• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.155-162
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• 2010

To ensure accurate measurements of wireless network performance, it should be collected real-time data which are transmitted between a large number of nodes in the actual network environment. Therefore, it is necessary to develop simulation tool for finding optimal network system design method such as media access control, routing technique, ad-hoc algorithm of node deployment while overcoming spatial and temporal constraints. Our research attempts to provide an improved architecture and design method of simulation tool for wireless network is an application of multi-threading technique in these issues. We finally show that usability of the proposed simulator by comparing results derived from same test environment in the wireless LAN model of our simulator and widely used network simulation package, NS-2.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.4
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• pp.404-413
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• 2015

The target classification for ballistic target(BT) is one of the most critical issues of ballistic defence mode(BDM) in multi-function radar(MFR). Radar responds to the target according to the result of classifying BT and air breathing target(ABT) on BDM. Since the efficiency and accuracy of the classification is closely related to the capacity of the response to the ballistic missile offense, effective and accurate classification scheme is necessary. Generally, JEM(Jet Engine Modulation), HRR(High Range Resolution) and ISAR(Inverse Synthetic Array Radar) image are used for a target classification, which require specific radar waveform, data base and algorithms. In this paper, the classification method that is applicable to a MFR system in a real environment without specific waveform is proposed. The proposed classifier adopts kinematic data as a feature vector to save radar resources at the radar time and hardware point of view and is implemented by fuzzy logic of which simple implementation makes it possible to apply to the real environment. The performance of the proposed method is verified through measured data of the aircraft and simulated data of the ballistic missile.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.791-793
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• 2015

Recently, UCC (User Created Contents) and VoD (Video on Demand), and multimedia content are growing, IP-TV, Smart TV, OHTV (Open Hybrid TV) various services such as multi platform (Multi-platform) environment, services and QoS issues. To solve this problem, the network efficiently, and improve the quality of content is necessary for the system. In this paper, the network of channels State and transmission of multimedia data based on dynamic resource usage, TCP and UDP, Adaptive dual-streaming system used for design and analysis. In addition, the existing TCP and UDP streaming system using a single protocol for analysis and verification of the effectiveness of the difference between and. This is a disaster, and medical/first aid system will be utilized in the field of feed, are ubiquitous.

• Journal of the Korea Computer Graphics Society
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• v.21 no.4
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• pp.11-17
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• 2015

As the needs of real-time visual object tracking are increasing in various kinds of application fields such as surveillance, entertainment, etc., kernel-based mean-shift tracking has received more interests. One of major issues in kernel-based mean-shift tracking is to be robust under partial or full occlusion status. This paper presents a real-time mean-shift tracking which is robust in partial occlusion by applying multi-mode local kernel weight. In the proposed method, a kernel is divided into multiple sub-kernels and each sub-kernel has a kernel weight to be determined according to the location of the sub-kernel. The experimental results show that the proposed method is more stable than the previous methods with multi-mode kernels in partial occlusion circumstance.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.257-257
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• 2010

One of the critical issues for applications of flexible organic thin film transistors (OTFTs) for flexible electronic systems is the electrical stabilities of the OTFT devices, including variation of the current on/off ratio ($I_{on}/I_{off}$), leakage current, threshold voltage, and hysteresis, under repetitive mechanical deformation. In particular, repetitive mechanical deformation accelerates the degradation of device performance at the ambient environment. In this work, electrical stabilities of the pentacene organic thin film transistors (OTFTs) employing multi-stack hybrid encapsulation layers were investigated under mechanical cyclic bending. Flexible bottom-gated pentacene-based OTFTs fabricated on flexible polyimide substrate with poly-4-vinyl phenol (PVP) dielectric as a gate dielectric were encapsulated by the plasma-deposited organic layer and atomic layer deposited inorganic layer. For cyclic bending experiment of flexible OTFTs, the devices were cyclically bent up to $10^5$ times with 5mm bending radius. In the most of the devices after $10^5$ times of bending cycles, the off-current of the OTFT with no encapsulation layers was quickly increased due to increases in the conductivity of the pentacene caused by doping effects from $O_2$ and $H_2O$ in the atmosphere, which leads to decrease in the $I_{on}/I_{off}$ and increase in the hysteresis. With encapsulation layers, however, the electrical stabilities of the OTFTs were improved significantly. In particular, the OTFTs with multi-stack hybrid encapsulation layer showed the best electrical stabilities up to the bending cycles of $10^5$ times compared to the devices with single organic encapsulation layer. Changes in electrical properties of cyclically bent OTFTs with encapsulation layers will be discussed in detail.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.977-985
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• 2021

The LiDAR sensor is attracting attention as a key sensor for autonomous driving vehicle. LiDAR sensor provides measured three-dimensional lengths within range using LASER. However, as much data is provided to the external system, it is difficult to process such data in an external system or processor of the vehicle. To resolve these issues, we develop integrated processing system for LiDAR sensor. The system is configured that client receives data from LiDAR sensor and processes data, server gathers data from clients and transmits integrated data in real-time. The test was carried out to ensure real-time processing of the system by changing the data acquisition, processing method and process driving method of process. As a result of the experiment, when receiving data from four LiDAR sensors, client and server process was operated using background or multi-core processing, the system response time of each client was about 13.2 ms and the server was about 12.6 ms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.127-135
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• 2023

Accurate segmentation of histopathology whole slide images (WSIs) is a crucial task for disease diagnosis and treatment planning. However, conventional automated segmentation algorithms may not always be applicable to WSI segmentation due to their large size and variations in tissue appearance, staining, and imaging conditions. Recent advances in interactive segmentation, which combines human expertise with algorithms, have shown promise to improve efficiency and accuracy in WSI segmentation but also presented us with challenging issues. In this paper, we propose a novel interactive segmentation method, ZoomISEG, that leverages multi-resolution WSIs. We demonstrate the efficacy and performance of the proposed method via comparison with conventional single-scale methods and an ablation study. The results confirm that the proposed method can reduce human interaction while achieving accuracy comparable to that of the brute-force approach using the highest-resolution data.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.43-50
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• 2024

Large planar military antenna boasts a range of electrical components, including TRA(Transmit-Receive Assembly), signal processors, etc. which engage in computations and calculations. These processes generate a significant amount of heat, leading to unforeseen consequences for the equipment. To mitigate these adverse effects, it's imperative to implement a cooling system that can effectively reduce heat-related issues. Given the antenna's intricate nature and the multitude of components it houses, a two-step estimation process is necessary. The first step involves a comprehensive model calculation to determine the total flow characteristics, while the second step entails a thermal analysis of individual TRA set. In this study, we depicted an antenna set using simplified 3D models of its components, considering their material and thermal properties. The sequential analysis process facilitated the calculation of branched flow rates, providing insights into the individual TRA. This approach also allowed us to design a cooling system for the TRA set, assessing its thermal stability in high-temperature environments. To ensure the optimal performance of TRA, breaking down the analysis into stages based on the cooling system's structure can assist operators in predicting numerical results more effectively.