithm

Weapon Target Assignment for Surface-to-air Defense with an ...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Liu, Dalong Zhang, Yong Xu, Ke School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

Decision optimization in surface-to-air defense and coordinated weapon target assignment can improve the defense effect and the utilization rate of weapons. Considering the constraints of weapon capability, a multi-target optimization model is established to maximize the damage probability and minimize the weapon consumption cost. A dual population genetic algorithm improved by reinforcement learning is designed to solve this model. The chromosome exchange ratio is controlled by reinforcement learning to ensure population diversity. Experimental results verify the convergence speed and accuracy of the improved reinforcement genetic algorithm. © 2021 IEEE

关键词： Genetic algorithms

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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Computer Systems science & Engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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Dynamic Threat Assessment of Air multi-target Based on DBN-TOPSIS Method

Dynamic Threat Assessment of Air multi-target Based on DBN-T...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Yu, Xiaoye Zhang, Yong Liu, Fang School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

In the traditional air target threat assessment, only quantitative threat degree is considered, and the target threat range assessment is missing. Aiming at the problem, a comprehensive dynamic threat assessment method is designed in this paper. This comprehensive method considers two terms of the target threat: threat degree and threat range. In terms of threat degree assessment, a dynamic Bayesian network (DBN) and a technique for order preference by similarity to an ideal solution (TOPSIS) are fused. The multi-target fusion assessment method DBN-TOPSIS is proposed. This method maps the probability of multi-target threat level obtained by dynamic Bayesian network to TOPSIS evaluation matrix. This strategy solves the problem of multi-target threat ranking. In terms of threat range assessment, a grey prediction threat range assessment method GM(1,1) is obtained. This method solves the problem of small sample threat range assessment. The experimental results show that the multi-target fusion threat assessment method is reasonable, and the target threat range can directly reflect the change of target threat degree in airspace. © 2021 IEEE

关键词： Bayesian networks

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Seamless Decision-Making in the Big Data Era: A Modular Approach to Integrating IoT, Cloud Computing, and Data Lakes

Seamless Decision-Making in the Big Data Era: A Modular Appr...

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International Conference on Electrical, communication, and Computer Engineering (ICECCE)

作者： Mohamed Zemmouri Fatma Zohra Laalam Okba Kazar Yassine Himeur Adel Oulefki Chahrazad Toumi Wathiq Mansoor Shadi Attala Artificial Intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla Algeria Department of Computer Science - LINFI Laboratory Mohamed Khider University Biskra Algeria Department of Information Systems and Security - College of Information Technology United Arab Emirate University Abu Dhabi United Arab Emirates College of Engineering and Information Technology University of Dubai Dubai UAE Department of Computer Science University of Sharjah Sharjah United Arab Emirates

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source.

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Pruning CNN based on Combinational Filter Deletion

Pruning CNN based on Combinational Filter Deletion

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Annual Conference of Industrial Electronics Society

作者： Wenli Huang Xiujie Wang Zhihong Zhao Li Su Shuai Sui Jinjun Wang School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Science and Technology on Communication Networks Laboratory Shijiazhuang China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Ningbo University of Technology Ningbo China Liaoning University of Technology Jinzhou China Institute of Artificial Intelligence and Robotic Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Network pruning is a figurative model compression technique designed to lighten and accelerate neural network models. Most existing pruning methods prioritize the selection of filters by their importance or apply regularization based on the properties of individual filters, neglecting the internal connections within combinations of multiple filters. This work introduces a pruning method termed Combinational Filter Deletion (CFD), which incorporates a straightforward yet effective evaluation metric based on the diversity of filter combination distributions to reveal the characteristics inherent to multiple filter interactions. CFD enables the exploration of an expanded search space, offering a greater array of choices and leveraging the intrinsic information of conventional layers. Moreover, this method is both general and non-exclusive, capable of enhancing the efficacy of other single-filter-based pruning techniques.

关键词： Measurement Industrial electronics Filters Neural networks Diversity reception Excavation

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GPU-accelerated multipath clutter cancellation algorithm for dtmb-based passive radar

GPU-accelerated multipath clutter cancellation algorithm for...

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2020 International Conference on Engineering Management of communication and technology, EMCTECH 2020

作者： Bu, Zirong Zhu, Huijie Qian, Jiachuan Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China No. 36 Research Institute of China Electronics Technology Group Corporation Jiaxing314033 China

ISBN: (纸本)9781665404488

In order to extract the target echo in the monitoring channel, the direct wave and the clutter should be suppressed in the signal processing of the passive radar. The batch version of the Extensive Cancellation Algorithm (ECA-B) is an effective time-domain clutter cancellation algorithm that can suppress direct waves and multipath clutter. And it has better robustness than ECA. However, ECA-B is a computationally intensive algorithm. Therefore, the Graphic Processing Unit (GPU) accelerated ECA-B algorithm has been studied and implemented on GPU by exploring the parallelism of the ECA-B algorithm. The paper contains the description of the algorithm implementation and the performance results analysis of the measured data. © 2020 IEEE.

关键词： Graphics processing unit

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Design Compact YOLO based Network for Small Target Detection on Infrared Image

Design Compact YOLO based Network for Small Target Detection...

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Chinese Automation Congress (CAC)

作者： Shuang Liu Zhicheng Liu Yuhai Li Wancheng Liu Chenyang Ge Longjun Liu Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

most YOLO object detection neural networks prefer to focus on traditional RGB image, but previous studies rarely consider special YOLO network with compact architecture for infrared image. In this paper, we analyze original YOLO network architecture, and we propose a compact YOLO based network by using different blocks from previous work for small target detection on infrared image. We use small target detection layer, GhostConv convolution, Focus structure, Focal EIOU Loss and soft NMS modules to improve original YOLO network structure, which improves the accuracy and speed of target detection for infrared image. The experimental results show that the accuracy of target detection of original model can be effectively improved by adding the small target detection layer, the Focus structure and the BI-FPN structure. By replacing the GhostConv convolution, the calculation speed is significantly improved. At last, after the model is quantized and deployed with NCNN framework, the object detection speed can reached to 35ms per infrared image.

关键词： Automation Convolution Neural networks Object detection Network architecture

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Secrecy Outage Probability Analysis for Downlink RIS-NOMA Networks with On-Off control

arXiv

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arXiv 2024年

作者： Pei, Yingjie Yue, Xinwei Yi, Wenqiang Liu, Yuanwei Li, Xuehua Ding, Zhiguo Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering Princeton University Princeton United States School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

Reconfigurable intelligent surface (RIS) has been regarded as a promising technology since it has ability to create the favorable channel conditions. This paper investigates the secure communications of RIS assisted non-orthogonal multiple access (NOMA) networks, where both external and internal eavesdropping scenarios are taken into consideration. More specifically, novel approximate and asymptotic expressions of secrecy outage probability (SOP) for the k-th legitimate user (LU) are derived by invoking imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC). To characterize the secrecy performance of RIS-NOMA networks, the diversity order of the k-th LU with ipSIC/pSIC is obtained in the high signal-to-noise ratio region. The secrecy system throughput of RIS-NOMA networks is discussed in delay-limited transmission mode. Numerical results are presented to verify theoretical analysis that: i) The SOP of RIS-NOMA networks is superior to that of RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication schemes;ii) As the number of reflecting elements increases, the RIS-NOMA networks are capable of achieving the enhanced secrecy performance;and iii) The RIS-NOMA networks have better secrecy system throughput than that of RIS-OMA networks and conventional cooperative communication schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Cooperative communication

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