• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.17-23
• /
• 2024

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.1057-1062
• /
• 2007

얼굴표정 인식은 심리학 연구, 얼굴 애니메이션 합성, 로봇공학, HCI(Human Computer Interaction) 등 다양한 분야에서 중요성이 증가하고 있다. 얼굴표정은 사람의 감정 표현, 관심의 정도와 같은 사회적 상호작용에 있어서 중요한 정보를 제공한다. 얼굴표정 인식은 크게 정지영상을 이용한 방법과 동영상을 이용한 방법으로 나눌 수 있다. 정지영상을 이용할 경우에는 처리량이 적어 속도가 빠르다는 장점이 있지만 얼굴의 변화가 클 경우 매칭, 정합에 의한 인식이 어렵다는 단점이 있다. 동영상을 이용한 얼굴표정 인식 방법은 신경망, Optical Flow, HMM(Hidden Markov Models) 등의 방법을 이용하여 사용자의 표정 변화를 연속적으로 처리할 수 있어 실시간으로 컴퓨터와의 상호작용에 유용하다. 그러나 정지영상에 비해 처리량이 많고 학습이나 데이터베이스 구축을 위한 많은 데이터가 필요하다는 단점이 있다. 본 논문에서 제안하는 실시간 얼굴표정 인식 시스템은 얼굴영역 검출, 얼굴 특징 검출, 얼굴표정 분류, 아바타 제어의 네 가지 과정으로 구성된다. 웹캠을 통하여 입력된 얼굴영상에 대하여 정확한 얼굴영역을 검출하기 위하여 히스토그램 평활화와 참조 화이트(Reference White) 기법을 적용, HT 컬러모델과 PCA(Principle Component Analysis) 변환을 이용하여 얼굴영역을 검출한다. 검출된 얼굴영역에서 얼굴의 기하학적 정보를 이용하여 얼굴의 특징요소의 후보영역을 결정하고 각 특징점들에 대한 템플릿 매칭과 에지를 검출하여 얼굴표정 인식에 필요한 특징을 추출한다. 각각의 검출된 특징점들에 대하여 Optical Flow알고리즘을 적용한 움직임 정보로부터 특징 벡터를 획득한다. 이렇게 획득한 특징 벡터를 SVM(Support Vector Machine)을 이용하여 얼굴표정을 분류하였으며 추출된 얼굴의 특징에 의하여 인식된 얼굴표정을 아바타로 표현하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.5
• /
• pp.60-67
• /
• 2014

Wind power is one of the fastest growing renewable energy sources. The blades length and tower height of wind turbine have been growing steadily in the last 10 years in order to increase the output amount of wind power energy. The amount of wind turbine energy is increased by increasing the capacity of wind turbine, but the costs of preventive, corrective and replacement maintenance are also increased accordingly. Recently, Condition Monitoring System that can repair the fault diagnose and repair of wind turbine in the real-time. However, these system have a problem that cannot predict and diagnose of the fault. In this paper, wind turbine predict methodology is proposed by using the SVM method. In the case study, correlation analysis between wind turbine fault and external environmental factors is performed by using the SVM method.

• The Journal of Korean Institute of Information Technology
• /
• v.16 no.12
• /
• pp.25-30
• /
• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.

• Journal of the Korea Safety Management & Science
• /
• v.23 no.3
• /
• pp.83-88
• /
• 2021

Recently, due to the aging of workers and the weakening of the labor base in the automobile industry, research on quality inspection methods through ICT(Information and Communication Technology) convergence is being actively conducted. A lot of research has already been done on the development of an automated system for quality inspection in the manufacturing process using image processing. However, there is a limit to detecting defects occurring in the automotive sunroof sealer application process, which is the subject of this study, only by image processing using a general camera. To solve this problem, this paper proposes a system construction method that collects image information using a infrared thermal imaging camera for the sunroof sealer application process and detects possible product defects based on the SVM(Support Vector Machine) algorithm. The proposed system construction method was actually tested and applied to auto parts makers equipped with the sunroof sealer application process, and as a result, the superiority, reliability, and field applicability of the proposed method were proven.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.3
• /
• pp.289-293
• /
• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

• Journal of Information Processing Systems
• /
• v.12 no.1
• /
• pp.109-128
• /
• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• Journal of Korea Multimedia Society
• /
• v.21 no.5
• /
• pp.558-570
• /
• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2006.05a
• /
• pp.435-438
• /
• 2006

Single Nucleotide Polymorphism(SNP)는 인간 유전자 서열의 0.1%에 해당하는 부분으로 이는 각 개인의 체질 및 각종 유전질환과 밀접한 관련이 있다고 알려져 있으며 이 SNP 정보를 이용 각종 질환의 유전적 원인규명에 대한 많은 생물학적 연구가 진행되고 있다. 그러나 아직 SNP를 이용한 효율적인 분석방법에 대한 전산학적 연구는 많지 않다. 본 논문에서는 대표적인 패턴인식기 중 하나인 Support Vector Machine(SVM)을 이용 한국인의 대표적인 유전질환으로 알려진 위암에 대한 예측율을 실험하였다. 실험 데이터는 간 및 소화기 질환 유전체 센터에서 얻어진 위 질환 환자를 대상으로 하였으며 실험 결과 예측율은 67.3%로 이는 Case Based Reasoning(CBR)방법의 55% 보다 더 좋은 예측 결과를 보였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.04c
• /
• pp.175-177
• /
• 2003

This paper addresses a problem of classifying human breast cancer into its subtypes. A main ingredient in our approach is kernel machines such as support vector machine (SVM). kernel principal component analysis (KPCA). and kernel partial least squares (KPLS). In the task of breast cancer classification, we employ both SVM and KPLS and compare their results. In addition to this classification. we also analyze the patterns of clinical outcomes in the feature space. In order to visualize the clinical outcomes in low-dimensional space, both KPCA and KPLS are used. It turns out that these methods are useful to identify correlations between clinical outcomes and the nonlinearly protected expression profiles in low-dimensional feature space.