• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.106-108
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• 2012

스마트TV는 스마트폰과 달리 가족이 사용하는 디바이스로 인식되고 있다. 그러나 스마트TV도 SNS(Social Networking Service), 웹 서비스 등 가족인 아닌 개인 단위로 사용하는 앱(application)뿐만 아니라 각 개인마다 선호하는 게임을 포함한 앱(application), VOD 리스트, TV 채널 등이 다르다고 볼 수 있다. 그리하여 스마트TV에서도 개인 맞춤형 사용자 인터페이스가 필요하다. 이에 따라 본 논문에서는 스마트TV 환경에서 기존 윈도우 로그인 시스템처럼 개인만의 맞춤형 사용자 인터페이스를 제시한다. 그리하여 각각의 사용자들은 개인만의 환경에서 앱(application)을 실행하거나 TV를 볼 수 있다. 기존 연구되고 있는 채널 네비게이션 기법을 토대로 VOD list, 게임을 포함한 앱(App)을 개인 맞춤형 인터페이스로 보다 편리하게 사용할 수 있는 방법도 제시한다. 채널 네비게이션은 로그인한 각 자신만의 환경에서 사용자가 과거에 시청한 TV 채널을 조사하여 많이 시청한 TV 채널을 보여 주게 한다. 그리하여 보다 자신이 선호하는 채널을 쉽게 시청할 수 있다. 이와 더불어 VOD 리스트와 게임을 포함한 앱(application)을 과거에 시청하거나 사용했던 패턴을 분석하고 선호하는 VOD 리스트와 앱(application)을 예측하여 보여주어 개인의 맞춤형 인터페이스에서 보다 빠르게 접근이 가능하며 편리하게 사용할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1434-1440
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• 2008

Performance of TCP/IP is studied on the wireless network using flow control and congestion control mechanism based on transmission rate. We discuss the elimination or the reduction of various phenomena of burst by flow controlling on transmission rate and verify that there are TCP ACK compression promblems on the queue by burst reaction while executing transmission rate controlled channels. Analyzing periodic burst reaction on the queue of source IP, the maximum value of queue is expected, which represents the applible expectation of throughput reduce and shows the improvement of performance by the reduce of throughput due to hi-directional traffic.

• Journal of the Korea Society of Computer and Information
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• v.7 no.3
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• pp.143-148
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• 2002

Congestion may take place in the ATM network because of high-speed cell transmission features, and cell delay and loss also can be caused by unexpected traffic variation. Thus, traffic control mechanisms are needed. One of them to decrease congestion is the Cell shaping. This paper proposes a hybrid type cell shaper composed of a Leaky Bucket with token pool, Tn with time window, and a spacing control buffer. The simulator BONeS with the ON/OFF traffic source model evaluates the performance of the proposed cell shaping method. Simulation results show that the cell shaping concerning the respective source traffics is adapted to and then controlled on the mean bit rate.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.2
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• pp.151-162
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• 2009

This study proposes a new methodology for quantitative rock classification in unsampled rock zone, which occupies the most of tunnel design area. This methodology is to train an ANN (artificial neural network) by using results from a drilling investigation combined with electric resistivity survey in sampled zone, and then apply the trained ANN to making a prediction of grade of rock classification in unsampled zone. The prediction is made at the center point of a shifting window by using a number of electric resistivity values within the window as input reference information. The ANN training in this study was carried out by the RPROP (Resilient backpropagation) training algorithm and Early-Stopping method for achieving a generalized training. The proposed methodology is then applied to generate a rock grade distribution on a real tunnel site where drilling investigation and resistivity survey were undertaken. The result from the ANN based prediction is compared with one from a conventional kriging method. In the comparison, the proposed ANN method shows a better agreement with the electric resistivity distribution obtained by field survey. And it is also seen that the proposed method produces a more realistic and more understandable rock grade distribution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1237-1242
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• 2017

Computer vision is coming more interesting with new imaging sensors' new capabilities which enable it to understand more its surrounding environment by imitating human vision system with artificial intelligence techniques. In this paper, we made experiments with Kinect camera, a new depth sensor for object detection and distance measurement functions, most essential functions in computer vision such as for unmanned or manned vehicles, robots, drones, etc. Therefore, Kinect camera is used here to estimate the position or the location of objects in its field of view and measure the distance from them to its depth sensor in an accuracy way by checking whether that the detected object is real object or not to reduce processing time ignoring pixels which are not part of real object. Tests showed promising results with such low-cost range sensor, Kinect camera which can be used for object detection and distance measurement which are fundamental functions in computer vision applications for further processing.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.192-200
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• 2003

The object of this research is to develop a computerized algorithm of cost estimation method to forecast the total construction cost in the bidding stage by the historical and elemental work cost data. Traditional cost models to prepare Bill of Quantities in the korea construction industry since 1970 are not helpful to forecast the project total cost in the bidding stage because the BOQ is always constant data according to the design factors of a particular project. On the contrary, statistical models can provide cost quicker and more reliable than traditional ones if the collected cost data are sufficient enough to analyze the trends of the variables. The estimation system considers non-deterministic methods which referred to as the 'Monte Carlo simulation. The method interprets cost data to generate a probabilistic distribution for total costs from the deficient elemental experience cost distribution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• Tunnel and Underground Space
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• v.13 no.6
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• pp.455-464
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• 2003

Four assessment systems for planning and evaluation of TBM tunnelling are discussed, and their characteristics and input data are analyzed. Two of the systems are determined to be adequate for post-evaluation of TBM performance because the time, such as repair time, downtime, installation time and transport time, must be included for calculations. The others are adequate for pre-planning because the basic data of the systems consist of only the basic properties of rocks and rock masses, and the specification of TBM. In order to apply these assessment systems, a number of equations, graphs and charts are generally required, which seems to be very inconvenient and complicated. In this study, therefore, a user-friendly program operated on Windows system is developed, and each system can be selected by the corresponding input data. It will be possible fer tunnel engineers to select a system according to their objectives and available input data, and to apply the system to TBM tunnel project.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.4
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• pp.35-44
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• 2004

Recently announced TCP Vegas predicts the degree of congestion in the network and then control the congestion window size. Thus it shows better performance than TCP Reno. however, TCP vegas does not assume any network model, its congestion window control is very limited. Because or this limitation, TCP vegas still can not adapt to fast changing available bandwidth. In this paper, we introduce a new TCP algorithm which adapts to fast changing available bandwidth well. To devise such a TCP, we model the end to end network of TCP connection as a queueing system and finds congestion window size which can utilize the available bandwidth sufficiently but not make the network congested. The simulation results show that our algorithm adapts to the avaliable bandwidth faster than TCP vegas and as a results, when the available bandwidth is changing rapidly, our algorithm not only operates more stably than TCP Vegas, but also it shows higher thruput than TCP Vegas.