• 제목/요약/키워드: state space optimization

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Topology, shape, and size optimization of truss structures using modified teaching-learning based optimization

  • Tejani, Ghanshyam G.;Savsani, Vimal J.;Patel, Vivek K.;Bureerat, Sujin
    • Advances in Computational Design
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    • 제2권4호
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    • pp.313-331
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    • 2017
  • In this study, teaching-learning based optimization (TLBO) is improved by incorporating model of multiple teachers, adaptive teaching factor, self-motivated learning, and learning through tutorial. Modified TLBO (MTLBO) is applied for simultaneous topology, shape, and size optimization of space and planar trusses to study its effectiveness. All the benchmark problems are subjected to stress, displacement, and kinematic stability constraints while design variables are discrete and continuous. Analyses of unacceptable and singular topologies are prohibited by seeing element connectivity through Grubler's criterion and the positive definiteness. Performance of MTLBO is compared to TLBO and state-of-the-art algorithms available in literature, such as a genetic algorithm (GA), improved GA, force method and GA, ant colony optimization, adaptive multi-population differential evolution, a firefly algorithm, group search optimization (GSO), improved GSO, and intelligent garbage can decision-making model evolution algorithm. It is observed that MTLBO has performed better or found nearly the same optimum solutions.

On Thermal and State-of-Charge Balancing using Cascaded Multi-level Converters

  • Altaf, Faisal;Johannesson, Lars;Egardt, Bo
    • Journal of Power Electronics
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    • 제13권4호
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    • pp.569-583
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    • 2013
  • In this study, the simultaneous use of a multi-level converter (MLC) as a DC-motor drive and as an active battery cell balancer is investigated. MLCs allow each battery cell in a battery pack to be independently switched on and off, thereby enabling the potential non-uniform use of battery cells. By exploiting this property and the brake regeneration phases in the drive cycle, MLCs can balance both the state of charge (SoC) and temperature differences between cells, which are two known causes of battery wear, even without reciprocating the coolant flow inside the pack. The optimal control policy (OP) that considers both battery pack temperature and SoC dynamics is studied in detail based on the assumption that information on the state of each cell, the schedule of reciprocating air flow and the future driving profile are perfectly known. Results show that OP provides significant reductions in temperature and in SoC deviations compared with the uniform use of all cells even with uni-directional coolant flow. Thus, reciprocating coolant flow is a redundant function for a MLC-based cell balancer. A specific contribution of this paper is the derivation of a state-space electro-thermal model of a battery submodule for both uni-directional and reciprocating coolant flows under the switching action of MLC, resulting in OP being derived by the solution of a convex optimization problem.

Learning the Covariance Dynamics of a Large-Scale Environment for Informative Path Planning of Unmanned Aerial Vehicle Sensors

  • Park, Soo-Ho;Choi, Han-Lim;Roy, Nicholas;How, Jonathan P.
    • International Journal of Aeronautical and Space Sciences
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    • 제11권4호
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    • pp.326-337
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    • 2010
  • This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

The effects of construction related costs on the optimization of steel frames

  • Choi, Byoung-Han;Gupta, Abhinav;Baugh, John W. Jr.
    • Structural Engineering and Mechanics
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    • 제43권1호
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    • pp.31-51
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    • 2012
  • This paper presents a computational study that explores the design of rigid steel frames by considering construction related costs. More specifically, two different aspects are investigated in this study focusing on the effects of (a) reducing the number of labor intensive rigid connections within a frame of given geometric layout, and (b) reducing the number of different member section types used in the frame. A genetic algorithm based optimization framework searches design space for these objectives. Unlike some studies that express connection cost as a factor of the entire frame weight, here connections and their associated cost factors are explicitly represented at the member level to evaluate the cost of connections associated with each beam. In addition, because variety in member section types can drive up construction related costs, its effects are evaluated implicitly by generating curves that show the trade off between cost and different numbers of section types used within the frame. Our results show that designs in which all connections are considered to be rigid can be excessively conservative: rigid connections can often be eliminated without any appreciable increase in frame weight, resulting in a reduction in overall cost. Eliminating additional rigid connections leads to further reductions in cost, even as frame weight increases, up to a certain point. These complex relationships between overall cost, rigid connections, and member section types are presented for a representative five-story steel frame.

