• 한국소음진동공학회논문집
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• 제22권5호
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• pp.413-421
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• 2012

In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2007년도 한국지능정보시스템학회
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• 디지털융복합연구
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• 제16권7호
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• pp.223-229
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• 2018

최근 전기기기로 인한 화재발생이 급증함에 따라 기기에 대한 안전진단의 필요성이 높아지고 있다. 본 연구는 지능형의 Fuzzy기술을 이용한 전기기기의 안전진단에 관한 것으로 기기의 사용전류특성, 누적사용시간, 열화특성 및 Arc특성 등의 복합적인 전기안전 요인을 검출하여 진단한다. 이들 안전요인을 실시간으로 추출하기 위하여 각종 Sensor회로, DSP(Digital Signal Processor) 신호처리회로, 무선통신회로 등으로 구성된 Board를 설계하였고, 추출된 4가지 진단정보를 이용하여, 기기의 안전정도를 퍼지수치 값으로 표시하기 위하여 각 정보마다 Gaussian function을 사용한 퍼지 알고리즘을 설계하고 DSP에 실장 하였다. 지능적인 퍼지알고리즘은 4가지의 진단정보를 입력받아 퍼지엔진으로 추론하고 해당기기의 종합적인 안전 상태를 사람의 감성에 익숙한 100단계의 아날로그 퍼지 값으로 출력한다. 본 연구에서 구현된 DSP 하드웨어와 퍼지 알고리즘을 융합한 보드의 실험을 통하여, 전기기기의 운전 중 실시간 안전 상태를 복합적으로 검출하고, 사람에게 친화적인 감성적 퍼지 값으로 진단결과를 출력하는 기능을 소형의 DSP Board에서 구현할 수 있음을 입증하였다. 향후 인공지능 전용 Micom이 출시된다면 지능을 바탕으로 보다 진보된 진단 시스템을 연구할 수 있을 것으로 기대한다.

• 한국컴퓨터정보학회논문지
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• 제28권10호
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• pp.1-8
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• 2023

자동차의 주요 부품인 휠 베어링에 결함이 생기면 교통사고등 문제를 발생시켜 이를 해결하기 위해 빅데이터를 수집해서 예측진단 및 관리 기술을 통한 휠 베어링의 고장 유무 및 고장 유형을 조기에 알려 주는 알고리즘과 모니터링 시스템 개발이 필요하다. 본 논문에서는 이러한 지능형 휠 허브 베어링 정비 시스템 구현을 위해 신뢰성 및 건전성에 대한 모니터링용 센서 및 예측 진단하는 알고리즘이 탑재된 임베디드 시스템을 개발하였다. 사용된 알고리즘은 휠 베어링에 설치된 가속도 센서로부터 진동 신호를 취득하고 이를 신호 처리기법, 결함주파수 분석, 건전성 특징 인자정의 등의 과정을 빅데이터 기술을 통해 고장을 예측하고 진단할 수 있다. 구현된 알고리즘은 진동 주파수 성분들은 최소화하고 휠 베어링에서 발생하는 진동 성분을 극대화할 수 있는 안정 신호 추출 알고리즘을 적용하고, 필터를 활용한 노이즈 제거에서는 인공지능 기반의 건전성 추출 알고리즘을 적용하였으며, FFT를 통한 결함 주파수를 분석하여 고장 특성인자 추출을 통한 고장을 진단하였다. 본 시스템의 성능 목표는 12,800ODR 이상으로 시험 결과를 통해 목표치를 만족하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.355-357
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• 2019

고령화 사회로 진입하고 있는 지금, 시니어 세대에게는 새로운 디바이스나 IoT기술에 대한 사용자 접점은 매우 불편하다. 이를 개선하기 위하여 우리는 AI 기술 기반 지능형 시니어 도우미 음성인식 시스템을 제안한다. 제안 시스템은 Cloud platform기반 API를 구현하여 머신러닝 처리 활용을 위한 데이터를 축적하며, 치매진단, 치매예방활동을 위한 콘텐츠를 제공하며,시니어 세대를 위한 챗봇 콘텐츠를 제공한다. 우리가 제안한 개념모델을 이용한 서비스를 API로 제공함으로서 시니어 세대에 대한 IoT기반 등 새로운 디바이스의 접근성 및 편리성을 증대하는 계기가 될 것으로 기대한다.

• Journal of applied mathematics & informatics
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• 제41권6호
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2006년도 춘계학술대회
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• pp.327-335
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• 2006

Case Based Reasoning (CBR) has come to be considered as an appropriate technique for diagnosis, prognosis and prescription in medicine. However, canventional CBR has a limitation in that it cannot incorporate asymmetric misclassification cast. It assumes that the cast of type1 error and type2 error are the same, so it cannot be modified according ta the error cast of each type. This problem provides major disincentive to apply conventional CBR ta many real world cases that have different casts associated with different types of error. Medical diagnosis is an important example. In this paper we suggest the new knowledge extraction technique called Cast-Sensitive Case Based Reasoning (CSCBR) that can incorporate unequal misclassification cast. The main idea involves a dynamic adaptation of the optimal classification boundary paint and the number of neighbors that minimize the tatol misclassification cast according ta the error casts. Our technique uses a genetic algorithm (GA) for finding these two feature vectors of CSCBR. We apply this new method ta diabetes datasets and compare the results with those of the cast-sensitive methods, C5.0 and CART. The results of this paper shaw that the proposed technique outperforms other methods and overcomes the limitation of conventional CBR.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.144-146
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• 2021

Idiopathic pulmonary fibrosis (IPF) is one of the most dreadful lung diseases which effects the performance of the lung unpredictably. There is no any authentic natural history discovered yet pertaining to this disease and it has been very difficult for the physicians to diagnosis this disease. With the advent of Artificial intelligent and its related technologies this task has become a little bit easier. The aim of this paper is to develop and to explore the machine learning models for the prediction and diagnosis of this mysterious disease. For our study, we got IPF dataset from Haeundae Paik hospital consisting of 2425 patients. This dataset consists of 502 features. We applied different data preprocessing techniques for data cleaning while making the data fit for the machine learning implementation. After the preprocessing of the data, 18 features were selected for the experiment. In our experiment, we used different machine learning classifiers i.e., Multilayer perceptron (MLP), Support vector machine (SVM), and Random forest (RF). we compared the performance of each classifier. The experimental results showed that MLP outperformed all other compared models with 91.24% accuracy.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 춘계학술대회 논문집
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• pp.421-423
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• 2007

The paper used to the Neral Netwok for a forecasting conservation system A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. The true power and advantage of neural network lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Form results of this study, the Neral Netwok is will play an important role for insulation diagnosis system of real site GIS and power equipment using $SF_6$ gas.