• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5878-5904
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• 2017

This paper proposes a novel intelligent wheelchair vehicle system that enables upper limb exercises, lower limb standing exercises and rehabilitation training in a daily life. The proposed system, which can be used to prevent at least the degeneration of body movements and further atrophy of musculoskeletal system functions, considers the characteristics and mobility of the old and the disabled. Its main purpose is to help the old and the disabled perform their daily activities as much as they can, minimizing the extent of secondary disabilities. In other words, the system will provide the old and the disabled with regular and quantitative rehabilitation exercises and diagnosis using the wheelchair-based upper/lower limb rehabilitation vehicle system and then verify their effectiveness. The system comprises an electric wheelchair, a biometric module to identify individual characteristics, and an upper/lower limb rehabilitation vehicle. In this paper the design and configuration of the developed vehicle is described, and its operation method is presented. Moreover, to verify the tracking performance of the proposed system, dangerous situations according to biosignal changes occurring during the rehabilitation exercise of a non-disabled examinee are analyzed and the performance of the upper/lower limb rehabilitation exercise function depending on muscle strength is evaluated through a neural network algorithm.

• 반도체디스플레이기술학회지
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• 제16권3호
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• pp.65-70
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• 2017

This paper presents the implementation for the master changing algorithm between nodes in a general electric vehicle. The packet processing method based on the unique network method of an electric vehicle is that the method of processing a communication packet has the priority from the node of a vehicle installed at both ends. An important factor in deciding master or slave in a train is that the request data, the status data, and transmits or control codes of sub-devices are controlled from the node which master becomes. If the request data or the status data is transmitted from the non- master side, it is very important that only one of the devices of both stages be master since the data of the request data may collide with each other. This paper proposes an algorithm to select master or slave depending on which vehicle is started first, which node is master or slave, and whether the vehicle key is operation. Finally experimental results show the stable performance and effectiveness of the proposed algorithm.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.15-23
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• 2020

In this paper, we propose a new method to detect traffic accidents in video from vehicle-mounted cameras (vehicle black box). We use the distance between vehicles to determine whether an accident has occurred. To calculate the position of each vehicle, we use object detection and tracking method. By the way, in a crowded road environment, it is so difficult to decide an accident has occurred because of parked vehicles at the edge of the road. It is not easy to discriminate against accidents from non-accidents because a moving vehicle and a stopped vehicle are mixed on a regular downtown road. In this paper, we try to increase the accuracy of the vehicle accident detection by using not only the motion of the surrounding vehicle but also ego-motion as the input of the Recurrent Neural Network (RNN). We improved the accuracy of accident detection compared to the previous method.

• 한국인터넷방송통신학회논문지
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• 제17권6호
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• pp.127-135
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• 2017

대부분의 최신 자동차들은 편안하고 안전한 운전 환경을 위해 다양한 종류의 ECU들을 탑재하고 있다. ECU들 사이의 효율적인 통신을 위해 대부분의 자동차 제조사들은 Controller Area Network(CAN) 프로토콜을 사용하고 있다. 그러나 CAN은 데이터 인증을 제공하지 않는다. 이러한 취약점 때문에 CAN은 메시지 재생공격에 취약하다. 본 논문은 자동차 내부 네트워크에 적용 가능한 현실적인 메시지 인증 기법을 제안한다. CAN 데이터 프레임의 제한적인 공간을 고려하여, 데이터와 메시지 인증 코드 (MAC)를 동시에 전송하기 위해서는 짧은 길이의 MAC을 사용하는 것이 가장 적합하다. 그러나 짧은 길이의 MAC은 암호학적 안전성을 충분히 보장하지 않기 때문에 안전성을 보장하기 위한 추가적인 조치가 필요하다. 본 연구에서 제안한 메시지 인증 기술은 CAN의 제한된 데이터 페이로드를 고려하기 때문에 차량 내부의 안전한 네트워크를 설치하는데 유용하게 활용될 수가 있다.

