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Enhanced enterprise-student matching with meta-path based graph neural network

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2024年第6期36卷

作者： Li, Fu Ma, Guangsheng Chen, Feier Lyu, Qiuyun Wang, Zhen Zhang, Jian Hangzhou Dianzi Univ Student Off Cyber Ctr Hangzhou 310000 Peoples R China Hangzhou Dianzi Univ ZhuoYue Honors Coll Hangzhou 311100 Peoples R China Shanghai Natl Accounting Inst Shanghai 201702 Peoples R China Hangzhou Dianzi Univ Sch Cyberspace 11582nd StBaiyang St Hangzhou 310000 Peoples R China Hangzhou Dianzi Univ Pinghu Digital Technol Innovat Res Inst Jiaxing 314200 Peoples R China

Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between students, , enterprises and industries. . And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect fiveyear employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method.

关键词： Job recommendation Graph neural network Meta-path genetic algorithm optimization

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A numerical tool for efficient analysis and optimization of offshore wind turbine jacket substructure considering realistic boundary and loading conditions

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MARINE STRUCTURES 2024年 95卷

作者： Wang, Zhenyu Mantey, Selase Kwame Zhang, Xin Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

The jacket substructure is a critical component of the offshore wind turbine (OWT) that is the interface between the transition piece at the top and the grouted connection. This paper presents a comprehensive study on the optimization of a jacket substructure to achieve greater cost efficiency while maintain acceptable structural performance. A fast parametric finite element modelling (FEM) approach for jacket substructures was firstly proposed. The generated models took into account realistic loading conditions, including self-weight, wind load and sectiondependent wave load, and soil-pile interaction. Parametric studies were conducted afterwards to investigate the trends of the mass and response of the jacket substructure with respect to the variation of geometric and sectional parameters. optimizations of the jacket substructure were carried out using parametric optimization and numerical genetic algorithm (GA) optimization under three different optimization strategies corresponding to three groups of objective and constraint functions. The trends obtained by parametric analysis were used to guide the parameter selection in parametric optimization, while a rank-based mutation GA was established with the proposed efficient FEM embedded in as the solver to the optimization objective and constraint functions. Parametric optimization gained its advantage in computational efficiency, and the mass reduction were 6.2%, 10% and 14.8% for the three strategies respectively. GA optimization was more aggressive as the mass reductions were 16.8%, 22.3% and 34.3% for the three strategies, but was relatively more computational intense. The two proposed optimization methods and the three optimization strategies are both expected to be applied in practical engineering design of OWT jacket substructure with good optimization output and high computational efficiency.

关键词： Offshore wind turbine Jacket substructure Parametric modelling Parametric optimization genetic algorithm optimization

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Experimental investigation and optimization of delamination factors in the drilling of jute fiber-reinforced polymer biocomposites with multiple estimators

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第9-10期116卷 2885-2907页

作者： Adda, Bachir Belaadi, Ahmed Boumaaza, Messaouda Bourchak, Mostefa Univ 20 Aout 1955 Dept Genie Mecan Fac Technol Skikda Algeria Univ 20 Aout 1955 Lab LGMM Skikda Algeria Univ 8 Mai 1945 Lab LSPN Guelma Algeria Univ 8 Mai 1945 Lab Genie Civil & Hydraul LGCH Guelma Algeria King Abdulaziz Univ Aerosp Engn Dept Jeddah Saudi Arabia

Currently, the manufacture of composite structures often requires material removal operations using a cutting tool. Indeed, since biocomposites are generally materials that do not conduct electricity, electro-erosion cannot be utilized. As a result, the processes that can be used not only the unconventional method of abrasive water jet but also conventional machining, such as drilling. Delamination factors (F-d) are widely recognized for controlling the damaged area (delamination) induced by drilling in industry. As discussed in the literature, several approaches are available to evaluate and quantify the delamination surrounding a hole. In this context, the objective of this study is to compare the three F-d evaluation methods that have been most frequently used in previous investigations. To this end, three spindle and feed speeds and three Brad and Spur drills (BSD) tool diameters were selected (L-27) for drilling 155-g/m(2) density jute fabric-reinforced polyester biocomposites. The desirability function (DF) was further made to optimize the drilling parameters. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to validate the results obtained experimentally as well as to predict the behavior of the structure depending on the cutting conditions.

