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Academic literature on the topic 'Virtual models, optimal control, cycling'
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Journal articles on the topic "Virtual models, optimal control, cycling"
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Dembia, Christopher L., Nicholas A. Bianco, Antoine Falisse, Jennifer L. Hicks, and Scott L. Delp. "OpenSim Moco: Musculoskeletal optimal control." PLOS Computational Biology 16, no. 12 (December 28, 2020): e1008493. http://dx.doi.org/10.1371/journal.pcbi.1008493.
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Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves—which typically requires extensive technical expertise—and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.
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Никитин, Д. А., Д. Е. Цыбин, А. М. Хафизов, С. А. Мишин, А. Ф. Тайчинов, and Э. М. Сафин. "ADAPTIVE VIRTUAL ANALYZER IN ADVANCED PROCESS CONTROL." Южно-Сибирский научный вестник, no. 2(36) (April 30, 2021): 149–57. http://dx.doi.org/10.25699/sssb.2021.36.2.004.
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В данной статье рассматривается разработка адаптивного виртуальногоанализаторов (ВА) показателей качества для проекта системы усовершенствованного управления Advanced Process Control (СУУТП, далее «APC»). Построение моделей ВА произведено с помощью регрессионных методов по параметрам, влияющих в наибольшей степени на выброс монооксида углерода, содержание кислорода в дымовых газах, температуры змеевика. В качестве метода оценки моделей предложен векторный критерий, определяющий значения ширины окна ВА с наиболее оптимальными значениями коэффициента детерминации и среднеквадратичной ошибкой. Интеграция моделей ВА в систему управления обеспечивает оптимизацию параметров работы с целью улучшения качественных и количественных показателей производимой продукции, а также позволяет существенно снизить затраты в области оборудования поточной аналитики. This article discusses the development of adaptive virtual analyzers (VA) of quality indicators for the project of the advanced control system Advanced Process Control (SUUTP, hereinafter referred to as "APC"). The construction of VA models was carried out using regression methods for the parameters that affect to the greatest extent the emission of carbon monoxide, the oxygen content in the flue gases, and the coil temperature. As a method for evaluating models, a vector criterion is proposed that determines the values of the VA window width with the most optimal values of the determination coefficient and the root-mean-square error. Integration of VA models into the control system ensures optimization of operating parameters in order to improve the qualitative and quantitative indicators of manufactured products, as well as significantly reduce costs in the field of flow analytics equipment.
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Feng, Jinfeng. "Optimal Control Strategy Model of Marketing Management Based on Consumer Psychology." Mathematical Problems in Engineering 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/8689244.
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This paper uses an optimal control strategy approach to conduct an in-depth study and analysis of consumer psychology and to design marketing management in this way. The process simulation enhances consumer participation in virtual CSR co-creation by enhancing task meaning perceptions, and the outcome simulation weakens consumer participation in virtual CSR co-creation by decreasing task meaning perceptions. Second, the proximity of the target distance positively moderates the relationship between psychological stimulation and task meaning perceptions, with process simulation having a stronger effect on task meaning perceptions at closer target distances than at longer target distances; the proximity of the target distance positively moderates the relationship between psychological simulation and consumer engagement in virtual CSR co-creation, with process simulation having a stronger effect on consumer engagement in virtual CSR co-creation at closer target distances than at longer target distances. The effect of simulation on consumers’ participation in virtual CSR co-creation is stronger at a closer target distance than at a farther target distance. A multi-input and multioutput block diagram structure is proposed in the frequency domain to describe the characteristics of the multisubject system with transfer functions. Males and females accounted for 58.6% and 41.4% of the total sample population, respectively. The entire multisubject system is decomposed according to matrix theory, and the consistency problem of the multisubject system is converted into the stability problem of multiple subsystems. The global stability domain of the distributed PID controller is obtained by finding the stability domain of the PID controller for each subsystem after decomposition and taking the intersection set. The product green preferences of consumers are incorporated into the demand model, and the correlation between the inventory strategy of products, green inputs, and the manufacturer’s confident preferences is investigated. The article considers two models of centralized inventory management and decentralized inventory management for manufacturers and conducts a cross-sectional comparison. The article finds through numerical experiments that the manufacturer’s assertive behavior in most cases makes its gains suffer but is often beneficial for the interests of retailers, overall supply chain efficiency, and environmental friendliness.
