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A Hardware Descriptive Approach to Beetle Antennae Search

IEEE ACCESS 2020年 8卷 89059-89070页

作者： Yue, Zongcheng Li, Gang Jiang, Xiangyuan Li, Shuai Cheng, Jian Ren, Peng China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China Swansea Univ Coll Engn Swansea SA1 8EN W Glam Wales

Beetle antennae search (BAS) is a newly developed meta-heuristic algorithm which is effectively used for optimizing objective functions of complex forms or even unknown forms. The common practice for implementing meta-heuristic algorithms including the BAS largely relies on programming in a high-level language and executing the code on a computer platform. However, the high-level implementation of the BAS algorithm hinders it from being used in an embedding system, where real-time operations are normally required. To address this limitation, we present an approach to implementing the BAS algorithm on a field-programmable gate array (FPGA). Specifically, we program the BAS function in the Verilog hardware description language (HDL), which provides a tractable vehicle for implementing the BAS algorithm at the gate level on the FPGA chip. We simulate our Verilog HDL based BAS module with the Modelsim platform. Simulation results validate the feasibility of our proposed Verilog HDL implementation of the BAS. Additionally, we implement the BAS model on the Zynq XC7Z010 platform, with 132.5 mu s latency for model implementation.

关键词： Field programmable gate arrays Hardware design languages optimization Directive antennas Genetic algorithms Hardware FPGA beetle antennae search Verilog HDL optimization algorithm

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Review of optimized layout of electric vehicle charging infrastructures

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Journal of Central South University 2021年第10期28卷 3268-3278页

作者： TAO Ye HUANG Miao-hua CHEN Yu-pu YANG Lan School of Automotive Engineering Wuhan University of TechnologyWuhan 430070China

Electric vehicle is a kind of new energy vehicle which uses batteries as energy supply unit.A huge gap in charging infrastructures will be created by the expansion of electric *** effectiveness and rationality of charging facilities will directly affect the convenience and economy of the users,as well as the safe operation of the power *** types of charging facilities:charging pile,charging station and battery swap station are introduced in this *** to the different methods of charging infrastructure planning,the research status of the method of determining charging demand points is *** the spatial distribution of charging demand points extracted by the current site selection method has a certain *** the models and algorithms of charging infrastructure optimized layout are ***,many researches focus on three categories optimization objectives:benefit of power company side,investment cost of charging facility and user side cost,and the genetic algorithm and particle swarm optimization are the main solving ***,the relative methods and development trend of the charging infrastructures optimized layout are summarized,and some suggestions on the optimized layout of electric vehicle charging infrastructures are given forward.

关键词： electric vehicle charging infrastructures layout optimal modeling optimization algorithm

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Fireworks algorithm Applied to Trajectory Design for Earth to Lunar Halo Orbits

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第2期57卷 235-246页

作者： He, Guanwei Conte, Davide Melton, Robert G. Spencer, David B. Penn State Univ Dept Aerosp Engn 229 Hammond Bldg University Pk PA 16802 USA Embry Riddle Aeronaut Univ Dept Aerosp Engn 3700 Willow Creek Rd Prescott AZ 86301 USA

A simple and efficient method using the fireworks algorithm (FWA) is presented for pruning the search space of an impulsive maneuver transfer from a user-defined low Earth orbit (LEO) to a desired lunar halo orbit around Earth-moon Lagrange point 2. The dynamical framework adopted is the circular restricted three-body problem. The FWA method is used to optimize both the two-burn direct impulsive transfer and the impulsive transfer that uses stable manifolds. Sample LEO to lunar halo trajectories, along with their required Delta V costs and times of flight (TOFs), are provided and compared to known numerical techniques. The results obtained are close in both Delta V and TOFs to what the existing literature reports, which demonstrates the validity of the proposed method.

