• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권11호
• /
• pp.3544-3564
• /
• 2022

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm Optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a large space.

• 한국전자파학회논문지
• /
• 제29권1호
• /
• pp.50-60
• /
• 2018

고출력 응용환경에 적용 가능한 광대역 다층 구조 레이돔을 개발하였다. 이를 위해 다층 레이돔의 전파전파특성을 ABCD 행렬로 표현하였고, Particle swarm 최적화 알고리즘을 상용 수치 모델링 툴로 구현하여 레이돔의 최적화된 층별 두께와 물질상수를 구하였다. 바람, 눈, 얼음 등 외부 기상 환경을 고려한 기계적 특성을 감안하여 레이돔을 재설계하였다. 대형구조물의 제작 제한조건을 고려한 두께를 재산출하여 전력 전달특성을 재분석하였다. 대기 정전파괴 때보다 10 dB 높은 첨두 전기장의 세기 조건에서 상용 해석 툴을 이용하여, 설계된 레이돔의 RF에 대한 대기 정전파괴 특성을 분석하였다. 설계된 다층 레이돔을 제작하여 소신호 및 대신호 시험을 수행하였고, 상용 도구들을 사용한 계산값과 비교하여 목표 성능을 획득하였다.

• 제어로봇시스템학회논문지
• /
• 제16권12호
• /
• pp.1150-1158
• /
• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of Power Electronics
• /
• 제11권4호
• /
• pp.408-417
• /
• 2011

The plug-in hybrid electric vehicles (PHEVs) are specialized hybrid electric vehicles that have the potential to obtain enough energy for average daily commuting from batteries. The PHEV battery would be recharged from the power grid at home or at work and would thus allow for a reduction in the overall fuel consumption. This paper proposes an integrated power electronics interface for PHEVs, which consists of a novel Eight-Switch Inverter (ESI) and an interleaved DC/DC converter, in order to reduce the cost, the mass and the size of the power electronics unit (PEU) with high performance at any operating mode. In the proposed configuration, a novel Eight-Switch Inverter (ESI) is able to function as a bidirectional single-phase AC/DC battery charger/ vehicle to grid (V2G) and to transfer electrical energy between the DC-link (connected to the battery) and the electric traction system as DC/AC inverter. In addition, a bidirectional-interleaved DC/DC converter with dual-loop controller is proposed for interfacing the ESI to a low-voltage battery pack in order to minimize the ripple of the battery current and to improve the efficiency of the DC system with lower inductor size. To validate the performance of the proposed configuration, the indirect field-oriented control (IFOC) based on particle swarm optimization (PSO) is proposed to optimize the efficiency of the AC drive system in PHEVs. The maximum efficiency of the motor is obtained by the evaluation of optimal rotor flux at any operating point, where the PSO is applied to evaluate the optimal flux. Moreover, an improved AC/DC controller based Proportional-Resonant Control (PRC) is proposed in order to reduce the THD of the input current in charger/V2G modes. The proposed configuration is analyzed and its performance is validated using simulated results obtained in MATLAB/ SIMULINK. Furthermore, it is experimentally validated with results obtained from the prototypes that have been developed and built in the laboratory based on TMS320F2808 DSP.

• 디지털융복합연구
• /
• 제14권8호
• /
• pp.293-303
• /
• 2016

근래에 진화 연산을 활용한 교섭 게임의 분석은 게임 이론 분야에서 중요한 문제로 다루어지고 있다. 본 논문은 교섭 게임에서 진화 연산을 사용하여 이기종 인공 에이전트 간의 상호 작용 및 공진화 과정을 조사하였다. 교섭게임에 참여하는 진화전략 에이전트들로서 유전자 알고리즘(GA), 입자군집최적화(PSO) 및 차분진화알고리즘(DE) 3종류를 사용하였다. GA-agent, PSO-agent 및 DE-agent의 3가지 인공 에이전트들 간의 공진화 실험을 통해 교섭게임에서 가장 성능이 우수한 진화 계산 에이전트가 무엇인지 관찰 실험하였다. 시뮬레이션 실험결과, PSO-agent가 가장 성능이 우수하고 그 다음이 GA-agent이며 DE-agent가 가장 성능이 좋지 않다는 것을 확인하였다. PSO-agent가 교섭 게임에서 성능이 가장 우수한 이유를 이해하기 위해서 게임 완료 후 인공 에이전트 전략들을 관찰하였다. PSO-agent는 거래 실패로 인해 보수를 얻지 못하는 것을 감수하고서라도 가급적 많은 보수를 얻기 위한 방향으로 진화하였다는 것을 확인하였으며, 반면에 GA-agent와 DE-agent는 소량의 보수를 얻더라도 거래를 성공시키는 방향으로 진화하였다는 것을 확인하였다.