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Zeng, Jinhui, Yao Rao, Zheng Lan, Dong He, Fan Xiao, and Bei Liu. "Multi-Objective Unified Optimal Control Strategy for DAB Converters with Triple-Phase-Shift Control." Energies 14, no. 20 (October 9, 2021): 6444. http://dx.doi.org/10.3390/en14206444.
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To solve the problems of large current stress, difficult soft-switching of all switches, and slow dynamic response of dual active bridge converters, a multi-objective unified optimal control strategy based on triple-phase-shift control was proposed. The forward power flow global modes of triple-phase-shift control were analyzed, and three high-efficiency modes were selected to establish the analytical models of current stress and soft-switching. Combined with these models, the optimal solutions in different modes were derived by using the cost function-optimization equation to overcome the limitation of the Lagrange multiplier method, such that the DAB converter achieved the minimum current stress, and all switches operated in the soft-switching state over the entire power range. At the same time, the virtual power component was introduced in the phase-shift ratio combination, which improved the dynamic response of output voltage under the input voltage or load steps changed by power control. The theoretical analysis and experimental results show that the proposed control strategy can optimize the performance of the DAB converter from three aspects, such as current stress, soft-switching, and dynamic response, which achieves multi-objective optimization of the steady-state and dynamic performance of DAB converters.
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Kuutti, Sampo, Richard Bowden, and Saber Fallah. "Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages." Sensors 21, no. 6 (March 13, 2021): 2032. http://dx.doi.org/10.3390/s21062032.
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The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement learning based approach to autonomous vehicle longitudinal control, where the rule-based safety cages provide enhanced safety for the vehicle as well as weak supervision to the reinforcement learning agent. By guiding the agent to meaningful states and actions, this weak supervision improves the convergence during training and enhances the safety of the final trained policy. This rule-based supervisory controller has the further advantage of being fully interpretable, thereby enabling traditional validation and verification approaches to ensure the safety of the vehicle. We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance. Additionally, we show that when the model parameters are constrained or sub-optimal, the safety cages can enable a model to learn a safe driving policy even when the model could not be trained to drive through reinforcement learning alone.
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Wang, Qi, Ming Chai, Hongjie Liu, and Tao Tang. "Optimized Control of Virtual Coupling at Junctions: A Cooperative Game-Based Approach." Actuators 10, no. 9 (August 27, 2021): 207. http://dx.doi.org/10.3390/act10090207.
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Recently, virtual coupling has aroused increasing interest in regard to achieving flexible and on-demand train operations. However, one of the main challenges in increasing the throughput of a train network is to couple trains quickly at junctions. Pre-programmed train operation strategies cause trains to decelerate or stop at junctions. Such strategies can reduce the coupling efficiency or even cause trains to fail to reach coupled status. To fill this critical gap, this paper proposes a cooperative game model to represent train coupling at junctions and adopts the Shapley theorem to solve the formulated game. Due to the discrete and high-dimensional characteristics of the model, the optimal solution method is non-convex and is difficult to solve in a reasonable amount of time. To find optimal operation strategies for large-scale models in a reasonable amount of time, we propose an improved particle swarm optimization algorithm by introducing self-adaptive parameters and a mutation method. This paper compares the strategy for train coupling at junctions generated by the proposed method with two naive strategies and unimproved particle swarm optimization. The results show that the operation time was reduced by using the proposed cooperative game-based optimization approach.
