• Structural Engineering and Mechanics
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• v.72 no.6
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• pp.689-712
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• 2019

Time-series models like AR-ARX and ARMAX, provide a robust way to capture the dynamic properties of structures, and their residuals can be effectively used as features for damage detection. Even though several research papers discuss the implementation of AR-ARX and ARMAX models for damage diagnosis, they are basically been exploited so far for detecting the time instant of damage and also the spatial location of the damage. However, the inverse problem associated with damage quantification i.e. extent of damage using time series models is not been reported in the literature. In this paper, an approach to detect the extent of damage by combining the ARMAX model by formulating the inverse problem as a multi-constrained optimization problem and solving using a newly developed hybrid adaptive differential search with dynamic interaction is presented. The proposed variant of the differential search technique employs small multiple populations which perform the search independently and exchange the information with the dynamic neighborhood. The adaptive features and local search ability features are built into the algorithm in order to improve the convergence characteristics and also the overall performance of the technique. The multi-constrained optimization formulations of the inverse problem, associated with damage quantification using time series models, attempted here for the first time, can considerably improve the robustness of the search process. Numerical simulation studies have been carried out by considering three numerical examples to demonstrate the effectiveness of the proposed technique in robustly identifying the extent of the damage. Issues related to modeling errors and also measurement noise are also addressed in this paper.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.8-17
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• 2005

This paper dealt with a kind of heterogeneous vehicle routing problem with known demand and time deadline of customers. The customers are supposed to have one of tight deadline and loose deadline. The demand of customers with tight deadline must be fulfilled in the deadline. However, the late delivery is allowed to customers with loose deadline. That is, the paper suggests a model to minimize total acquisition cost, total travel distance and total violation time for a fleet size and mix vehicle routing problem with time deadline, and proposes a heuristic algorithm for the model. The proposed algorithm consists of two phases, i.e. generation of an initial solution and improvement of the current solution. An initial solution is generated based on a modified insertion heuristic and iterative Improvement procedure is accomplished using neighborhood generation methods such as swap and reallocation. The proposed algorithm is evaluated using a well known numerical example.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.59-66
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• 2011

This work presents an algorithm for generating multi-RUN (m-RUN) deadlock avoidance policy based on simulated annealing algorithm. The basic idea of this method is to gradually improve the current m-RUN DAP after constructing an initial m-DAP by using simple m RUN DAPs. The search for a neighbor of the current m-RUN DAP is done by selecting and changing only one component of the current m-RUN, while accepting some unimproved solutions with some probability. It is examined for its performance by generating some sample system configurations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.1
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• pp.127-135
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• 2009

This paper presents a new technique for corner shape based object retrieval from a database. The proposed feature matrix consists of values obtained through a neighborhood operation of detected corners. This results in a significant small size feature matrix compared to the algorithms using color features and thus is computationally very efficient. The corners have been extracted by finding the intersections of the detected lines found using Hough transform. As the affine transformations preserve the co-linearity of points on a line and their intersection properties, the resulting corner features for image retrieval are robust to affine transformations. Furthermore, the corner features are invariant to noise. It is considered that the proposed algorithm will produce good results in combination with other algorithms in a way of incremental verification for similarity.

• Transactions of the Society of Information Storage Systems
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• v.1 no.2
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• pp.137-142
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• 2005

In this paper, we propose a new servo system design strategy to reduce the position error signal(PES) and track mis-registration(TMR) in magnetic disk drive systems. The proposed method provides a systematic design procedure based on the plant model and an optimal solution via an optimization with a 'Robust Random Neighborhood Search(RRNS)' algorithm. In addition, it guarantees the minimum PES level as well as stability to parametric uncertainties. Furthermore, the proposed method can be used to estimate the performance at the design stage and thus can reduce the cost and time for the design of the next generation product. The reduction of PES as well as robust stability is demonstrated by simulation and experiments.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2687-2696
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• 2011

A Clustering problem is one of the organizational problems to improve network lifetime and scalability in underwater acoustic sensor networks. This paper propose an algorithm to obtain an optimal clustering solution to be able to minimize a total transmission power for all deployed nodes to transmit data to the sink node through its clusterhead. In general, as the number of nodes increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time, we propose a genetic algorithm to obtain the optimal solution of the cluster configuration. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the total transmission power of nodes and the execution time of the proposed algorithm. The evaluation results show that the proposed algorithm is efficient for the cluster configuration in underwater acoustic sensor networks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.79-86
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• 2020

To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.869-873
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• 2008

Optimization method for the open loop commutation time intervals in HDD spindle start-up control is presented in this paper. A hard disk drive(HDD) uses a sensorless brushless DC motor(BLDC) for the platter rotation. Because there is no direct sensor for the rotor position, open loop commutations after sensing the rotor position at a standstill using inductive sensing method are performed to speed up the rotor up to a certain speed where the zero crossings of the back electromotive force(EMF) are measurable. Therefore successful open loop commutations are necessary for the stable start-up control of the spindle motors. Random neighborhood search(RNS) algorithm is introduced as a optimization technic in this paper. Rotor speed and its standard deviation are used as a cost function and commutation intervals obtained from the spindle motion equation are used as initial parameter values for the RNS. With the help of the proposed method optimized open loop commutation time intervals for the very low start-up current are acquired and tested. The experimental results shows that the proposed method can decrease the start-up failure rate of a HDD spindle motor.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.23-31
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• 2000

Iterative curve evolution techniques are powerful methods for image segmentation. Classical methods proposed curve evolutions which guarantee close contours at convergence and, combined with the level set method, they easily handled curve topology changes. In this paper, we present a new geometric active contour model based on level set methods introduced by Osher & Sethian for detection of object boundaries or shape and we adopt anisotropic diffusion filtering method for removing noise from original image. Classical methods allow only one-way curve evolutions : shrinking or expanding of the curve. Thus, the initial curve must encircle all the objects to be segmented or several curves must be used, each one totally inside one object. But our method allows a two-way curve evolution : parts of the curve evolve in the outward direction while others evolve in the inward direction. It offers much more freedom in the initial curve position than with a classical geodesic search method. Our algorithm performs accurate and precise segmentations from noisy images with complex objects(jncluding sharp angles, deep concavities or holes), Besides it easily handled curve topology changes. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image.