• Annual Conference of KIPS
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• 2011.04a
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• pp.625-628
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• 2011

본 논문에서는 다수의 이동 가능한 관리 대상을 소수의 관리자가 효율적으로 관리하기 위하여 Ad-hoc 네트워크 환경에서 DSDV(Destination Sequenced Distance Vector) 라우팅 알고리즘을 활용하는 위치 정보 시스템을 구현한다. 제안된 시스템에서는 다수의 위치 정보 노드들이 Ad-hoc 네트워크 환경에서 DSDV 라우팅 알고리즘으로 네트워크를 구축하고, 구축된 네트워크에서 하나 이상의 위치 정보 노드가 사라지면 진동과 같은 경고 시스템을 활성화한다. 또한 제안된 시스템은 안드로이드 환경에서 블루투스 통신을 통해 시각적으로 노드들의 위치를 확인시켜 주어 관리 대상에 대한 관리를 도와준다. 본 시스템으로 모의 성능 평가를 수행한 결과, 사용자 위치 정보 노드 간 1:1 통신을 직선거리에서 수행하였을 때, 약 250m까지 통신이 가능하였으며, 1:N 통신의 경우 약 100m 이내의 거리에서 정상적으로 통신이 이루어지는 것을 확인하였다. 본 논문에서 구현된 시스템은 유치원 아동 관리 시스템, 놀이공원 미아 방지 시스템, 관광객 관리 시스템 등 여러 시스템에 매우 유연하게 적용이 가능하여, 그 활용도가 높을 것으로 예상된다.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1287-1299
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• 2023

A new Monte-Carlo-based computer program (RDS-BASIC) is developed to simulate the transport of energetic ions in pure matter. This computer program is utilizing an algorithm that uses detailed numerical solutions for the classical scattering integral for evaluating the outcomes of the binary collision processes. This approach is adopted by several prominent similar simulation programs and is known to provide results with higher accuracy compared to other approaches that use approximations to shorten the simulation time. Furthermore, RDS-BASIC simulation program contains special methods to reduce the displacement energy threshold of surface atoms. This implementation is found essential for accurate simulation results for sputtering yield in the case of very low energy ions irradiation (near sputtering energy threshold) and also successfully solve the problem of simultaneously obtaining an acceptable number of atomic displacements per incident ions. Results of our simulation for several irradiation systems are presented and compared with their respective TRIM (SRIM-2013) and the state-of-the-art SDTrimSP simulation results. Our sputtering simulation results were also compared with available experimental data. The simulation execution time for these different simulation programs has also been compared.

• Annual Conference of KIPS
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• 2020.11a
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• pp.57-60
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• 2020

순위다중패턴매칭문제는 길이가 n인 텍스트 T와 패턴들의 집합 P' = {P₁,P₂…,P_k}가 주어졌을 때, P'에 속하는 패턴들과 상대적인 순위가 일치하는 T의 모든 부분문자열들의 위치를 찾는 문제이다. P'에서 가장 짧은 패턴의 길이가 m, 가장 긴 패턴의 길이를 $\bar{m}$, 모든 패턴들의 길이의 합을 M, q개의 연속된 문자들을 q-그램이라 할 때, 기존에 텍스트의 핑거프린트를 이용하여 순위다중패턴매칭문제를 $O(q!+nqlogq+Mlog\bar{m}+nM)$ 시간에 해결하는 알고리즘이 제시되었다. 본 논문에서는 텍스트의 핑거프린트를 활용하여 O(max(q!,M,n))개의 스레드를 이용하여 순위다중패턴매칭문제를 평균적으로 $O(\bar{m}+qlogq+n/q!)$ 시간에 해결하는 병렬 구현 방법을 제시한다. 실험 결과, n = 1,000,000, k = 1,000, m = 5, q = 3일 때, 본 논문에서 제시하는 병렬 구현 방법은 기존의 순차 알고리즘보다 약 19.8배 빠르게 수행되었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.577-578
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• 2021

Recently, artificial intelligence systems are being used in various fields. The accuracy of the decision algorithm of artificial intelligence is greatly affected by the amount of learning and the accuracy of the learning data. In the case of the amount of learning, a large amount of data is required because it has a decisive effect on the performance of AI. In this paper, we propose a data collection system for constructing a system that analyzes future conditions and changes in infants' conditions based on the body temperature data of infants and toddlers. The proposed system is a system that collects and transmits data, and it is believed that it can minimize the resource consumption of the server system in existing big data analysis and training data construction.

• Annual Conference of KIPS
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• 2021.11a
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• pp.217-220
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• 2021

ICISC'19에서 기존 CHAM과 동일한 구조와 규격을 갖지만, 라운드 수만 증가시킨 revised CHAM이 발표되었다. CHAM은 사물인터넷에서 사용되는 저사양 프로세서에서 효율적인 구현이 가능한 특징을 갖고 있다. AVR, ARM 프로세서 상에서의 CHAM 암호 알고리즘에 대한 최적 구현은 존재하지만, 아직 RISC-V 프로세서 상에서의 CHAM 구현은 존재하지 않는다. 따라서, 본 논문에서는 RISC-V 프로세서 상에서의 Revised CHAM 알고리즘을 최초로 구현을 제안한다. CHAM 라운드 함수의 내부 구조의 일부를 생략하여 최적 구현하였다. 그리고 홀수 라운드와 짝수 라운드를 모듈별로 구현하여 필요에 따라 모듈을 호출하여 손쉽게 사용할 수 있게 하였다. 결과적으로, RISC-V 상에서 제안 기법 적용하기 전보다 제안 기법 적용 후에 12%의 속도 향상을 달성하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.385-387
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• 2021

Marine deposition waste threatens the book ecosystem and causes a decrease in catch due to ghost fishing, causing damage of about 370 billion won per year. In order to collect this, a current status survey is conducted using two-way ultrasonic detectors, diving, and lifting frames. However, the scope of the investigation is small to investigate a lot of sedimentary waste, and there is a possibility of causing casualties. This paper deals with the implementation of a high-accuracy marine deposition detection AI model by learning the coastal sediment image data of AI-Hub using the YOLOv5 algorithm suitable for real-time object detection.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.71-78
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• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.