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Sands, Timothy. "Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems." Sensors 21, no. 13 (July 5, 2021): 4603. http://dx.doi.org/10.3390/s21134603.
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To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters emphasizing deterministic options rather than stochastic ones. The computational model is formulated using Pontryagin’s treatment of Hamiltonian systems resulting in optimal and near-optimal results dependent upon the algorithm option chosen. Feedback is proposed to re-initialize the initial values of a reformulated two-point boundary value problem rather than using state feedback to form errors that are corrected by tuned estimators. Four algorithm options are proposed with two optional branches, and all of these are compared to three manifestations of classical estimation methods including linear-quadratic optimal. Over ten-thousand simulations were run to evaluate each proposed method’s vulnerability to variations in plant parameters amidst typically noisy state and rate sensors. The proposed methods achieved 69–72% improved state estimation, 29–33% improved rate improvement, while simultaneously achieving mathematically minimal costs of utilization in guidance, navigation, and control decision criteria. The next stage of research is indicated throughout the manuscript: investigation of the proposed methods’ efficacy amidst unknown wave disturbances.
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Alexandru, Cătălin. "Modeling and Simulation in Virtual Prototyping Environment of a Photovoltaic Tracking System." Applied Mechanics and Materials 436 (October 2013): 100–107. http://dx.doi.org/10.4028/www.scientific.net/amm.436.100.
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The paper presents the modeling and simulation of the mechatronic tracking system used for a photovoltaic module. The mechanical model of the sun tracker is developed by using the MBS environment ADAMS, while the DFC software EASY5 is used for the control system design. The study is approached in concurrent engineering concept, integrating the mechanical and control models at the virtual prototype level. The main task of the design refers to the energetic efficiency, by maximizing the energetic gain (i.e. the solar input), and minimizing the energy consumption for performing the tracking. The design of the motion law is focused on determining the optimal actuating times, considering the energetic efficiency as design objective for the optimization.
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Budia, I., A. Alvarez-Arenas, T. E. Woolley, G. F. Calvo, and J. Belmonte-Beitia. "Radiation protraction schedules for low-grade gliomas: a comparison between different mathematical models." Journal of The Royal Society Interface 16, no. 161 (December 2019): 20190665. http://dx.doi.org/10.1098/rsif.2019.0665.
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We optimize radiotherapy (RT) administration strategies for treating low-grade gliomas. Specifically, we consider different tumour growth laws, both with and without spatial effects. In each scenario, we find the optimal treatment in the sense of maximizing the overall survival time of a virtual low-grade glioma patient, whose tumour progresses according to the examined growth laws. We discover that an extreme protraction therapeutic strategy, which amounts to substantially extending the time interval between RT sessions, may lead to better tumour control. The clinical implications of our results are also presented.
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Sefercik, U. G., T. Kavzoglu, M. Nazar, C. Atalay, and M. Madak. "UAV-BASED 3D VIRTUAL TOUR CREATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 493–99. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-493-2021.
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Abstract. Lately, improvements in game engines have increased the interest in virtual reality (VR) technologies, that engages users with an artificial environment, and have led to the adoption of VR systems to display geospatial data. Because of the ongoing COVID-19 pandemic, and thus the necessity to stay at home, VR tours became very popular. In this paper, we tried to create a three-dimensional (3D) virtual tour for Gebze Technical University (GTU) Southern Campus by transferring high-resolution unmanned air vehicle (UAV) data into a virtual domain. UAV data is preferred in various applications because of its high spatial resolution, low cost and fast processing time. In this application, the study area was captured from different modes and altitudes of UAV flights with a minimum ground sampling distance (GSD) of 2.18 cm using a 20 MP digital camera. The UAV data was processed in Structure from Motion (SfM) based photogrammetric evaluation software Agisoft Metashape and high-quality 3D textured mesh models were generated. Image orientation was completed using an optimal number of ground control points (GCPs), and the geometric accuracy was calculated as ±8 mm (~0.4 pixels). To create the VR tour, UAV-based mesh models were transferred into the Unity game engine and optimization processes were carried out by applying occlusion culling and space subdivision algorithms. To improve the visualization, 3D object models such as trees, lighting poles and arbours were positioned on VR. Finally, textual metadata about buildings and a player with a first-person camera were added for an informative VR experience.
