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

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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Calibration of a hypoplastic model using genetic algorithms

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ACTA GEOTECHNICA 2021年第7期16卷 2031-2047页

作者： Mendez, Francisco Jose Pasculli, Antonio Mendez, Miguel Alfonso Sciarra, Nicola Univ G DAnnunzio Dept Engn Geol INGEO I-66013 Chieti Italy Von Karman Inst Fluid Dynam Dept Environm & Appl Fluid Dynam B-1640 Rhode St Genese Belgium Univ G DAnnunzio INDAM Res Grp GNCS Natl Inst Adv MathNatl Grp Sci Comp Chieti Italy

This article proposes an optimization framework, based on genetic algorithms (GA), to calibrate the constitutive law of von Wolffersdorff. This constitutive law, known as Sand Hypoplasticity (SH), allows for robust and accurate modelling of the soil behaviour but requires a complex calibration involving eight parameters. The proposed optimization can automatically fit these parameters from the results of an oedometric and a triaxial drained compression test, by combining the GA with a numerical solver that integrates the SH in the test conditions. By repeating the same calibration several times, the stochastic nature of the optimizer enables the uncertainty quantification of the calibration parameters and allows studying their relative importance on the model prediction. After validating the numerical solver on the ExCalibre-Laboratory software from the SoilModels' website, the GA calibration is tested on a synthetic dataset to analyse the convergence and the statistics of the results. In particular, a correlation analysis reveals that two couples of the eight model parameters are strongly correlated. Finally, the calibration procedure is tested on the results from von Wolffersdorff, 1996, and Herle and Gudehus, 1999, on the Hochstetten sand. The model parameters identified by the GA optimization improves the matching with the experimental data and hence lead to a better calibration.

关键词： genetic algorithm optimization Hypoplasticity model calibration Nonlinear regression

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Classification of wellhead device defects based on CNN-GA-BP model

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2023年 572卷

作者： Minghui, Wei Aihua, Deng Hongjun, Chen Qiuyang, Tao Yilin, He Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China

During the long-term operation of wellhead devices, various types of defects appear due to scouring and corrosion, poor construction, and other reasons. Timely and accurate identification of wellhead device defects is an important factor in ensuring wellhead integrity. In this paper, we design a wellhead device defect classifi-cation model based on a combination of convolutional neural network (CNN) and genetic algorithm optimization-bp neural network (GA-BP) using the finite element simulation data of leakage detection and experimental data as samples. Based on the characteristics of leakage defect data with three dimensions: axial, radial and circumferential, the three-axis components of leakage defects are used instead of the color image R, G and B channels, and input to the convolutional neural network to achieve data fusion and automatic feature extraction of the three-axis components of leakage defects. In order to improve the problem that bp neural networks are prone to fall into local optima, a genetic algorithm is used for optimization to obtain optimal initial weights and thresholds. The GA-BP model is used instead of the self-contained softmax classifier in CNN to accurately classify the extracted features and achieve intelligent classification and recognition of wellhead device defects. Based on the leakage data for experimental analysis and comparison of various classification methods, the CNN-GA-BP combination model established in this paper has the highest classification accuracy, reaching 91.25%. The experimental results illustrate that the CNN-GA-BP model established in this paper has a good classification and recognition effect on various types of defects of wellhead devices.

关键词： Wellhead device Magnetic leakage detection Convolutional neural network genetic algorithm optimization bp neural network Defect classification

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Robot-aided human evacuation optimal path planning for fire drill in buildings

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JOURNAL OF BUILDING ENGINEERING 2023年 72卷

作者： Sriniketh, Konduri Le, Anh Vu Mohan, Rajesh Elara Sheu, Bing J. Tung, Vo Dinh Van Duc, Phan Vu, Minh Bui Singapore Univ Technol & Design ROAR Lab Engn Prod Dev Singapore 487372 Singapore Chang Gung Univ Coll Engn Dept Elect Engn Taoyuan 330 Taiwan Ton Duc Thang Univ Fac Elect & Elect Engn Commun & Signal Proc Res Grp Ho Chi Minh City Vietnam HUTECH Univ HUTECH Inst Engn Ho Chi Minh City Vietnam Van Lang Univ Fac Automot Engn Sch Technol 69-68 Dang Thuy Tram Ho Chi Minh City 700000 Vietnam Nguyen Tat Thanh Univ Fac Engn & Technol 300A Nguyen Tat Thanh StWard 13Dist 4 Ho Chi Minh City Vietnam

