• 한국자동차공학회논문집
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• 제20권4호
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• pp.16-24
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• 2012

This paper proposes a method utilizing Differential Global Position System (DGPS) with Real-Time Kinematic (RTK) and pre-built Geo-graphic Information System (GIS) to detect lane departure of a vehicle. The position of a vehicle measured by DGPS with RTK has 18 cm-level accuracy. The preconditioned GIS data giving accurate position information of the traffic lanes is used to set up coordinate system and to enable fast calculation of the relative position of the vehicle within the traffic lanes. This relative position can be used for safe driving by preventing the vehicle from departing lane carelessly. The proposed system can be a key component in functions such as vehicle guidance, driver alert and assistance, and the smart highway that eventually enables autonomous driving supporting system. Experimental results show the ability of the system to meet the accuracy and robustness to detect lane departure of a vehicle at high speed.

• 한국지능시스템학회논문지
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• 제21권2호
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• pp.254-259
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• 2011

일반적으로 어떤 순간에 발생할지 모르는 응급 상황을 CCTV의 영상 정보만으로 상황을 항상 모니터링하기에는 인력과 비용의문제점이 발생되고 있다. 본 논문에서는 응급상황을 동적으로 보여주는 CCTV환경에서 감지하기 위해 GMM을 이용한 응급단어와 비응급단어의 검출 및 인식기법을제안하고자 한다. 제안된 방법은 Global GMM 모델에 의해 응급단어와 일반단어를 검출하고 이 모델에 의해 응급단어라 판정된 경우에는 Local GMM 모델에 응급단어 인식을 수행하게 된다. 제안된 방법은 다양한 환경하에서 취득한 응급단어와 일반단어에 대해 적용하여 타당성을 검증하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권12호
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• pp.1445-1454
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• 1999

광역 조건식의 계산은 분산 프로그램의 수행을 테스트 또는 디버깅하기 위한 방법으로 활 발히 연구되고 있다. 이제까지 주로 연구된 광역조건식은 AND 또는 OR 광역 조건식 등이 있는데, 특히 AND 광역 조건식은 분산 프로그램의 동시적 조건을 표현하는데 유용하여 효율적인 검색 알고리즘이 활발히 연구되었다. 분산프로그램의 수행오류로서 공유자원의 배타적 접근조건은 가장 중요하고 일반적인 경우라 할 수 있다. 본 논문에서는 XOR 연산을 이용하여 공유자원 프로그램의 오류 검색을 위한 광역조건식을 기술하는 방식에 대해 제안하였다. XOR 연산을 이용한 광역 조건식은 연산자 중 많아야 하나의 지역조건식만이 참일 때 전체 조건식이 참이 되는데 이러한 성질은 여러 프로세스 중 한번에 하나만이 공유자원에 배타적으로 접근할 수 있는 조건을 표현하는데 매우 유용하다. n 개의 프로세스로 이루어진 분산프로그램에서 한개의 공유자원에 대한 배타적 접근 조건을 기술하기 위해서 AND로 연결된 광역조건식을 이용하면 O(n2)개의 광역 조건식이 필요한데 반해 XOR 연산으로는 하나의 조건식으로 나타낼 수 있다. 더구나 XOR 연산을 이용한 광역조건식은 최근 소개된 겹치는 구간의 개념을 활용하면 매우 간단하게 검색할 수 있다. 본 논문에서는 겹치는 구간을 찾는 검색 알고리즘을 소개하고 증명하였다.Abstract Detecting global predicates is an useful tool for debugging and testing a distributed program. Past research has considered several restricted forms of predicates, including conjunctive or disjunctive form of predicates. Especially, conjunctive predicates have attracted main attention not only because they are useful to describe simultaneous conditions in a distributed program, but also because it is possible to find algorithms to evaluate them within reasonable time bound. Detecting errors in accessing shared resources are the most popular and important constraints of distributed programs. In this paper, we introduced an exclusive OR predicates as a model of global predicates to describe shared resource conditions in distributed programs. An exclusive OR predicate holds only when at most one operand is true, which is useful to describe mutual exclusion conditions of distributed programs where only one process should be allowed to access the shared resource at a time. One exclusive OR predicate is enough to describe mutual exclusion condition of n processes with one shared resource, while it takes O(n2) conjunctive predicates. Moreover, exclusive OR condition is easily detectable using the concept of overlapping intervals introduced by Venkatesan and Dathan. An off-line algorithm for evaluating exclusive OR global predicates is presented with a correctness proof.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.374-377
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• 2005

The distribution and changes of tropospheric nitrogen dioxide ($NO_2$) are analyzed using the satellite measurements data from GOME (Global Ozone Monitoring Experiment) and SCIMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY). We produced global maps of tropospheric $NO_2$ for 4 seasons using GOME measurements from January 1997 to June 2003. The global distribution shows high values in regions with dense population and high industrialization. Tropospheric $NO_2$ shows obvious seasonal changes depending on its emission and lifetime. Based on the good agreement between two instruments in the time period of overlapping measurements (January 2003-June2003), we linked SClAMACHY data to the GOME time series. The combined time series over the past decade indicate that $NO_2$ 1evels over China are rapidly increasing while those over Europe are decreasing. We also discussed potential application of spaceborne instruments in detecting and characterizing long-distance transport of $NO_2$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제2권3호
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• pp.135-149
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• 2008

