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Optimization of the driver's seat belt and injury biomechanical analysis in real-world minivan small offset impact accident scenarios

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 10卷 965206页

作者： Yang, Xiuju Shi, Jingjing Fu, Qianying Pu, Shanshan Lian, Chunxiao Li, Kui Yin, Zhiyong Liu, Shengxiong Wang, Guixue Chongqing Univ State & Local Joint Engn Lab Vasc Implants Key Lab Biorheol Sci & Technol Coll BioengnMinist Educ Chongqing Peoples R China Chongqing Univ Technol Dept Biomed Engn Coll Pharm & Bioengn Chongqing Peoples R China Army Med Univ Chongqing Key Lab Vehicle Crash Bioimpact & Traff Dept 4 Inst Surg ResDaping Hosp Chongqing Peoples R China China Automot Engn Res Inst Co Ltd Chongqing Peoples R China

To minimize injuries and protect the safety of the driver in minivan small offset collisions, an optimized pre-tensioned force-limiting seat belt was proposed herein. An accident with detailed information, such as medical reports, vehicle inspection reports, and accident scene photographs, was reconstructed using HyperMesh software. The effectiveness of both the accident model and the pre-tensioned force-limiting seat belt was evaluated. To obtain the optimal seat belt parameters for driver protection, first, force-limiting A, pre-tensioned force B, and pre-tensioned time C factors were selected in designing an orthogonal test with different factor levels. The influence laws of each factor on the injury biomechanical characteristics of the driver were analyzed via the direct analysis method. Moreover, each kind of critical injury value of the human body was synthesized, and the radial basis function surrogate model was constructed. The three seat belt parameters were optimized using the NSGA-II multi-objective genetic algorithm. The results showed that the optimal balance variable parameter of the seat belt was 4751.618 N-2451.839 N-17.554 ms (A-B-C). Finally, the optimal scheme was verified in a system simulating a minivan small offset collision. The results showed that after optimization, the skull von Mises stress was reduced by 36.9%, and the stress of the cervical vertebra cortical bone and cancellous bone decreased by 29.1% and 30.8%, respectively. In addition, the strains of the ribs and lungs decreased by 31.2% and 30.7%, respectively.

关键词： small offset collision accident reconstruction driver injury pre-tensioned force-limiting seat belt multi-objective genetic algorithm biomechanics analysis genetic algorithm

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Optimisation of Francis turbine draft tube using response surface model

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PROGRESS IN COMPUTATIONAL FLUID DYNAMICS 2022年第6期22卷 343-359页

作者： Abbas, Ali Kumar, Arun Flovel Energy Pvt Ltd Faridabad 121003 Haryana India IIT Roorkee Hydro & Renewable Energy Dept Rorrkee 247667 Haridwar India

The draft tube design of low and medium head hydraulic turbines plays an important role in determining the efficiency and power output. Proposed Francis draft tube geometry was optimised with two different optimisation goals, i.e.: 1) rehabilitation;2) a new design. Using the design of experiments approach, 81 design points were generated for both cases separately and response surface methodology (RSM) was used for optimisation. Two objective functions, i.e., pressure recovery (C-p) and loss factor (zeta) were considered in the optimisation process. A multi-objective genetic algorithm coupled with RSM was adapted to optimise the proposed draft tube geometry for achieving desired optimisation goal. For Case 1, optimum values of C-p and zeta value were found as 0.8290 and 0.1158, respectively. The value of pressure recovery was enhanced by 7.0% at the best efficiency point. For Case 2, an optimum value of C-p was found as 0.851 which is 10.0% higher compared with the performance of the existing draft tube.

