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Landing Gear Mechanical Network Synthesis for Improving Comfort at Landing Considering Aircraft Flexibility

JOURNAL OF AIRCRAFT 2021年第6期58卷 1242-1253页

作者： Stachiw, Terrin Khouli, Fidel Langlois, Robert G. Afagh, Fred F. Carleton Univ Mech & Aerosp Engn Ottawa ON K1S 5B6 Canada

The landing impact that occurs during aircraft touchdown results in the development of significant loads and accelerations within the airframe. Accurate knowledge of the landing loads is necessary not only for predicting the resulting stresses for design of the airframe, but also for designing strategies to mitigate the vibratory loads and improve the ride quality. Perceived passenger comfort is dependent both on the magnitude of the acceleration experienced by the passengers and on the frequency content of the vibrations. Using a flexible airframe model to better capture the loading regime and frequency response at landing impact, this study optimizes various single-port (two-terminal) passive mechanical networks that consist of an arrangement of springs, dampers, and inerters to minimize passenger discomfort and peak forces applied to the aircraft. The performance is compared with a baseline oleo-pneumatic shock absorber. First, the candidate layouts are optimized, and then the observations from this exercise are used to synthesize a mechanical network with a desired frequency response. All considered mechanical networks demonstrated the ability to control the frequency content of the input loading, thus resulting in a reduction in accelerations and an improvement in all comfort parameters used in this study over the oleo-pneumatic baseline.

关键词： Model Aircraft Aircraft Structures Frequency Response Functions Maximum Landing Weight Aircraft Operations Finite Element Modeling Federal Aviation Regulation optimization algorithm Damping Coefficient Recurrent Neural Network

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Analysis and Optimal Design of a Novel Actuator System for a Camera Module

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IEEE ACCESS 2021年 9卷 3441-3450页

作者： Kwon, Tae-Hoon Ro, Jong-Suk Chung Ang Univ Dept Elect & Elect Engn Seoul 06974 South Korea

Owing to the fourth industrial revolution and the recent trend for compact, high-performance, and multifunctional electrical devices, the demand for a small and high-performance camera module has dramatically increased. To address this, in this study, a novel, miniaturized, high-performance, low-cost multifunctional camera module with optical image stabilization (OIS) via a novel two-axis motional actuator with 1-Stage is proposed. To date, a systematic analysis and optimal design method for the actuators of camera modules has not been proposed yet. Accurate and rapid analysis and optimal design methods are proposed herein. Based on the proposed methods, it is possible to reduce the time and cost for the development of actuators for camera modules. The usefulness of the proposed actuator system for small, high performance, and multifunctional camera modules, and the proposed analysis and optimal design method were verified experimentally in this study.

关键词： Cameras Actuators Magnetic resonance imaging Lenses Force Springs Optical imaging Actuator chamfered magnet coil diameter optical image stabilization (OIS) optimization algorithm voice coil motor (VCM)

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Application Of Chimp-based ANFIS Model For Forecasting The Compressive Strength Of The Improved High-performance Concrete

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淡江理工学刊 2024年第4期27卷 2295-2306页

作者： Yan Yuan

In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp optimizer ( CO) to identify optimal values of determinative factors of Support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which could be adjusted to improve performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The outcomes were then contrasted with those found in the body of existing scientific literature. Calculation results point to the potential benefit of combining CO - SVR and CO - ANFIS study. When compared to the CO - SVR, the CO - ANFIS showed much higher R^2 and lower Root means square error values. Comparing the findings shows that the created CO - ANFIS is superior to anything that has previously been published. In conclusion, the suggested CO - ANFIS analysis might be used to determine the proposed approach for estimating the CS of HPC augmented with blast furnace slag and fly ash.

关键词： High-performance concrete Compressive strength Support vector regression Adaptive neuro-fuzzy inference system optimization algorithm

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Trimming Analysis Method of Quad Tilt Rotor Based on Aerodynamic Interference Model

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JOURNAL OF AIRCRAFT 2021年第2期58卷 253-265页

作者： Wang, Zhigang Wang, Qiannan Yu, Hongrui Duan, Dengyan Ding, Zhiwei Li, Jianbo Yangzhou Collaborat Innovat Res Inst Shenyang Aircraft Design & Res Inst Yangzhou 225000 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Natl Key Lab Rotorcraft Aeromech Nanjing 210016 Peoples R China

The quad tilt rotor (QTR) has complex aerodynamic characteristics and strong aerodynamic interference. The flight dynamic model is the basis of control. To effectively account for the aerodynamic interference between the multiple aerodynamic surfaces of the QTR unmanned aerial vehicle, a hybrid optimization trimming analysis method based on Immune algorithm/Levenberg-Marquardt (IA/LM) and lattice Boltzmann method (LBM) is proposed. The aerodynamic characteristics between the wing and the rotor calculated by the LBM are compensated into the flight dynamics model. The IA/LM algorithm is used to reduce the dependence on the initial trimming value. The effectiveness of the hybrid optimal trimming analysis method is verified through the analysis of trimming simulation results.