A two-stage structural damage detection method using dynamic responses based on Kalman filter and particle swarm optimization

  • Beygzadeh, Sahar;Torkzadeh, Peyman;Salajegheh, Eysa
    • Structural Engineering and Mechanics
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    • 제83권5호
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    • pp.593-607
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    • 2022
  • To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

The hybrid uncertain neural network method for mechanical reliability analysis

  • Peng, Wensheng;Zhang, Jianguo;You, Lingfei
    • International Journal of Aeronautical and Space Sciences
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    • 제16권4호
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    • pp.510-519
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    • 2015
  • Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.

Optimal Admission Control and State Space Reduction in Two-Class Preemptive Loss Systems

  • Kim, Bara;Ko, Sung-Seok
    • ETRI Journal
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    • 제37권5호
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    • pp.917-921
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    • 2015
  • We consider a multiserver system with two classes of customers with preemption, which is a widely used system in the analysis of cognitive radio networks. It is known that the optimal admission control for this system is of threshold type. We express the expected total discounted profit using the total number of customers, thus reducing the stochastic optimization problem with a two-dimensional state space to a problem with a one-dimensional birth-and-death structure. An efficient algorithm is proposed for the calculation of the expected total discounted profit.

Passive vibration control of plan-asymmetric buildings using tuned liquid column gas dampers

  • Fu, Chuan
    • Structural Engineering and Mechanics
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    • 제33권3호
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    • pp.339-355
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    • 2009
  • The sealed, tuned liquid column gas damper (TLCGD) with gas-spring effect extends the frequency range of application up to about 5 Hz and efficiently increases the modal structural damping. In this paper the influence of several TLCGDs to reduce coupled translational and rotational vibrations of plan-asymmetric buildings under wind or seismic loads is investigated. The locations of the modal centers of velocity of rigidly assumed floors are crucial to select the design and the optimal position of the liquid absorbers. TLCGD's dynamics can be derived in detail using the extended non-stationary Bernoulli's equation for moving reference systems. Modal tuning of the TLCGD renders the optimal parameters by means of a geometrical transformation and in analogy to the classical tuned mass damper (TMD). Subsequently, fine-tuning is conveniently performed in the state space domain. Numerical simulations illustrate a significant reduction of the vibrations of plan-asymmetric buildings by the proposed TLCGDs.

A Novel Binary Ant Colony Optimization: Application to the Unit Commitment Problem of Power Systems

  • Jang, Se-Hwan;Roh, Jae-Hyung;Kim, Wook;Sherpa, Tenzi;Kim, Jin-Ho;Park, Jong-Bae
    • Journal of Electrical Engineering and Technology
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    • 제6권2호
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    • pp.174-181
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    • 2011
  • This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

매개변수 종속 최적화에서 최대치형 목적함수 처리에 관한 연구 (A study on the treatment of a max-value cost function in parametric optimization)

  • 김민수;최동훈
    • 대한기계학회논문집A
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    • 제21권10호
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    • pp.1561-1570
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    • 1997
  • This study explores the treatment of the max-value cost function over a parameter interval in parametric optimization. To avoid the computational burden of the transformation treatment using an artificial variable, a direct treatment of the original max-value cost function is proposed. It is theoretically shown that the transformation treatment results in demanding an additional equality constraint of dual variables as a part of the Kuhn-Tucker necessary conditions. Also, it is demonstrated that the usability and feasibility conditions on the search direction of the transformation treatment retard convergence rate. To investigate numerical performances of both treatments, typical optimization algorithms in ADS are employed to solve a min-max steady-state response optimization. All the algorithm tested reveal that the suggested direct treatment is more efficient and stable than the transformation treatment. Also, the better performing of the direct treatment over the transformation treatment is clearly shown by constrasting the convergence paths in the design space of the sample problem. Six min-max transient response optimization problems are also solved by using both treatments, and the comparisons of the results confirm that the performances of the direct treatment is better than those of the tranformation treatment.