• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.2
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• pp.1-15
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• 2003

This paper presents a new evolutionary algorithm to solve complex multi-level integration problems, which is called multi-level symbiotic evolutionary algorithm (MEA). The MEA uses an efficient feedback mechanism to flow evolution information between and within levels, to enhance parallel search capability, and to improve convergence speed and population diversity. To show the MEA's applicability, It is applied to the integrated planning of production and distribution in supply chain management. The encoding and decoding methods are devised for the integrated problem. A set of experiments has been carried out, and the results are reported. The superiority of the algorithm's performance is demonstrated through experiments.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.190-193
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• 2004

Optimal Power Flow (OPF) is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. So this paper presents parallel genetic algorithm-tap search for the solution of the OPF. The control variables modeled unit active power outputs, generator-bus voltage magnitudes and transformer-tap settings. A number of functional operating constraints, such as branch flow limits, load bus boltage magnitude limits and generator reactive capabilities are included as penalties in the fitness function. In parallel GA-TS, GA operators are executed for each process. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper three populations to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on an IEEE 30-bus system in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient for the OPF.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.170-178
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• 2004

In this work a high speed 8-error correcting Reed-Solomon decoder is designed using the modified Euclid algorithm. Decoding algorithm of Reed-Solomon codes consists of four steps, those are, compute syndromes, find error-location polynomials, decide error-locations, and determine error values. The decoding speed is increased and the latency is reduced by using the parallel architecture in the syndrome generator and a faster clock speed in the modified Euclid algorithm block. In addition. the error locator polynomial in Chien search block is separated into even and odd terms to increase the overall speed of the decoder. All the functionalities of the decoder are verified first through C++ programs. Verilog is used for hardware description, and then the decoder is synthesized with a $.25{\mu}m$ CMOS TML library. The functionalities of the chip is also verified through test vectors. The clock speed of the chip is 250MHz, and the maximum data rate is 1Gbps.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.99-105
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• 2018

k-Nearest neighbor join (k-NN Join) is a computationally intensive algorithm that is designed to find k-nearest neighbors from a dataset S for every object in another dataset R. Most related studies on k-NN Join are based on single-computer operations. As the data dimensions and data volume increase, running the k-NN Join algorithm on a single computer cannot generate results quickly. To solve this scalability problem, we introduce the locality-sensitive hashing (LSH) k-NN Join algorithm implemented in Spark, an approach for high-dimensional big data. LSH is used to map similar data onto the same bucket, which can reduce the data search scope. In order to achieve parallel implementation of the algorithm on multiple computers, the Spark framework is used to accelerate the computation of distances between objects in a cluster. Results show that our proposed approach is fast and accurate for high-dimensional and big data.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.2
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• pp.41-50
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• 2003

In this paper, nonlinear parallel structural analyses are introduced by using the parallel multifrontal solver and damage localization for 2D and 3D crack models is presented as the application of nonlinear parallel computation. The parallel algorithms related with nonliear reduce the amount of memory used is carried out because many variables should be utilized for this highly nonlinear damage analysis. Also, Riks' continuation method is parallelized to search the solution when strain softening occurs due to damage evolution. For damage localization problem, several computational models having up to around 1-million degree of freedoms are used. The parallel performance in this nonlinear parallel algorithm is shown through these examples and the local variation of damage at crack tip is compared among the models with different degree of freedoms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.159-165
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• 2002

진화연산에는 교배, 돌연변이, 경쟁, 선택이 있다. 이러한 과정 중에서 선택은 새로운 개체를 생산하지는 않지만, 모든 해중에서 최적의 해가 될만한 해는 선택하고, 그러지 않은 해는 버리는 판단의 역할을 한다. 따라서 아무리 좋은 해를 만들었다고 해도, 취사 선택을 잘못하면, 최적의 해를 찾지 못하거나, 또 많은 시간이 소요되게 된다. 따라서 본 논문에서는 stochastic한 성질을 갖고 있는 Tournament selection에 Local selection개념을 도입하여, 지역 해에서 벗어나 전역 해를 찾는데, 개선이 될 수 있도록 하였고 Fast Evolutionary Programming의 mutation과정을 개선하고, Genetic Algorithm의 연산자인 crossover와 mutation을 도입하여 Parallel search로 지역 해에서 벗어나 전역 해를 찾는 하이브리드 알고리즘을 제안하고자 한다.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.956-959
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• 1996

In this study, we propose a neuro-genetic controller combined with a linear controller in parallel to improve the tracking performance of the Line of Sight(LOS) stabilization system and reject the effect of disturbances. A Genetic Algorithm(GA) is used to optimize weights of the neuro-genetic controller since this algorithm can search a global minimum without derivatives or other auxiliary knowledge. The LOS system is very complex and has limited measurable output data. Under these specific circumstances GA solves many problems that other training methods have. Computer simulation results show that the, proposed controller makes better tracking response and rejection of disturbance than a linear controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.683-686
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• 2000

A new approach to the task of aligning a robot using camera is presented in this paper. We apply an elitist GA to find the joints angles of manipulator to reach target position instead of using nonlinear least error method. Since it employs parallel search and have good performance in solving optimization problems. In order to improve convergence speed, the floating coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using elitist genetic algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.213-220
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• 2007

This paper considers a two-stage hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. Each job is processed through the two production stages in stages, each of which has multiple identical parallel machines. The problem is to determine the allocation and sequence of jobs at each stage. A branch and bound algorithm that gives the optimal solutions is suggested that incorporates the methods to obtain the lower and upper bounds. Dominance properties are also suggested to reduce the search space. To show the performance of the algorithm, computational experiments are done on randomly generated problems, and the results are reported.