Conference papers on the topic "Virtual models, optimal control, cycling"
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Rothenberger, Michael J., Joel Anstrom, Sean Brennan, and Hosam K. Fathy. "Maximizing Parameter Identifiability of an Equivalent-Circuit Battery Model Using Optimal Periodic Input Shaping." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6272.
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This paper shapes the periodic cycling of a lithium-ion battery to maximize the battery’s parameter identifiability. The paper is motivated by the need for faster and more accurate lithium-ion battery diagnostics, especially for transportation. Poor battery parameter identifiability makes diagnostics challenging. The existing literature addresses this challenge by using Fisher information to quantify battery parameter identifiability, and showing that test trajectory optimization can improve identifiability. One limitation is this literature’s focus on offline estimation of battery model parameters from multi-cell laboratory cycling tests. This paper is motivated, in contrast, by online health estimation for a target battery or cell. The paper examines this “targeted estimation” problem for both linear and nonlinear second-order equivalent-circuit battery models. The simplicity of these models leads to analytic optimal solutions in the linear case, providing insights to guide the setup of the optimization problem for the nonlinear case. Parameter estimation accuracy improves significantly as a result of this optimization. The paper demonstrates this improvement for multiple electrified vehicle configurations.
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Huang, Zhizhong, Yiqun Pan, and Gang Wu. "Emulation-Based Optimal Control of Chiller Plants." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-37151.
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Chiller plants of HVAC systems usually operate in part-load conditions during most time of a year. Energy efficiency (i.e. energy input ratio, EIR) of chiller plant components such as chillers, pumps, and cooling towers are the functions of part-load ratio (PLR) and other variables. However, different chiller plant components have different functions of EIR to PLR and don’t reach their respective peaks at the same part-load ratio. Based on this fact, there are possibilities that overall efficiency of a chiller plant can be improved to and maintained at the maximum via optimal control of operating numbers and set-points of various plant components at various part-load ratios. An emulation-based optimal control strategy for chiller plants is introduced in the paper. The main idea of this control strategy is to set up a virtual chiller plant as a mirror of a real system. The virtual system is composed of mathematical models that are obtained through theoretical derivation, numerical calculation or off-line test. These models can emulate energy performances of the physical components in the real system. An optimization algorithm is first run on the virtual system to search for an optimal combination of the operating number and set-points of various components to achieve the highest overall efficiency of a chiller plant. An objective function of the optimization algorithm is the overall efficiency of a chiller plant, having real-time cooling load and meteorological data as inputs, and energy and mass balance and component capacities and restrictions as constraint conditions. Once an optimal combination is identified, it will be used to control the real system operation. A mockup system has been installed and operated in a 50,000 m2 office building in Shanghai, China in order to test and verify the optimal control strategy. Preliminary testing results show that the annual overall energy efficiency of the chiller plant (with constant speed centrifugal chillers) is from 0.7 kW/Ton to 0.75 kW/Ton, about 25% less than that of the same plant controlled by normal strategy.
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Yu, Victor, and Dongmei Chen. "Estimating the Concentration Imbalance of a Vanadium Redox Flow Battery With Crossover Using a Constrained Extended Kalman Filter." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3925.
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One of the main issues with vanadium redox flow batteries is that vanadium ions travels across the membrane during operation which leads to a concentration imbalance and capacity loss after long-term cycling. Precise state of charge (SOC) monitoring allows the operator to effectively schedule electrolyte rebalancing and devise a control strategy to keep the battery running under optimal conditions. However, current SOC monitoring methods are too expensive and impractical to implement on commercial VRFB systems. Furthermore, physical models alone are neither reliable nor accurate enough to predict long-term capacity loss. In this paper, we present an application of using an extended Kalman filter (EKF) to estimate the total vanadium concentration in each half-cell by combining three voltage measurements and a state prediction model without crossover effects. Simulation results show that the EKF can accurately predict capacity loss for different crossover patterns over a few hundred cycles.
