• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.22-29
• /
• 2010

A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.

• Journal of Advanced Marine Engineering and Technology
• /
• v.38 no.6
• /
• pp.710-715
• /
• 2014

This paper investigates the design scheme of fuzzy-based adaptive controller to give adaptability for controlling nonlinear systems. For this, a nonlinear system is linearized by the several subsystems depending on the operating point or parameter changes. Then, the sub-controller is designed by linear control scheme for each subsystem and the sub-controllers are fused with each gain of sub-controllers using fuzzy rules. The proposed method is applied to an inverted pole system which has structurally instability and nonlinearity, and simulation works are shown to illustrate the effectiveness by comparison with the interpolation-based adaptive Controller.

• Journal of the Institute of Convergence Signal Processing
• /
• v.10 no.3
• /
• pp.158-163
• /
• 2009

LMS(Least Mean Square) algorithm requires simple equation and is used widely because of the low complexity. If the convergence speed increase, LMS algorithm has a divergence in case of sharp environment changes. And if a stability increase, the convergence speed becomes slow. This algorithm based on a trade off between fast convergence and system stability. To improve this problem, VSSLMS (Variable Step Size LMS) algorithm was developed. The VSSLMS algorithm improved the convergence speed and performance as adjusting step size using error signal. In this paper, I-VSSLMS algorithm is proposed tor improve the performance of adaptive noise canceller in real-time environments. The proposed algorithm is applied to adaptive noise canceller using TMS320C6713 DSP board and we did simulation by real time. Then we compared performance of each algorithm and demonstrated that proposed algorithm has superior performance.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.6
• /
• pp.195-202
• /
• 2005

An adaptive segmentation algorithm is used to efficiently target decisions in local non-stationary images. Until now, several adaptive approaches have been proposed as a method of segmentation. However, they can't be directly used for radar target detection because a radar signal has different characteristics from general images. Generally, a histogram of radar signal shows that targets have a relatively small number of frequency functions compared to the background and distribution of background, which have several shapes as the environment changes. In this paper, we propose an adaptive segmentation algorithm using a histogram chord which is a right-down line from maximum pick of frequency function. The proposed method provides thresholds which are optimum for several radar environments because the used chord for threshold search is not significantly effected by interference conditions. Simulation results show that the proposed method is superior to the traditional algorithms, global threshold method and distribution median method, with respect to detection performance.

• Proceedings of the Korea Association of Crystal Growth Conference
• /
• 1996.06b
• /
• pp.285-309
• /
• 1996

Non oxide ceramics such as nitrides of transition metals have shown significant potential for future economic impact, in diverse applications in ceramic, aerospace and electronic industries, as refractory products, abrasives and cutting tools, aircraft components, and semi-conductor substrates amid others. Combustion synthesis has become an attractive alternative to the conventional furnace technology to produce these materials cheaply, faster and at a higher level of purity. However he process os highly exothermic and manifests complex dynamics due to its strongly non-linear nature. In order to develop an understanding of this process and to study the effect of operational parameters on the final outcome, numerical modeling is necessary, which would generated essential knowledge to help scale-up the process. the model is based on a system of parabolic-hyperbolic partial differential equations representing the heat, mass and momentum conservation relations. The model also takes into account structural change due to sintering and volumetric expansion, and their effect on the transport properties of the system. The solutions of these equations exhibit steep moving spatial gradients in the form of reaction fronts, propagating in space with variable velocity, which gives rise to varying time scales. To cope with the possibility of extremely abrupt changes in the values of the solution over very short distances, adaptive mesh techniques can be applied to resolve the high activity regions by ordering grid points in appropriate places. To avoid a control volume formulation of the solution of partial differential equations, a simple orthogonal, adaptive-mesh technique is employed. This involves separate adaptation in the x and y directions. Through simple analysis and numerical examples, the adaptive mesh is shown to give significant increase in accuracy in the computations.

