• 제어로봇시스템학회논문지
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• 제7권11호
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• pp.912-917
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• 2001

In this study, we define the measuring method of crank angle precisely using an event and perform a study on the hardware structure and software algorithms which is applicable for the commercial engine. Also we developed a Computer-ECU(Personal computer based electronic control unit) using a computer and a microprocessor, for performing the ignition at a desire position(angle) and for controlling a duty ratio a pulse for ISC(Idle speed control). We applied these algorithms to the modeling which is induced a concept of event and got a better result than a conventional ECU in the state of transient as a result of performing air fuel ratio control in a commercial engine. This technique can be used for the back to improve ECU performance. It the present type of Hybrid I. C voltage regulator is altered to the new type of regulator, we will be surely able to reduce the production cost as well as simplify the design of alternator\`s rear bracket and rectifier part because of the removal of trio diode. Experiment is taken by MS-R004.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• Geomechanics and Engineering
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• 제24권3호
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• pp.237-251
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• 2021

The main purpose of this study is to investigate the performance of the proposed hybrid teaching-learning based optimization algorithm on the optimum design of reinforced concrete (RC) cantilever retaining walls. For this purpose, three different design examples are optimized with 100 independent runs considering continuous and discrete variables. In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful meta-heuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms. Moreover, Rao-1 and Rao-2 algorithms are applied to this design problem for the first time. The objective function is defined as minimizing the total material and labor costs including concrete, steel, and formwork per unit length of the cantilever retaining walls subjected to the requirements of the American Concrete Institute (ACI 318-05). Furthermore, the effects of peak ground acceleration value on minimum total cost is investigated using various stem height, surcharge loads, and backfill slope angle. Finally, the most robust results were obtained by HTLBO with 50 populations. Consequently the optimization results show that, depending on the increase in PGA value, the optimum cost of RC cantilever retaining walls increases smoothly with the stem height but increases rapidly with the surcharge loads and backfill slope angle.

• Advances in concrete construction
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• 제13권1호
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• pp.11-23
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• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• 한국항행학회논문지
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• 제19권2호
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• pp.116-122
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• 2015

지속적으로 증가하고 있는 항공수요에 따라, 공항운영 측면에서 이동지역 내 항공기 이동에 대한 효율성을 증대할 수 있는 방안의 중요성이 대두되고 있다. 본 논문은 공항 이동지역을 운항하는 항공기에게 최단경로를 적시에 제공하여 공항운영의 효율성을 증대시키기 위한 경로 탐색 알고리즘을 제시하고자 한다. 기존 문헌들에서 여러 알고리즘이 개발되었는데, 대표적으로 Dijkstra 알고리즘 $A^*$ 알고리즘이 있다. Dijkstra 알고리즘은 상대적으로 느린 연산속도로 인해 공항구조가 복합해질 경우 최단경로를 적시에 제공하기 어려울 수 있다는 단점이 있으며, $A^*$ 알고리즘은 최적성을 보장하지 못한다는 단점이 있다. 본 논문에서는 두 알고리즘을 병합하여, 각 알고리즘의 단점을 보완한 새로운 Hybrid $A^*$ 알고리즘을 제시하였다. 성능분석 결과, Hybrid $A^*$ 알고리즘은 경로탐색에 있어 빠른 연산속도와 최적성이 개선됨을 확인하였다.

• 정보보호학회논문지
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• 제33권1호
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• pp.75-86
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• 2023

현재 금융거래 서비스에서 보편적으로 사용하는 RSA와 ECC 같은 공개키 암호 알고리즘은 양자 컴퓨터가 실현되면 더 이상 안전성을 보장할 수 없으므로 기존 레거시 알고리즘을 양자내성암호로 전환해야 한다. 하지만 다양한 서비스에 사용 중인 알고리즘을 교체하는 데에는 상당한 시간이 소요될 것으로 예상된다. 다가올 전환기를 대비하기 위하여 두 알고리즘을 결합하는 하이브리드 방식에 관한 연구가 필요하다. 본 논문에서는 레거시 알고리즘인 ECDH 알고리즘과 양자내성암호 알고리즘인 NTRU 알고리즘을 결합하여 세션키를 생성하는 하이브리드 세션키교환 프로토콜을 제안한다. TLS 1.3 기반 하이브리드 키 교환을 위해 IETF에서 제안한 방식들을 적용해본 결과 기존 금융거래 세션 보호 솔루션에 우리가 제안한 프로토콜을 사용하면 안전성을 강화할 수 있을 것으로 기대된다.

