• International Journal of CAD/CAM
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• v.12 no.1
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• pp.9-19
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• 2012

Generation of optimum planar B-spline curve in terms of minimum deviation and required fairness to approximate a target shape defined by a strip-shaped unorganized 2D point cloud is studied. It is proposed to use the location of control points as variables within the geometric optimization framework of point distance minimization. An adaptive simulated annealing heuristic optimization algorithm is developed to iteratively update an initial approximate curve towards the target shape. The new implementation comprises an adaptive cooling procedure in which the temperature change is adaptively dependent on the objective function evolution. It is shown that the proposed method results in an improved convergence speed when compared to the standard simulated annealing method. A couple of examples are included to show the applicability of the proposed method in the surface model reconstruction directly from point cloud data.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.381-389
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• 1996

A simple modification of the standard back-propagation algorithm to eliminate redundant connections(weights and biases) is described. It was motivated by speculations from the distribution of the magnitudes of the weights and the biases, analysis of the classification boundary, and the nonlinearity of the sigmoid function. After initial training, this algorithm eliminates all connections of which magnitude is below a threshold by setting them to zero. The algorithm then conducts retraining in which all weights and biases are adjusted to allow important ones to recover. In studies with Boolean functions, the algorithm reconstructed the theoretical minimum architecture and eliminated the connections which are not necessary to solve the functions. For simulated random signal classification problems, the algorithm produced the result which is consistent with the idea that easier problems require simpler networks and yield lower misclassification rates. Furthermore, in comparison, our algorithm produced better generalization than the standard algorithm by reducing over fitting and pattern memorization problems.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.64-70
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• 2003

Current growth of VLSI design depends critically on the research and development (If automatic layout tool. Automatic layout is composed of placement assigning a specific shape to a block and arranging the block on the layout surface and routing finding the interconnection of all the nets. Algorithms Performing placement and routing impact on Performance and area of VLSI design. Channel routing is a problem assigning each net to a track after global routing and minimizing the track that assigned each net. In this paper we propose a genetic algorithm searching solution space for the netlist partitioning problem for 4-layer channel routing. We compare the performance of proposed genetic algorithm(GA) for channel routing with that of simulated annealing(SA) algorithm by analyzing the results which are the solution of given problems. Consequently experimental results show that out proposed algorithm reduce area over the SA algorithm.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.676-682
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• 2018

In wireless sensor networks, we propose an optimization algorithm in order to minimize the consumed energy of nodes for maritime search and rescue. In the marine environment, search and rescue operations are mainly performed on the surveillance side and passively on the rescued side. A self-configurable wireless sensor network can build a system that can send rescue signals in the operations. A simulated annealing algorithm is proposed to minimize the consumed energy of nodes in the networks with many nodes. As the density of nodes becomes higher, the algorithmic computation will increase highly. To search the good result in a proper execution time, the proposed algorithm proposes a new neighborhood generating operation and improves the efficiency of the algorithm. The proposed algorithm was evaluated in terms of the consumed energy of the nodes and algorithm execution time, and the proposed algorithm performed better than other optimization algorithms in the performance results.

• Journal of Advanced Navigation Technology
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• v.21 no.4
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• pp.339-346
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• 2017

We propose the design of an INS simulated equipment to evaluate the enhanced jamming resistance of an INS-aided GPS receiver with INS, when a jamming signal is injected into a simulated high dynamic platform. We propose the design of a relative cheap INS simulated equipment, instead of an expensive INS simulator connected to GPS simulators. Based on the design, we implement the equipment and setup high dynamic test environment using the equipment to evaluate an INS-aided GPS receiver. In the GPS L1 C/A and L2C simulations that inject jamming signals of the narrow and wide bandwidth into the GPS receiver with and without INS-aided algorithm, we obtain increased jamming resistance performance as +5 dB compared with the GPS receiver without INS-aided algorithm in all kinds of jamming bandwidth. Based on the simulations, we verified that the INS simulated equipment can be used to evaluate the enhanced jamming resistance of INS-aided GPS receivers.

• Journal of the Korea Society for Simulation
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• v.9 no.2
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• pp.15-25
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• 2000

Since data security problems are very important in the information age, cryptographic algorithms for encryption and decryption have been studied for a long time. The GOST(Gosudarstvennyi Standard or Government Standard) algorithm as a data encryption algorithm with a 256-bit key is a 64-bit block algorithm developed in the former Soviet Union. In this paper, we describe how to design an encryption chip based on the GOST algorithm. In addition, the GOST algorithm is compared with the DES(Data Encryption Standard) algorithm, which has been used as a conventional data encryption algorithm, in modeling techniques and their performance. The GOST algorithm whose key size is relatively longer than that of the DES algorithm has been expanded to get better performance, modeled in VHDL, and simulated for implementation with an CPLD chip.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.633-642
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• 2008

Variable selection algorithm for partial least square regression using penalty function is proposed. We use the fact that usual partial least square regression problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Then simulated annealing algorithm can be used in searching for optimal solutions of above maximization problem with penalty functions added. The HARD penalty function would be suggested as the best in several aspects. Illustrations with real and simulated examples are provided.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.121-130
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• 2021

Foot bionic robot could be supported and towed through a series of discrete footholds and be adapted to rugged terrain through attitude adjustment. The vibration isolation of the robot could decouple the fuselage from foot-end trajectories, thus, the robot walked smoothly even if in a significant terrain. The gait programming and foot end trajectory algorithm were simulated. The quadruped robot of parallel five linkages with eight degrees of freedom were tested. The kinematics model of the robot was established by setting the corresponding coordinate system. The forward and inverse kinematics of both supporting and swinging legs were analyzed, and the angle function of single leg driving joint was obtained. The trajectory planning of both supporting and swinging phases was carried out, based on the control strategy of compound cycloid foot-end trajectory planning algorithm with zero impact. The single leg was simulated in Matlab with the established kinematic model. Finally, the walking mode of the robot was studied according to bionics principles. The diagonal gait was simulated and verified through the foot-end trajectory and the kinematics.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.530-539
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• 2007

Many construction projects such as highways, pipelines, tunnels, and high-rise buildings typically contain repetitive activities. Research has shown that the Critical Path Method (CPM) is not efficient in scheduling linear construction projects that involve repetitive tasks. Linear Scheduling Method (LSM) is one of the techniques that have been developed since 1960s to handle projects with repetitive characteristics. Although LSM has been regarded as a technique that provides significant advantages over CPM in linear construction projects, it has been mainly viewed as a graphical complement to the CPM. Studies of scheduling linear construction projects with resource consideration are rare, especially with multiple resource constraints. The objective of this proposed research is to explore a resource assignment mechanism, which assigns multiple critical resources to all activities to minimize the project duration while satisfying the activities precedence relationship and resource limitations. Resources assigned to an activity are allowed to vary within a range at different stations, which is a combinatorial optimization problem in nature. A heuristic multiple resource allocation algorithm is explored to obtain a feasible initial solution. The Simulated Annealing search algorithm is then utilized to improve the initial solution for obtaining near-optimum solutions. A housing example is studied to demonstrate the resource assignment mechanism.