• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.325-331
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• 2016

Computational fluid numerical analysis using the commercial code CFX was performed to develop a Pelton turbine for a pico hydro power generator using the circulating water of a cooling tower in a large building. The performance of the Pelton turbine was examined for different design factors, such as the bucket shape, in which the Pelton wheel was connected in an appropriate manner to the pipe section, and the number of buckets in order to find the optimal design of Pelton turbine for a pico hydro power using surplus water. A benchmark test was carried out on the manufactured small scale Pelton turbine to validate the design method of the Pelton turbine by numerical analysis. The results obtained by comparing the flow characteristics and power output measured using the ultrasonic flowmeter, the pressure transducer and the oscilloscope with the numerical results confirmed the validity of the analytical design method. The possibility of developing Pelton turbines for kW class pico hydro power generators using surplus water with an average circulation velocity of 1.2 m/s for the chosen bucket shape and number of buckets in a 30 m high building was confirmed.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1217-1228
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• 2005

Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.1-7
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• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.6
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• pp.113-125
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• 2017

In spite of bulk literature about the tuning of TMD, the effectiveness of TMD in reducing the seismic response of engineering structures is still in a row. This paper deals with the optimum tuning parameters of a passive TMD and simulated on MATLAB with a ten-story numerical shear building. A weighted multi-objective optimization method based on computer experiment consisting of coupled with central composite design(CCD) central composite design and response surface methodology(RSM) was applied to find out the optimum tuning parameters of TMD. After the optimization, the so-conceived TMD turns out to be optimal with respect to the specific seismic event, hence allowing for an optimum reduction in seismic response. The method was employed on above structure by assuming first the El Centro seismic input as a sort of benchmark excitation, and then additional recent strong-motion earthquakes. It is found that the RSM based weighted multi-objective optimized damper improves frequency responses and root mean square displacements of the structure without TMD by 31.6% and 82.3% under El Centro earthquake, respectively, and has an equal or higher performance than the conventionally designed dampers with respect to frequency responses and root mean square displacements and when applied to earthquakes.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.85-97
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• 2011

Wireless sensor networks have emerged as one of the key enabling technologies for ubiquitous computing since wireless intelligent sensor nodes connected by short range communication media serve as a smart intermediary between physical objects and people in ubiquitous computing environment. We recognize the wireless sensor network as a massively distributed and deeply embedded system. Such systems require concurrent and asynchronous event handling as a distributed system and resource-consciousness as an embedded system. Since the operating environment and architecture of wireless sensor networks, with the seemingly conflicting requirements, poses unique design challenges and constraints to developers, we propose a very new operating system for sensor nodes based on finite state machine. In this paper, we clarify the design goals reflected from the characteristics of sensor networks, and then present the heart of the design and implementation of a compact and efficient state-driven operating system, SenOS. We describe how SenOS can operate in an extremely resource constrained sensor node while providing the required reactivity and dynamic reconfigurability with low update cost. We also compare our experimental results after executing some benchmark programs on SenOS with those on TinyOS.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.4 s.74
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• pp.429-440
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• 2006

This paper comprises two specific objectives. The first is to examine the applicability of ordinary kriging interpolation(OK) to the p-adaptivity of the finite element method that is based on variogram modeling. The second objective Is to present the adaptive procedure by the hierarchical p-refinement in conjunction with a posteriori error estimator using the modified S.P.R. (superconvergent patch recovery) method. The ordinary kriging method that is one of weighted interpolation techniques is applied to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by experimental and theoretical variograms for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In the p-refinement, the analytical domain has to be refined automatically to obtain an acceptable level of accuracy by increasing the p-level non-uniformly or selectively. To verify the performance of the modified S.P.R. method, the new error estimator based on limit value has been proposed. The validity of the proposed approach has been tested with the help of some benchmark problems of linear elastic fracture mechanics such as a centrally cracked panel, a single edged crack, and a double edged crack.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.3
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• pp.63-76
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• 2004

