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본 논문은 기존의 프로젝트 수행 방식과 틀을 과감히 벗어나 기존 프로젝트 전체 기간 30% 단축 및 전체비용 절감을 가능케 하는 급진적이며 위험을 수반하는 복합적 프로젝트 일정 개발을(integrated project scheduling) 제안하는 것을 목표로 한다. 복합적 프로젝트 일정 개발을 위하여 기존의 프로젝트 일정 기법들인 공정압축법과 공정중첩 단축법을 통합적으로 적용하였으며, 정량적 위험분석 실행을 위하여 3점 추정을 사용하여 프로젝트 전체 기간을 파악하였다. 복합적 프로젝트 일정 개발은 프로젝트 관리 계획 개발 단계에서 프로젝트 실행을 위한 다양한 일정 선택의 가능성을 열어준다.

The aim of this paper is to introduce a radical and innovative way of project scheduling, so called integrated project scheduling, enabling a company or an organization to achieve 30% reduction on project total duration along with project total cost reduction. This scheduling technique could bring high risks to a project, and could be against current common project scheduling practice amongst many companies. To develop a integrated project schedule, current well-known scheduling technique such as crashing and fast tracking has been utilized simultaneously. Three point estimate technique as a quantitative risk analysis also has been used for finding out project total duration. The integrated project scheduling technique is expected to provide various schedule alternatives for you to select at the project planning phase in accordance with strategy of your project.

In this paper, we present a robust and fast tracking method based on joint collaborative representation. Traditional sparse coding based tracking methods code the candidates as a sparse linear combination of a series of object and trivial templates and perform time consuming L1 regularizations. In contrast to these methods, this paper adopts the L2-regularized least square models to reduce the computational complexity. The tracked object can be represented by the linear combination of a series of object templates, and also can be represented by candidate samples in the current frame. We propose a joint objective function to handle the tracking process. In addition, we introduce an effective update scheme to deal with the change of target appearance over time. Experiments on several challenging image sequences show that our proposed tracking method is robust and efficient.

Recently, due to the interest for personal safety, intelligent image recognition technology using the CCTV has received a lot of attention in many areas. Real-time video or image tracking technology is the technology to continuously track the movement of the target object and maintain the specified information of the object. However, the problem is that unnecessary noise or environmental changes of the input video from CCTV make it difficult to accurately detect and track the target object. In this paper, we propose a fast object tracking algorithm, which is divided into two parts: i) object detection process and ii) object tracking process. In the background image replacement technique, we compute the difference between the initial input image and input video image. Then, it replaces N×M blocks based on the changed pixels, finally leading to replacing the background images. If it changes over the pre-defined threshold value, we set the block size and generate the variable search window for object tracking. Since the variable search window only generates the minimum search area for object tracking, our proposed method is quickly able to compute the variable region for object tracking. Through various experiments, our proposed scheme outperforms the other object detection and tracking methods in terms of object tracking counts and tracking error counts.

광학시스템에서 가장 중요한 기술 중의 하나는 고속으로 이동하는 표적을 지속적으로 추적할 수 있는 고속 자동 표적 추적 시스템이다. 본 논문은 원거리 소형 표적에 대해서 고속으로 이동하는 표적에 대해서 급격한 형태 변화에도 강인한 상관 트렉커 기반에 자동 표적 추적 시스템을 설계한다. 본 논문은 IR 영상에서 3x3 이상의 표적에 대해서 4ms 내에 고속으로 표적을 추적하기 위한 커널 함수와 correlation filter 설계 최적화 방법을 제시하고, 이를 VxWorks와 같은 실시간 O/S 하에서 짐벌과 함께 연동하여 시험을 수행한다. 제안된 알고리즘의 성능 검증을 위해서 실제로 복잡환 경에서 기동하는 드론, 비행체를 대상으로 IR 카메라로 영상을 획득한 후 이를 실시간 보드상에서 시험을 진행한 결과 응답시간 4ms이하에서 약 98% 추적 성공률를 보였다.

On today, optical system are the next generation weapon systems being studied in many countries, starting from USA. One of the most important technologies in optical system is a high-speed automatic target tracking system that can continuously track high-speed moving small targets. This paper designs an automatic target tracking system based on a correlated trekker that is robust against rapid shape changes for fast moving targets and small targets at a distance. The proposed system showed about 98% success rate in response to the targets that are under a complex background such as drone, ranger, etc.

