• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.180-186
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• 2017

In this paper we propose a traffic aware Demand Wakeup MAC(TADW-MAC) protocol, in which low data delay and high throughput can be achieved, for wireless sensor networks. With the TADW-MAC protocol, the problem of the DW-MAC protocol, which schedules only one packet to deliver during the Sleep period in a multi-hop transmission is resolved. DW-MAC is not adequate for the applications such as object tracking and fire detection, in which busty data should be transmitted in a limited time when an event occurs [6-8]. When an event occurs, duty cycle can be adjusted in the TADW-MAC protocol to get less energy consumption and low latency. The duty cycle mechanism has been widely used to save energy consumption of sensor node due to idle listening in wireless sensor networks. But additional delay in packet transmission may be increased in the mechanism. Our simulation results show that TADW-MAC outperforms RMAC and DW-MAC in terms of energy efficiency while achieving low latency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.203-208
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• 2021

At the manufacturing site, workers have been operating by inputting materials into the manufacturing process and leaving input records according to the work instructions, but product LOT tracking has been not possible due to many omissions. Recently, it is being carried out as a system to automatically input materials using RFID-Tag. In particular, the initial automatic recognition rate was good at 97 percent by automatically generating input information through RACK (TAG) ID and RACK input time analysis, but the automatic recognition rate continues to decrease due to multi-material RACK, TAG loss, and new product input issues. It is expected that it will contribute to increasing speed and yield (normal product ratio) in the overall production process by improving automatic recognition rate and real-time monitoring through the establishment of artificial intelligent smart factory datasets.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.9
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• pp.771-780
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• 2021

The space Debris laser ranging system is called to be a definite type of satellite laser ranging system that measures the distance to satellites. It is a system that performs POD (Precise Orbit Determination) by measuring time of flight by firing a laser. Distance precision can be measured in mm-level units, and it is the most precise system among existing systems. Currently, KASI has built SLR in Sejong and Geochang, and utilized SLR data to verify the precise orbits of the STSAT-2C and KOMASAT-5. In recent years, due to the fall or collision of space debris, its satellites have been threatened, and in terms of security, laser tracking of space objects is receiving great interest in order to protect their own space assets and protect the safety of the people. In this paper, a 1.5m-class main mirror was applied for the system design of a multipurpose laser tracking system that considers satellite laser ranging and space object laser tracking. System preliminary performance analysis was performed based on Link Budget analysis considering specifications of major components.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.402.1-402.1
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• 2014

Photonic force microscopy (PFM) is an optical tweezers-based scanning probe microscopy, which measures the forces in the range of fN to pN. The low stiffness leads proper to measure single molecular interaction. We introduce a novel photonic force microscopy to stably map various chemical properties as well as topographic information, utilizing weak molecular bond between probe and object's surface. First, we installed stable optical tweezers instrument, where an IR laser with 1064 nm wavelength was used as trapping source to reduce damage to biological sample. To manipulate trapped material, electric driven two-axis mirrors were used for x, y directional probe scanning and a piezo stage for z directional probe scanning. For resolution test, probe scans with vertical direction repeatedly at the same lateral position, where the vertical resolution is ~25 nm. To obtain the topography of surface which is etched glass, trapped bead scans 3-dimensionally and measures the contact position in each cycle. To acquire the chemical mapping, we design the DNA oligonucleotide pairs combining as a zipping structure, where one is attached at the surface of bead and other is arranged on surface. We measured the rupture force of molecular bonding to investigate chemical properties on the surface with various loading rate. We expect this system can realize a high-resolution multi-functional imaging technique able to acquire topographic map of objects and to distinguish difference of chemical properties between these objects simultaneously.