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Lo, Janzen, and Dimitris Metaxas. "Efficient Human Motion Planning Using Recursive Dynamics and Optimal Control Techniques." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8210.
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Abstract We present an efficient optimal control based approach to simulate dynamically correct human movements. We model virtual humans as a kinematic chain consisting of serial, closed-loop, and tree-structures. To overcome the complexity limitations of the classical Lagrangian formulation and to include knowledge from biomechanical studies, we have developed a minimum-torque motion planning method. This new method is based on the use of optimal control theory within a recursive dynamics framework. Our dynamic motion planning methodology achieves high efficiency regardless of the figure topology. As opposed to a Lagrangian formulation, it obviates the need for the reformulation of the dynamic equations for different structured articulated figures. We use a quasi-Newton method based nonlinear programming technique to solve our minimum torque-based human motion planning problem. This method achieves superlinear convergence. We use the screw theoretical method to compute analytically the necessary gradient of the motion and force. This provides a better conditioned optimization computation and allows the robust and efficient implementation of our method. Cubic spline functions have been used to make the search space for an optimal solution finite. We demonstrate the efficacy of our proposed method based on a variety of human motion tasks involving open and closed loop kinematic chains. Our models are built using parameters chosen from an anthropomorphic database. The results demonstrate that our approach generates natural looking and physically correct human motions.
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Huang, Gengxun, Angran Xiao, and Kenneth M. Bryden. "A Virtual Engineering Tool for Product Design Using High Fidelity CFD Analysis Models." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85404.
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Product design optimization is a complex decision-making process requiring intensive interactions between designers and the designed product. However, most current optimization tools do not support this type of direct interaction. Typically, resolving a converged result with an optimization tool takes a long solution time and high computing cost. However, designers are not involved in the optimization process and cannot control the quality of the so-called optimal result. In this paper, we introduce a virtual engineering design tool that expands the application scope of virtual reality from visualization to interaction and decision support. This design tool allows designers to easily experiment with different product designs using high fidelity CFD solver and observe the effects in an almost real-time manner. This can help designers understand the nature of the product and make superior decisions. Most importantly, the design tool enables designers to control the optimization computing process by selecting superior starting points or changing an obviously unpromising search direction. Hence, by adding human creativity and experience into the optimization process, designers can resolve the design optimization problem more efficiently. A coal pipe design and optimization scenario is presented to demonstrate the efficacy of this virtual engineering design tool. The goal of this tool is to enable a designer to modify the size and shape of a coal pipe to obtain evenly distributed coal at the outlet. In this tool after the initial population was chosen, a standard evolutionary algorithm was used to find the most superior pipe design within a much shorter time.
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Davuluri, Pavan, Abhijit Dasgupta, and Steven Young. "Thermomechanical Durability of High I/O BGA Packages." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2253.
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Abstract Efficient modeling strategies are developed to study thermomechanical durability of high I/O Ball Grid Array (BGA) packages, in order to facilitate virtual qualification and accelerated testing of component designs. A viscoplastic stress analysis technique is developed where the critical solder joint(s) (joint(s) at which failure first occurs) are modeled in detail with a multi-domain Rayleigh-Ritz (MDRR) methodology (Ling and Dasgupta, 1996; Ling and Dasgupta 1997; Ling 1997; and Rassian and Lee, 1998) while the load-sharing offered by non-critical joints is modeled with a simplified compact model. This hybrid technique is used to study the behavior of solder interconnects in selected Ball Grid Array (BGA) package under thermal cycling environments. Parametric studies are conducted to determine the optimal scheme for allocating a critical number of solder joints to the MDRR model, and the remaining non-critical joints to the compact models. Damage calculations are made with the Energy Partitioning Solder Durability model (Dasgupta et al., 1992) and cycles-to-failure predictions are compared with both finite element model predictions as well as experimental failure data provided by CALCE EPSC sponsors. Parametric studies on change in solder joint durability with interconnect volume are also discussed in this paper.