This paper presents LRB-based hybrid base isolation systems employing additional active/semiactive control devices for mitigating earthquake-induced vibration of a cable-stayed 29 bridge. Hybrid base isolation systems could improve the control performance compared with the passive type-base isolation system such as LRB-installed bridge system due to multiple control devices are operating. In this paper, the additional response reduction by the two typical additional control devices, such as active type hydraulic actuators controlled by LQG algorithm and semiactive-type magnetorheological dampers controlled by clipped-optimal algorithm, have been evaluated bypreliminarily investigating the slightly modified version of the ASCE phase I benchmark cable-stayed bridge problem (i.e., the installation of LRBs to the nominal cable-stayed bridge model of the problem). It shows from the numerical simulation results that all the LRB based hybrid seismic isolation systems considered are quite effective to mitigate the structural responses. In addition, the numerical results demonstrate that the LRB based hybrid seismic isolation systems employing MR dampers have the robustness to some degree of the stiffness uncertainty of in the structure, whereas the hybrid system employing hydraulic actuators does not. Therefore, the feasibility of the hybrid base isolation systems employing semiactive additional control devices could be more appropriate in realfor full-scale civil infrastructure applications is clearly verified due to their efficacy and robustness.

• International Commerce and Information Review
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• v.14 no.2
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• pp.147-172
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• 2012

The aim of this research is to identify the competencies required by freight forwarding company logistics managers or supply chain managers. And this research also attempts to show their relative importance and key knowledge areas that require improvement. Using a survey questionnaire, data was collected against forty three logistics and supply chain management skills or competencies, which were then grouped into four categories and analysed. The Analysis revealed that supply chain awareness, ability to make decisions, analytical skill, communication skill, supply chain cost, people skill, and integration of internal or external information flow which belong to logistics planning group are considered the most important competencies for effective and efficient logistics functioning. On the other hand, reverse logistics and IATA regulations from environmental awareness group show little influence on logistics managers for improving their logistics performances. The results have implications for a variety of parties including prospective logisticians, students, teachers and companies considering expanding their business to Chinese market. For example, the results permit companies to employ appropriate logistics managers who are qualified with sufficient skills and competencies suggested in this research. In the case of practitioners, the results provide a benchmark for comparison with their current level of abilities and suggested competencies.

• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.3
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• pp.9-18
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• 2017

Construction Management at Risk (CMAR) is a project delivery method that enables CM companies to deliver projects at a Guaranteed Maximum Price (GMP). General contractors can apply CMAR from the initial design phase right through the construction phase to reduce risks and improve project performance. One of the major advantages CMAR offers is that it permits a general contractor to provide a comprehensive suite of preconstruction services, including estimating, a constructability review, value engineering, drawings and a specification review, green building, and Building Information Modeling(BIM), among others. However, general contractors in South Korea currently provide only limited preconstruction services using CMAR because few CMAR projects have yet been implemented in Korea and their experience using the method is therefore limited. This benchmark study of how foreign general contractors utilize CMAR in their projects, particularly during the preconstruction process, its purpose, and the roles and responsibilities of each of the different participants in successful implementations thus provides invaluable information and will serve as a useful guide for Korean contractors seeking to incorporate CMAR preconstruction services in their projects and thus improve the competitiveness of their construction businesses.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1314-1323
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• 1999

컴퓨터의 사용이 일반화됨에 따라 데이타를 생성하고 수집하는 것이 용이해졌다. 이에 따라 데이타로부터 자동적으로 유용한 지식을 얻는 기술이 필요하게 되었다. 데이타 마이닝에서 얻어진 지식은 정확성과 이해성을 충족해야 한다. 본 논문에서는 데이타 마이닝을 위하여 퍼지 결정트리에 기반한 효율적인 퍼지 규칙을 생성하는 알고리즘을 제안한다. 퍼지 결정트리는 ID3와 C4.5의 이해성과 퍼지이론의 추론과 표현력을 결합한 방법이다. 특히, 퍼지 규칙은 속성 축에 평행하게 판단 경계선을 결정하는 방법으로는 어려운 속성 축에 평행하지 않는 경계선을 갖는 패턴을 효율적으로 분류한다. 제안된 알고리즘은 첫째, 각 속성 데이타의 히스토그램 분석을 통해 적절한 소속함수를 생성한다. 둘째, 주어진 소속함수를 바탕으로 ID3와 C4.5와 유사한 방법으로 퍼지 결정트리를 생성한다. 또한, 유전자 알고리즘을 이용하여 소속함수를 조율한다. IRIS 데이타, Wisconsin breast cancer 데이타, credit screening 데이타 등 벤치마크 데이타들에 대한 실험 결과 제안된 방법이 C4.5 방법을 포함한 다른 방법보다 성능과 규칙의 이해성에서 보다 효율적임을 보인다.Abstract With an extended use of computers, we can easily generate and collect data. There is a need to acquire useful knowledge from data automatically. In data mining the acquired knowledge needs to be both accurate and comprehensible. In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods.In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets including the IRIS data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.