• Convergence Security Journal
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• v.19 no.3
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• pp.37-41
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• 2019

Recently, due to the appearance of various types of malware, the existing static analysis exposes many limitations. Static analysis means analyzing the structure of a code or program with source code or object code without actually executing the (malicious) code. On the other hand, dynamic analysis in the field of information security generally refers to a form that directly executes and analyzes (malware) code, and compares and examines and analyzes the state before and after execution of (malware) code to grasp the execution flow of the program. However, dynamic analysis required analyzing huge amounts of data and logs, and it was difficult to actually store all execution flows. In this paper, we propose and implement a preprocessor architecture of a system that performs malware detection and real-time multi-dynamic analysis based on 2nd generation PT in Windows environment (Windows 10 R5 and above).

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.892-903
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• 2018

The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.113-117
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• 2020

Images were taken under various weather such as rain, haze, snow often show low visibility, which can dramatically decrease accuracy of some tasks in computer vision: object detection, segmentation. Besides, previous work to enhance image usually downsample the image to receive consistency features but have not yet good upsample algorithm to recover original size. So, in this research, we jointly implement removal streak in heavy rain image and super resolution using a deep network. We put forth a 2-stage network: a multi-model network followed by a refinement network. The first stage using rain formula in the single image and two operation layers (addition, multiplication) removes rain streak and noise to get clean image in low resolution. The second stage uses refinement network to recover damaged background information as well as upsample, and receive high resolution image. Our method improves visual quality image, gains accuracy in human action recognition task in datasets. Extensive experiments show that our network outperforms the state of the art (SoTA) methods.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1065-1072
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• 2024

Previous approaches to 3D excavator pose estimation via synthetic data training utilized a single virtual excavator model, low polygon objects, relatively poor textures, and few background objects, which led to reduced accuracy when the resulting models were tested on differing excavator types and more complex backgrounds. To address these limitations, the authors present a realism-centric synthetization and labeling approach that synthesizes results with improved image quality, more detailed excavator models, additional excavator types, and complex background conditions. Additionally, the data generated includes dense pose labels and depth maps for the excavator models. Utilizing the realism-centric generation method, the authors achieved significantly greater image detail, excavator variety, and background complexity for potentially improved labeling accuracy. The dense pose labels, featuring fifty points instead of the conventional four to six, could allow inferences to be made from unclear excavator pose estimates. The synthesized depth maps could be utilized in a variety of DNN applications, including multi-modal data integration and object detection. Our next step involves training and testing DNN models that would quantify the degree of accuracy enhancement achieved by increased image quality, excavator diversity, and background complexity, helping lay the groundwork for broader application of synthetic models in construction robotics and automated project management.

• Journal of the Korea Convergence Society
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• v.6 no.3
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• pp.103-109
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• 2015

TOLED(Transparent, Organic Light Emitting Diodes) based edutainment system has been studied to solve the actual feeling training and educational experience problem of e-learning. However, edutainment system using TOLED has a problem for the non-detection of multi marker array and rotate marker array, and it has problem for the dissonance phenomena caused by Illumination Environment between real world and virtual object. It also has a do not provide services through a variety of devices problem. Therefore, in this paper, we designed a system that provides a realistic actual feeling edutainment contents by recognizes the marker array rotation and a plurality of marker arrangement via an improved marker detection technique. And to unify the real space and virtual space of the lighting environment through a nested block layer.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.72-79
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• 2006

Recently, a MMORPG(Massively Multi-play Online Role Playing Game) has built distribute server by Seamless world. This paper proposes an efficient collision detection method. DLS is used to dynamically adjust spatial subdivisions in each the boundary regions of distribute server We use an index table to effectively utilize the relationships between in the nodes and can perform the collision detection efficiently by reconstructing nodes of the tree. Also, we maintain the information for the boundary region to efficiently detect the collections and adjust the boundary regions between distributed servers by using DLS. As the DLS uses pointers, the information for each server is not needed and the boundary regions between the distributed servers are efficiently searched. Using node index points, the construction table can be made to find between ray and neighborhood node, In addition, processes for Network traffic reduce because a copy of the boundary regions is not needed when a object moves with realtime.