PURPOSES : This study aimed to develop a quantitative structure property relationships (QSPR) model to predict the density from the molecular structure information of the asphalt binder AAA1, a non-full connected structure mixed with a total of 12 molecules. METHODS : The partial least squares regression (PLSR) model, which models the relationship between predictions and responses and the structure of these variables, was applied to predict the density of a binder with molecule descriptors. The PLSR model could also analyze data with collinear, noisy, and multiple dimensional independent variables. The density and additive-free AAA1 binder’s molecule systems generated by an asphalt binder’s molecules-related study were used to fit the PLSR model with the molecular descriptors produced using alvaDesc software. In addition to developing the relationship, a systematic feature selection framework (i.e., the V-WSP- and PLSR-modelbased genetic algorithm (GA)) was applied to explore sets of predictors which contributed to predicting the physical property. RESULTS : The PLSR model accurately predicted the density for the AAA1 binder’s molecules using the condition of the temperature and aging level (R2 was 0.9537, RMSE was 0.00424, and MAP was 0.00323 for the test data) and provided a set of features which correlated well to the property. CONCLUSIONS : Through the establishment of the physical property prediction model, it was possible to evaluate the physical properties of construction materials without limited experiments or simulations, and it could be used to comprehensively design the modified material composition.

In this study, we propose an optimal design method by applying the Prefabricated Buckling Restrained Brace (PF-BRB) to structures with asymmetrically rigidity plan. As a result of the PF-BRB optimal design of a structure with an asymmetrically rigidity plan, it can be seen that the reduction effect of dynamic response is greater in the case of arrangement considering the asymmetric distribution of stiffness (Asym) than in the case of arrangement in the form of a symmetric distribution (Sym), especially It was confirmed that at an eccentricity rate of 20%, the total amount of reinforced PF-BRBs was also small. As a result of analyzing the dynamic response characteristics according to the change in eccentricity of the asymmetrically rigidity plan, the distribution of the reinforced PF-BRB showed that the larger the eccentricity, the greater the amount of damper distribution around the eccentric position. Additionally, when comparing the analysis models with an eccentricity rate of 20% and an eccentricity rate of 12%, the response reduction ratio of the 20% eccentricity rate was found to be large.

In this study, the load fluctuation of the main engine is considered to be a disturbance for the jacket coolant temperature control system of the low-speed two-stroke main diesel engine on the ships. A nonlinear PID temperature control system with satisfactory disturbance rejection performance was designed by rapidly transmitting the load change value to the controller for following the reference set value. The feed-forwarded load fluctuation is considered the set points of the dual loop control system to be changed. Real-coded genetic algorithms were used as an optimization tool to tune the gains for the nonlinear PID controller. ITAE was used as an evaluation function for optimization. For the evaluation function, the engine jacket coolant outlet temperature was considered. As a result of simulating the proposed cascade nonlinear PID control system, it was confirmed that the disturbance caused by the load fluctuation was eliminated with satisfactory performance and that the changed set value was followed.

부분구조화 기법은 자유도가 많고 복잡한 구조물의 유한요소 해석 모델 단순화에 효율적으로 적용될 수 있는 기법이다. 대표적으 로 선형 문제에 대해서는 Craig-Bampton method 등이 있다. Craig-Bampton method는 경계 요소를 제외한 나머지 요소의 불필요한 자 유도를 제거함으로써 선형 구조물의 축소를 수행한다. 최근에는 부분구조화 기법과 더불어 구조물의 최적설계를 위해 멀티레벨 최적 화 기법이 많이 활용되고 있다. 시스템의 목표를 달성하기 위해 각 부구조에 새로운 목표를 할당하는 기법이다. 본 연구에서는 유전자 알고리즘을 이용하여 시스템 목표 달성을 위한 각 부구조별 내부 자유도 개수를 새로운 목표로 할당하고 최적화를 수행하였다. 최적 화 절차로부터 도출된 부구조별 내부 자유도 개수를 이용하여 시스템의 축소를 수행하였다. 다양한 수치예제들을 통해 축소 모델에 대한 결과를 확인하였으며, 90% 이상의 정확도를 가지는 것을 확인하였다.

