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A joint optimization algorithm Using Adaptive Minimum Coset Number Based Discrete Multi-Coset Sampling

IEEE ACCESS 2020年 8卷 168659-168670页

作者： Dong, Jiannan Li, Hongkun Fan, Zhenfang Zhou, Qiang Hua, Shungang Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

We address the problem of sparse multi-band signal reconstruction in the case of unknown band position through the discrete multi-coset sampling (DMCS). In this article, the signal has complex frequency components, and the minimum coset number is determined on the assumption that there is only one frequency component with same characteristics. According to the frequency characteristics, we analyze the influence of the parameterized compressed matrix on the two reconstruction algorithms, and get that a single algorithm does not have universal adaptability to different frequency components. In order to solve this problem, under the discrete multi-coset sampling model, a joint optimization algorithm with discriminant factor (DF-JOA) is proposed to identify the different characteristics and automatically select an appropriate algorithm for signal reconstruction, numerical simulation experiments show the effectiveness of the algorithm. We also simulate the reconstruction success ratio of amplitude and the total coset number under different compressed matrices, determine the influence law, and confirm the improvement of signal reconstruction probability by joint optimization algorithm. Our method ensures the spectrum reconstruction of the multi-band signal. This article can guide how to better select the coset parameters under the condition that the channels of the discrete multi-coset sampling system are limited but the minimum coset number can be guaranteed. It will have a great significance to the sub-Nyquist sampling technique.

关键词： Discrete Fourier transforms Signal reconstruction optimization Compressed sensing Sparse matrices Mathematical model Discrete multi-coset sampling sparse reconstruction minimum coset number discriminant factor joint optimization algorithm

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joint optimization of Computational Tasks Offloading for Efficient and Secure Manufacturing Through Cloud-Fog-Edge-Terminal Architecture

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年

作者： Liu, Zhifeng Lin, Zhiwen Zhang, Yueze Chen, Chuanhai Guo, Jinyan Qi, Baobao Tao, Fei Jilin Univ Key Lab CNC Equipment Reliabil Minist Educ Changchun 130025 Peoples R China Jilin Univ Jilin Prov Key Lab Adv Mfg & Intelligent Technol H Changchun 130025 Peoples R China Beijing Univ Technol Machinery Ind Key Lab Heavy Machine Tool Digital D Beijing 100124 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Beijing Univ Technol Beijing Key Lab Adv Mfg Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Within the realm of smart manufacturing, the growing complexity and scale of computational tasks expose the limitations of conventional computing architectures with rigid task allocation and fragmented collaboration, which impede the efficiency and security of smart manufacturing systems. To address these challenges, this paper presents an innovative cloud-fog-edge-terminal collaborative strategy that provides an adaptive and comprehensive offloading solution for tasks with complex dependencies and large scale, thereby enhancing the seamless collaborative potential of hierarchical computing structures. Additionally, a joint optimization mathematical model for collaborative computational offloading is developed, aiming to minimize task offloading time and assess manufacturing risk. To refine the solution, an advanced multi-objective optimization algorithm is formulated to identify the optimal solutions. The effectiveness and practical applicability of the proposed method are substantiated through simulation experiments and empirical case studies, demonstrating a performance enhancement of 12-29% over other benchmarks. The joint optimization method effectively synchronizes cloud-fog-edge-terminal computing resources, realizing efficient and secure task offloading and execution in smart manufacturing scenarios.

关键词： Manufacturing optimization Collaboration Computational modeling Computer architecture Cloud computing Smart manufacturing Security Edge computing Delays computational task offloading cloud-fog-edge-terminal architecture optimization of latency optimization of manufacturing risk joint optimization algorithm

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Applying an auction optimization algorithm to mobile edge computing for security

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IET COMMUNICATIONS 2022年第10期16卷 1130-1141页

作者： Wang, Yi Tang, Tao Jiang, Fang Xu, Yaohua Li, Liping Huang, Rong Anhui Univ Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Anhui Peoples R China Chinese Acad Sci Key Lab Wireless Sensor Network & Commun Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China

The great demand for mobile blockchain computing power is often unsatisfied by terminal devices, so computational tasks are offloaded to edge computing servers. This paper proposes a new mobile communication blockchain assumption, forms a computing power alliance (CPA), and builds a smart contract-based security model. First, mining difficulty compensates for the personal computing power outside the CPA to increase the block generation difficulty. Second, contract account funds are used to increase the cost of malicious nodes seeking to launch forking attacks, and the duration is used to limit mining. Finally, a court trial is opened to select validators to verify the fork. The auction algorithm is used to allocate computing power in the CPA, and a price utility function is constructed to maximize social welfare. The joint optimization algorithm increases the transaction price and improves the system security. Simulation results verify that the system security increases with the blocked funds and duration, and the forking attack success rate approaches zero as the number of validators increases. The proposed algorithm provides considerable sum utility and average revenue gains versus traditional methods under different numbers of mobile users and computational capabilities, and the security of the algorithm is also higher.

