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3rd International E-Conference on Frontiers in Mechanical Engineering and NanoTechnology (ICFMET)

作者： Mishra, Ruby KIIT Bhubaneswar 751024 India

The usages of linkage mechanisms in diverse applications, including manufacturing, are extensively appreciated. The synthesis of mechanisms for given motion and path generation has becomemore significant. The data of the kinematics of machines, thus, is a very important aspect of modern-day manufacturing. From conventional perception, kinematics' knowledge prevents many realistic mechanisms like pin deformation, joint clearances, and manufacturing inexactitudes, which cannot be overlooked. This study aims to explain the different design optimization processes and the analysis of flexible four-bar linkages for achieving a better solution. This study could be an essential step for setting the appropriate resources and optimizing the four-bar mechanism design to reduce the deviation from the desired output. The high-speed rotation of the mechanism may cause vibration and deformation in the system. So, different methods may be applied for improved four-bar mechanisms. This study provides us withprobable to resolve such conditions as precise load values regarding other vibrating elements. Like MATLAB, ADAMS, and ANSYS, the soft-computing method has been formulated to strengthen the researcher's analysis ability. These software packages may provide the improved design data in the form of displacement, deformation, and vibration frequency of the four-bar Mechanisms. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International e-Conference on Frontiers in Mechanical Engineering and nanoTechnology.

关键词： Mechanism Path generation Virtual link Optimization evolution algorithm

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Static Analysis and Optimization of Voltage and Reactive Power Regulation Systems in the HV/MV Substation with Electronic Transformer Tap-Changers

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ENERGIES 2022年第13期15卷 4773-4773页

作者： Korpikiewicz, Jaroslaw Mohamed-Seghir, Mostefa Gdynia Maritime Univ Fac Elect Engn Departamet Ship Automat PL-81225 Gdynia Poland

The quality of electricity is a very important indicator. The durability and reliable operation of all connected devices depend on the quality of the network voltage. Rapid changes in loads, changes in network connections and the presence of uncontrolled energy sources require the development of new voltage regulation systems. This requires voltage regulation systems capable of responding quickly to sudden voltage changes. In substations with control transformers, it is possible thanks to the use of semiconductor tap changers. Moreover, voltage regulation and reactive power compensation systems should be built as one system. This is due to the close dependence of voltage and reactive power in the network node. Therefore, it was proposed to use artificial intelligence methods to build a new voltage regulation and reactive power compensation system using all measurement voltages of network nodes. In the first stage of the research, active and reactive powers, as well as the voltage of the reference node, were selected for 6420 periods of the mains voltage. The simulation results were compared for the classic voltage regulation system with semiconductor tap changers and the evolution algorithm based on voltage measurements from the entire MV network. A significant improvement in the quality of voltage regulation with the use of an evolutionary algorithm was demonstrated. Then, a second set of input data with increased values of reactive power was generated. The results of the evolutionary algorithm after the application of the classic, independent reactive power compensation system and two-criteria optimization were compared. It has been shown that only the two-criteria optimization algorithm keeps both |tg phi| within the acceptable range and the quality of voltage regulation is the best. The article compares different working algorithms for semiconductor tap changers.

关键词： power system voltage control control tap-changer evolution algorithm multi-criteria optimization

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Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

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APPLIED INTELLIGENCE 2020年第6期50卷 1889-1907页

作者： Nguyen Thi My Binh Huynh Thi Thanh Binh Nguyen Van Linh Yu, Shui Hanoi Univ Ind Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam Univ Technol Sydney Sydney NSW Australia

One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid evolutionary algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.

关键词： Internet of things Heterogeneous wireless multimedia sensor network Directional sensing coverage model Minimal exposure path evolution algorithm Particle swarm optimization algorithm

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DNA Epidemic Model Construction and Dynamics Optimization

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2020年第3期14卷 97-117页

作者： Zhou, Yinglian Hunan Int Econ Univ Informat Sci & Technol Changsha Peoples R China

In order to solve some complex optimization problems, the SIR-DNA algorithm was constructed based on the DNA-based SIR (susceptible-infectious-recovered) infectious disease model. Since infectious diseases attack a very small part of the individual's genes, the number of variables per treatment is small;thus, the natural dimensionality reduction of the algorithm is achieved. Based on the DNA-SIR infectious disease model, different infections can be distinguished in the pathogenesis of viruses. The mechanisms of disease transmission are described by the SIR model, and these are used to construct operators such as SS, SI, II, IR, RR, and RS, so that individuals can naturally exchange information naturally through disease transmission. The test results show that the algorithm has the characteristics of strong search ability and has a high convergence speed for solving complex optimization problems.