• 대한전자공학회논문지TC
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• 제49권3호
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• pp.47-61
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• 2012

현재 HFC(Hybrid Fiber Coaxial) 망에서 UHD(Ultra High Definition) 콘텐츠와 같은 대용량 방송콘텐츠 전송을 위한 대표적인 전송기술로, 다수의 채널을 결합시켜 논리적인 하나의 광대역 채널로 변경하여 데이터를 병렬 전송하는 방법이 대두되고 있다. 하지만 채널 결합을 통해 콘텐츠를 전송하는 시스템의 경우 하나의 콘텐츠 전송을 위해서 다수의 채널을 결합하기 때문에 채널 자원의 부족이 예상된다. 따라서 결합된 채널을 효율적으로 사용할 수 있는 기술이 필요하다. 본 논문에서는 결합된 채널의 효율적인 대역폭 사용을 위하여 세 가지 방식의 VBR(Variable Bit Rate) 대역폭 추정 알고리즘을 분석하였다. 세 가지의 유효 대역폭 추정 방식은 Guerin이 제안한 가우시안 근사를 통한 유효 대역폭 추정, Lee가 제안한 비디오 프레임 특성을 기반으로 한 유효 대역폭 추정과 Nagarajan이 제안한 가우시안 트래픽을 기반으로 한 유효 대역폭 추정 알고리즘이다. 또한, 이들을 분석하여 대용량 방송 시스템으로의 적용가능성을 평가하였다. 성능 분석을 위한 시뮬레이션은 OPNET 시뮬레이터를 사용하였고, 성능 분석의 정확성을 위해 실제 HD 방송 트래픽을 분석하여 대용량 방송 트래픽을 생성하였다.

• Smart Structures and Systems
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• 제26권4호
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• pp.451-468
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• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5904-5927
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• 2019

Many artificial intelligence (AI) techniques have been embedded into various engineering technologies to assist them in achieving different goals. The integration of modern technologies with energy consumption management system and occupant's comfort inside buildings results in the introduction of intelligent building concept. The major aim of this integration is to manage the energy consumption effectively and keeping the occupant satisfied with the internal environment of the building. The last few couple of years have seen many applications of AI techniques for optimizing the energy consumption with maximizing the user comfort in smart buildings but still there is much room for improvement in this area. In this paper, a hybrid of two AI algorithms called firefly algorithm (FA) and genetic algorithm (GA) has been used for user comfort maximization with minimum energy consumption inside smart building. A complete user friendly system with data from various sensors, user, processes, power control system and different actuators is developed in this work for reducing power consumption and increase the user comfort. The inputs of optimization algorithms are illumination, temperature and air quality sensors' data and the user set parameters whereas the outputs of the optimization algorithms are optimized parameters. These optimized parameters are the inputs of different fuzzy controllers which change the status of different actuators according to user satisfaction.

• 한국전산구조공학회논문집
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• 제16권4호
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• pp.407-417
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• 2003

최근 구조최적화분야에서 활발하게 사용되고 있는 유전알고리즘은 해집단을 운용하기 때문에, 많은 반복수와 적응도 평가를 위하여 해집단의 수에 해당하는 구조해석을 필요로 하며, 또한 교배율과 돌연변이율 등의 파라미터에 따라 알고리즘의 성능이 변화하므로 문제에, 따라 적합한 파라미터 설정이 필요한 근본적인 단점을 지니고 있다. 본 연구에서는 기존 유전알고리즘의 단점을 극복할 수 있는 복합유전알고리즘을 마이크로유전알고리즘과 단순유전알고리즘을 결합한 형식으로 그리고, 최적화에 요구되는 연산을 다수의 개인용 컴퓨터에서 동시에 분산하여 수행할 수 있는 고성능 분산 복합유전알고리즘으로 개발하였다. 개발된 알고리즘은 철골 가새골조 구조물의 최소중량설계에 적용하여 그 성능을 평가하였다.