关键词： Tiltrotor Aircraft Aerodynamic Characteristics LBM UAV Aircraft Flight Dynamics Fuselages Helicopters CFD optimization algorithm Lift to Drag Ratio

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A systematic review on state of modeling, clinical issues, and advanced control approaches for artificial ventilator: traditional control to soft computing-based control approach

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2025年第1期13卷 25-25页

作者： Acharya, Debasis Das, Dushmanta Kumar Brainware Univ Dept Comp Sci & Engn Cyber Secur & Data Sci Kolkata India Natl Inst Technol Nagaland Dept Elect & Elect Engn Dimapur India

An artificial ventilator is crucial in medical emergencies, providing severely ill patients with sufficient oxygen to reduce their breathing effort. For critical patients, clinicians must set various parameters on the ventilator to ensure safe and comfortable ventilation. Accurate tracking of the patient's breathing profile by the ventilator is essential for synchronization, reducing the patient's workload and effort. Designing an effective ventilator controller requires proper system modeling and a clear understanding of its purpose. This paper offers a brief review of modeling, physiological and clinical issues, and state-of-the-art advanced control practices, guiding future control design for ventilators. It also presents detailed ventilator modeling and parameters from the literature, along with a comprehensive review of traditional and soft-computing-based control approaches.

关键词： Artificial ventilator Fuzzy logic controller PID controller optimization algorithm Patient-hose ventilator Piston-motor-driven ventilator

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Testing coverage criteria for optimized deep belief network with search and rescue

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JOURNAL OF BIG DATA 2021年第1期8卷 1-20页

作者： Jammalamadaka, Kiran Parveen, Nikhat Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

A new data-driven programming model is defined by the deep learning (DL) that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the data labeling and manual group. Nowadays, a lot of coverage criteria have been developed, but these criteria basically count the neurons' quantity whose activation during the implementation of a DL structure fulfilled certain properties. Also, existing criteria are not adequately fine-grained to capture delicate behaviors. This paper develops an optimized deep belief network (DBN) with a search and rescue (SAR) algorithm for testing coverage criteria. For an optimal selection of DBN structure, the SAR algorithm is introduced. The main objective is to test the DL structure using different criteria to enhance the coverage accuracy. The different coverage criteria such as KMNC, NBC, SNAC, TKNC, and TKNP are used for the testing of DBN. Using the generated test inputs, the criteria is validated and the developed criteria are capable to capture undesired behaviors in the DBN structure. The developed approach is implemented by Python platform using three standard datasets like MNIST, CIFAR-10, and ImageNet. For analysis, the developed approach is compared with the three LeNet models like LeNet-1, LeNet-4 and LeNet-5 for the MNIST dataset, the VGG-16, and ResNet-20 models for the CIFAR-10 dataset, and the VGG-19 and ResNet-50 models for the ImageNet dataset. These models are tested on the four adversarial test input generation approaches like BIM, JSMA, FGSM, and CW, and one DL testing method like DeepGauge to validate the efficiency of the suggested approach. The simulation results proved that the proposed approach obtained high coverage accuracy for each criterion on four adversarial test inputs and one DL testing method as compared to other models.

关键词： Deep learning Coverage criteria optimization algorithm Adversarial example Software testing

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Using Deep Learning and Swarm Intelligence to Achieve Personalized English-Speaking Education

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2024年第1期15卷 1页

作者： Liu, Yang Huanghe Sci & Technol Univ Zhengzhou Peoples R China

This paper presents a pioneering approach to personalized English oral education through the integration of deep learning and swarm intelligence algorithms. Leveraging deep learning techniques, our system offers precise evaluation of various aspects of spoken language, including pronunciation, fluency, and grammatical accuracy. Furthermore, we combine swarm intelligence algorithms to optimize model parameters to achieve optimal performance. We compare the proposeda* optimization algorithm based on swarm intelligence and its corresponding original algorithm for training comparison to test the effect of the proposed optimizer. Experimental results show that in most cases, the accuracy of the test set using thea* optimization algorithm based on the swarm intelligence algorithm is better than the corresponding original version, and the training results are more stable. Our experimental results demonstrate the efficacy of the proposed approach in enhancing personalized English oral education, paving the way for transformative advancements in language learning technologies.