关键词： Bionic Optimization Epidemic Dynamics evolution algorithm Immune Genes Meta-Heuristic Search Population Intelligent Optimization Random Search SIR Epidemic Model

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An adaptive ensemble predictive strategy for multiple scale electrical energy usages forecasting

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SUSTAINABLE CITIES AND SOCIETY 2021年 66卷 102654-102654页

作者： Tian, Jing Li, Kangji Xue, Wenping Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China

Electrical energy prediction plays an important role in energy management, power plant scheduling, peak demand and grid security conflict. To deal with prediction scenarios at multiple energy and time scales, an adaptive ensemble model strategy with wider applicability is proposed. The strategy consists of three parts. Firstly, the data preprocessing part is composed of expert knowledge, recursive feature elimination (RFE) and fuzzy c-means clustering (FCM). RFE and FCM methods are used for feature identification and data clustering for different energy usage patterns. Secondly, the primary prediction part consists of a model library containing five commonly used data-driven models. To improve their prediction accuracies, key model parameters are optimized by swarm evolution algorithms (EAs). An algorithm package containing three EAs is combined with the model library. The choice of algorithm for each model depends on the comparison of accuracy in specific prediction scenarios. In the third part, a linear regression model provides the final result based on primary predictors' outputs. Its weights are also optimized by EAs from the algorithm package. To verify the performance of this strategy, four case studies are carried out representing different energy and time scales' prediction scenarios. Case A is a benchmark case from the first energy prediction competition organized by American Society of Heating Refrigerating and Air-conditioning Engineer (ASHRAE). Case B perform hourly electrical load prediction of a whole building from University of Wyoming, USA. Case C is a city scale daily electrical load forecasting (Yizheng City, Jiangsu Province, China), and in Case D, national wide monthly electrical load prediction of the United States is carried out. Results of the four cases indicate that: (1) The proposed ensemble strategy performs best in all four case studies compared with five primary predictors. Compared to the best primary predictors in four case studies, t

关键词： Electrical energy prediction Ensemble learning Hybrid data-driven model evolution algorithm Recursive feature elimination Fuzzy c-means clustering Multiple scales

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High-Pressure Phases of SnO and PbO: A Density Functional Theory Combined with an evolutionary algorithm Approach

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MATERIALS 2021年第21期14卷 6552-6552页

作者： Long Truong Nguyen Makov, Guy Ben Gurion Univ Negev Fac Engn Sci Dept Mat Engn IL-84105 Beer Sheva Israel

Tin monoxide, SnO, and its analog, lead monoxide, PbO, have the same tetragonal P4/nmm structure, shaped by nonbonding dispersion forces and lone pairs. The high-pressure phases of SnO and PbO have been explored in several experimental and theoretical studies, with conflicting results. In this study, the high-pressure structures of SnO and PbO are investigated using density functional theory calculations combined with an evolutionary algorithm to identify novel high-pressure phases. We propose that the monoclinic P2(1)/m SnO and orthorhombic Pmmn PbO phases, which are metastable at 0 GPa, are a slight rearrangement of the tetragonal P4/nmm-layered structure. These orthorhombic (and their closely related monoclinic) phases become more favored than the tetragonal phase upon compression. In particular, the transition pressures to the orthorhombic gamma-phase Pmn2(1) of SnO/PbO and the monoclinic phase P2(1)/m of SnO are found to be consistent with experimental studies. Two new high-pressure SnO/PbO polymorphs are predicted: the orthorhombic Pbcm phase of SnO and the monoclinic C-2/m of PbO. These phases are stabilized in our calculations when P > 65 GPa and P > 50 GPa, respectively. The weakening of the lone pair localization and elastic instability are the main drivers of pressure-induced phase transitions. Modulations of the SnO/PbO electronic structure due to structural transitions upon compression are also discussed.