• Smart Structures and Systems
• /
• v.13 no.2
• /
• pp.203-217
• /
• 2014

Due to the shift in paradigm from passive control to adaptive control, smart tuned mass dampers (STMDs) have received considerable attention for vibration control in tall buildings and bridges. STMDs are superior to tuned mass dampers (TMDs) in reducing the response of the primary structure. Unlike TMDs, STMDs are capable of accommodating the changes in primary structure properties, due to damage or deterioration, by tuning in real time based on a local feedback. In this paper, a novel adaptive-length pendulum (ALP) damper is developed and experimentally verified. Length of the pendulum is adjusted in real time using a shape memory alloy (SMA) wire actuator. This can be achieved in two ways i) by changing the amount of current in the SMA wire actuator or ii) by changing the effective length of current carrying SMA wire. Using an instantaneous frequency tracking algorithm, the dominant frequency of the structure can be tracked from a local feedback signal, then the length of pendulum is adjusted to match the dominant frequency. Effectiveness of the proposed ALP-STMD mechanism, combined with the STFT frequency tracking control algorithm, is verified experimentally on a prototype two-storey shear frame. It has been observed through experimental studies that the ALP-STMD absorbs most of the input energy associated in the vicinity of tuned frequency of the pendulum damper. The reduction of storey displacements up to 80 % when subjected to forced excitation (harmonic and chirp-signal) and a faster decay rate during free vibration is observed in the experiments.

• Smart Media Journal
• /
• v.12 no.10
• /
• pp.19-28
• /
• 2023

In the fruit harvesting field, interest in automatic robot harvesting is increasing due to various seasonality and rising harvesting costs. Accurate apple detection is a difficult problem in complex orchard environments with changes in light, vibrations caused by wind, and occlusion of leaves and branches. In this paper, we introduce a dataset and an adaptive heatmap regression model that are advantageous for robot automatic apple harvesting. The apple dataset was labeled with not only the apple location but also the visibility. We propose a method to detect the center point of an apple using an adaptive heatmap regression model that adjusts the Gaussian shape according to visibility. The experimental results showed that the performance of the proposed method was applicable to apple harvesting robots, with MAP@K of 0.9809 and 0.9801 when K=5 and K=10, respectively.

• Journal of Digital Convergence
• /
• v.11 no.2
• /
• pp.243-248
• /
• 2013

This paper proposes an effective method using Adaptive Boosting to track a person's head in complex background. By only one way to feature extraction methods are not sufficient for modeling a person's head. Therefore, the method proposed in this paper, several feature extraction methods for the accuracy of the detection head running at the same time. Feature Extraction for the imaging of the head was extracted using sub-region and Haar wavelet transform. Sub-region represents the local characteristics of the head, Haar wavelet transform can indicate the frequency characteristics of face. Therefore, if we use them to extract the features of face, effective modeling is possible. In the proposed method to track down the man's head from the input video in real time, we ues the results after learning Harr-wavelet characteristics of the three types using AdaBoosting algorithm. Originally the AdaBoosting algorithm, there is a very long learning time, if learning data was changes, and then it is need to be performed learning again. In order to overcome this shortcoming, in this research propose efficient method using cascade AdaBoosting. This method reduces the learning time for the imaging of the head, and can respond effectively to changes in the learning data. The proposed method generated classifier with excellent performance using less learning time and learning data. In addition, this method accurately detect and track head of person from a variety of head data in real-time video images.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.169-183
• /
• 2010

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a freeway and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, existing technologies, this freeway attribute, velocity changes, volume changes, occupancy changes reflect judge the primary. Furthermore, We pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian Mixture model analytical method which has been considered the best among well-known environmental obstacle reduction methods. Therefore, in this way, the accident was the final decision. Also, environmental factors occur frequently, and with the index finger situations, effectively reducing that can actively and environmentally adaptive techniques through accident final judgment. This implementation of the evaluate performance of the experiment road of 12 incidents in simulated and the jang-hang IC's real-time accident experiment. As a result, the do well detection 93.33%, false alarm 6.7% as showed high reliability.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.9
• /
• pp.361-368
• /
• 2014

The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.