Human evacuation during an emergency in high-rise buildings is a life-risking task and has to be performed in the least possible time. With the advancements in robotics, deploying autonomous robots in those situations could ease human efforts in evacuation processes but pose challenges in finding the optimal path to guide the people toward the exits. This paper proposes a novel path planning for the autonomous mobile robot to lead evacuees to the nearest staircase exits. The proposed algorithm is modeled by the moving-target TSP (Traveling Salesman Problem) concept for humans who need to be evacuated and uses the genetic algorithm optimization framework to generate the optimal path in consideration of muti-staircases as exits. We evaluated the performance of the proposed algorithm by benchmarking it with traditional algorithms in a simulated environment. Also, we conducted controlled real-time experiments to validate the proposed method with the conventional methods. In both cases, the proposed algorithm demonstrated significantly better with the gain of 22.4% than other algorithms regarding distance traveled by the robot.

关键词： Human evacuation Robot guided evacuation Moving-target TSP Optimal path planning genetic algorithm optimization

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Artificial neural network based optimization for Ag grated D-shaped optical fiber surface plasmon resonance refractive index sensor

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OPTICS COMMUNICATIONS 2023年 534卷

作者： Dogan, Yusuf Katirci, Ramazan Erdogan, Ilhan Yartasi, Ekrem Sivas Univ Sci & Technol Dept Elect & Elect Engn TR-58000 Sivas Turkiye Sivas Univ Sci & Technol Dept Met & Mat Engn TR-58000 Sivas Turkiye Sivas Univ Sci & Technol Dept Engn Fundamental Sci TR-58000 Sivas Turkiye

This study reports the optimization of fiber optic SPR refractive index sensor parameters with the simulation of finite element method (FEM) and artificial neural network (ANN) model. To demonstrate the applicability of the algorithm, we examined an Ag-grated D-shaped fiber optic sensor configuration with 4 basic input parameters with the aim of reaching the highest sensitivity. Through the conventional optimization, the best parameter set appeared to be a 10 nm air gap distance between the gratings (a), 20 gratings (N), 50 nm residual cladding thickness (d), and 70 nm silver layer thickness (Ag_th) at the indices of 1.35 and 1.39 yielding a sensitivity of 3775 nm/RIU. A close match is found between the actual and predicted sensitivity. 199 input data obtained from FEM are used for training by Leave One Out Cross-Validation (LOOCV) approach with R-squared value of 0.98, and the trained model with R-squared value of 0.97 is implemented in the genetic algorithm. We achieved the sensitivity of 3890 nm/RIU at the predicted a, N, d, and Ag_th of 10 nm, 20, 50 nm, and 75 nm, respectively. Future studies may further improve these results by integrating other algorithms. This method may apply to different and more complex structures to observe the correlation between the parameters, cover an entire range of parameters, and get more accurate results, especially with a high number of inputs requiring less time and computing effort. The proposed method carries great potential to improve the sensing ability and bring a new perspective to the literature.

关键词： Surface plasmon resonance (SPR) Optical fiber sensors genetic algorithm optimization Artificial intelligence

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optimization of Hydraulic Fine Blanking Press Control System Based on System Identification

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PROCESSES 2023年第1期11卷 59页

作者： Shu, Yuwen Liu, Yanxiong Xu, Zhicheng Zhao, Xinhao Chen, Mingzhang Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Collaborat Innovat Ctr Automot Components Te Wuhan 430070 Peoples R China

Fine-blanking is a molding process based on the common blanking process, which obtains hydrostatic stress through blank holder reverse jacking, in order to increase material plasticity. It requires special equipment, namely a fine-blanking press, to complete the fine-blanking process. In this paper, the problem of the speed of the slide block fluctuation found in the actual use of a 12,000 kN hydraulic fine-blanking press after multi-stage pressure source optimization is studied. Firstly, the mathematical model of the motion of the slide block in the blanking stage of the hydraulic fine blanking press is established, and the accurate mathematical model in the blanking stage of the hydraulic fine-blanking press is obtained through the least square method system identification experiment. Aiming at the complex working situation of the fine-blanking press, a phased PID control strategy is creatively proposed. The optimal PID control parameters are obtained by a genetic algorithm, and established a fuzzy PID controller for the blanking stage to accurately control the movement speed of the slide block. The results show that the new control strategy is very effective in improving the movement accuracy of the slide block, effectively improving the machining accuracy and reducing the impact vibration of the hydraulic system.