关键词： Francis turbine draft tube computational fluid dynamics CFD response surface methodology RSM design of experiment DOE multi-objective genetic algorithm MOGA

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Reduction of Insolvency Risk and Total Costs in Banking Sector using Partners Selection Approach with genetic algorithm and multilayer Perceptron Neural Network

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INTERNATIONAL JOURNAL OF ENGINEERING 2024年第8期37卷 1667-1690页

作者： Azarbad, M. Shojaie, A. A. Abdi, F. Ghezavati, V. R. Khalili-Damghani, K. Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran

Banking, a vital economic pillar worldwide, thrives with effective management, aiding economic growth. Mitigating risks and addressing cost control are key challenges. Prioritizing strategies to enhance performance in both risk management and cost efficiency is crucial for the banking sector's success and economic stability. One approach is to select partners in such a way that the risk of bank insolvency and total costs are reduced, and the capital adequacy of the bank is increased. So, in this work, we first created a mathematical model to achieve the above goals in the field of banking using the approach of selecting partners. In this model, three objective functions are considered for the optimal selection of partners, two of which aim to minimize risk and cost, and the last objective is to maximize capital adequacy. To solve this multi -objective model, we implemented an integrated intelligent system. A combination of a multiobjective genetic algorithm and a neural network was used in this system. A multilayer perceptron neural network is used to calculate the nondeterministic parameters based on the data from different periods. The proposed method was evaluated using a numerical example in MATLAB software. The obtained results and their comparison with one of the classic algorithms show the superiority and reliability of this intelligent system. Using this system, the optimal partners can be selected to achieve the set goals. The most important factors in the field of risk have been identified. Then, a meta -heuristic multiobjective algorithm (NSGA-II) along with an intelligent neural network system has been used to optimally select partners. According to this intelligent system, a suitable methodology is presented along with the optimization algorithm.

关键词： Banking Banks Insolvency Risk Selecting Partners multi-objective genetic algorithm multilayer Perceptron Neural Network

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Improved multiobjective genetic algorithm for Partitioning Distributed Photovoltaic Clusters: Balancing Spatial Distance and Power Similarity

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Journal of Computing and Information Technology 2024年第4期32卷 251-264页

作者： Chen, Yansen Cheng, Kai Li, Zhuohuan Pan, Shixian Hu, Xudong China Southern Grid Digital Grid Research Institute Co. Ltd. Guangzhou China China Southern Power Grid Artificial Intelligence Technology Co. Ltd. Guangzhou China

The prediction of power output from photovoltaic generation clusters is crucial for optimizing the dispatch of regional photovoltaic generation. Enhancing the accuracy of power prediction for photovoltaic power plant clusters requires the segmentation of distributed photovoltaic systems into clusters. This paper proposes a method for partitioning distributed photovol-taic clusters using a multiobjective genetic algorithm NSGA2, with spatial distance modularity and electricity similarity as optimization objectives to determine the optimal cluster partitioning scheme. The numerical examples and experimental results of the case analysis demonstrate a significant improvement in the conver-gence speed of the prediction system when employing the clustering partitioning method. This cluster segmentation algorithm significantly reduces the complexity and investment cost of the prediction system. © 2024, University of Zagreb Faculty of Electrical Engineering and Computing. All rights reserved.

关键词： cluster partitioning distributed photovoltaic multi-objective genetic algorithm

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Evolutionary Optimization of Residual Neural Network Architectures for Modulation Classification

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第2期8卷 542-556页

作者： Perenda, Erma Rajendran, Sreeraj Bovet, Gerome Pollin, Sofie Zheleva, Mariya Katholieke Univ Leuven ESAT WaveCore Leuven Belgium EluciDATA Lab Sirris Belgium Armasuisse Sci & Technol Swiss Dept Def CH-3602 Thun Switzerland SUNY Albany Dept Comp Sci Albany NY 12222 USA