Traditional attack detection schemes based on packets or flows have very high computational complexity. And, network based anomaly detection schemes can reduce the complexity, but they have a limitation to figure out the pattern of the distributed global scale network attack. In this paper, we propose an efficient and fast method for detecting distributed global-scale network attack symptoms in high-speed backbone networks. The proposed method is implemented at the aggregate traffic level. So, our proposed scheme has much lower computational complexity, and is implemented in very high-speed backbone networks. In addition, the proposed method can detect attack patterns, such as attacks in which the target is a certain host or the backbone infrastructure itself, via collaboration of edge routers on the backbone network. The effectiveness of the proposed method are demonstrated via simulation.

• 로봇학회논문지
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• 제6권2호
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• pp.182-188
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• 2011

This paper describes a method of road tracking by using a vision and laser with extracting road boundary (road lane and curb) for navigation of intelligent transport robot in structured road environments. Road boundary information plays a major role in developing such intelligent robot. For global navigation, we use a global positioning system achieved by means of a global planner and local navigation accomplished with recognizing road lane and curb which is road boundary on the road and estimating the location of lane and curb from the current robot with EKF(Extended Kalman Filter) algorithm in the road assumed that it has prior information. The complete system has been tested on the electronic vehicles which is equipped with cameras, lasers, GPS. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision system by our approach for detecting the curb of road and lane boundary detection.

• Industrial Engineering and Management Systems
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• 제9권1호
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• pp.10-19
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• 2010

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

• 제어로봇시스템학회논문지
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• 제11권2호
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• pp.137-143
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• 2005

Autonomous mobile robot has an ability to navigate using both map in known environment and sensors for detecting obstacles in unknown environment. In general, autonomous mobile robot navigates by global path planning on the basis of already made map and local path planning on the basis of various kinds of sensors to avoid abrupt obstacles. This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• International Journal of Advanced Culture Technology
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• 제12권3호
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• pp.148-158
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• 2024

We investigate Autism Spectrum Disorder (ASD), which is typified by deficits in social interaction, repetitive behaviors, limited vocabulary, and cognitive delays. Traditional diagnostic methodologies, reliant on expert evaluations, frequently result in deferred detection and intervention, particularly in South Korea, where there is a dearth of qualified professionals and limited public awareness. In this study, we employ advanced deep learning algorithms to enhance early ASD screening through automated video analysis. Utilizing architectures such as Convolutional Long Short-Term Memory (ConvLSTM), Long-term Recurrent Convolutional Network (LRCN), and Convolutional Neural Networks with Gated Recurrent Units (CNN+GRU), we analyze video data from platforms like YouTube and TikTok to identify stereotypic behaviors (arm flapping, head banging, spinning). Our results indicate that the LRCN model exhibited superior performance with 79.61% accuracy on the augmented platform video dataset and 79.37% on the original SSBD dataset. The ConvLSTM and CNN+GRU models also achieved higher accuracy than the original SSBD dataset. Through this research, we underscore AI's potential in early ASD detection by automating the identification of stereotypic behaviors, thereby enabling timely intervention. We also emphasize the significance of utilizing expanded datasets from social media platform videos in augmenting model accuracy and robustness, thus paving the way for more accessible diagnostic methods.

• 한국컴퓨터정보학회논문지
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• 제18권9호
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• pp.53-62
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• 2013

본 논문에서는 입력된 자연영상으로부터 도로 영역을 검출하기 위한 소실점 자동 예측 방법을 제안한다. 제안하는 방법에서는 도로 환경에서 안정적으로 소실점을 검출하기 위해 영상의 주방향성을 분석하여 영상 특징성분들이 집중되는 곳을 소실점으로 예측한다. 이를 위해 첫번째 단계에서는, 영상을 일정크기의 서브블록으로 분할하고 분할된 서브블록 내에서 임의의 에지 샘플을 선택하고 RANSAC을 적용하여 직선 모델을 예측함으로서 각 서브블록의 주방향성을 분석한다. 모든 블록에 대하여 주방향성을 검출한 후, 두 번째 단계에서 임의의 직선 샘플을 선택하고 RANSAC을 적용하여 교점 모델을 예측함으로서 각 직선들로 인한 교점 모델의 비용값을 측정하고 가장 높은 비용값의 교점 모델에 의한 평균점으로 소실점을 예측한다. 마지막으로 성능 검증을 위해 다양한 상황에 따른 정량적, 정성적 분석을 통해 제안하는 소실점 검출 알고리즘의 타당성과 효율성을 입증한다.