• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.617-635
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• 2020

The purpose of this study is to discuss how to improve the maneuverability of lifting and diving for underwater vehicle's vertical motion. Therefore, to solve these problems, applied the 3-D numerical simulation, Taguchi's Design of Experiment (DOE), and intelligent parameter design methods, etc. We planned four steps as follows: firstly, we applied the 2-D flow simulation with NACA series, and then through the Taguchi's dynamic method to analyze the sensitivity (β). Secondly, take the data of pitching torque and total resistance from the Taguchi orthogonal array (L9), the ignal-to-noise ratio (SNR), and analysis each factorial contribution by ANOVA. Thirdly, used Radial Basis Function Network (RBFN) method to train the non-linear meta-modeling and found out the best factorial combination by Particle Swarm Optimization (PSO) and Weighted Percentage Reduction of Quality Loss (WPRQL). Finally, the application of the above methods gives the global optimum for multi-quality characteristics and the robust design configuration, including L/D is 9.4:1, the foreplane on the hull (Bow-2), and position of the sail is 0.25 Ls from the bow. The result shows that the total quality is improved by 86.03% in comparison with the original design.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• 전자공학회논문지
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• 제54권6호
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• pp.106-113
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• 2017

본 논문에서는 자율주행형 다관절 차량용 제어 시스템 구축 시 장치 간 네트워크로 이더넷 기반의 Train Communication Network(TCN)를 적용할 경우 적합한 네트워크 토폴로지를 제안하고 실험을 통하여 그 결과를 측정하여 검증한다. 케이블 수, 포트 수 등 구조적인 제한조건과 네트워크 응답시간, 최대 전송량 등 성능적인 제한조건을 고려하여 네트워크 토폴로지를 제안한다. 스타 토폴로지, 데이지체인 토폴로지, 그리고 이들을 절충한 하이브리드 토폴로지를 각각 적용하여 비교하며 본 논문에서는 특히 하이브리드 토폴로지의 적절한 구성을 위해 그룹으로 묶이는 노드 수를 구한다. 적절하게 노드의 그룹이 구성된 하이브리드 토폴로지는 본 논문에서 최적 토폴로지로 제안하는 구조이다. 먼저 시뮬레이션을 통해 각각의 토폴로지 구성 시의 네트워크 성능에 대한 예상치를 도출하며 이 후 실제 장치를 연결하여 네트워크를 구현한다. 다양한 네트워크 성능 측정 프로그램을 이용하여 각 토폴로지에서의 성능을 측정하고 비교를 통해 제안한 방안의 우수성을 기술한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.564-567
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• 2002

This paper evaluates the stability of the terminal group for he inter-vehicle communication (IVC) network with an autonomous relay access scheme. Some stability criterions such as updating rate, terminal group convergence probability and total path average holding time have been conducted by computer simulation. As the results, dynamic moving of the terminal is the serious problem that can degrade the stability of the terminal group and directly affect to overall performance of the IVC network.

• 한국지능시스템학회논문지
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• 제13권4호
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• pp.409-415
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• 2003

Recently, the modular learning are very popular and receive much attention for pattern classification. The modular learning method based on the "divide and conquer" strategy can not only solve the complex problems, but also reach a better result than a single classifier′s on the learning quality and speed. In the neural network area, some researches that take the modular learning approach also have been made to improve classification performance. In this paper, we propose a simple modular neural network for characters recognition of vehicle number plate and evaluate its performance on the clustering methods of feature vectors used in constructing subnetworks. We implement two clustering method, one is grouping similar feature vectors by K-means clustering algorithm, the other grouping unsimilar feature vectors by our proposed algorithm. The experiment result shows that our algorithm achieves much better performance.

• 경영과학
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• 제17권2호
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• pp.87-95
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• 2000

This paper presents two analytical approaches to determine the vehicle fleet size for container shuttle service. The shuttle service can be defined as the repetitive travel between the designated places during working period. In the first approach, the transportation model is adopted in order to determine the number of vehicles required. Its advantages and disadvantages in practical application are also discussed. In the second approach, a logical network which is oriented on job is transformed from a physical network which is focused on demand site. Nodes on the logical network represent jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next job. Then a mathematical formulation is constructed similar to the multiple traveling salesman problem (TSP). A solution procedure is carried out based on the well-known insertion heuristic with the real world data.

• 한국자동차공학회논문집
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• 제12권3호
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• pp.170-176
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• 2004

MR dampers show highly nonlinear and histeretic dynamic behavior. Therefore, for a vehicle dynamic simulation with MR dampers, this dynamic characteristics should be accurately reflected in the damper model. In this paper, an artificial neural network technique was developed for modeling MR dampers. This MR damper model was successfully verified through a random input forcing test. This MR damper model can be used for semi-active suspension vehicle dynamics and control simulations with practical accuracy.

• 한국위성정보통신학회논문지
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• 제12권1호
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• pp.43-48
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• 2017

Recently, demands on civilian UAV (Unmanned Aerial Vehicle) has been increasing and appropriate communication system is required for the UAV. In this paper, the performance of the UAV communication system using commercial wireless mobile network is discussed. The main service area of the wireless mobile network is ground level however the flying range of the UAV is normally in high altitude. Because of this mismatch of service area the performance of the UAV communication system is degraded in high altitude. To compensate performance degradation of the UAV communications system in high altitude, adaptive array antenna is introduced which is able to overcome altitude limitation of the UAV communication system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.468-471
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• 2005

The Air Quality Sensor(AQS), located near the fresh air inlet, serves to reduce the amount of pollution entering the vehicle cabin through the HVAC(heating, ventilating, and air conditioning) system by sending a signal to close the fresh air inlet door/ventilation flap when the vehicle enters a high pollution area. One chip sensor module which include above two sensing elements, humidity sensor and bad odor sensor was developed for AQS (air quality sensor) in automobile. With this sensor module, PIC microcontroller was designed with back propagation neural network to reduce detecting error when the motor vehicles pass through the dense fog area. The signal from neural network was modified to control the inlet of automobile and display the result or alarm the situation. One chip microcontroller, Atmega128L (ATmega Ltd., USA) was used. For the control and display. And our developed system can intelligently detect the bad odor when the motor vehicles pass through the polluted air zone such as cattle farm.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.