• 한국시뮬레이션학회논문지
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• 제24권4호
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• pp.11-21
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• 2015

클라우드 컴퓨팅 환경은 다수의 가상서버 처리요청으로 인해 필요 자원을 충분히 제공하지 못한 경우, 특정 서버에 부하가 걸리는 문제가 발생할 수 있다. 이주관리자는 물리서버 내에 존재하는 가상서버들의 이주 효율성 향상을 위해 각 물리서버의 자원 정보를 모니터링 시스템으로부터 전달받고, 시뮬레이션 결과 값을 토대로 이주 목적지 물리서버를 결정한다. 하지만 모든 물리서버의 미래 자원 사용량을 예측하여 시뮬레이션 과정을 거쳐 이주 목적지 물리서버를 결정하는 것은 소수의 서버 네트워크 컴퓨팅 환경보다 거대하고 복잡한 클라우드 컴퓨팅 환경에서는 오버헤드가 크다. 본 논문에서는 퍼지로직 기반 이주 결정 모델(FPLM)을 제안하고 DEVS 형식론을 적용하여 이주 발생 횟수 및 성능을 비교 측정하였다. FPLM은 이주 발생 횟수 및 이주 목적지 결정 오버헤드를 감소시킴으로써 이주 발생으로 인한 물리서버 자원 사용 효율성을 증가시킨다.

• 한국컴퓨터정보학회논문지
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• 제18권1호
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• pp.11-19
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• 2013

자율적인 다개체 모바일 로봇 시스템에 관해 경로 계획과 충돌회피는 중요한 기능이며 동시에 협력과 협동적으로 주어진 일을 수행하는데 필요한 기능이다. 본 논문에서는 이러한 중요하고도 도전적인 문제를 다룬다. 제안된 방법은 포텐셜 필드 방법과 퍼지로직 시스템에 기반을 두고 있다. 첫째로, 전역경로 계획은 포텐셜 필드를 이용하여 로봇이 목적지까지 가는데 비용을 최소화할 수 있는 경로를 선택한다. 그러고 나서 지역경로 계획은 퍼지로직 시스템을 이용하여 정적이거나 동적인 장애물과의 충돌을 피하기 위해 전역경로에서 경로를 변경시킨다. 본 논문에서는 각각의 로봇은 독립적으로 목적지를 선택하며 동시에 다른 로봇은 동적인 장애물로 고려한다. 또한 장애물의 움직임을 예측할 필요도 없다. 이러한 과정은 각각의 로봇이 해당되는 목적지를 찾을 때 까지 지속된다. 이 방법을 테스트하기 위해 자율 다개체 로봇 시뮬레이터(AMMRS)를 개발했으며 시뮬레이션과 실험기반의 결과물을 제공한다. 본 결과는 다개체 모바일 로봇 시스템에 대하여 경로계획과 충돌회피 전략이 효율적이며 유용하다는 것을 보인다.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권2호
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• pp.101-106
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• 2010

We propose an approach to estimate the real-time moving trajectory of an object in this paper. The object's position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Extended Kalman Filter(EKF) and neural networks are utilized cooperatively. Since the EKF needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach the Kohonen networks, which have a high adaptability to the memory of the inputoutput relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the EKF is demonstrated through real experiments.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1195-1202
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• 2017

Induction motors often reach their maximum efficiency at the nominal load. In most applications, the machine load is not equal to the nominal load, thus reduces the motor efficiency and turns a greater percent of power into loss. In this paper, the induction motor control problem has been investigated to reduce the system losses. The Field Oriented Control method (FOC) has been employed in this paper. In this research, the mathematical equations related to system losses are calculated in relation to torque and speed, and then the q- and d-axis are summarized according to the current components. After that, the proposed method is applied along with d- and q-axis. In the recent three decades, many techniques have been suggested to improve the induction motor performance using smart and non-smart methods. In this paper, a new PSO-Fuzzy method have used in real time. The fuzzy logic method serves as speed controller in q-axis and PSO algorithm controls the optimum flux in d-axis. It will be proved that the use of this combined method will lead to a significant improvement in motor efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Structural Engineering and Mechanics
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• 제82권4호
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• pp.445-458
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• 2022