关键词： Earth Near Rectilinear Halo Orbit Moon Orbital Transfer Particle Swarm optimization optimization algorithm Flight Path Angle Propellant Right Ascension Orbital Period

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Computer-aided SPT-based reliability model for probability of liquefaction using hybrid PSO and GA

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2020年第1期7卷 107-127页

作者： Goharzay, Maral Noorzad, Ali Ardakani, Ahmadreza Mahboubi Jalal, Mostafa Shahid Beheshti Univ Fac Civil Water & Environm Engn Shahid Shahriari SqDaneshjou Blvd Tehran *** Iran Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77840 USA

In this paper, an approach for soil liquefaction evaluation using probabilistic method based on the world-wide SPT databases has been presented. In this respect, the parameters' uncertainties for liquefaction probability have been taken into account. A calibrated mapping function is developed using Bayes' theorem in order to capture the failure probabilities in the absence of the knowledge of parameter uncertainty. The probability models provide a simple, but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction triggering thresholds. Within an extended framework of the first-order reliability method considering uncertainties, the reliability indices are determined through a well-performed meta-heuristic optimization algorithm called hybrid particle swarm optimization and genetic algorithm to find the most accurate liquefaction probabilities. Finally, the effects of the level of parameter uncertainty on liquefaction probability, as well as the quantification of the limit state model uncertainty in order to incorporate the correct model uncertainty, are investigated in the context of probabilistic reliability analysis. The results gained from the presented probabilistic model and the available models in the literature show the fact that the developed approach can be a robust tool for engineering design and analysis of liquefaction as a natural disaster.

关键词： liquefaction hazard probabilistic approach reliability analysis optimization algorithm hybrid PSO-GA

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Telemonitoring of Daily Activity Using Accelerometer and Gyroscope in Smart Home Environments

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2020年第6期15卷 2801-2809页

作者： Batool, Mouazma Jalal, Ahmad Kim, Kibum Air Univ Dept Comp Sci & Engn Islamabad Pakistan Hanyang Univ Dept Human Comp Interact Seoul South Korea

Wearable sensors in the smart home environment have been actively developed as assistive systems to detect behavioral anomalies. Smart wearable devices incorprated into daily life can respond immediately to anomalies and process and dispatch important information in real-time. Artificially intelligent technology monitoring of the user's daily activities and smart home ambience is especially useful in telehealthcare. In this paper, we propose a behavioral activity recognition framework which uses inertial devices (accelerometer and gyroscope) for activity detection within the home environment via multi-fused features and a reweighted genetic algorithm. The procedure begins with the segmentation and framing of data to enable efficient processing of useful information. Features are then extracted and transformed into a matrix. Finally, biogeography-based optimization and a reweighted genetic algorithm are used for the optimization and classification of extracted features. For evaluation, we used the "leave-one-out" cross validation scheme. The results outperformed existing state-of-the-art methods, achieving higher recognition accuracy rates of 88%, 88.75%, and 93.33% compared with CMU-Multi-Modal Activity, WISDM, and IMSB datasets respectively.

关键词： Daily life activity recognition Local binary pattern Mel frequency cepstral coefficients optimization algorithm Reweighted genetic algorithm

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The spherical search algorithm for bound-constrained global optimization problems

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APPLIED SOFT COMPUTING 2019年 85卷 105734-000页

作者： Kumar, Abhishek Misra, Rakesh Kumar Singh, Devender Mishra, Sujeet Das, Swagatam Indian Inst Technol BHU Dept Elect Engn Varanasi 221005 Uttar Pradesh India Diesel Locomot Works Varanasi Uttar Pradesh India Indian Stat Inst Elect & Commun Sci Unit Kolkata India

In this paper, a new optimization algorithm called Spherical Search (SS) is proposed to solve the bound-constrained non-linear global optimization problems. The main operations of SS are the calculation of spherical boundary and generation of new trial solution on the surface of the spherical boundary. These operations are mathematically modeled with some more basic level operators: Initialization of solution, greedy selection and parameter adaptation, and are employed on the 30 black-box bound constrained global optimization problems. This study also analyzes the applicability of the proposed algorithm on a set of real-life optimization problems. Meanwhile, to show the robustness and proficiency of SS, the obtained results of the proposed algorithm are compared with the results of other well-known optimization algorithms and their advanced variants: Particle Swarm optimization (PSO), Differential Evolution (DE), and Covariance Matrix Adapted Evolution Strategy (CMA-ES). The comparative analysis reveals that the performance of SS is quite competitive with respect to the other peer algorithms. (C) 2019 Published by Elsevier B.V.

