• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.271-276
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• 2006

To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC (Cerebellar Model Articulation Controller) is integrated into ASE controller. ASE-reinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training for CMAC is utilized to establish the look-up table and to produce discrete control outputs. In computer simulation, an ASE controller and a couple of ASE-CMAC neural network are trained to balance the inverted pendulum on a cart. The number of trials until the controllers are established and the learning performance of the controllers are evaluated to find that generalization ability of the CMAC improves the speed of the ASE-reinforcement learning enough to realize the cartpole control system.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.219-223
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• 2001

Within a mechanical system such as an automotive the number of standard machine parts is increasing, so that the parts selection becomes more important than ever before. Selection of appropriate bearings in the preliminary design phase of a machine is also important. In this paper, three decision-making approaches are compared to find out a model that is appropriate to bearing selection problem. An artificial neural network, which is trained with real design cases, is used to select a bearing mechanism at the first step. Then, the subtype of the bearing is selected by the weighting factor method. Finally, types of peripherals such as lubrication methods are determined by a rule-based expert system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1983-1989
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• 2005

Industrial robots are powerful, extremely accurate multi-jointed systems, but they are heavy and highly rigid because of their mechanical structure and motorization. Therefore, sharing the robot working space with its environment is problematic. A novel pneumatic artificial muscle actuator (PAM actuator) has been regarded during the recent decades as an interesting alternative to hydraulic and electric actuators. Its main advantages are high strength and high power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available and cheap power source, inherent safety and mobility assistance to humans performing tasks. The PAM is undoubtedly the most promising artificial muscle for the actuation of new types of industrial robots such as Rubber Actuator and PAM manipulators. However, some limitations still exist, such as the air compressibility and the lack of damping ability of the actuator bring the dynamic delay of the pressure response and cause the oscillatory motion. In addition, the nonlinearities in the PAM manipulator still limit the controllability. Therefore, it is not easy to realize motion with high accuracy and high speed and with respect to various external inertia loads in order to realize a human-friendly therapy robot To overcome these problems a novel controller, which harmonizes a phase plane switching control method with conventional PID controller and the adaptabilities of neural network, is newly proposed. In order to realize satisfactory control performance a variable damper - Magneto-Rheological Brake (MRB) is equipped to the joint of the manipulator. Superb mixture of conventional PID controller and a phase plane switching control using neural network brings us a novel controller. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practical PAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through experiments, which had proved that the stability of the manipulator can be improved greatly in a high gain control by using MRB with phase plane switching control using neural network and without regard for the changes of external inertia loads.

• 한국ITS학회 논문지
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• 제18권2호
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• pp.144-155
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• 2019

오늘날 자동차 산업의 화두 중 하나는 자율주행차량이다. 국제자동차기술자협회(SAE International)가 정의한 레벨 3이상을 달성하기 위해서는 자율주행 기술과 커넥티드 기술의 조화가 필수적이다. 현재의 차량은 자율주행과 같은 새로운 기능을 가지게 됨에 따라 전장 부품의 수뿐 만 아니라 소프트웨어의 양과 복잡성도 늘어났다. 이로 인해 공격 표면(Attack surface)이 확대되고, 소프트웨어에 내재된 보안 취약점도 늘어나고 있다. 실제로 커넥티드 기능을 가진 차량의 보안 취약점을 악용하여 차량을 강제 제어할 수 있음이 연구자들에 의해 증명되기도 했다. 하지만 차량에 적용 되어야 하는 필수적인 보안 요구 사항은 정의되어 있지 않는 것이 현실이다. 본 논문에서는 실제 공격 및 취약점 사례를 바탕으로 차량내부네트워크(In-Vehicle Network)에 존재하는 자산을 식별하고, 위협을 도출하였다. 또한 보안요구사항을 정의 하였고, 위험 분석을 통해 사이버 보안으로 인한 안전 문제를 최소화하기 위한 필수 보안 요구 사항을 도출하였다.