• 한국지능시스템학회논문지
• /
• 제22권6호
• /
• pp.722-728
• /
• 2012

도심지역의 하수관거 시스템은 우수 수용능력 및 하수 월류 발생 등의 시스템의 한계점을 가지고 있어, 강우시 우수 유출수로 인한 침수저감과 더불어 도시비점오염원의 저감에 모두 대응할 수 있는 저류시설의 도입이 주목받고 시작하였다. 최근 환경부에서는 방재적 우수관리와 더불어 합류식 하수관거 월류수, 분류식 우수관거 유출수 처리를 포함하는 다기능 저류시설을 "하수저류시설"이라 통칭하고, 이의 도입을 적극 추진하고 있는 실정이다. 반면 대규모 단일 저류시설 설치의 경우에는 공간 확보의 문제가 발생할 수 있으며, 이에 대안으로는 중 소규모의 분산형 저류시설 설치 및 운영을 들 수 있다. 본 연구에서는 분산형 저류시설-하수관망 네트워크 시스템의 최적 운용을 위한 모델 예측 제어기법을 제안한다. 이를 위해 첫째로 네트워크 시스템의 각 구성 요소의 수리모델을 제시함으로써 보다 정밀한 하수관망 네트워크의 거동을 모사하고자 한다. 둘째로 제안된 모델을 기반으로 현재의 강우 유입량을 고려하여 각 저류조의 수위, 하수관로의 유입/유출량을 예측하여, 입자군집 최적화 알고리즘을 이용한 모델 예측 제어기법을 바탕으로 주어진 제약조건을 만족하며 상황을 바탕으로 제안된 제어기법의 사용여부에 따른 효과를 비교 분석하고, 이의 타당성을 검증하고자 한다.

• Journal of Electrical Engineering and Technology
• /
• 제12권1호
• /
• pp.161-172
• /
• 2017

In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.

• 한국항공우주학회지
• /
• 제41권7호
• /
• pp.524-531
• /
• 2013

본 연구에서는 헬리콥터 로터 블레이드의 제작 과정 및 여러 가지 요인으로 인해 발생하는 불균형성을 해소하기 위한 RTB(Rotor Track and Balance) 알고리즘을 개발하였다. 비행 시험 결과로부터 RTB 조절 값과 트랙 및 기체 진동 사이의 상호관계를 선형모델을 이용한 회귀분석을 통하여 RTB 모델을 구축하였다. 개발된 RTB 알고리즘을 실기 시험 결과에 적용하여 RTB 모델을 검증하였고 선형화 모델만으로도 비교적 정확한 모델링이 가능함을 확인하였다. RTB 조절값 설정을 위해 최적화 문제를 정식화하고 유전자 알고리즘에 입자 군집 최적화(PSO) 알고리즘을 결합하여 빠른 수렴성을 갖는 최신의 최적화 기법을 적용하였다. 또한 최적화 해석을 통하여 얻은 RTB 조절값을 이용하여 트랙 편차와 기체 진동을 허용 기준치 아래로 감소시키고, 다양한 비행 조건에 대하여 효율적인 RTB를 수행할 수 있음을 보였다.

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

• 한국군사과학기술학회지
• /
• 제23권4호
• /
• pp.407-416
• /
• 2020

Recent complex battle field demands Network Centric Warfare(NCW) ability to control various parts into a cohesive unit. In path planning filed, the NCW ability increases complexity of path planning algorithm, and it has to consider a communication coverage map as well as traditional parameters such as minimum radar exposure and survivability. In this paper, pros and cons of various propagation models are summarized, and we suggest a coverage map generation method using a Longley-Rice propagation model. Previous coverage map based on line of sight has significant discontinuities that limits selection of path planning algorithms such as Dijkstra and fast marching only. If there is method to remove discontinuities in the coverage map, optimization based path planning algorithms such as trajectory optimization and Particle Swarm Optimization(PSO) can also be used. In this paper, the Longley-Rice propagation model is used to calculate continuous RF strengths, and convert the strength data using smoothed leaky BER for the coverage map. In addition, we also suggest other types of rough coverage map generation using a lookup table method with simple inputs such as terrain type and antenna heights only. The implemented communication coverage map can be used various path planning algorithms, especially in the optimization based algorithms.