• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.424-431
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• 2019

In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.277-283
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• 2019

In this paper, we propose a distributed fusion estimation for sensor networks using a receding horizon strategy. Communication channels were modelled as Markov jump systems, and a posterior probability distribution for communication channel characteristics was calculated and incorporated into the filter to allow distributed fusion estimation to handle path loss observation situations automatically. To implement distributed fusion estimation, a Kalman-Consensus filter was then used to obtain the average consensus, based on the estimates of sensors randomly distributed across sensor networks. The advantages of the proposed algorithms were then verified using a large-scale sensor network example.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.121-122
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• 2019

본 논문에서는 웹쉘을 탐지하기 위한 방법 중 하나로 슬라이딩윈도우 기반 머신러닝을 활용하는 방안을 제안하고자 한다. 웹 공격에 많이 활용되는 웹쉘의 탐지를 위하여 제안하는 슬라이딩윈도우 기반의 탐지 기법은 시간이 지남에 따라 발전해가는 웹쉘 탐지 우회 기술에 대응하여 보다 정확한 탐지를 제공하는 기술이며, 이를 기반으로 웹쉘의 다양한 변종 또한 탐지할 수 있다. 본제안의 경우 코드의 부분별 위험도를 측정 및 제공하여 보다 효과적으로 대응할 수 있을 것으로 전망된다.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.890-903
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• 2019

The single carrier frequency domain equalization (SC-FDE) technology is an important part of the broadband wireless access communication system, which can effectively combat the frequency selective fading in the wireless channel. In SC-FDE communication system, the accuracy of timing synchronization directly affects the performance of the SC-FDE system. In this paper, on the basis of Schmidl timing synchronization algorithm a timing synchronization algorithm suitable for FPGA (field programmable gate array) implementation is proposed. In the FPGA implementation of the timing synchronization algorithm, the sliding window accumulation, quantization processing and amplitude reduction techniques are adopted to reduce the complexity in the implementation of FPGA. The simulation results show that the algorithm can effectively realize the timing synchronization function under the condition of reducing computational complexity and hardware overhead.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.67-90
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• 2019

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1157-1158
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• 2023

자율주행 시스템에서 차선 인지는 주행의 성능과 안전에 중요한 역할을 한다. 차선 인지 분야에서는 다양한 알고리즘이 사용된다. 본 논문에서는 슬라이딩 윈도우 기법을 사용한 알고리즘을 기반으로, 더 정확하고 효율적인 차선 인지를 위한 개선 방안을 소개한다.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.188-195
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• 2024

This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a neural network-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. At the same time, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.110-116
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• 2024

Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5284-5289
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• 2011

This study focused to evaluate thermal transmittance(U-value) performance of sliding type of aluminum alloy window frame(AAWF) with double glazing(DG) and glazing spacer and that without thermal breaker in winter and summer season by two dimensional steady state heat transfer analysis. The AAWE was installed to an existing educational facilities in Seosan area which is the southern region of the Korean Peninsula. Analysis of 2D steady-state heat transfer was performed through the use of BISCO as calculation and simulation program. U-value and temperature factors were calculated. The results are as followed. First, the isotherm simulation shows that AAWF with double glazing have serious differences from recently proposed window thermal performance standards such as Insulation Performance of Windows and Doors of Building Energy Saving Design Standards and the results of calculation of thermal transmittance performance of AAWF and DG are U=9.631 W/$m^2K$, U=2.382 W/$m^2K$ respectively during winter and summer season. Second, the results of analysis of heat transfer analysis, calculated by simulation, shows that 225% of heat is lost comparing with thermal performance standards U=4.0 W/$m^2K$ of general double glazing among those standards on AAWF without thermal breaker.