关键词： auction optimization algorithm block generation difficulty CPA mobile communication blockchain assumption price utility function contract account funds computational capabilities system security increases validators increases mobile blockchain computing power terminal devices joint optimization algorithm edge computing optimisation mobile edge blocked funds computer network security forking attack success rate approaches forking attacks blockchains computational tasks personal computing power mobile users mobile computing mining difficulty compensates smart contract-based security model contracts auction algorithm computing power alliance

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Data-driven adaptive assembled joints decision-making model for prefabricated underground stations

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年第1期140卷

作者： Qiu, Tong Chen, Xiangsheng Su, Dong Wang, Lei Shenzhen Univ Coll Civil & Transportat Engn Shenzhen Peoples R China Shenzhen Key Lab Green Efficient & Intelligent Con Shenzhen Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Key Lab Resilient Infrastructures Coastal Cities MOE Shenzhen 518060 Peoples R China

In recent years, the construction of prefabricated underground stations (PUS) has become an important aspect of low-carbon urban development. At present, the design of PUS assembled joints involving design-fabricationtransportation-assembly industry chain information encounters technical bottleneck. To address this problem, this study proposed a big data decision-making model for PUS assembled joints considering multi-source massive data. First, the databases of assembled joints, precast components and assembled structures were established. Second, the finite element analysis analyzer linked to the databases was built. Third, the structure optimization algorithm and joint decision-making algorithm were developed. Finally, the Data-driven Adaptive Decisionmaking Model (DADM) was constructed by combining the above results. This study used DADM for case study and compared with the empirical design, the main conclusions were as follows: (1) DADM revealed the adaptability of joint properties in PUS, including the joint properties influence and the joint type decision. (2) DADM performed joint-component-structure planning more accurately than empirical design. This compensates the disadvantage of empirical decision making under massive data. (3) DADM presented significant advantages of semi-rigid joints for PUS applications. And DADM also achieved economical planning that precisely matched the joint performance requirements. (4) The advantages of the adaptive scheme were comprehensive, including good economics and industry chain benefits and improved station quality. This study addresses difficulty of designing PUS assembled joints with multi-source massive data, which has important application value and practical significance.

关键词： Prefabricated underground station Assembled joint Big data Structure optimization algorithm joint optimization algorithm Adaptive scheme

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Design of a Reconfigurable Intelligent Surface algorithm Based on Multiple-Input Multiple-Output

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TRAITEMENT DU SIGNAL 2022年第6期39卷 1943-1950页

作者： Jiang, Fuchun Lin, Weiming Zhang, Hongyi Lin, Xinhua Feng, Chenwei Xiamen Univ Technol Fujian Key Lab Optoelect Technol & Devices Xiamen 361024 Peoples R China

Reconfigurable Intelligent Surface (RIS) can improve the security of the physical layer of wireless communication by adjusting the phase of the reflective unit. After analyzing the common theoretical models and potential problems, this study proposes an alternative iterative model based on multiple input multiple output (MIMO), and designs the transmitter, channel and receiver. In addition, passive beamforming precoding matrix of RIS was jointly optimized, and the Lagrangian dual relaxation (LDR) was adopted to decouple the nonconvex problem. After that, the active and passive beamforming matrices were subjected to iterative calculation, and the beamforming was optimized through cyclic programming at the base station (BS). In addition, the high convergence of the proposed algorithm was proved by MATLAB simulation. The results of simulation demonstrate that the joint precoding framework algorithm can maximize the weighted sum rate (WSR), which in turn demonstrates the feasibility of our method. Finally, the authors analyzed the strong applicability of the RIS technology in complex wireless networks with different volatility, revealing the possibility of future development.

关键词： reconfigurable intelligent surface (RIS) cell-free massive multiple input multiple output (MIMO) joint optimization algorithm weighted sum rate (WSR)

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Secrecy Capacity Maximization of UAV-Enabled Relaying Systems with 3D Trajectory Design and Resource Allocation

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SENSORS 2022年第12期22卷 4519页

作者： An, Qi Pan, Yu Han, Huizhu Hu, Hang Air Force Engn Univ Sch Informat & Nav Xian 710077 Peoples R China

Unmanned aerial vehicles (UAVs) have attracted considerable attention, thanks to their high flexibility, on-demand deployment and the freedom in trajectory design. The communication channel quality can be effectively improved by using UAV to build a line-of-sight communication link between the transmitter and the receiver. Furthermore, there is increasing demand for communication security improvement, as the openness of a wireless channel brings serious threat. This paper formulates a secrecy capacity optimization problem of a UAV-enabled relay communication system in the presence of malicious eavesdroppers, in which the secrecy capacity is maximized by jointly optimizing the UAV relay's location, power allocation, and bandwidth allocation under the communication quality and information causality constraints. A successive convex approximation-alternative iterative optimization (SCA-AIO) algorithm is proposed to solve this highly coupled nonconvex problem. Simulation results demonstrate the superiority of the proposed secrecy transmission strategy with optimal trajectory design and resource allocation compared with the benchmark schemes and reveal the impacts of communication resources on system performance.

关键词： unmanned aerial vehicle (UAV) communication mobile relay secrecy capacity joint optimization algorithm

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