As a new entertainment and social way, online games now have a huge and increasing user group, so it is of great significance to identify the data stream of online games. Using the excellent nonlinear fitting ability of BP neural network and the advantages of global search of genetic algorithm, the initial weights and thresholds of BP neural network are optimized, and the BP neural network model optimized by genetic algorithm is established. The muti-dimensional input information is proposed to identify online game data streams. Through the experimental simulation, it shows that the selected muti-dimensional information and the established model can be well applied to online game stream recognition.

전역 최적화 문제의 해를 유전 알고리즘을 사용하여 얻어 완전파형역산을 수행하고 층상 반무한체의 물성치를 추정하는 기법을 제안한다. 조화 수직 하중이 작용하는 층상 반무한체의 동적 응답을 측정하고, 이를 추정 물성치를 사용하여 계산된 응답과 비교한다. 응답의 추정치는 mid-point integrated finite element와 perfectly matched discrete layer를 사용하여 구성된 thin-layer model로부터 얻는다. 전역 최적화 문제의 목적 함수는 응답의 관측치와 추정치의 차이에 대한 L2-norm으로 계산된다. 유전 알고리즘을 사용하여 전역 최적화 문제의 해를 구하여 완전파형역산을 수행한다. 제안된 기법을 기본 진동 모드 뿐만이 아니라 고차 진동 모드도 우세한 다양한 층상 반무한 매질에 적용하여, 측정치가 잡음을 포함하지 않는 경우와 포함하는 경우 모두에 대해서 제안된 완전파형역산 기법은 층상 반무한체의 재료 특성을 추정하는데 적합함을 확인할 수 있다.

A60 급 갑판 관통 관은 선박과 해양플랜트에서 화재사고가 발생할 경우 화염의 확산을 방지하고 인명을 보호하기 위해 수평구조에 설치되는 방화장치이다. 본 연구에서는 다양한 대리모델과 다중 섬유전자 알고리즘을 이용하여 A60 급 갑판 관통 관의 방화설계에 대한 이산변수 근사최적화를 수행하였다. A60 급 갑판 관통 관의 방화설계는 과도 열전달해석을 통해 평가하였다. 근사최적화에서 관통 관의 길이, 지름, 재질, 그리고 단열재의 밀도는 이산설계변수로 적용하였고, 제한조건은 온도, 생산성 및 가격을 고려하였다. 대리모델 기반의 근사최적설계 문제는 제한조건을 만족하면서 A60 급 갑판 관통 관의 중량을 최소화할 수 있는 이산설계변수를 결정하도록 정식화 하였다. 반응표면모델, 크리깅, 그리고 방사기저함수 신경망과 같은 다양한 대리모델이 근사최적화에 사용되었다. 근사최적화의 정확도를 검토하기 위해 최적해의 결과는 실제 계산 결과와 비교하였다. 근사최적화에 사용된 대리모델 중 방사기저함수 신경망 모델이 A60 급 갑판 관통 관의 방화설계에 대해 가장 정확한 최적설계 결과를 나타내었다.

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

본 논문은 유한요소법과 유전알고리즘을 연동하여 지진하중을 받는 구조물의 강성저하(손상) 및 보강 후 효과를 추정하는 방법을 다루었다. 본 연구의 독창성은 지진하중을 적용하였고, 그 응답으로부터 구조물의 미지 변수를 추정한다는 점이다. 본 연구에서 제안한 방법은 지진하중으로부터 손상된 부위를 추정할 뿐 아니라, 그 위치와 정도를 규명할 수 있다. 제안한 방법을 검증하기 위하여 El Centro 및 포항 지진하중을 적용하여 저층 뼈대구조물와 트러스 교량을 대상으로 알고리즘을 실행하였다. 수치해석 예제는 제안한 방법이 수치해석적인 효율성 뿐 아니라 지진으로부터의 심각한 피해를 예방하는 데 적용할 수 있음을 보여주었다.

