• Advances in Computational Design
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• 제4권3호
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• pp.197-210
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• 2019

Motion generation of a four-bar linkage is a type of mechanism synthesis that has a wide range of applications such as a pick-and-place operation in manufacturing. In this research, the use of meta-heuristics for motion generation of a four-bar linkage is demonstrated. Three problems of motion generation were posed as a constrained optimization probably using the weighted sum technique to handle two types of tracking errors. A simple penalty function technique was used to deal with design constraints while three meta-heuristics including differential evolution (DE), self-adaptive differential evolution (JADE) and teaching learning based optimization (TLBO) were employed to solve the problems. Comparative results and the effect of the constraint handling technique are illustrated and discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.3841-3861
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• 2017

Virtualized small cell network is a promising architecture which can realize efficient utilization of the network resource. However, conventional full duplex self-backhauls lead to residual self-interference, which limits the network performance. To handle this issue, this paper proposes a virtual resource allocation, in which the residual self-interference is fully exploited by employing a physical-layer network coding (PNC) aided self-backhaul scheme. We formulate the features of PNC as time slot and information rate constraints, and based on that, the virtual resource allocation is formulated as a mixed combinatorial optimization problem. To solve the problem efficiently, it is decomposed into two sub problems, and a two-phase iteration algorithm is developed accordingly. In the algorithm, the first sub problem is approximated and transferred into a convex problem by utilizing the upper bound of the PNC rate constraint. On the basis of that, the convexity of the second sub problem is also proved. Simulation results show the advantages of the proposed scheme over conventional solution in both the profits of self-backhauls and utility of the network resource.

• 한국통신학회논문지
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• 제38B권1호
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• pp.18-25
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• 2013

본 논문에서는 차세대 이종망 환경에서 펨토셀이 자율적으로 커버리지를 조절하는 방안을 제안한다. 펨토셀의 커버리지가 펨토셀이 설치된 실내 영역보다 큰 경우 펨토셀 커버리지를 통과하는 실외 단말에 의해 핸드오버 요청이 발생하여 불필요한 signaling을 증가시키고 이에 따라 overhead가 커지게 된다. 펨토셀의 커버리지가 펨토셀이 설치된 실내 영역보다 작은 경우 실내에 위치한 단말이 펨토셀에 연결되지 못하는 문제가 발생한다. 따라서 본 논문에서는 펨토셀 커버리지가 실내영역과 일치하도록 자율적으로 전송 전력을 조절하는 방법을 제안한다. 제안 기법은 펨토셀이 스스로 얻을 수 있는 정보인 핸드오버 요청 및 단말의 펨토셀에 대한 결합등록(membership) 여부를 이용함으로써 자율적인 커버리지 조절을 가능 하게 한다. 제안 기법의 성능 분석을 위해 먼저 커버리지를 실내영역과 일치시키는 펨토셀 전송 전력의 이론값을 도출한다. 이후 제안 기법을 모의실험에 적용하여 분석한 결과에서 펨토셀의 전송 전력이 자율적으로 조절되어 이론값으로 수렴함을 보인다.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3150-3163
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• 2023

To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multiphysics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.

• Structural Engineering and Mechanics
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• 제84권4호
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.88-93
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• 2008

The electromagnetic parameters of a permanent magnet synchronous motor (PMSM) such as the open load permanent magnet flux, d axis reactance $X_d$, and q axis reactance $X_q$, are most essential to the performance analysis and optimization design of the motor. Based on the numerical analysis of the 3D electromagnetic field, the three electromagnetic parameters of permanent magnet synchronous motors with U form interior rotor structures are calculated by FEA. The rules of the leakage coefficient and reactance parameters changing with the air gap length, permanent magnet magnetism length, and isolation magnetic bridge dimensions in the rotor are given. The calculated values agree well with the measured values. The FEA results are integrated with the self compiled electromagnetic design program to optimize the prototype motor. The tested performances of the prototype motor prove that the method is suitable for the optimization of motor structure.

• 스마트미디어저널
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• 제13권2호
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• pp.129-135
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• 2024

이 연구에서는 MAP-Elites 알고리즘의 연속 최적화 성능을 향상한 새로운 접근법을 제안한다. 기존의 자기 참조 MAP-Elites 알고리즘은 차분 진화 알고리즘의 "DE/rand/1/bin" 연산자를 사용했는데, 이 연산자는 회전 불변이 아니라서 각 변수 간의 상관관계가 높은 경우 성능이 감소하는 문제가 존재한다. 제안하는 알고리즘은 "DE/rand/1/bin" 연산자 대신에 "DE/current-to-rand/1" 연산자를 사용한다. 이 연산자는 회전 불변성을 가지므로 각 변수 간의 상관관계가 높은 분리 불가능 최적화 문제에서도 강건한 성능을 보장할 수 있다. 실험 결과, 제안하는 알고리즘이 비교 알고리즘들에 비해 높은 성능을 발휘함을 확인했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권3호
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• pp.205-223
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• 2010

In this paper, we analyze the impact of different frame types on self-similarity and burstiness characteristics of the aggregated frame traffic from a real 802.11 wireless local area network. We find that characteristics of aggregated frame traffic are affected by both mean frame size and the proportion of specified frame types. Based on this new knowledge, an adaptative frame size optimization (AFSO) mechanism is proposed to improve the transmission efficiency by adaptively adjusting data frame size according to the proportions of different frame types. Simulation results show that our proposed mechanism can effectively regulate the burstiness of aggregated frame traffic and improve the successful delivery rate of data frames when a fixed throughput target is set for 802.11 wireless networks.

• 전기전자학회논문지
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• 제23권4호
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• pp.1400-1407
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• 2019

본 논문은 실시간 자가학습과 분류 기능을 모두 지원하는 support vector machine (SVM) 기반 가스 분류기의 하드웨어 구조 설계 및 구현 결과를 제시한다. 제안된 가스 분류기는 학습 알고리즘으로 modified sequential minimal optimization(MSMO)을 사용하였고, 학습과 분류 기능을 공유구조를 사용하여 설계함으로써 기존 논문 대비 하드웨어 면적을 35% 감소시켰다. 설계된 가스 분류기는 Xilinx Zynq UltraScale+ FPGA를 사용하여 구현 및 검증되었고, 108MHz의 동작 주파수에서 3,337개의 CLB LUTs로 구현 가능함을 확인하였다.