가변구조 제어시스템(variable structure control system)을 위해 제안된 기존의 대부분 슬라이딩 서피스 (sliding surface)는 주어진 초기조건과 무관하게 설계되었으며, 서피스 계수 또는 임의로 설정되었다. 이러한 서피스를 갖는 제어시스템은 슬라이딩 운동이 일어나기 전까지 외란등에 매우 민감하며 또한 느린 추적시간 (tracking time)을 초래한다. 이러한 단점을 극복하기 위해 본 연구에서는 임의로 주어진 초기조건을 항상지나 며 시간에 따라 기울기 및 절편이 변하는 새로운 슬라이딩 서피스를 설계하였다. 이 서피스와 연계된 제어시스 템의 슬라이딩 모드(sliding mode) 존재성을 증명하였고, 서피스의 움직임 절차를 상세히 기술하였다. 2차 선형 시스템과 2자유도계 로봇의 추적제어를 통해 제안된 방법의 효율성과 우수성을 입증하였다.

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

Maximum power point tracking (MPPT) is necessary for photovoltaic (PV) power system application to extract the maximum possible power under changing irradiation and temperature conditions. The β-parameter-based method has many advantages over conventional MPPT methods; such advantages include fast tracking speed in the transient stage, small oscillations in the steady state, and moderate implementation complexity. However, a problem in the implementation of the conventional beta method is the choice of an appropriate scaling factor N, which greatly affects both the steady-state and transient performance. Therefore, this paper proposes a modified β-parameter-based method, and the determination of the N is discussed in detail. The study shows that the choice of the scaling factor N is determined by the changes of the value of β during changes in irradiation or temperature. The proposed method can respond accurately and quickly during changes in irradiation or temperature. To verify the proposed method, a photovoltaic power system with MPPT function was built in Matlab/Simulink, and an experimental prototype was constructed with a solar array emulator and dSPACE. Simulation and experimental results are illustrated to show the advantages of the improved β-parameter-based method with the optimized scaling factor.

The F!T tube is a new type of CRT without a shadow mask. Correct color reproduction is performed by an electronic system that measures the landing positions of the electron beams and corrects through a dedicated deflection system. This paper describes in detail the position sensor and some of the electronics needed to build a functional control system. The principle has been shown in single- and triple-beam 17" and 32" tubes, of which results are included.

This paper proposes a 250 mV supply voltage digital low-dropout (LDO) regulator. The proposed LDO regulator reduces the supply voltage to 250 mV by implementing with all digital circuits in a$0.11{\mu}m$ CMOS process. The fast current tracking scheme achieves the fast settling time of the output voltage by eliminating the ringing problem. The over-voltage and under-voltage detection circuits decrease the overshoot and undershoot voltages by changing the switch array current rapidly. The switch bias circuit reduces the size of the current switch array to 1/3, which applies a forward body bias voltage at low supply voltage. The fabricated LDO regulator worked at 0.25 V to 1.2 V supply voltage. It achieved 250 mV supply voltage and 220 mV output voltage with 99.5% current efficiency and 8 mV ripple voltage at $20{\mu}A$ to $200{\mu}A$ load current.

This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.

A Snake is an active contour for representing object contours. Traditional snake algorithms are often used to represent the contour of a single object. However, if there is more than one object in the image, the snake model must be adaptive to determine the corresponding contour of each object. Also, the previous initialized snake contours risk getting the wrong results when tracking multiple objects in successive frames due to the weak topology changes. To overcome this problem, in this paper, we present a new snake method for efficiently tracking contours of multiple objects. Our proposed algorithm can provide a straightforward approach for snake contour rapid splitting and connection, which usually cannot be gracefully handled by traditional snakes. Experimental results of various test sequence images with multiple objects have shown good performance, which proves that the proposed method is both effective and accurate.