Process mining is an analytical technique aimed at obtaining useful information about a process by extracting a process model from events log. However, most existing process models are deterministic because they do not include stochastic elements such as the occurrence probabilities or execution times of activities. Therefore, available information is limited, resulting in the limitations on analyzing and understanding the process. Furthermore, it is also important to develop an efficient methodology to discover the process model. Although genetic process mining algorithm is one of the methods that can handle data with noises, it has a limitation of large computation time when it is applied to data with large capacity. To resolve these issues, in this paper, we define a stochastic process tree and propose a tabu search-genetic process mining (TS-GPM) algorithm for a stochastic process tree. Specifically, we define a two-dimensional array as a chromosome to represent a stochastic process tree, fitness function, a procedure for generating stochastic process tree and a model trace as a string of activities generated from the process tree. Furthermore, by storing and comparing model traces with low fitness values in the tabu list, we can prevent duplicated searches for process trees with low fitness value being performed. In order to verify the performance of the proposed algorithm, we performed a numerical experiment by using two kinds of event log data used in the previous research. The results showed that the suggested TS-GPM algorithm outperformed the GPM algorithm in terms of fitness and computation time.

본 논문에서는 AISC 표준 단면을 설계 변수로 하는 캔틸레버 타입 헬리데크 모델의 유전 알고리즘 최적설계를 소개한다. AISC 표준 단면을 단면 형상별로 분류하고 단면적 순으로 정렬한 후 정수 단면 번호를 부여하여 설계 변수로 최적설계를 수행하였다. 이 과정을 통하여 이산화된 설계 변수를 가지는 최적설계 문제를 해결하기 위해 유전 알고리즘을 적용하였다. 또한, 제약조건으로 허용응력 및 허용응력비 검사 조건을 모두 고려하여 구조물의 구조 안정성을 고려한 설계를 수행하였다. 최적설계 과정중 매 반복계산 마다 수행되는 구조해석 시간을 단축시키기 위해 선형 중첩법을 사용하였고, 이를 통해 구조 해석 시간을 약 75% 감소시킬 수 있었다. 또한 헬리데크 최적설계의 경량 효과를 높이기 위해 부재 그룹 세분화를 하였고, 그 결과를 선행 연구 모델, 기존의 부재 그룹 모델과 비교하였다. 그 결과 선행연구 대비 약 30톤의 부재를 절감할 수 있었으며, 구조적으로도 보다 안전한 헬리데크 설계를 얻을 수 있었다.

신재생 에너지 자원중 풍력발전은 비약적인 기술 발전과 시장 규모가 급속하게 성장하고 있다. 최근 육상풍력발전단지의 공간적 한계, 환경 문제 등으로 인하여 설치 공간이 해상으로 이동되었고, 더욱 풍부한 풍황 조건을 가진 깊은 수심에 설치되는 부유식 해상 풍력단지의 개발이 활발하게 진행되고 있다. 해상교통관점에서 해상풍력단지의 최적위치 선정은 선박과 풍력기들의 간섭을 최소화 하고 사고 확률이 적은 곳이며, 선박 밀집도가 낮은 해역이 최적위치로 선정된다. 본 연구에서는 유전 알고리즘 기반의 계절별 1주일 기간 선박자동식별장치 데이터를 유전자 및 염색체로 구성하였다. 80개의 유전자로 구성하고 유전 알고리즘의 적합도 평가를 거쳐 부유식 해상 풍력단지의 계절별 최적위치를 선정하였다. 더 나아가 계절별 최적위치 점수를 합산하여 최종 최적위치를 선정하였다. 분석 해역에서 최적위치는 11개로 나타났으며, 해상교통관점에서 유전 알고리즘을 통한 최적위치 선정이 적용 가능함을 확인하였다.

Robot manipulators are highly nonlinear system with multi-inputs multi-outputs, and various control methods for the robot manipulators have been developed to acquire good trajectory tracking performance and improve the system stability lately. The computed torque controller has nonlinear feedforward control elements and so it is very effective to control robot manipulators. If the control gains of the computed torque controller is adjusted according the payload, then more precise control performance is attained. This paper extends the conventional computed torque controller in the joint space to the Cartesian space, and optimize the control gains for some specified payloads in both joint and Cartesian spaces using genetic algorithms. Also a neural network is employed to have proper control gains for arbitrary payloads using generalization properties of the neural network. Computer simulation results show that the proposed control system for robot manipulators has excellent performance in various conditions.

