• 대한원격탐사학회지
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• 제38권5_3호
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• pp.967-977
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• 2022

산불의 발생과 강도는 기후 변화로 인하여 증가하고 있다. 산불 연기에 의한 배출가스 대기질과 온실 효과에 영향을 미치는 주요 원인 중 하나로 인식되고 있다. 산불 연기의 효과적인 탐지를 위해서는 위성 산출물과 기계학습의 활용이 필수적이다. 현재까지 산불 연기 탐지에 대한 연구는 구름 식별의 어려움 및 모호한 경계 기준 등으로 인한 어려움이 존재하였다. 본 연구는 우리나라 환경위성 센서인 Geostationary Environment Monitoring Spectrometer (GEMS)의 Level 1, Level 2 자료와 기계학습을 이용한 산불 연기 탐지를 목적으로 한다. 2022년 3월 강원도 산불을 사례로 선정하여 산불 연기 레이블 영상을 생성하고, 랜덤 포레스트 모델에 GEMS Level 1 및 Level 2 자료를 투입하여 연기 픽셀 분류 모델링을 수행하였다. 훈련된 모델에서 입력변수의 중요도는 Aerosol Optical Depth (AOD), 380 nm 및 340 nm의 복사휘도 차, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), 포름알데히드, 이산화질소, 380 nm 복사휘도, 340 nm 복사휘도의 순서로 나타났다. 또한 2,704개 픽셀에 대한 산불 연기 확률(0≤p≤1) 추정에서 Mean Bias Error (MBE)는 -0.002, Mean Absolute Error (MAE)는 0.026, Root Mean Square Error (RMSE)는 0.087, Correlation Coefficient (CC)는 0.981의 정확도를 보였다.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1984년도 추계학술발표회 논문집
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• pp.66-69
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• 1984

This paper is a study on the Endpoint Detection for Korean Speech Recognition. In speech signal process, analysis parameter was classification from Zero Crossing Rate(Z.C.R), Log Energy(L.E), Energy in the predictive error(Ep) and fundamental Korean Speech digits, /영/-/구/ are selected as date for the Recognition of Speech. The main goal of this paper is to develop techniques and system for Speech input ot machine. In order to detect the Endpoint, this paper makes choice of Log Energy(L.E) from various parameters analysis, and the Log Energy is very effective parameter in classifying speech and nonspeech segments. The error rate of 1.43% result from the analysis.

• 한국항행학회논문지
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• 제27권4호
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• pp.391-395
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• 2023

Helicopter accidents due to spatial disorientation in low visibility conditions continue to persist as a major issue. These incidents often stem from human error, typically induced by stress, and frequently result in fatal outcomes. This study employs machine learning to analyze flight data and evaluate the efficacy of a flight illusion detection algorithm, laying groundwork for further research. This study collected flight data from approximately 20 pilots using a simulated flight training device to construct a range of flight scenarios. These scenarios included three stages of flight: ascending, level, and descent, and were further categorized into good visibility conditions and 0-mile visibility conditions. The aim was to investigate the occurrence of flight illusions under these conditions. From the extracted data, we obtained a total of 54,000 time-series data points, sampled five times per second. These were then analyzed using a machine learning approach.

• 센서학회지
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• 제20권6호
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• pp.388-392
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• 2011

In this paper, a face detection method for interactive TV control system using a new feature, edge histogram feature, with a support vector machine(SVM) in the near-infrared(NIR) images is proposed. The edge histogram feature is extracted using 16-directional edge intensity and a histogram. Compared to the previous method using local binary pattern(LBP) feature, the proposed method using edge histogram feature has better performance in both smaller feature size and lower equal error rate(EER) for face detection experiments in NIR databases.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2588-2593
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• 2003

Any system has the possibility of an error occurrence. Even if trivial errors were occurred, the original system would be fatally affected by the occurring errors. Accordingly, the error detection must be demanded. In this paper, we developed a real-time error detection system would be able to apply to an electronic Jacquard system. A Jacquard is a machine, which controls warps while weaving textiles, for manufacturing patterned cloth. There are two types of mechanical and electronic Jacquard. An electronic Jacquard is better than a mechanical Jacquard in view of the productivity and realizability for weaving various cloths. Recent weaving industry is growing up increasingly due to the electronic Jacquard. But, the problem of wrong weaving from error data exists in the electronic Jacquard. In this research, a real-time error detection system for an electronic Jacquard is developed for detecting errors in an electronic Jacquard in real-time. The real-time system is constructed using PC-based embedded system architecture. The system detects the occurring errors in real-time by storing 1344 data transferred in serial from an electronic Jacquard into memory, and then by comparing synchronously 1344 data stored into memory with 1344 data in a design file before the next data would be transferred to the Jacquard for weaving. The information of detected errors are monitored to the screen and stored into a file in real-time as the outputs of the system. In this research, we solve the problem of wrong weaving through checking the weaving data and detecting the occurred errors of an electronic Jacquard in real-time.

• 지능정보연구
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• 제25권4호
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• pp.53-65
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• 2019

영상 기반의 보안 시스템의 증가함에 따라 각 용도마다 다른 다양한 객체들에 대한 처리들이 중요해지고 있다. 객체 추적은 객체 인식, 검출과 같은 작업들과 함께 필수적인 작업으로 다뤄진다. 이 객체 추적을 달성하기 위해서 다양한 머신러닝이 적용될 수 있다. 성공적인 분류기로써 전체 에러율 최소화(total-error-rate minimization) 기반의 방법론이 사용될 수 있다. 이 전체 에러율 최소화 기반의 방법론은 오프라인 학습을 기반으로 하고 있다. 객체 추적은 실시간으로 처리하며 갱신해야하는 것이 필수적이므로 온라인 학습(online learning)을 기반으로 하는 것이 적합하다. 온라인 전체 에러율 최소화 방법론이 개발되었지만 점근적으로 재가중되는(approximately reweighted) 작업이 포함되어 에러를 누적시킬 수 있다는 단점이 있다. 본 논문에서는 정확하게 재가중되는(exactly reweighted) 방법론을 제안하면서 온라인 전체 에러율 최소화가 달성되었다. 이 제안된 온라인 학습 방법론을 객체 추적에 적용하여 총 8개의 데이터베이스에서 다른 추적 방법론들 보다 좋은 성능이 달성되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.468-473
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• 2002

This paper presents the methodology for measuring eccentricity of the cylindricaliy machined part using CCS(Cylindrical Capacity Spindle Sensor) signal in the CNC turning process. In order to investigate the relationships between CCS orbits and eccentricities, the initial conditions for various eccentricity state and machining process is applied to the experimental strategy. AS a result, it is considered the linearities of CCS signal and magnitude of eccentricity of machined cylindrical surfaces based on the possibility as a automatic detection apparatus for the CNC lathe.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.410-413
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• 2008

Existing Outlier detections have been widely studied in geosensor networks. Recently, machine learning and data mining have been applied the outlier detection method to build a model that distinguishes outliers based on anchored criterion. However, it is difficult for the existing methods to detect outliers against incoming time-variant data, because outlier detection needs to monitor incoming data and classify irregular attacks. Therefore, in order to solve the problem, we propose a time-variant outlier detection using 2-dimensional grid method based on unanchored criterion. In the paper, outliers using geosensor data was performed to classify efficiently. The proposed method can be utilized applications such as network intrusion detection, stock market analysis, and error data detection in bank account.

• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.