Automatic modulation classification receives significant interest in the context of current and future wireless communication systems. Deep learning emerged as a powerful tool for modulation classification, as it allows for joint discriminative features learning and signal classification. However, the optimization of deep neural network architectures for modulation classification is a manual and time-consuming process that requires profound domain knowledge and much effort. Most state-of-the-art solutions focus mainly on classification accuracy, while optimization of network complexity is neglected. This paper presents a novel bi-objective memetic algorithm, BO-NSMA, to search optimal deep neural network architectures for modulation classification to maximize classification accuracy and minimize network complexity. The experiments show that BO-NSMA, with an initial population of six individuals and only ten generations, finds a deep neural network architecture that outperforms all human-crafted architectures. Furthermore, BO-NSMA discovered the first low-complexity Convolutional neural network architecture, which achieves slightly better performance than costly Recurrent neural network architectures, allowing a 2.9-fold reduction in network complexity with 1.43% performance improvement. Compared to counterparts from network architecture search, BO-NSMA finds the best architecture, which achieves up to 18.73% accuracy gain and up to an 82-fold reduction in network complexity. The results are validated using the Wilcoxon signed-rank test.

关键词： Modulation classification deep learning network architecture search multi-objective genetic algorithm

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The Optimal Location algorithm of SAPFs in Distribution Network Based on Harmonic State Estimation 8

The Optimal Location Algorithm of SAPFs in Distribution Netw...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Yin, Xiaoqing Yi, Hao Yang, Zebin Zhuo, Fang Zhang, Yao Wei, Wei Xi'An Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipmen Xi'an China

ISBN: (纸本)9798350328813

With the development of high-proportion renewable energy systems, the number of distributed nonlinear loads has been increasing, which causes a growing harmonic problem in the distribution network. In this situation, the traditional point-to-point harmonic compensation method is inefficient, since it needs to configure plenty of control equipment. This article presents a multi-objective optimal location algorithm of multiple shunt active power filters (SAPFs), considering that some harmonic sources are unknown in the distribution network. Firstly, based on the measurement equations, the least-square method is used to estimate the unmeasured power quality data, so as to improve the accuracy of the multi-bus harmonic mitigation model. Secondly, a multi-objective genetic algorithm is proposed for determining the Pareto-optimal set of the equipment layout to suppress multi-bus harmonic distortion. Finally, a standard IEEE-18 buses simulation model with 6 nonlinear loads is built to verify the proposed optimal location strategy. The simulation results show that the proposed algorithm can ensure that harmonic distortion indexes of each bus meet the recommended standards with few devices and total capacity. © 2023 IEEE.

关键词： harmonic comprehensive suppression harmonic state estimation multi-objective genetic algorithm optimal configuration

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Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期158卷 106799-106799页

作者： Reis, Diogo R. Santos, Bruno C. Bleicher, Lucas Zarate, Luis E. Nobre, Cristiane N. Pontifical Catholic Univ Minas Gerais PUC Minas 500 Dom Jose Gaspar St Bldg 20 BR-30535901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Ave Pres Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and has been the focus of several works in bioinformatics. In this work, we investigated the characteristics of proteins, representing the primary, secondary, tertiary, and quaternary structures of the protein, that improve the model's quality by applying dimensionality reduction techniques and using the Support Vector Machine classifier for predicting the enzymes' classes. During the investigation, two approaches were evaluated: feature extraction/transformation, which was performed using the statistical technique Factor Analysis, and feature selection methods. For feature selection, we proposed an approach based on a genetic algorithm to face the optimization conflict between the simplicity and reliability of an ideal representation of the characteristics of the enzymes and also compared and employed other methods for this purpose. The best result was accomplished using a feature subset generated by our implementation of a multi-objective genetic algorithm enriched with features that this work identified as relevant to represent the enzymes. This subset representation reduced the dataset by about 87% and reached 85.78% of F-measure performance, improving the overall quality of the model classification. In addition, we verified in this work a subset addressed with only 28 features out of a total of 424 that reached a performance above 80% of F-measure for four of the six evaluated classes, showing that satisfactory classification performance can be achieved with a reduced number of enzymes's characteristics. The datasets and implementations are openly available.