In this paper, the problem of a viewer's head tracking in a desktop-based interactive stereoscopic display system is considered. A fast and low-cost approach to the problem is important for such a computing environment. The system under consideration utilizes a shuttle glass for stereoscopic display. The proposed method makes use of an image taken from a single low-cost video camera. By using a simple feature extraction algorithm, the obtained points corresponding to the image of the user-worn shuttle glass are used to estimate the glass center, its local 'yaw' angle, as measured with respect to the glass center, and its global 'yaw' angle as measured with respect to the camera location. With these estimations, the stereoscopic image synthetic program utilizes those values to interactively adjust the two-view stereoscopic image pair as displayed on a computer screen. The adjustment is carried out such that the so-obtained stereoscopic picture, when viewed from a current user position, provides a close-to-real perspective and depth perception. However, because the algorithm and device used are designed for fast computation, the estimation is typically not precise enough to provide a flicker-free interactive viewing. An error concealment method is thus proposed to alleviate the problem. This concealment method should be sufficient for applications that do not require a high degree of visual realism and interaction.

We propose a fast macroblock (MB) mode prediction and decision algorithm based on temporal correlation for P-slices in the H.264/AVC video standard. There are eight block types for temporal decorrelation, including SKIP mode based on rate-distortion (RD) optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a thresholding method for fast inter mode decision using a MB tracking scheme to find the most correlated block and RD cost of the correlated block is suggested for early stop of the inter mode determination. We propose a two-step inter mode candidate selection method using statistical analysis. In the first step, a mode is selected based on the mode information of the co-located MB from the previous frame. Then, an adaptive thresholding scheme is applied using the RD cost of the most correlated MB. Secondly, additional candidate modes are considered to determine the best mode of the initial candidate modes that does not satisfy the designed thresholding rule. Comparative analysis shows that a speed-up factor of up to 70.59% is obtained when compared with the full mode search method with a negligible bit increment and a minimal loss of image quality.

$\textbullet$ 3-D object tracking framework $\textbullet$ Using fast stereo vision system for range image $\textbullet$ Using CONDENSATION algorithm to tracking object $\textbullet$ For recognizing object, superquardrics model is used $\textbullet$ Our target object is like coils in steel works

When size and shape of moving object have been changed, a correlator often accumulates walk-off error. A success of correlation-based tracking largely depends on choosing suitable window size and position and thus transferring the proper reference image to the next frame. For this, we propose the Adaptive Window Algorithm with Four-Direction Sizing Factors (AWA-FSF) for fast adjusting a reference region to enhance reliability of correlation-based image tracking in complex cluttered environments. Since the AWA-FSF is capable of adjusting a reference image size more rapidly and properly, we can minimize the influence of complex background and clutter. In addition, we can finely tune the center point of the reference image repeatedly after main tracking process. Thus we have increased stability and reliability of correlation-based image tracking. We tested performance of the AWA-FSF using 45 real image sequences made of over 3400 images and had the satisfied results for most of them.

In this paper, we propose an obstacle identification system and a corresponding algorithm for object motion tracking. The object motion tracking device detects only ships rather than all objects so it can be applied to unmanned surface vehicles’ operation in marine environments. When an obstacle is detected, the object motion tracking device identifies and tracks it in real-time according to its movement. Objects are recognized using the Fast-RCNN algorithm, and their positions are measured through the transformation of an image coordinate system. A gimbal control method for a camera using only the image center and distance information of objects without using the camera's focus and zoom function was proposed. The control of the gimbal were made using serial communication based on the video system control architecture protocol, and object tracking was performed by placing the detected object toward the center of the image frame. The object tracking performance of the proposed object identification system has been verified using images in a limited environment.

본 논문은 비디오 연속 영상에서 얼굴의 위치를 추적하는 알고리즘에 관하여 기술한다. 컴퓨터 비젼에서 대량의 비디오 연속 영상내 물체 추적은 실시간에 처리되는 빠른 알고리즘이 요구된다. 기존의 방법은 형태에 기반한 알고리즘으로 물체의 회전, 크기 변화, 겹침 등에 대한 문제에 민감하여 여러 가지 어려움이 발생한다. 그러나 칼라를 이용한 알고리즘은 이러한 문제에 대하여 둔감하여 훨씬 효과적이다. 본 논문은 칼라 3D 히스토그램을 이용한 Swain과 Ballard의 역 투사(backprojection) 방법을 적용하여 비디오 연속 영상에서 얼굴의 위치를 빠르고 정확히 추적하는 알고리즘을 제안한다.