• International Journal of Advanced Culture Technology
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• 제8권1호
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• pp.135-140
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• 2020

The main battlefield of 'made in China 2025' proposed by the Chinese government is the deep integration of industrialization and informatization, among which intelligent manufacturing and new energy vehicles are the key links. New energy vehicle refers to the use of unconventional automotive fuel as a power source (or use conventional automotive fuel, the use of new vehicle-mounted power plant), integrated vehicle power control and driving aspects of advanced technology, the formation of advanced technical principles, with new technology and the structure of the vehicle. BYD's success in the battery, I T and automobile industries has attracted the attention of the industry, making it a shining new star in the Chinese business community. BYD 's innovation, diversification and corporate culture construction have certain enlightenment to the development of China's small and medium-sized enterprises. Therefore, by looking at the Chinese network literature, about BYD's research mainly focused on the development strategy, corporate finance, corporate performance, and corporate marketing, etc. This paper will take BYD as the research object and focus on corporate culture. Through literature analysis and qualitative analysis, it will summarize and further analyze the unique corporate culture of BYD, its important role, and provide relevant theoretical references for the construction and development of corporate culture in other industries.

• 한국통신학회논문지
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• 제37C권11호
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• pp.1036-1044
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• 2012

IT기술의 발전에 힘입어 지능형 센서 및 지능형 액추에이터 채택이 증가하면서 차량내 통신 네트워크를 통한 데이터 전송은 꾸준한 증가 추세를 보이고 있다. 2015년에는 차량내 통신 네트워크를 통한 전송 데이터량이 2010년 대비 2배 이상 증가하며, 차량제어에 필요한 네트워크 노드 수는 2010년 대비 1.5배 이상 증가할 것으로 전망된다. 이와 같이 차량내 데이터량의 증가가 예상됨에 따라 최근 자동차 산업계에서는 차량용 통신 네트워크로서 EtherCAT, TTEthernet 등 산업용 Ethernet에 대한 관심이 증대되고 있다. 본 논문에서는 차량내 데이터 전송을 위한 산업용 Ethernet으로 주목받고 있는 EtherCAT의 전송지연 특성과 고장복구 특성을 분석할 수 있는 모델을 제안한다. 실험용 EtherCAT 네트워크를 구성하여 제안한 모델의 정확성을 검증하였다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.741-746
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• 2001

This paper presents an investigation of a vehicle-to-vehicle distance control using a Hardware-in-the-Loop Simulation(HiLS) system. Since vehicle tests are costly and time consuming, how to establish a efficient and low cost development tool is an important issue. The HiLS system consists of a stepper motor, an electronic vacuum booster, a controller unit and two computers which are used to form real time simulation and to save vehicle parameters and signals of actuator through a CAN(Controller Area Network). Adoption of a CAN for communication is a trend in the automotive industry. Since this environment is the same as that of a real vehicle, a distance control logic verified in laboratory can be easily transfered to a test vehicle.

• Advances in nano research
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• 제13권6호
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• pp.587-597
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• 2022

In the present study, a novel optimization method in formation control of multi -system vehicles based on the trajectory of the nearest neighbor trajectory is presented. In this regard, the state equations of each vehicle and multisystem is derived and the optimization scheme based on minimizing the differences between actual positions and desired positions of the vehicles are conducted. This formation control is a position-based decentralized model. The trajectory of the nearest neighbor are optimized based on the current position and state of the vehicle. This approach aids the whole multi-agent system to be optimized on their trajectory. Furthermore, to overcome the cumulative errors and maintain stability in the network a semi-centralized scheme is designed for the purpose of checking vehicle position to its predefined trajectory. The model is implemented in Matlab software and the results for different initial state and different trajectory definition are presented. In addition, to avoid collision avoidance and maintain the distances between vehicles agents at a predefined desired distances. In this regard, a neural fuzzy network is defined to be utilized in conjunction with the control system to avoid collision between vehicles. The outcome reveals that the model has acceptable stability and accuracy.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.7-12
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• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.