• 한국통신학회논문지
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• 제37권7C호
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• pp.527-537
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• 2012

본 논문은 다수의 자율무인차량 (Autonomous Unmanned Ground Vehicle) 들이 서로 협력하여 그룹 이동하는 타겟을 추적하고 지속적인 커버리지를 제공하는 무인차량 기반의 추적 네트워크 (UVTN: Unmanned Vehicle based Tracking Network) 구조와 알고리즘을 제안한다. UVTN은 움직이는 사람 또는 사물을 추적 감시하거나 이동하는 구조팀 또는 병사들에게 지속적인 네트워크 Access를 제공해 주기 위하여 커버리지를 최대화 하는 것을 목적으로 한다. 이러한 목적을 달성하기 위하여 UVTN은 주기적인 네트워크 확장과 수축 과정을 통한 무인차량 노드 재배치 및 네트워크 토폴로지 최적화를 수행한다. 또한 본 논문에서는 평균 커버리지비율과 이동거리 관점에서의 성능향상을 위한 개선 알고리즘들이 제안된다. 시뮬레이션을 통해 UVTN과 개선 알고리즘들이 그룹이 동성을 갖는 대상을 효율적으로 추적하여 지속적인 커버리지를 제공할 수 있음을 보인다.

• 경영과학
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• 제28권3호
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• pp.1-13
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• 2011

Hub-and-spoke transportation network is a powerful and useful network structure that takes full advantage of economies of scale on routes between hubs. In recent studies, the network structure is extended to hybrid hub-andspoke that allows direct transportation between spokes. In this study, we considered more extended network structure which is called hybrid multiple hub-and-spoke that has multiple hubs and allows direct transportation between spokes. We developed a mathematical optimization model for automotive service parts transportation planning under hybrid multiple hub-and-spoke network structure. The model suggests a long-term transportation route planning and a short-term vehicle assignment planning. The model is verified by simulation and validated in real world application to Hyundai Mobis automotive service parts transportation planning. From the simulation result, the model reduced the transportation cost about 24.7%, the total distance about 6.8% and the CO2 emissions about 8.8%. In real world application for 6 months from July to December 2010, the model reduced the transportation cost about 9.1% by changing the long-term transportation route without daily vehicle assignment planning.

• 정보통신설비학회논문지
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• 제8권3호
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• pp.122-131
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• 2009

Ubiquitous-Transportation sensor network is able to realize a vehicle ad-hoc network. Since there are some problems in an existing ITS system, the new technology and traffic information strategies are requirements in this advanced system, u-TSN. The purposes of this paper is to introduce the components on u-TSN system, establish new traffic strategies for this system, and then evaluate these strategies by making a comparative study of ITS and using micro traffic simulator, AIMSUN. The strategy evaluated by AIMSUN is position-based multicast strategy which provides traffic information to vehicles using V2I (vehicle to Infrastructure) communication. This paper focuses on the providing real-time route guidance information when congestion is occurred by the incidents. This study estimates total travel time on each route by API modules. Result from simulation experiments suggests that position-based multicast strategy can achieve more optimal network performance and increased driver satisfaction since the total accumulated travel times of both the major road and the total system on position-based multicast strategy are less than those on VMS.

• 한국정밀공학회지
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• 제21권10호
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• pp.65-77
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• 2004

In this paper, we propose an adaptive mechanism based on immune algorithm and neural network identifier technique. It is also applied fur an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To solve this problem, we use the neural network identifier (NNI) technique fur modeling the plant and humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using an immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. Finally, the simulation and experimental result fur the control of steering and speed of AGV system illustrate the validity of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

• ETRI Journal
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• 제42권5호
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• pp.700-711
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• 2020

Because of the rapid increase of mobile traffic, flexible broadband supportive unmanned aerial vehicle (UAV)-based 6G mobile networks using free space optical (FSO) links have been recently proposed. Considering the advancements made in UAVs, big data processing, and artificial intelligence precision control technologies, the formation of an additional wireless network based on UAV aerial platforms to assist the existing fixed base stations of the mobile radio access network is considered a highly viable option in the near future. In this paper, a combined time bound optimization scheme is proposed that can adaptively satisfy the control and communication time constraints as well as the processing time constraints in FSO-based 6G UAV aerial networks. The proposed scheme controls the relation between the number of data flows, input data rate, number of worker nodes considering the time bounds, and the errors that occur during communication and data processing. The simulation results show that the proposed scheme is very effective in satisfying the time constraints for UAV control and radio access network services, even when errors in communication and data processing may occur.

• 자동차안전학회지
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• 제14권3호
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.