• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.720-730
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• 2002

Automated design of large structures requires efficient and accurate optimization algorithms because of a large number of design variables and design constraints. The objective of this study is to examine the characteristics of the Kreisselmeier -Steinhauser envelope function and to investigate va tidily of accumulating constraint functions into a small number of constraint functions or even into a single constraint function. The commercial package DOT is adopted as a local optimizer. The optimum results using the envelope function are compared with those of the conventional method for a number of numerical examples and the differences between them are shown to be negligible.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.318-321
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• 2006

멀티미디어 디지털 기기의 발전으로 다양한 형태의 저장장치에 저장되어 있는 디지털 데이터 등을 다른 멀티미디어 디지털 기기에서 이용하기 위한 외부 인터페이스로 멀티카드리더기를 사용하며 이러한 멀티미디어 디지털 기기는 임베디드 리눅스를 기반으로 하고 있다. 이에 본 논문에서는 멀티미디어 디지털 기기에서 사용되는 uClinux기반의 운영체제와 멀티카드리더기 모듈과의 인터페이스 최적화 방안에 관하여 제안한다. 기존의 멀티카드리더기 모듈과의 인터페이스 방법은 시스템의 리소스를 많이 사용하여 시스템의 안전성이 떨어지며 성능 또한 좋지 못하다. 이러한 문제를 디바이스 드라이버와 운영체제 인터페이스를 최적화 하는 방법을 본 논문에서 논의 하고자 한다.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.6
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• pp.562-568
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• 2009

Hull form generation and variation methods to be mainly discussed in this study are based on the fairness optimized B-Spline form parameter curves (FOBFC). These curves can be used both as indirect modification function for variation and as geometric entities for hull form generation. The flexibility and functionality of geometric control technique play the most important role for the success of hull form optimization. This study shows the hydrodynamic optimization process and the characteristics of optimum design hull forms of a 14,000TEU containership and 60K LPG carrier. SHIPFLOW has been used as a CFD solver and FS-Framework as a geometric modeler and optimizer.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.3
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• pp.478-489
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• 2015

In the present study, a hydrodynamic hull-form optimization algorithm for a container ship was presented in terms of the minimum wave-making resistance. Bell-shaped modification functions were developed to modify the original hull-form and a sequential quadratic programming algorithm was used as an optimizer. The wave-making resistance as an objective function was obtained by the Rankine source panel method in which non-linear free surface conditions and the trim and sinkage of the ship were fully taken into account. Numerical computation was performed to investigate the validity and effectiveness of the proposed hull-form modification algorithm for the container carrier. The computational results were validated by comparing them with the experimental data.

• Proceedings of the Optical Society of Korea Conference
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• 1989.02a
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• pp.85-88
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• 1989

Introducing and optimizing it-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers "0" to "9", are successfully stored and retrieved in a 6x8 node system. Unlike many other neural networks models, this model has stronger error correction capability for correlated images such as "6", "8", "3", and "9". the bit-significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with another neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.uding the adaptive optimization networks is also introduced.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3772-3783
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• 2021

This work proposes a new efficient meta-heuristic optimization algorithm called Gamma Ray Interactions Based Optimization (GRIBO). The algorithm mimics different energy loss processes of a gamma-ray photon during its passage through a matter. The proposed novel algorithm has been applied to search for the global minima of 30 standard benchmark functions. The paper also considers solving real optimization problem in the field of nuclear engineering, radioisotope identification. The results are compared with those obtained by the Particle Swarm Optimization, Genetic Algorithm, Gravitational Search Algorithm and Grey Wolf Optimizer algorithms. The comparisons indicate that the GRIBO algorithm is able to provide very competitive results compared to other well-known meta-heuristics.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.223-229
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• 2021

Classification of different blood cell types is an essential task for human's medical treatment. The white blood cells have different types of cells. Counting total White Blood Cells (WBC) and differential of the WBC types are required by the physicians to diagnose the disease correctly. This paper used transfer learning methods to the pre-trained deep learning models to classify different WBCs. The best pre-trained model was Inception ResNetV2 with Adam optimizer that produced classification accuracy of 98.4% for the dataset comprising four types of WBCs.

• Steel and Composite Structures
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• v.47 no.4
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• pp.513-521
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• 2023

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.466-469
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• 2021

최근 암호화폐가 발전함에 따라 다양한 연구들이 진행되고 있지만 그 중에서도 암호화폐의 가격 예측 연구들이 활발히 진행되고 있다. 특히 이러한 예측 분야에서도 인공지능 기술을 접목시켜 암호화폐 가격의 예측 정확도를 높이려는 노력들이 지속되고 있다. 인공지능 기반의 기법들 중 시간적 정보를 가진 데이터를 기반으로 하고 있는 LSTM(Long Short-Term Memory) 모델이 다각도로 활용되고 있으나 급등락하는 암호화폐 가격 데이터가 많을 경우에는 그 성능이 상대적으로 낮아질 수 밖에 없다. 따라서 본 논문에서는 가격이 급등락하고 있는 Bitcoin, Ethereum, Dash 암호화폐 데이터 환경에서 LSTM 모델의 예측 성능이 향상될 수 있는 세부 하이퍼 파라미터 값을 실험 및 분석하고, 그 결과의 의미에 대해 고찰한다. 이를 위해 LSTM 모델에서 향상된 예측률을 보일 수 있는 epoch, hidden layer 수, optimizer 에 대해 분석하였고, 최적의 예측 결과를 도출해 줄 수 있는 최소 training data 개수도 함께 살펴보았다.

• Bulletin of the Korean Mathematical Society
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• v.61 no.3
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• pp.585-595
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• 2024

We show that a pseudo-Anosov map constructed as a product of the large power of Dehn twists of two filling curves always has a geodesic axis on the curve graph of the surface. We also obtain estimates of the stable translation length of a pseudo-Anosov map, when two filling curves are replaced by multicurves. Three main applications of our theorem are the following: (a) determining which word realizes the minimal translation length on the curve graph within a specific class of words, (b) giving a new class of pseudo-Anosov maps optimizing the ratio of stable translation lengths on the curve graph to that on Teichmüller space, (c) giving a partial answer of how much power is needed for Dehn twists to generate right-angled Artin subgroup of the mapping class group.