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Hinricher, Niels, Chris Schröer, and Claus Backhaus. "Design of control elements in Virtual Reality - Investigation of factors influencing operating efficiency, user experience and presence." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002066.
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The ergonomic design of control elements in real life has been researched in detail. Various studies exist on the optimal dimensioning, their haptic and acoustic feedback to achieve high control accuracy and user experience. But the development of products is increasingly done with virtual prototypes. Virtual reality (VR) allows these prototypes to be tested in a highly immersive environment. However, the findings from reality cannot be transferred to VR directly. For example, users in VR interact with the prototypes using controllers, which affects haptic feedback. This study investigates how rotary dials and joysticks must be designed and programmed in VR so that control tasks can be performed efficiently and generate a high user experience and perceived presence.In user tests, subjects (n = 25) evaluate the control of a joystick and a rotary dial in VR. In a virtual crane operator's cabin or at a virtual table, the subjects (f = 10, m = 15, age: 24 +- 3) perform four predefined tasks per control element. On two screens in VR, subjects see a vertical bar graph with a scale from 0 to 100 % controlled by the joystick and separately a numerical value between 0 and 100 % which is controlled by the rotary dial. The screens display the task to the subjects, e.g., "Set the value from 0 % to 42 %". According to the method “design of experiments”, 14 factors, such as vibration feedback, acoustic feedback, position of the subject or the sensitivity of the control element are systematically varied on two respectively three levels (e.g., diameter of actuator 40 mm, 80 mm or 110 mm). For each trial, the control accuracy and the time required to complete the task are determined. In addition, the perceived presence is assessed using the Slater-Usoh-Steed-Questionnaire and the user experience is surveyed using the User Experience Questionnaire. The effect of a change of level on the response parameters is investigated using multifactorial ANOVA (α = .05). Linear regression is used to calculate a mathematical relationship between factor and response parameter. These mathematical models are used to calculate which factor values can be used to achieve a high level of control accuracy with a low time requirement and a high level of user experience and perceived presence. The factors angular resolution, inclination, shape of the rotary dial and position of the subject have a highly significant effect (p ≤ .001) on the time required to complete the tasks with the rotary dial. On the control accuracy of the rotary dial, the angular resolution, the VR-controller and the interaction of angular resolution and diameter of the rotary dial have a significant effect. On the user experience, a total of six factors and two interactions have a significant effect. On the perceived presence of the subjects, the VR environment and the diameter of the rotary dial have a significant effect. The calculated optimized design is a rotary dial with vibration feedback, without acoustic feedback, with visualization of a rough knurling, an angular resolution of 10-12 degree/value, a 40 mm diameter and no inclination. Visualization of the hand should be avoided.Sensitivity, size, subject position, VR environment, and the interaction of subject position and VR environment have a significant effect on the time required to perform the control tasks with the joystick. Three factors and one interaction have a significant effect on the control accuracy of the joystick. The interaction of the factors vibration feedback and visualization of the hand has a significant effect on the perceived presence of the subjects. On user experience, nine factors and five interactions have a significant effect. The calculated optimized levels of factors for the joystick are vibration and acoustic feedback, no visual feedback, vertical handle with a height of 20-24 cm, a five-level angular resolution, a maximum deflection angle of +- 15°, a sensitivity of 8 %/sec and a visualization of the hand.The trials show a high degree of scatter. The residuals show outliers in the experiments. These deviations are mainly due to the individual previous experience of the test subjects in handling VR systems. Nevertheless, significant effects could be identified. A screening experimental design was used in this study. In a follow-up study, detailed investigations with a full factorial experimental design must be performed with the significant factors. The factors will be tested at multiple levels and with a significantly increased number of trials to further increase the accuracy of the mathematical models.