关键词： Spherical search algorithm Real-life optimization problems Bound constrained optimization problem optimization algorithm Global optimization

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Offloading and Resource Allocation With General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2020年第8期19卷 5404-5419页

作者： Yan, Jia Bi, Suzhi Zhang, Ying-Jun Angela Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

In this paper, we consider a mobile-edge computing (MEC) system, where an access point (AP) assists a mobile device (MD) to execute an application consisting of multiple tasks following a general task call graph. The objective is to jointly determine the offloading decision of each task and the resource allocation (e.g., CPU computing power) under time-varying wireless fading channels and stochastic edge computing capability, so that the energy-time cost (ETC) of the MD is minimized. Solving the problem is particularly hard due to the combinatorial offloading decisions and the strong coupling among task executions under the general dependency model. Conventional numerical optimization methods are inefficient to solve such a problem, especially when the problem size is large. To address the issue, we propose a deep reinforcement learning (DRL) framework based on the actor-critic learning structure. In particular, the actor network utilizes a DNN to learn the optimal mapping from the input states (i.e., wireless channel gains and edge CPU frequency) to the binary offloading decision of each task. Meanwhile, by analyzing the structure of the optimal solution, we derive a low-complexity algorithm for the critic network to quickly evaluate the ETC performance of the offloading decisions output by the actor network. With the low-complexity critic network, we can quickly select the best offloading action and subsequently store the state-action pair in an experience replay memory as the training dataset to continuously improve the action generation DNN. To further reduce the complexity, we show that the optimal offloading decision exhibits an one-climb structure, which can be utilized to significantly reduce the search space of action generation. Numerical results show that for various types of task graphs, the proposed algorithm achieves up to 99.1% of the optimal performance while significantly reducing the computational complexity compared to the existing optimization me

关键词： Task analysis Wireless communication Computational modeling Servers Resource management Edge computing optimization Mobile edge computing optimization algorithm deep reinforcement learning resource allocation

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Sequence set design for waveform-agile coherent radar systems

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2020年第1期2020卷 31-31页

作者： Zhang, Jindong Xu, Naiqing Song, Hu Zhang, Chao Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

With increased degrees of freedom of the transmitter, coherent waveform-agile radar system can change its transmission on-the-fly in response to target detection's requirement. This approach can provide better performance than a single waveform. In this paper, we consider unimodular sequence set design (USSD) problem based on summed range-Doppler ambiguity function (SRDAF) for coherent waveform-agile radar system. We present two algorithms for constructing unimodular sequence sets under the constant module constraint with desired minimized sidelobes on a predefined area of range-Doppler plane. The proposed algorithms are constructed based on singular value decomposition (SVD) and cyclic algorithm (CA), respectively. Numerical examples show the effectiveness of the proposed algorithms.

关键词： Unimodular sequence set Coherent radar Waveform agility Sidelobes optimization algorithm

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Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samso

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ENERGY 2020年 198卷 117188-000页

作者： Carli, Raffaele Dotoli, Mariagrazia Jantzen, Jan Kristensen, Michael Ben Othman, Sarah Politecn Bari Elect & Informat Engn Dept Via Orabona 4 I-70125 Bari Italy Univ Aegean Dept Financial & Management Engn Chios Greece Samso Energy Acad Samso Denmark Ecole Cent Lille CRIStAL Lab Lille France

This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samso (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master's office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naive control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naive control. Optimizati

关键词： Microgrid Demand side management Renewable energy Energy storage Energy management On-line scheduling Model predictive control optimization algorithm

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Source Exploration for an Under-Actuated System: A Control-Theoretic Paradigm

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2020年第3期28卷 1100-1107页

作者： Jiang, Xiangyuan Li, Shuai Luo, Bing Meng, Qinghao Shandong Univ Inst Marine Sci & Technol Qingdao 251007 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 307459 Peoples R China

The task of seeking an unknown source with the measurements of the emitted signal strength is named as the source-exploration problem, whose challenging issues with conducting autonomous source-exploration are the lack of a priori knowledge about the distribution of the emitted signal and the presence of unignorable noise in both the propagation situation and equipped sensor readings. This paper presents a planner for an autonomous robot engaged in seeking for an odor source. The guidance strategy for the planner is based on a control-theoretic paradigm which incorporates an optimization technique into the structure. The control law is inspired by two prominent behaviors widely observed in biology, namely, chemotaxis and anemotaxis. The two behaviors are formulated in the control development. The derived control law is rigorously analyzed and guarantees the converge of a nonlinear under-actuated robot to reach the unknown source, which provides a case study on using run-time optimization to guide rigorous control algorithm development. Besides theoretical proof, our results are also validated in numerical simulations and physical experiments.

关键词： Robot sensing systems Robot kinematics optimization Kinematics Mathematical model Chemicals optimization algorithm source exploration under-actuated systems

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