• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.63-80
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• 2022

In this paper, we study structural changes and performance gains in condition-based maintenance process redesign when mobile IoT technology is embedded into urban railway rolling stock. We first develop condition-based maintenance To-Be process model in accordance with the IoT deployment scheme. Secondly, we draw upon theoretical framework of redesign process analysis to develop performance evaluation method suitable to predictive maintenance shift from As-Is ordinary maintenance practice. Subsequently, To-Be process performance evaluations are conducted adopting both the quantitative and qualitative method for time, cost, and dependability dimensions. The results ascertain the considerable benefits captured through detection abnormality prior to actual rolling stock failure occurrence, and details of performance improvements and enhancement of standardization level is revealed. The procedures and results presented in this paper offers useful insights in the fields of IoT economic analysis, condition based maintenance, and business process redesign.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Korean Journal of Biological Psychiatry
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• v.3 no.1
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• pp.57-65
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• 1996

The Evaluation of patients complaining of psychiatric symptoms following head injury is much affected by the results of various tests. The objecive of this paper is to investigate the effectiveness of each lest by comparing the structual and fuctional brain studies. The subjects were 93 organic menial disorder in and out patients at the Dept. of Neuropsychiatry of the Kyung Hee University Hospital. After carrying out MRI, CT, SPECT, EEG, the results of each were analysed for the sesitivity and ability to detect focal lesion. The degree of inter-test correlations of lest results were also investigated. Furthermore, the characteristic features of psychological tests were studied and the relationship between each of above mentioned tests and psychological test was examined. As for the test sensitivity to diagnosis, the SPECT was the most superior followed by MRI, CT, EEG in thai order. In the case of abnormality, SPECT ranked 1st in detection of focal lesion, followed by MRI, CT in that order. In the inter-test result correlation, the correlation of SPECT-MRI was statistically significant. When mare than moderate abnormality EEG finding was reported, it correlated significantly with that of MRI findings. In the MMPI, the average scores on F, Hs, D, Hy, Pa, Pt, Sc subscales were above 60. Abnormal SPECT group scored significantly high on the F, Pd, Pa, Sc, Ma scales and therefore in comparison ot the SPECT normal group, displayed more psychotic features. In K-WAIS, the mean full scale IQ was down to 77. 23(Verbal IQ : 78.76, Performance IQ : 77.44) but there was no characterogic significant relationship between the lowered to and abnormal SPECT, MRI, CT and EEG results. In conclusion, 1) The SPECT was mast superior in sensitivity and detection of focal lesions. In comparision with other tests, the results of SPECT correlated well with MRI had thus is thought to be very usefull testing method in the evaluation of organic mental disorder patients. 2) The MRI had relatively high sensitivity, ability to detect focal lesion and superior correlation with other test. 3) Although EEG fared less an sensitivity in comparison to other tests, the results of above moderate abnormal grade group and that of MRI correlated significantly. 4) In the MMPI highly scored in F, Hs, D, Hy, Pa, Pt, Sc subscales and abnormal SPECT patients were shown to display more sever psychotic features. There was no significant character relationship between the lowered IQ(in K-WAIS) and abnormal findings on MRI, CT, SPECT, EEG.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1587-1595
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• 2016

P wave is cardiac parameters that represent the electrical and physiological characteristics, it is very important to diagnose atrial arrhythmia. However, It is very difficult to detect because of the small size compared to R wave and the various morphology. Several methods for detecting P wave has been proposed, such as frequency analysis and non-linear approach. However, in the case of conduction abnormality such as AV block or atrial arrhythmia, detection accuracy is at the lower level. We propose P wave detection algorithm through adaptive threshold and QRS peak variability. For this purpose, we detected Q, R, S wave from noise-free ECG signal through the preprocessing method. And then we classified three pattern of P wave by peak variability and detected adaptive window and threshold. The performance of P wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 92.60%.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4567-4570
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• 2012