关键词： Drilling machining Composite delamination Statistical properties methods RSM ANN genetic algorithm optimization

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Development of a Generalized Mathematical Model for Slider-Crank Mechanism Based on Multiobjective Concurrent Engineering with Application

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第8期46卷 8037-8053页

作者： Dang, Hoang Minh Bui, Van Phuong Phung, Van Binh Thom, Do Van Van Minh, Phung Gavriushin, Sergey Sergeevich Duc, Nguyen Viet Ind Univ Ho Chi Minh City Ho Chi Minh City Vietnam Bauman Moscow State Tech Univ Moscow Russia Le Quy Don Tech Univ Hanoi Vietnam Russian Acad Sci Mech Engn Res Inst Moscow Russia Thuyloi Univ 175 Tay Son Hanoi Vietnam

A design optimization of a slider-crank mechanism (SCM) based upon multiobjective concurrent engineering is presented in this paper. The proposed generalized mathematical model includes geometric equations, kinetic-dynamic expressions, fatigue strength and stability requirements and allows to take into consideration the manufacturing capabilities available. The model also allows for describing the actual mechanism characteristics and can be used directly for the detailed design stage of SCMs, in contrast to the simplified physical models available for preliminary design. Based on this approach, the multiobjective model of SCM which includes 11 variables, seven constrained expressions and the three most important objective functions (total mass, required power and maximum dynamic reaction) was developed. The closed-form analytical expressions were established to the relationship between objective functions, constraints and variables. The correctness of the model was checked by using the finite element method. By using the developed model, the optimization design of the main transmission of a fruit/vegetable washer was carried out. The genetic algorithm optimization has yielded 73 Pareto optimal solutions, and the three most suitable solutions according to the specific manufacturing capability were determined by employing a decision-making process based on the method of successive concessions. Further, a comparative study showed that the solutions obtained by the developed model have excelled the one obtained by means of the experience-based approach. It is noteworthy that the generalized mathematical model and the problem-solving approach adopted in this work can also be used for the SCM synthesis of other mechanical systems.

关键词： Slider-crank mechanism synthesis Multiobjective optimization model Concurrent engineering Decision-making process genetic algorithm optimization Fruit vegetable washer

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A Novel Design Method for the Knee Joint of the Exoskeleton Based on the Modular Wearable Sensor

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JOURNAL OF MEDICAL DEVICES-TRANSACTIONS OF THE ASME 2023年第4期17卷 041002-041002页

作者： Cao, Jian Wang, Chang Zhang, Jianhua Li, Kexiang Zhang, Jianjun Hebei Univ Technol Sch Mech Engn Dept Mech Engn Tianjin 300401 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Dept Mech & Elect Engn Beijing 100083 Peoples R China North China Univ Technol Sch Mech & Mat Engn Dept Mech & Elect Engn Beijing 100144 Peoples R China

This paper innovatively proposes an artificial knee joint customization design scheme based on modular wearable sensors. It aims to solve the compatibility and security problem of wearable devices for different individual knee joints. The method consists of two main parts: measurement and customization. A wearable sensor with three joints is proposed and analyzed. The sensor can measure the kinematic characteristics of human knee joints to obtain the customized design parameters of artificial joints. Designed a bionic four-link knee joint, and the parameters of the connecting rod were optimized by a genetic algorithm based on the measured data. In particular, the measuring device and knee joint are designed in a modular way, and they can be used on the same platform. The modular design method can be used to customize joints for different individuals, which simplifies the difficulty of customization and effectively reduces the cost. After the modular knee joint's optimized design, this paper mainly conducted a number of comparative tests. The comparative test results of three joints show that the dynamic tracking accuracy of customized joints is 54.9% higher than that of ordinary joints and 70.5% higher than that of hinge joints. The results show that personalized customization for individuals can improve human-machine coupling performance.

关键词： custom design genetic algorithm optimization kinematics of the knee joint rehabilitation exoskeleton wearable sensors

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Optimal design of Multiresonant Layered Acoustic Metamaterials (MLAM) via a homogenization approach

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ENGINEERING STRUCTURES 2023年第1期293卷

作者： Sal-Anglada, G. Yago, D. Cante, J. Oliver, J. Roca, D. Ctr Int Metodes Numer Engn CIMNE Campus Nord UPC Ed C1C-Gran Capita S-N Barcelona 08034 Spain Univ Politecn Catalunya Barcelona Tech UPC Escola Super Enginyeries Ind Aerosp & Audiovisuals Campus Terrassa UPC C-Colom 11 Terrassa 08222 Spain Univ Politecn Catalunya BarcelonaTech UPC Escola Tecn Super Enginyers Camins Canals & Ports Campus Nord UPC Ed C1C-Jordi Girona 1 Barcelona 08034 Spain