关键词： fine-blanking system identification phased PID control genetic algorithm optimization adaptive fuzzy PID control

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Hybrid vibration control of the membrane antenna experiencing maneuver with cable actuators

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THIN-WALLED STRUCTURES 2023年第1期192卷

作者： Shao, Qi Lu, Yifan Shi, Chuang Yue, Honghao Lv, Liangliang Liu, Rongqiang Fang, Guangqiang Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China China Aerosp Sci & Technol Grp Co Ltd Space Struct & Mech Technol Lab Shanghai 201108 Peoples R China

This work investigates a novel hybrid vibration control approach of a membrane antenna with cable actuators considering the rigid-flexible coupling effect induced by maneuver. The dynamic model of the membrane antenna is first formulated using finite element analysis, incorporating nonlinearity and rigid-flexible coupling. Tension cables are selected as actuators due to their wide influence range and lightweight properties, without the need for additional accessories on the membrane. Based on these foundations, a hybrid control strategy is proposed, comprising two sub-controllers for the during-maneuver and after-maneuver stages. The feedforward sub-controller during maneuver is designed based on the system's transfer function and optimized using the genetic algorithm, with the purpose of minimizing shape deflection. The feedback sub-controller for postmaneuver vibration suppression is designed based on the linear quadratic regulator (LQR), with a general controller expression of cable actuators provided. Finally, numerical simulations demonstrate the robustness and efficiency of the proposed control scheme for the membrane antenna. The proposed scheme leverages the rapid responses of feedforward control and reliable suppression of feedback control, offering a new perspective on control scheme design for maneuver-induced vibration and presenting a general controller expression for cable actuators based on state space equations.

关键词： Vibration control Membrane antenna Cable-driven actuator Hybrid control genetic algorithm optimization

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Study on strengthening power generation by using LNG cold energy with multi-stream heat exchange

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APPLIED THERMAL ENGINEERING 2023年第1期234卷

作者： Zhao, Yiming Hu, Dapeng Cai, Dongxu Wang, Yingguang Liang, Yong Dalian Univ Technol Dept Chem Machinery Dalian 116012 Peoples R China PipeChina Engn Technol Innovat Co Ltd Tianjin 300450 Peoples R China

The utilization of cold energy released during the liquefied natural gas vaporization to enhance power generation is an efficient method for energy conservation and emission reduction. In this study, a novel multi-stream cascade combined cycle (MS-CCC) is proposed, which integrates multi-stream heat exchange into the power generation system. In contrast to the cycles of the previous research, MS-CCC improves the utilization efficiency of cold exergy while simultaneously recovering the cold exergy of the working fluid without the need for additional heat exchangers in the system. The thermodynamic performance and economic benefits of MS-CCC were evaluated by comparing them with traditional combined cycle (CC), parallel combined cycle (PCC) and cascade combined cycle (CCC) by genetic algorithm, and the study also investigated the influence of operating parameters and cycle structure on the performance of MS-CCC. Compared to the CC, PCC and CCC, the exergy efficiency of MS-CCC can be higher by 88.6 %, 63.1 % and 12.4 % respectively when the same working fluid is employed. Additionally, the net annual income with the optimal working fluids can be increased by 1.039, 1.036 and 0.405 M$, respectively. The maximum net annual income of MS-CCC reaches 1.225 M$ with an exergy efficiency of 17.46 % when employing the optimum working fluid group of ethane + R32. The exergy efficiency of MS-CCC can be maximized at certain condensing temperatures, and it increases with the decline of the natural gas exhaust pressure of the system. Furthermore, the removal of direct expansion has minimal effect on the system efficiency, and by adding one stage of Rankine cycle, the exergy efficiency can be increased by more than 24.4 %.

关键词： Cold energy power generation Liquefied natural gas Multi-stream heat exchange Cascade combined cycle genetic algorithm optimization

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