关键词： phase transition high-pressure density-functional theory evolution algorithm

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A Test Restoration Method based on Genetic algorithm for effective fault localization in multiple-fault programs

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JOURNAL OF SYSTEMS AND SOFTWARE 2021年 172卷 110861-110861页

作者： Yan Xiaobo Liu Bin Wang Shihai Beihang Univ Sci & Technol Reliabil & Environm Engn Lab Beijing Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Automatic fault localization is essential for software engineering. However, fault localization suffers from the interactions among multiple faults. Our previous research revealed that the fault-coupling effect is responsible for the weakened fault localization performance in multiple-fault programs. On the basis of this finding, we propose a Test Case Restoration Method based on the Genetic algorithm (TRGA) to search potential coupling test cases and conduct a restoration process for eliminating the coupling effect. The major contributions of the current study are as follows: (1) the construction of a fitness function to measure the possibility of failed test cases becoming coupling test cases;(2) the development of a TRGA that searches potential coupling test cases;(3) and an evaluation of the TRGA efficiency across 14 open-source programs, three spectrum-based fault localizations, and two parallel debugging techniques. The results revealed the TRGA outperformed the original fault localization techniques in 74.28% and 78.57% of the scenarios in the best and worst cases, respectively. On average, the percentage improvement was 4.43% for the best case and 2% for the worst case. A detailed discussion of TRGA parameter settings is also provided. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Fault localization Software debugging Multiple-faults evolution algorithm Test restoration

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A Comparison of the Taguchi Method and evolutionary Optimization in Multivariate Testing

A Comparison of the Taguchi Method and Evolutionary Optimiza...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Jiang, Jingbo Legrand, Diego Severn, Robert Miikkulainen, Risto Univ Penn Philadelphia PA 19104 USA Criteo Paris France Evolv Technol San Francisco CA USA Univ Texas Austin Austin TX 78712 USA Cognizant Technol Solut San Francisco CA USA

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

Multivariate testing has recently emerged as a promising technique in web interface design. In contrast to the standard A/B testing, multivariate approach aims at evaluating a large number of values in a few key variables systematically. The Taguchi method is a practical implementation of this idea, focusing on orthogonal combinations of values. It is the current state of the art in applications such as Adobe Target. This paper evaluates an alternative method: population-based search, i.e. evolutionary optimization. Its performance is compared to that of the Taguchi method in several simulated conditions, including an orthogonal one designed to favor the Taguchi method, and two realistic conditions with dependences between variables. evolutionary optimization is found to perform significantly better especially in the realistic conditions, suggesting that it forms a good approach for web interface design and other related applications in the future.

关键词： evolution algorithm Taguchi method multivariate testing web interface design

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Dual Models to Facilitate Learning of Policy Network

Dual Models to Facilitate Learning of Policy Network

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Chinese Automation Congress (CAC)

作者： Mei, Yidong Gao, Yue Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Dept Automat Shanghai Peoples R China

ISBN: (纸本)9781728176871

Learning to control agents without the prior knowledge of its own kinematic is a challenging problem. Recently neural network architecture models can be utilized for robust nonlinear lilting. However, complex sensor inputs including RGB images and robot joints states bring difficulties to the convergence of the control policy due to large input space and complex network structure. Besides, adding temporal information can bring more perception capabilities to the agent but also increase the number of parameters for policy networks. We present a new method called DualM-Control, which exploits dual models including self model and image model. DualM-Control algorithm can compress high dimensional spatial and temporal sensor inputs into low dimension data, thus making it possible for policy network with a few thousand parameters to evolve with evolution strategy. We test the algorithm on a challenging simulation environment created on gym and the performance exceeds existing approaches.

关键词： evolution algorithm Robot control Image processing Reinforcement Learning

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Resource management in the cloud: An end-to-end Approach

Resource management in the cloud: An end-to-end Approach

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作者： Ma, Kun University of Western Cape

学位级别：博士

Cloud Computing enables users achieve ubiquitous on-demand , and convenient access to a variety of shared computing resources, such as serves network, storage ,applications and more. As a business model, Cloud Computing has been openly welcomed by users and has become one of the research hotspots in the field of information and communication technology. This is because it provides users with on-demand customization and pay-per-use resource acquisition methods.

关键词： Cloud computing evolution algorithm Fog computing Internet of things (IoT) Quality of service (Qos)

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