Broadband sound attenuation at low frequency ranges (below 500 Hz) has been a challenge in the acoustics field which cannot be solved, via conventional materials, unless impractical amounts of mass are employed. Multiresonant Layered Acoustic Metamaterials (MLAM) offer exceptional attenuating properties at lower frequencies, through novel coupled resonances mechanisms, in a layered configuration that make them amenable for large-scale manufacturing. To show the potential capabilities of MLAM, a novel computational design strategy has been developed to optimize the metamaterials' performance in terms of their Sound Transmission Loss (STL). First, a multiscale homogenization framework specifically derived for MLAM allows an accurate and extremely fast evaluation of their STL response to normal-incidence acoustic waves in the frequency range of interest. Then, the MLAM design is parameterized into a set of relevant geometric features, which are optimized by means of an optimization scheme based on standard genetic algorithms combined with the homogenization model. The results demonstrate how this design strategy is a powerful tool to obtain optimal MLAM panel designs subject to constraints imposed by the application, for instance, in terms of weight or thickness of the panel, or the manufacturing process (e.g. geometric tolerances).

关键词： Acoustic metamaterials Coupled resonances MLAM Computational homogenization genetic algorithm optimization

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optimization of cathode catalyst layer of membrane electrode assembly for polymer electrolyte fuel cells using machine learning

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JOURNAL OF POWER SOURCES 2023年第1期573卷

作者： Uenishi, Toru Imoto, Rui Toyota Motor Co Ltd Adv Mat Engn Div 1200 Mishuku Susono Shizuoka 4101193 Japan Kitami Inst Technol Sch Earth Energy & Environm Engn 165 Koen Cho Kitami Hokkaido 0908507 Japan

To optimize the cathode catalyst layer of MEAs for polymer electrolyte fuel cells, we developed a data-driven model to predict the performance of MEAs by using the physical properties of the catalyst layer as a property variable. To use the physical properties of the catalyst layer as an input variable, feature extraction through image processing and measurement data profiles from electron microscopy images, and XRD, were automated to eliminate variation due to differences among analysts. Furthermore, a sensitivity analysis using a data-driven model extracted important features and revealed that diffusion in the interparticle voids within the catalyst layer and intra-particle diffusion of carbon aggregates were the key factors. These results are consistent with the findings of previous numerical and experimental approaches for cathode catalyst layers and demonstrate that data-driven modeling is a powerful tool for developing cathode catalyst layers for MEAs. Furthermore, the combination of data-driven modeling and genetic algorithm optimization techniques guides the optimization of material properties to improve the performance of membrane electrode assemblies. These results are expected to provide valuable insights into improving the performance of polymer electrolyte fuel cells, promoting their widespread use, and realizing CO2 recycling in a carbon-neutral and green manner.

关键词： Polymer electrolyte membrane fuel cell Cathode catalyst layer Machine learning Data -driven model Sensitivity analysis genetic algorithm optimization

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H7N9 avian influenza diagnosis based on a multilayer belief rule-based inference methodology

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EXPERT SYSTEMS 2023年第7期40卷 e13296-e13296页

作者： Xu, Xiaojian Gao, Yucai Xu, Xiaobin Dai, Libo Liu, Shelan Zhang, Shuo Weng, Xu China Waterborne Transport Res Inst Beijing 100088 Peoples R China Hangzhou Dianzi Univ Sch Automation Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Otolaryngol Hangzhou 310018 Zhejiang Peoples R China Zhejiang Prov Ctr Dis Control & Prevent Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Tradit Chinese Med Hangzhou 310018 Zhejiang Peoples R China

H7N9 avian influenza is a novel virus with high morbidity and mortality that threatens human health and life. Therefore, it is necessary to diagnose H7N9 avian influenza in a timely and rapid manner to prevent further transmission of the virus and greatly reduce the infection and mortality rates. This paper proposes an H7N9 avian influenza diagnostic model that is based on a multilayer belief rule-based (BRB) inference methodology by considering five typical characteristics of influenza: epidemiology, clinical manifestations, complications, characteristics of imaging tests and positive pathogen test results. Specifically, the severity of H7N9 avian influenza is gradually identified by a multilayer BRB model, and then the diagnostic model is optimized by a genetic algorithm (GA) to improve the diagnostic accuracy. Finally, the feasibility of the model is verified by fivefold cross-validation with a real clinical dataset. The performance of the proposed diagnostic model is compared with those of the BP neural network (BPNN) model and support vector machine (SVM) model, and the results show that the multilayer BRB model can achieve rapid and satisfactory diagnostic results for H7N9 avian influenza. The experiment shows that the accuracy of the BRB model for H7N9 avian influenza hierarchical diagnosis provided in this paper is 0.903, which is higher than 0.818 of the BP neural network (BPNN) modules and 0.844 of the support vector machine (SVM) models. Especially when diagnosing the suspected and confirmed degree of H7N9 disease, it is more realized satisfactory diagnostic accuracy.