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Xie, Kang, Jaime A. Camelio, and L. Eduardo Izquierdo. "Dimensional Error Compensation in Compliant Assembly Processes Using Virtual Assembly Training." In ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing. ASMEDC, 2008. http://dx.doi.org/10.1115/msec_icmp2008-72219.
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Dimensional variation propagation and accumulation in multistage manufacturing processes are among the most important issues that affect quality. Although robust design and statistical process quality control help to reduce the effects of these problems, neither of these two methods can be used for instant variation reduction during assembly operations. This paper introduces a complete methodology for error compensation in compliant sheet metal assembly processes. The proposed methodology can be divided in two steps: (1) an off-line error control-learning module using virtual assembly models, and (2), an in-line control implementation using a feedforward control strategy. The off-line learning method focuses on determining the optimal control actions or corrections to a set of predefined deviations. Specifically, it utilizes a newly developed iterative sampling method based on Kriging fitting to efficiently determine an optimal control action. The in-line feedforward control uses measurements of incoming assembly components to select an appropriate pre-learned control action. Two case studies are presented; first, a mathematical case study is used as the empirical proof for the feasibility of the iterative sampling and fitting algorithm. Second, a simulation-based case study is used to illustrate the effectiveness of the proposed methodology to improve dimensional quality in assembly operations of compliant sheet metal parts.
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Xie, Jinyu, and Qian Wang. "A Personalized Diet and Exercise Recommender System in Minimizing Clinical Risk for Type 1 Diabetes: An In Silico Study." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5136.
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Risk of having hypoglycemia is one of the biggest barriers preventing Type 1 Diabetes (T1D) patients from performing exercise. In addition, management of diet and exercise levels needs to be personalized for each patient to avoid hypoglycemia and to achieve a good glycemic control. In this paper, we developed a model-based diet and exercise recommender system that could be used to provide an (optimal) personalized intervention on diet and exercise for T1D patients. The recommender system makes prediction of blood glucose at each intervention time based on a patient-specific model of glucose dynamics, and then provides the optimal meal sizes, target heart rates during exercise, pre-exercise carbohydrate and bedtime snack by minimizing a clinical risk function under constraints. Patient-specific models of glucose dynamics were identified for 30 virtual subjects generated from a modified UVa/Padova simulator with an added exercise-glucose subsystem. The performance of the recommender system was then compared to two self-management schemes (the Starter and the Skilled). The latter represents an off-line optimal solution providing a lower bound on the risk index. The average clinical risk under the recommender system was reduced by 49% compared to that under the Starter, and it was comparable to the risk of the Skilled. In addition, the recommender system had less number of post-exercise/nocturnal hypoglycemia events occurred than that under the Starter or the Skilled. Such recommender system can be implemented as an “App” patient advisor to improve T1D patients’ self-management of glucose control.
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Nielsen, David, and Ranga Pitchumani. "Neural Network Based Control of Preform Permeation in Resin Transfer Molding Processes With Real-Time Permeability Estimation." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1473.
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Abstract Variabilities in the preform structure in situ in the mold are an acknowledged challenge to effective permeation control in the Resin Transfer Molding (RTM) process. An intelligent model-based controller is developed which utilizes real-time virtual sensing of the permeability to derive optimal decisions on controlling the injection pressures at the mold inlet ports so as to track a desired flowfront progression during resin permeation. This model-based optimal controller employs a neural network-based predictor that models the flowfront progression, and a simulated annealing-based optimizer that optimizes the injection pressures used during actual control. Preform permeability is virtually sensed in real-time, based on the flowfront velocities and local pressure gradient estimations along the flowfront. Results are presented which illustrate the ability of the controller in accurately steering the flowfront for various fill scenarios and preform geometries.