关键词： ACO Deep Learning Multi-Feature Neural Networks optimization algorithm Personalized English Speaking Education Swarm Intelligence

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Fake Profile Detection Using Logistic Regression and Gradient Descent algorithm on Online Social Networks

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第1期11卷

作者： Raja, Eswara Venkata Sai Aditya, Bhrugumalla L. V. S. Mohanty, Sachi Nandan VIT AP Univ Sch Comp Sci & Engn Scope Amaravati Andhra Pradesh India

One of the most challenging issues on online social networks is identifying spam accounts. The concern stems from the fact that these personas pose a significant threat, as they may engage in harmful activities against other users, extending beyond mere annoyance or low-quality advertisements. The demand for accurate and effective spam detection algorithms for online social networks is increasing due to this risk. To address the problem of spam detection in online social networks, this research proposes a hybrid machine learning model based on logistic regression and a contemporary metaheuristic method called the Gradient Descent algorithm. The proposed approach automates spammer identification and provides insights into the factors that have the greatest impact on the detection process. Additionally, the model is evaluated and implemented on multiple datasets, and the experiments and findings demonstrate that the proposed model outperforms many other algorithms in terms of accuracy and delivers robust results in terms of precision, recall, f-measure, and AUC. It also aids in identifying the factors that influence detection the most.

关键词： social media spammers detection logistic regression optimization algorithm gradient descent accuracy precision

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Specific Error Mode Correction for Deformable Reflector by Structure-Actuator Simultaneous optimization

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第4期57卷 728-739页

作者： Satou, Yasutaka Furuya, Hiroshi Kogiso, Nozomu Japan Aerosp Explorat Agcy Dept Space Flight Syst Inst Space & Astronaut Sci Sagamihara Kanagawa 2528510 Japan Tokyo Inst Technol Dept Mech Engn Yokohama Kanagawa 2268502 Japan Osaka Prefecture Univ Dept Aerosp Engn Sakai Osaka 5998531 Japan

This paper proposes an efficient shape correction method for high-precision deformable reflectors. To this end, the specific error modes that affect the antenna performance are selectively corrected by simultaneous optimization of the structure and the actuator locations. First, a method to correct the specific error modes by design optimization is formulated, where a residual surface error vector is introduced to evaluate the contribution of each error mode. A constraint parameter for the actuator location, which helps thea* optimization algorithm find the global minimum, is proposed to improve the optimization performance. The results of the optimization indicated that the proposed constraint parameter as well as the simultaneous optimization of the structure and actuator distribution improve optimization performance. The optimized deformable reflector with the shape control actuators is then manufactured for experimental verification. The experimental results are in good agreement with the numerical results obtained in the design phase, thereby confirming the feasibility of the optimum design. Thus, the effectiveness of the proposed shape correction method is indicated.

关键词： Piezo Actuators Antenna Design Shape Control optimization algorithm Finite Element Analysis Particle Swarm optimization Electric Field Intensity Space Environment Numerical Simulation Institute of Space and Astronautical Science

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Quantitative Recognition of Granary Heat Source Using Quantum-Behaved Particle Swarm optimization Method

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2020年第3期34卷 498-503页

作者： Wang, Kun Liu, Juan Kong, Songtao Cai, Ping Xu, Hongmei Wan, Shibin Chongqing Univ Sci & Technol Coll Mech & Power Engn Chongqing 401331 Peoples R China Chongqing Med & Pharmaceut Coll Higher Educ Res Inst Chongqing 401331 Peoples R China Chongqing Univ Technol Sch Elect & Elect Engn Chongqing 400054 Peoples R China

On the basis of the inverse heat transfer methodology, the quantitative recognition of heat source parameters (location and intensity) in granary is studied using the finite element method and the quantum-behaved particle swarm optimization (QPSO) method. For the quantitative recognition problem of heat source parameters, a recognition system based on QPSO is established: the fitness function of QPSO is constructed according to the offsets between the measuring temperatures and calculating temperatures in granary, and the heat source parameters are obtained by optimizing the fitness function. The influence of heat source intensity, number of measuring points, measurement error, and initial guess value on the recognition results is studied through experiments, and compared with Levenberg-Marquardt method. The results show that the QPSO method in this paper can obviously weaken the influence of the number of measurement points and the initial guess on the recognition results and is able to ameliorate the anti-interference ability to the measurement errors.

关键词： Particle Swarm optimization Convective Heat Transfer Coefficient FEM Inverse Heat Conduction Thermal Conductivity Conjugate Gradient Method Artificial Neural Network Nondestructive Testing optimization algorithm Electron Beam Welding

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