关键词： Bioinformatics Proteins Enzyme class prediction Support vector machine Dimensionality reduction Feature selection multi-objective genetic algorithm

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Simulation-oriented model reuse in cyber-physical systems: A method based on constrained directed graph

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 91-110页

作者： Wenzheng Liu Heming Zhang Chao Tang Shuangfei Wu Hongguang Zhu National CIMS Engineering Research Center Tsinghua University Beijing 100084P.R.China Gridsum Holding Inc.Beijing 100086P.R.China Center of Intelligent Control and Telescience Tsinghua Shenzhen International Graduate School Shenzhen 518055 P.R.China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing 100084 P.R.China

Modeling and Simulation of Cyber-Physical Systems(MSCPS)is demanding in terms of immediate response to dynamic and complex changes of ***-oriented model reuse can be used to build a whole CPS model by reusing developed models in a new sim-ulation application,which avoid repeated modeling and thus reduce the redevelopment of *** composition,one of the important methods,enables model reuse by selecting and adopting diversified integration solutions of simulation components to meet the requirements of simulation application *** this paper,a real-time model integration approach for global CPS modeling is proposed,which reuses devel-oped submodels by compositing submodel ***,a constrained directed graph of submodels for the whole system which can meet the simulation requirements is constructed by reverse *** properties,including co-simulation distance between submodel nodes,reuse benefit and simulation performance of model nodes,are *** on the properties,the model-integrated solution for the whole CPS simulation is retrieved throughout the model constrained digraph by the genetic Algo-rithm(GA).In the experiment,the proposed method is applied to a typical model integrated computing scenario containing multiple model-integration solutions,among which the Pareto optimal solutions are *** show that the effectiveness of the model integration method proposed in this paper is verified.

关键词： CPS model reuse model composition co-simulation distance multi-objective genetic algorithm

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Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年第PartB期302卷 114073-114073页

作者： Ma, Changhui Xie, Yaowen Duan, Hanming Wang, Xiaoyun Bie, Qiang Guo, Zecheng He, Lei Qin, Wenhua Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Gansu Peoples R China China West Normal Univ Sch Geog Sci Nanchong 637002 Sichuan Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Gansu Peoples R China

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel;The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate t

关键词： Grassland utilization intensity Spatial quantification method Qinghai-Tibetan plateau Disturbance Artificial neural networks multi-objective genetic algorithm

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Prioritizing Natural Language Test Cases Based on Highly-Used Game Features 2023

Prioritizing Natural Language Test Cases Based on Highly-Use...

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31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Viggiato, Markos Paas, Dale Bezemer, Cor-Paul Univ Alberta Edmonton AB Canada Prodigy Educ Oakville ON Canada

ISBN: (纸本)9798400703270

Software testing is still a manual activity in many industries, such as the gaming industry. But manually executing tests becomes impractical as the system grows and resources are restricted, mainly in a scenario with short release cycles. Test case prioritization is a commonly used technique to optimize the test execution. However, most prioritization approaches do not work for manual test cases as they require source code information or test execution history, which is often not available in a manual testing scenario. In this paper, we propose a prioritization approach for manual test cases written in natural language based on the tested application features (in particular, highly-used application features). Our approach consists of (1) identifying the tested features from natural language test cases (with zero-shot classification techniques) and (2) prioritizing test cases based on the features that they test. We leveraged the NSGA-II genetic algorithm for the multi-objective optimization of the test case ordering to maximize the coverage of highly-used features while minimizing the cumulative execution time. Our findings show that we can successfully identify the application features covered by test cases using an ensemble of pre-trained models with strong zero-shot capabilities (an F-score of 76.1%). Also, our prioritization approaches can find test case orderings that cover highly-used application features early in the test execution while keeping the time required to execute test cases short. QA engineers can use our approach to focus the test execution on test cases that cover features that are relevant to users.

关键词： Test case prioritization multi-objective genetic algorithm Zeroshot classification Feature usage

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