Some of the control systems used in engineering structures that use sensors and decision systems have some time delay reducing efficiency of the control system or even might make it unstable. In this research, in addition to considering the effect of the time delay in vibration control process, predictive control is used to compensate the time delay. A semi-active vibration control approach with the help of magneto-rheological dampers is implemented. In addition to using fuzzy inference system to determine the appropriate control voltage for MR damper, structural behavior prediction system and specifying future responses are also used such that the time delays occurring within control process are overcome. For this purpose, determination of prediction horizon is conducted for one, five, and ten steps ahead for single degree of freedom structures with periods ranging from 0.1 to 4 seconds, subjected to twenty earthquake excitations. The amount of time delay applied to the control system is 0.1 seconds. The obtained results indicate that for 0.1 second time delay, average prediction error values compared to the case without time delay is 3.47 percent. Having 0.1 second time delay in a semi-active control system reduces its efficiency by 11.46 percent; while after providing the control system with structure behavior prediction, the difference in the results for the control system without time delay is just 1.35 percent on average; indicating a 10.11 percent performance improvement for the control system.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권9호
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• pp.712-721
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• 2010

본 논문에서는 화재 불꽃의 시각적 특징들을 확률적인 멤버십 함수로 모델링하고 이를 퍼지 유한상태 오토마타에 적용한 새로운 화재 불꽃 감지 알고리즘을 제안한다. 먼저 입력된 영상에서 배경모델을 이용하여 움직임 영역을 추출하고 불꽃 색상 모델을 적용하여 최종 화재 후보 영역을 결정한다. 불꽃영역은 일반적으로 연속적이며 불규칙한 패턴을 가지고 있으므로 명도와 웨이블릿 에너지의 왜도 값과 모션의 상승 방향성을 이용하여 확률모델을 생성하고 이를 퍼지 유한상태 오토마타에 적용한다. 퍼지 유한상태 오토마타는 오토마타의 성능과 퍼지 로직이 결합된 형태로 컴퓨터 시스템에서 불확실한 문제뿐 아니라 연속적인 공간에서 발생하는 문제를 처리하는 시스템적인 접근법을 제공한다. 제안된 알고리즘은 다양한 화재 영상에서 성공적으로 불꽃을 감지하였고 다른 알고리즘에 비해 더 좋은 성능을 보여주고 있다.

• 대한기계학회논문집
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• 제16권8호
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• pp.1468-1484
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• 1992

본 연구에서는 유한요소법을 이용해서 초기형상을 결정하는 새로운 방법으로 서 전방 투사법을 제안하고자 한다. 전방 투사법으로서 선형 보간을 이용한 방법과 소성 문제의 물리적인 특성을 고려하여 퍼지 로직을 도입한 퍼지시스템을 개발하려 한 다. 선형보간을 이용한 전방투사법은 임의의 초기 형상에 대한 유한 요소 해석 결과 얻어진 최종 형상에서의 미 충만 부피를 선형 보간하여 초기 형상에 적용함으로서 최 적 초기 형상을 결정하는 방법이다. 그러나 미 충만 부피의 변화가 미소할때에는 쉽 게 최적 초기 값을 찾지 못하는 경우가 발생하므로 유동 특성을 고려한 퍼지 로직을 구성하여 퍼지 시스템을 개발하였다. 이 방법을 리브-웨브(rbi-web)형태의 축대칭 단조 문제에 적용하고 유한 요소법에 의한 해석중 격자 재구성의 필요에 의해 단위체 격자 재구성법을 이용한다. 결정해야될 초기 형상의 변수로서는 형상비(aspect ra- tio=높이/지름)을 고려하기로 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1317-1341
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• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.