Objectives: The purposes of this study were to determine the prevalence and predictive value to detect significant neoplasia and invasive lesions, and to evaluate the correlation between clinical and histopathology of women with squamous cell carcinoma (SCCA) on Siriraj liquid-based cervical cytology (Siriraj-LBC). Methods: The computerized database of women who underwent Siriraj-LBC at Siriraj Hospital, Mahidol University from January 2007 to December 2010 were retrieved. The hospital records of women with SCCA cytology were reviewed. Results: The prevalence of SCCA cytology was 0.07%. A total of 86 women, mean age was 58.1 years. Sixty-one women (70.9%) were post-menopausal. Overall significant pathology and invasive gynecologic cancer were detected in 84 women (97.7%) and 71 women (82.5%), respectively. The positive predictive values for detection of significant neoplasia and invasive lesion were 97.7% and 82.6%, respectively. The cervical cancer was diagnosed in 69 women and among these 58 women were SCCA. Thirteen women (15.1%) had cervical intraepithelial neoplasia (CIN) 3 and two women (2.3%) had cervicitis. The sensitivity and specificity of colposcopy for cervical cancer detection in SCCA cytology were 83.3% and 75%, respectively. Median follow up period was 17.6 months and 64 patients were alive without cytologic abnormality. Conclusions: The final histopathology of SCCA cytology in our populations demonstrated a wide variety, from cervicitis to invasive cancer and the most common diagnosis was invasive cervical cancer. Colposcopy with biopsy and/or endocervical curettage and loop electrosurgical excision procedure should be undertaken to achieve histologic diagnosis.

• Journal of KIISE
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• v.43 no.5
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• pp.596-605
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• 2016

Since the Denial of Service Attack against multiple targets in the Korean network in private and public sectors in 2009, Korea has spent a great amount of its budget to build strong Internet infrastructure against DDoS attacks. As a result of the investments, many major governments and corporations installed dedicated DDoS defense systems. However, even organizations equipped with the product based defense system often showed incompetency in dealing with DDoS attacks with little variations from known attack types. In contrast, by following a capacity centric DDoS detection method, defense personnel can identify various types of DDoS attacks and abnormality of the system through checking availability of service resources, regardless of the types of specific attack techniques. Thus, the defense personnel can easily derive proper response methods according to the attacks. Deviating from the existing DDoS defense framework, this research study introduces a capacity centric DDoS detection methodology and provides methods to mitigate DDoS attacks by applying the methodology.

• BMB Reports
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• v.29 no.3
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• pp.241-247
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• 1996

The Philadelphia (Ph) chromosome is the single most intensively studied chromosome alteration characterizing a human malignancy. The specific genetic alteration of chronic myelogenous leukemia (CML) is the formation of the BCR/abl fusion gene in leukemic cells. The presence of the BCR/abl gene has important diagnostic and prognostic implications in CML. The detection of BCR/abl transcripts by reverse transcriptase based polymerase chain reaction (RT-PCR) was investigated in patients with CML in whom the Ph chromosome abnormality was documented by cytogenetic analysis. In a total of 68 CML patient cases, the Ph chromosome was found in 53 cases (77.9%) by cytogenetic analysis. On the other hand, sixty two cases (91.2%) were detected to have BCR/abl gene rearrangement Of these, b3a2 was 44 cases (64.7%) and b2a2 was 17 cases (25,0%). There was one case with both b3a2 and b2a2 (1.5%). Of the fifteen cases of Ph chromosome negative by cytogenetic anlaysis, the BCR/abl gene was observed in nine cases, The results of BCR/abl fusion gene confirmed by the direct sequencing method correlated well with PCR analysis, The amplified PCR products were detected by $1{\times}10^{-5}$ dilutions. In conclusion, PCR technique is sensitive, rapid and relatively simple for a laboratory test in detecting the BCR/abl fusion gene with CML regardless of the result of cytogenetic analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2037-2042
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• 2017

The manufacturing facility is generally operated by a pre-set program under the existing factory automation system. On the other hand, the manufacturing facility must decide how to operate autonomously in Industry 4.0. Determining the operation mode of the production facility itself means, for example, that it detects the abnormality such as the deterioration of the facility at the shop-floor, prediction of the occurrence of the problem, detection of the defect of the product, In this paper, we propose a manufacturing process modeling using a queue for detection of manufacturing process abnormalities at the shop-floor, and detect abnormalities in the modeling using SVM, one of the machine learning techniques. The queue was used for M / D / 1 and the conveyor belt manufacturing system was modeled based on ${\mu}$, ${\lambda}$, and ${\rho}$. SVM was used to detect anomalous signs through changes in ${\rho}$.