• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.3 s.18
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• pp.101-109
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• 2004

Image enhancement for Infrared imaging system is mainly based on the global histogram equalization. The global histogram equalization(GHE) is a method in which each pixel is equalized by using a whole histogram of an image. GHE is speedy and effective for real-time imaging system but its method fails to enhance the fine details. On the other hand, the basic local histogram equalization(LHE) method uses sliding a window and. the pixels under the window region are equalized over the whole output dynamic range. The LHE is adequate to enhance the fine details. But this method is computationally slow and noises are over-enhanced. So various local histogram equalization methods have been already presented to overcome these problems of LHE. In this paper, a new regional dynamic range histogram equalization (RDRHE) is presented. RDRHE improves the equalization quality while reducing the computational burden.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.450-453
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• 2011

고화질의 디지털 카메라 및 스마트폰, 감시용 카메라의 보급 등으로 인해 최근 패턴 인식 및 이미지 프로세싱 분야에서 고화질의 이미지 및 비디오를 처리해야 하는 경우가 많아지고 있다. 특히 차량 번호판 감지 등과 같은 객체 인식 분야의 경우, 고화질의 이미지로 인해 그만큼 인식에 필요한 계산 비용이 증가하게 되었는데 따라서 이러한 계산 비용을 효율적으로 줄이기 위한 기법이 요구되고 있다. 또한 기존의 차량 번호판 감지의 도메인과는 다르게 도로 상에서의 실시간 차량 번호판 감지의 필요성이 대두되고 있기에 본 논문에서는 도로 상에서의 실시간 번호판 감지 시스템을 위한 차량 번호판 주변정보 기반의 효율적인 윈도우 슬라이딩(window sliding) 방법을 제안한다. 본 논문의 시스템은 총 3단계로, (1) SVM(Supported Vector Machine) 을 통한 차량 번호판 주위 정보에 대한 학습, (2) 도로 상의 번호판 위치 확률 모델링을 통한 탐색 공간의 감소, (3) $context_{plate}$분류기를 통한 OCS(operator context scanning)의 수행이다. 이와 같은 $context_{plate}$분류기와 OCS를 통해 번호판 검출을 위한 윈도우 슬라이딩의 수가 크게 줄었음을 알 수 있었으며, 또한 번호판의 정보를 건너뛰지 않고, 신뢰성 있게 접근함을 알 수 있었다.

• Journal of Communications and Networks
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• v.16 no.5
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• pp.479-484
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• 2014

In this paper, we consider the construction of cyclic and quasi-cyclic structured q-ary low-density parity-check (LDPC) codes over a designated small field. The construction is performed with a pre-defined sliding-window, which actually executes the regular mapping from original field to the targeted field under certain parameters. Compared to the original codes, the new constructed codes can provide better flexibility in choice of code rate, code length and size of field. The constructed codes over small fields with code length from tenths to hundreds perform well with q-ary sum-product decoding algorithm (QSPA) over the additive white Gaussian noise channel and are comparable to the improved spherepacking bound. These codes may found applications in wireless sensor networks (WSN), where the delay and energy are extremely constrained.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.167.5-167
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• 2001

Single point attitude determination method provides an optimal attitude minimizing the Wahba loss function. However, for the insufficient number of measurement vectors, the conventional single point methods has no unique solution. Thus, we introduce the sequential method to and an optimal attitude minimizing the windowed loss function. In this paper, this function is de ned as the sum of square errors for all measurement vectors within the axed sliding window. For simple implementation, the proposed algorithm is rewritten as a recursive form. Moreover, the covariance matrix is derived and expressed as a recursive form. Finally, we apply this algorithm to the attitude determination system with three LOS measurement sensors.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.102.3-102
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• 2001

In this paper, we propose a novel method that can detect face region effectively with fuzzy theory and neural network We make fuzzy rules and membership functions to describe the face color. In this algorithm, we use a perceptually uniform color space to increase the accuracy and stableness of the nonlinear color information. We use this model to extract the face candidate, and then scan it with the pre-built sliding window by using a neural network-based pattern-matching method to find eye. A neural network examines small windows of face candidate, and decides whether each window contains eye. We can standardize the face candidate geometrically with detected eyes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.267-274
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• 2011

Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic's part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.673-682
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• 2009

XML data are widely used for data representation and exchange on the Web and the data type is an continuous stream in ubiquitous environment. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the sliding window. XML stream data are modeled as a tree set, called XFP_tree and we quickly extract the frequent structures over recent XML data in the XFP_tree.

• Journal of Astronomy and Space Sciences
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• v.24 no.4
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• pp.275-284
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• 2007

For the application to the numerical weather prediction (NWP) in active service, it is necessary to ensure that the GPS precipitable water vapor (PWV) data has less than one hour latency and three millimeter accuracy. The comparison and the verification between the daily products from GPS measurement by using the IGS final ephemeris and the conventional meteorological observation has been done in domestic researches. In case of using IGS final ephemeris, GPS measurements can be only post processed in daily basis in three weeks after the observation. Thus this method cannot be applied to any near real-time data processing. In this paper, a GPS data processing method to produce the PWV output with three mm accuracy and one hour latency for the data assimilation in NWP has been planned. For our new data processing strategy, IGS ultra-rapid ephemeris and the sliding window technique are applied. And the results from the new strategy has been verified. The GPS measurements during the first 10 days of January, April, July and October were processed. The results from the observations at Sokcho, where the GPS and radiosonde were collocated, were compared. As the results, a data processing strategy with 0.8 mm of mean bias and 1.7 mm of standard deviation in three minutes forty-three seconds has been established.

• Journal of Industrial Technology
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• v.21 no.A
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• pp.167-179
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• 2001

This paper discusses an index-based subsequence matching that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. In earlier work, we suggested an efficient method for whole matching under time warping. This method constructs a multidimensional index on a set of feature vectors, which are invariant to time warping, from data sequences. For filtering at feature space, it also applies a lower-bound function, which consistently underestimates the time warping distance as well as satisfies the triangular inequality. In this paper, we incorporate the prefix-querying approach based on sliding windows into the earlier approach. For indexing, we extract a feature vector from every subsequence inside a sliding window and construct a multi-dimensional index using a feature vector as indexing attributes. For query precessing, we perform a series of index searches using the feature vectors of qualifying query prefixes. Our approach provides effective and scalable subsequence matching even with a large volume of a database. We also prove that our approach does not incur false dismissal. To verily the superiority of our method, we perform extensive experiments. The results reseal that our method achieves significant speedup with real-world S&P 500 stock data and with very large synthetic data.