본 논문에서는 비냉각방식 적외선 스테레오 카메라 시스템을 이용하여 고속으로 이동하는 고온의 소형 물체의 3차원 위치를 추정함에 있어서 무작위로 추정되는 그 위치를 확률 모델로 표현하는 방법을 제안한다. 본 논문에서 제안하는 확률적 위치 모델은 디지털 영상으로 인한 픽셀위치의 오차(pixel position error)와 비냉각식 적외선 카메라에서 영상을 취득하는 순간의 차이에 의한 지터오차(jitter error)로부터 유도되는데, 두 가지 오차를 결합한 통합 오차확률모델을 수학적으로 제시하고 실험을 통하여 그 효용성을 보여준다. 우선 본 논문에서 고안한 적외선 카메라의 지터 측정기를 이용하여 적외선 카메라에서 발생하는 타이밍 지터를 통계적으로 관찰하여 확률모델을 설정한다. 또한 디지털 영상의 스테레오 정합 과정에서 발생하는 픽셀 오차에 의하여 정확도가 떨어지는 측정거리를 확률모델로 정의한다. 실험 측정 결과, 지터는 가우시안 확률분포로 모델링하는 것이 가능하며, 픽셀오차는 균일 확률분포로 모델링된다. 이 두 가지 확률분포를 갖는 오차는 상호 독립으로서 선형 결합되는데, 전체 오차에 대한 확률분포는 지터오차 변수의 확률분포와 픽셀위치오차 변수의 확률분포를 컨볼루션함으로써 유도된다. 실제 고속 이동체에 대하여 정밀한 3차원 궤적측정기와 자체 구현한 적외선 스테레오 카메라 시스템을 이용하여 제안한 확률모델을 3차원 위치추적 실험에 적용한 결과 95% 신뢰도 구간에서 물체의 위치를 추정하는 것을 확인하였다. 즉, 물체의 위치를 정확하게 측정하는 것은 이론적으로는 불가능하며, 확률모델을 통하여 물체의 위치를 표현하는 것이 타당함을 확인할 수 있다. 본 논문에서 제안한 확률모델은 적외선 스테레오 카메라를 이용한 거리측정에서 부정확함을 확률적으로 모델링하여 위치정보에 대한 불확실성을 보정해주며, 특히 적외선 스테레오 카메라를 이용한 고속 물체의 위치추적 및 거리측정에서 이론적, 실험적 토대를 제공할 것으로 기대된다.

This paper proposes a statistical model of 3-D positions when tracking moving targets using the uncooled infrared (IR) stereo camera system. The proposed model is derived from two errors. One is the position error which is caused by the sampling pixels in the digital image. The other is the timing jitter which results from the irregular capture-timing in the infrared cameras. The capture-timing in the IR camera is measured using the jitter meter designed in this paper, and the observed jitters are statistically modeled as Gaussian distribution. This paper derives an integrated probability distribution by combining jitter error with pixel position error. The combined error is modeled as the convolution of two error distributions. To verify the proposed statistical position error model, this paper has some experiments in tracking moving objects with IR stereo camera. The 3-D positions of object are accurately measured by the trajectory scanner, and 3-D positions are also estimated by stereo matching from IR stereo camera system. According to the experiments, the positions of moving object are estimated within the statistically reliable range which is derived by convolution of two probability models of pixel position error and timing jitter respectively. It is expected that the proposed statistical model can be applied to estimate the uncertain 3-D positions of moving objects in the diverse fields.

LCTV에서 대부분의 전력 소비가 이루어지는 곳은 백라이트 유닛이다. 따라서 백 라이트의 소비 전력을 절감하는 기술은 사용자 각자의 에너지 절약 효과만이 아니라 국가적인 에너지 절약 차원에서도 중요하다. 기존의 연구들은 이러한 에너지 절감을 위하여 영상 특성을 분석하여 백 라이트를 조절하는데 그 방법이 픽셀기반의 처리이므로 처리량이 많아 시간이 오래 걸린다. 본 연구에서는 픽셀 기반 처리를 최소화 할 수 있도록 영상의 특성에 따라 밝기 값에 대한 변화를 추적하여 최소 블록 처리로 그 속도를 개선하는 방법을 제안한다.