This study applies optimization-based algorithm to develop combination classification methods. We propose a genetic algorithm-based combination classification method of multiple decision trees to improve predictive accuracy, optimize classification rules, and interpret classification results. The basic algorithm for decision tree has been constructed in a top-down recursive divide-and-conquer manner. Based on different split measures (attribute selection measures), different decision tree algorithms can be produced, and then multiple decision trees can be formed. We proposed the parallel combination model of multiple decision trees. On top of the combination model, multiple decision trees are parallel combined. Each decision tree produces its own classification rules according to training samples from which one can present the classification result using probability distribution of target class label. At the bottom, the classification result of each decision tree serves as the input for combination algorithm in producing classification result and rules for the combination model. Combination algorithm adopts weighted summation of the outputs of probability measurement levels from individual decision trees, while genetic algorithm optimizes connection weight matrix. Finally the target class label with the largest probability output value is selected as the decision result for combination classification methods. The proposed method is applied to the issues of customer credit rating assessment and customer response behaviour pattern recognition in CRM. From the simulation results it is concluded that the proposed method has higher predictive accuracy than single decision tree. Moreover, it retains good interpretability and optimizes classification rules.

본 연구는 통합공정일정계획(Integrated Process Planning and Scheduling; IPPS)의 최적화를 위한 계산 효율성이 높은 탐욕적 휴리스틱과 유전알고리즘(Genetic Algorithm; GA)을 결합한 하이브리드형 유전 알고리즘을 제안한다. IPPS는 기존의 공정계획과 일정계획을 동시에 풀고자 하는 NP-Hard 문제이다. 특히, 본 연구에서 다루는 IPPS는 tool related constraints가 포함된 것으로서 전통적인 GA는 수행도중 infeasible schedule을 빈번히 발생시킨다. 제안하는 방법의 아이디어는 전체적인 schedule의 구조에 영향을 미치는 operation의 sequence와 machine의 결정은 GA의 procedure를 따르고, 목적함수의 부분계산이 가능한 tool과 Tool Access Direction(TAD)는 greedy heuristics을 통하여 infeasibility를 해소하자는 것이다. 이를 통하여 계산시간의 급격한 증가 없이 또는 기존에 비해 계산시간을 감소시키면서 좋은 품질의 해를 구할 수 있다. 본 연구에서 제안하는 알고리즘은 benchmark problems을 이용하여 성능을 평가한다.

Direct spring loaded pressure relief valve(DSLPRV) is a safety valve to relax surge pressure of the pipeline system. DSLPRV is one of widely used safety valves for its simplicity and efficiency. However, instability of the DSLPRV can caused by various reasons such as insufficient valve volume, natural vibration of the spring, etc. In order to improve reliability of DSLPRV, proper selection of design factors of DSLPRV is important. In this study, methodology for selecting design factors for DSLPRV was proposed. Dynamics of the DSLPRV disk was integrated into conventional 1D surge pressure analysis. Multi-objective genetic algorithm was also used to search optimum design factors for DSLPRV.

This paper seeks to present a multi-control method that can contribute to effective control of the production line with multiple bottleneck processes. The multi-control method is the production system that complements shortcomings of CONWIP and DBR, and it is designed to determine the raw material input according to the WIP level of two bottleneck processes and WIP level of total process. The effectiveness of the production system developed by applying the multi-control method was verified by the following three procedures. Raw material input conditions of the multi-control method are as follows. First, raw materials are go into the production line when the number of the total process WIP is lower than established number of WIP in total process and first process is idle. Second, raw materials are introduced when the number of WIP of two bottleneck processes is lower than the established number of WIP of each bottleneck process. Third, raw materials are introduced when the first process and in front of bottleneck process are idle even if the number of WIP in the total process is less than established number of WIP of the total process. The production line with two bottleneck processes was selected as the condition for production environment, and the production process modeling of CONWIP, DBR and multi-control production method was defined according to the production condition. And the optimum limited WIP level suitable for each system was obtained by applying a genetic algorithm to determine the total limited number of WIP of CONWIP, the limited number of WIP of DBR bottleneck process, the number of WIP in the total process of multi-control method and the limited number of WIP of bottleneck process. The limited number of WIP of CONWIP, DBR and multi-control method obtained by the genetic algorithm were applied to ARENA modeling, which is simulation software, and a simulation was conducted to derive result values on the basis of three criteria such as production volume, lead time and number of goods in-progress.

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.