• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.171-178
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• 2019

The use of anti-scatter grids in radiographic imaging has the advantage of preventing the image distortion caused by scattered radiation. However, it carries the side effect of leaving artifacts in the X-ray image. In this paper, we propose a grid line suppression technique using discrete cosine transform(DCT). In X-ray images, the grid lines have different characteristics depending on the shape of the object and the area of the image. To solve this problem, we adopt the DCT transform based on a dynamic segmentation, and propose a filter transfer function for each individual segment. An algorithm for detecting the band of grid lines in frequency domain and a band stop filter(BSF) with a filter transfer function of a combination of Kaiser window and Butterworth filter have been proposed. To solve the blocking effects, we present a method to determine the pixel values using multiple structured images. The validity of the proposed theory has been evaluated from the experimental results using 140 X-ray images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• Journal of Convergence for Information Technology
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• v.11 no.1
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• pp.12-19
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• 2021

For strengthening the national defense, the function of tactical network is essential. tactics and strategies in wartime situations are based on numerous information. Therefore, various reconnaissance devices and resources are used to collect a huge amount of information, and they transmit the information through tactical networks. In tactical networks that which use contention based channel access scheme, high-speed nodes such as recon aircraft may have performance degradation problems due to unnecessary channel occupation. In this paper, we propose a learning-backoff method, which empirically learns the size of the contention window to determine channel access time. The proposed method shows that the network throughput can be increased up to 25% as the number of high-speed mobility nodes are increases.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.19-32
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• 2009

In this paper, we design thin-client framework capable of application sharing & data access on the Internet, and apply related skills, such as X windows system, pseudo server, CODA file system, MPI(Message Passing Interface). We suggest a framework for the thin client to access data produced by working on a server optimally as well as to run server side application, even in the case of network down. Additionally, it needed to reflect all local computing changes to remote server when network is restored. To design thin client framework with these characteristics, in this paper, we apply distributed pseudo server and CODA file system to our framework, also utilize MPI for the purpose of more efficient computing & management. It allows for implementation of network independent computing environment of thin client, also provide scalable application service to numerous user through the elimination of bottleneck on caused by server overload. In this paper, we discuss the implementing method of thin client framework in detail.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.255-255
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• 2016

Resistive random access memory (ReRAM)는 낮은 동작 전압, 빠른 동작 속도, 고집적화 등의 장점으로 인해 차세대 비휘발성 메모리 소자로써 많은 관심을 받고 있다. 최근에 ReRAM 절연막으로 NiOx, TiOx, AlOx TaOx, HfOx와 같은 binary metal oxide 물질들을 적용하는 연구가 활발히 진행되고 있다. 특히, HfOx는 안정적인 동작 특성을 나타낸다는 점에서 ReRAM 절연막 물질로 적합하다고 보고되고 있다. ReRAM 절연막을 형성할 때, 물리 기상 증착 방법 (PVD)이나 화학 기상 증착법 (CVD)과 같은 방법이 많이 이용된다. 이러한 증착 방법들은 고품질의 박막을 형성시킬 수 있는 장점이 있다. 하지만, 높은 온도에서의 공정과 고가의 진공 장비가 이용되기 때문에 경제적인 문제가 있으며, 기판 또는 금속에 플라즈마 손상으로 인한 문제가 발생할 수 있다. 따라서 이러한 문제점들을 개선하기 위해 용액 공정이 많은 관심을 받고 있다. 용액 공정은 공정과정이 간단할 뿐만 아니라 소자의 대면적화가 가능하고 공정온도가 낮으며 고가의 진공장비가 필요하지 않은 장점을 가진다. 따라서 본 연구에서는, 용액공정을 이용하여 HfOx 기반의 ReRAM 제작하였고 $25^{\circ}C$와 $85^{\circ}C$에서 ReRAM의 동작특성에 미치는 compliance current의 영향을 평가하였다. 실험 방법으로는, hafnium chloride (0.1 M)를 2-methoxyethanol에 충분히 용해시켜서 precursor를 제작하였다. 이후, p-type Si 기판 위에 습식산화를 통하여 300 nm 두께의 SiO2 절연층을 성장시킨 후, 하부전극을 형성하기 위해 electron beam evaporation을 이용하여 10/100 nm 두께의 Ti/Pt 전극을 증착하였다. 순차적으로, 제작된 산화물 precursor를 이용하여 Pt 위에 spin coating 방법으로 1000 rpm 10 초, 6000 rpm 30초의 조건으로 두께 35 nm의 HfOx 막을 증착하였다. 최종적으로, solvent 및 불순물을 제거하기 위해 $180^{\circ}C$의 온도에서 10 분 동안 열처리를 진행하였으며, 상부 전극을 형성하기 위해 electron beam evaporation을 이용하여 Ti와 Al을 각각 50 nm, 100 nm의 두께로 증착하였다. ReRAM 동작에서 compliance current가 미치는 영향을 평가하기 위하여 compliance current를 10mA에서 1mA까지 변화시키면서 측정한 결과, $25^{\circ}C$에서는 compliance current의 크기와 상관없이 일정한 메모리 윈도우와 우수한 endurance 특성을 얻는 것을 확인하였다. 한편, $85^{\circ}C$의 고온에서 측정한 경우에는 1mA의 compliance current를 적용하였을 때, $25^{\circ}C$에서 측정된 메모리 윈도우 크기를 비슷하게 유지하면서 더 우수한 endurance 특성을 얻는 것을 확인하였다. 결과적으로, 용액공정 방법으로 제작된 ReRAM을 측정하는데 있어서 compliance current를 줄이면 보다 우수한 endurance 특성을 얻을 수 있으며, ReRAM 소자의 전력소비감소에 효과적이라고 기대된다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.563-569
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• 2018

This paper is on the implementation of a system for automatically monitoring window farm hydroponics based on Arduino (utilizing Arduino's open source code) emerging as the icon of the Fourth Industrial Revolution. A window farm, which means window-type hydroponics, is offered as an alternative to fulfill the desires of people who want to grow plants aside from the busy daily life in the city. The system proposed in this paper was developed to automatically monitor a window farm hydroponics cultivation environment using the Arduino UNO board, a four-charmel motor shield, temperature and humidity sensors, illumination sensors, and a real-time clock module. Modules for hydroponics have been developed in various forms, but power consumption is high because most of them use general power and motors. Since it is not a system that is monitored automatically, there is a disadvantage in that an administrator always has to manage its operational state. The system is equipped with a water supply that is most suitable for a plant growth environment by utilizing temperature, humidity, and light sensors, which function as Internet of Things sensors. In addition, the real-time clock module can be used to provide a more appropriate water supply. The system was implemented with sketch code in a Linux environment using Raspberry Pi 3 and Arduino UNO.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.105-113
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• 2017

This paper presents a novel on-line trajectory optimization framework based on automatic time warping, which performs the time warping of a reference motion while optimizing character motion control. Unlike existing physics-based character animation methods where sampling times for a reference motion are uniform or fixed during optimization in general, our method considers the change of sampling times on top of the dynamics of character motion in the same optimization, which allows the character to effectively respond to external pushes with optimal time warping. In order to do so, we formulate an optimal control problem which takes into account both the full-body dynamics and the change of sampling time for a reference motion, and present a model predictive control framework that produces an optimal control policy for character motion and sampling time by repeatedly solving the problem for a fixed-span time window while shifting it along the time axis. Our experimental results show the robustness of our framework to external perturbations and the effectiveness on rhythmic motion synthesis in accordance with a given piece of background music.