关键词： belief rule-based inference genetic algorithm optimization multilayer diagnostic model

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Detection and intelligent optimization of blood glucose signals based on MXene-assisted sensors

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2024年第8期19卷

作者： Bai, Minrui Zhou, Yuxiao Deng, Shilei Hanyuan Cty Hosp Tradit Chinese Med Lab Dept Hanyuan 625302 Peoples R China Hanyuan Cty Hosp Tradit Chinese Med Orthoped Dept Hanyuan Peoples R China

Two-dimensional materials exhibit unique properties that make them ideal for electronic sensing applications, particularly in the field of biosensors. In this study, a Ti3C2Tx MXene/graphene composite was investigated for its potential use in non-invasive blood glucose detection. The strong electrostatic interaction between MXene and graphene nanosheets effectively reduces the self-stacking of both components, resulting in the formation of a porous interlayer structure with enlarged interlayer spacing. This unique structure creates an efficient nanoscale transport channel, which increases the number of active sites and shortens ion diffusion paths. Consequently, the electrochemical properties of the composite membrane are enhanced, making it a promising candidate for sensing element applications. As the human blood glucose level is primarily determined by the glucose concentration in the blood, the concentration of glucose solution serves as the foundation for noninvasive blood glucose concentration detection. In this work, a prediction model was established using the partial least squares fitting method to correlate the electrochemical fingerprinting data with the glucose aqueous solution concentration, and the reliability of the model was verified. To address the limitations of the partial least squares method, which lacks nonlinear fitting capabilities, an RBF (radial basis function) network model was employed. Finally, a genetic algorithm was utilized to perform global optimization of the training parameters of the RBF network, and the quantitative relationship between the NIR spectral data and glucose concentration was validated. The findings of this study provide a theoretical basis for the development of equipment and the improvement of measurement accuracy in the field of human noninvasive glucose detection.

关键词： Electrochemical detection MXene Graphene Partial least squares fitting genetic algorithm optimization Neural network

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optimization of torsional vibration damper of cranktrain system using a hybrid damping approach

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2021年第4期24卷 959-973页

作者： Sezgen, Hasmet Cagri Tinkir, Mustafa Karatay Univ Vocat Sch Commerce & Ind TR-42020 Konya Turkey Necmettin Erbakan Univ Dept Mech Engn TR-42090 Konya Turkey

The focus of this research is to develop the optimum design of torsional vibration damper using hybrid damping approach to decrease the torsional vibrations in the cranktrain system of internal combustion engines (ICE). For this purpose, a double mass rubber and viscous torsional vibration damper (DMRVTVD) are combined. The optimization procedure is carried out using genetic algorithm (GA) to determine the best hybrid damping performance on cranktrain system of a four stroke and four cylinder diesel engine. Accordingly, twelve degrees of freedom lumped mass mathematical model of the proposed crank train system is created. The stiffness and damping coefficients of viscous and rubber materials used in DMRV-TVD model are verified by modal test and finite element natural frequency analysis. Then, the excitation torque is calculated considering the inertia forces and gas force, and Fourier series expansion is performed to obtain the harmonics of driven torque as the input load on the relevant masses. The relative angular deflection of the front end point of the crankshaft is determined. Additionally, in order to decrease the torsional vibrations of the crankshaft, DMRV-TVD model is optimized depending on the viscous material parameters by defining the boundary conditions and objective function of the genetic algorithm. The comparative results show that the developed hybrid design of optimized DMRV-TVD reduced the torsional vibrations by 50.17% when compared to the non-optimized DMRV-TVD. This achieved reduction in the torsional vibrations is expected to increase the engine performance and its durability as well as providing a better driving comfort and fuel efficiency. (C) 2021 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Cranktrain system Torsional vibration damper Rubber and viscous material Modal test and finite element method genetic algorithm optimization

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