• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.320-327
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• 2006

This paper introduces a new entertainment robot called ELIRO-II(Eating Lizard RObot version 2)which is a bio-mimetic quadruped walking robot with autonomous eating function. We focus on the realization of the behavior of an animal, i.e., wandering around to find food and eating food. The ELIRO-II is modeled after a lizard, which has four legs, 2-DOF waist-joint, an eye part, a mouth part and a stomach part. The effectiveness of the developed robot is shown through real experiments.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.113-119
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• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• Information and Communications Magazine
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• v.34 no.6
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• pp.11-19
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• 2017

V2X(Vehicle to Everything)는 차량 간 통신(Vehicle-to-Vehicle), 차량과 인프라 간 통신(Vehicle-to-Infrastructure), 차량과 보행자 간의 통신(Vehicle-to-Pedestrians) 등 운전 중 도로 인프라 및 다른 차량과 통신하면서 교통상황 등의 정보를 교환하거나 공유하는 기술을 말한다. V2X는 IT 기술의 발달과 여러 연구기관들의 연구개발 강화로 인해 'Connected/Smart car' 나아가 완전한 자율주행차를 구현하기 위해 빠르게 진화하고 있다. 이러한 V2X 시장이 향후 10년 간 급속한 확대가 전망되면서, 국내외 통신업체와 제조업 및 산학연은 자동차와 정보통신, 에너지, 서비스 산업을 융합한 스마트자동차 산업을 신성장 동력으로 하여 기술개발에 노력을 가속화하고 있다. 이에 본고에서는 V2X 차량 통신 기술의 주요선진국 기술개발 동향과 현 수준을 알아본다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.83-95
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• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• Journal of KIISE:Software and Applications
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• v.37 no.8
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• pp.641-646
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• 2010

This paper presents algorithms for real-time navigation of a humanoid robot with a stereo vision but no other sensors. Using the algorithms, a robot can recognize its 3D environment by retrieving SIFT features from images, estimate its position through the Kalman filter, and plan its path to reach a destination avoiding obstacles. Our approach focuses on estimating the robot’s central walking path trajectory rather than its actual walking motion by using an approximate model. This strategy makes it possible to apply mobile robot localization approaches to humanoid robot localization. Simple collision free path planning and motion control enable the autonomous robot navigation. Experimental results demonstrate the feasibility of our approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.5709-5715
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• 2012

Injection molding product is commonly used for reducing the weight of automotive vehicle, and door regulator guide rail with plastic material is also made by injection molding process. In order to improve the injection molding performance of plastic regulator guide rail, optimal molding condition is suggested by numerical simulation and DOE after obtaining the sensitivity of parameters for regulator rail manufacturing on warpage and fill time. Furthermore, multi direct gate method and optimal cooling circuit are introduced to get the uniform temperature distribution and better cooling efficiency in molding product. The effect of the proposed design on the injection molding performance is verified by the test of prototype of plastic regulator guide rail.

• Korean Journal of Optics and Photonics
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• v.32 no.3
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• pp.101-107
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• 2021

In this paper, a study of the design of automotive communication lamps using microlens arrays (MLAs) was conducted. With the development of autonomous driving technology, automobiles need communication lamps to communicate with pedestrians. To reduce the size of the optical system and secure high light intensity, the communication lamp's optical system was designed using an MLA. In addition, to secure a clear image on inclined ground, the design was carried out considering the overlap method. After that, the improved performance was confirmed by comparing it to the MLA optical system before overlapping.

• Clinical and Experimental Pediatrics
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• v.46 no.10
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• pp.996-1002
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• 2003

Purpose : In Rett syndrome patients, the incidence of sudden death is greater than that of the general population, and cardiac electrical instability including fatal cardiac arrhythmia is a main suspected cause. In this study, we are going to find out the possible cause of the higher risk of sudden death in Rett patients by the evaluation of heart rate variability, a marker of cardiac autonomic activity and corrected QT intervals. Methods : Diagnosis of Rett syndrome was made by molecular genetic study of Rett syndrome (MECP2 gene) or clinical diagnostic criteria of Rett syndrome. Heart rate variability and corrected QT intervals were measured by 24 h-Holter study in 12 Rett patients, and in 30 age-matched healthy children with chief complaints of chest pain or suspected heart murmurs. The were compared with the normal age-matched control. Results : Patients with total Rett syndrome, classic Rett syndrome, and Rett variants had significantly lower heart rate variability(especially rMSSD)(P<0.05) and longer corrected QT intervals than age-matched healthy children(P<0.05). Sympathovagal balance expressed by the ratio of high to low frequency(LF/HF ratio) also showed statistically significant differences between the three groups considered(P<0.05). Conclusion : A significant reduction of heart rate variability, a marker of autonomic disarray, suggests a possible explanation of cardiac dysfunction in sudden death associated with Rett syndrome.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.186-190
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• 2015

Intelligent vehicle plans motion and navigate itself based on the surrounding environment perception. Hence, the precise environment recognition is an essential part of self-driving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environment, the vehicle must percept all the dynamic obstacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. First, exceptional radar measurement model for vehicle, concentrated on the corner, is described by mixture density network (MDN), and applied to particle filter weighting. Also, to conquer the curse of dimensionality of particle filter and estimate the time-varying number of multi-target states, reversible jump markov chain monte carlo (RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstrated through several computer simulations.