• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.75-86
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• 2019

Purpose: It is essential for the steel industry to produce steel products without unexpected downtime to reduce costs and produce high quality products. A hot strip rolling mill consists of many mechanical and electrical units. In condition monitoring and diagnosis, various units could fail for unknown reasons. Methods: In this study, we propose an effective method to detect units with abnormal status early to minimize system downtime. The early warning problem with various units was first defined. An autoencoder was modeled to detect abnormal states. An application of the proposed method was also implemented in a simulated field-data analysis. Results: We can compare images of original data and reconstructed images, as well as visually identify differences between original and reconstruction images. We confirmed that normal and abnormal states can be distinguished by reconstruction error of autoencoder. Experimental results show the possibility of prediction due to the increase of reconstruction error from just before equipment failure. Conclusion: In this paper, hot strip roughing mill monitoring method using autoencoder is proposed and experiments are performed to study the benefit of the autoencoder.

• Management & Information Systems Review
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• v.37 no.4
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• pp.63-74
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• 2018

Recently, in the asset management industry, the smart beta strategy, which has an intermediate nature between passive and active strategies, is attracting attention. In this smart beta strategy, value, momentum, low volatility, and quality index are widely used. In this study, we analyzed the quality index which is not clear and complicated to calculate. According to the MSCI methodology, the quality index was calculated using three variables: return on equity, debt to equity, and earnings variability. In addition, we use the index using only return on equity variable, the index using only two variables of return on equity and debt to equity, and the KOSPI index as comparison targets for the quality index. In order to evaluate the performance of the indices used in the analysis, the arithmetic mean return, the coefficient of variation, and the geometric mean return were used. In addition, Fama and French (1993) model, which is widely used in related studies, was used as a pricing model to test whether abnormal returns in each index are occurring. The results of the empirical analysis are as follows. First, in all period analysis, quality index was the best in terms of holding period returns. Second, the quality index performed best in the currency crisis and the global financial crisis. Third, abnormal returns were not found in all indices before the global financial crisis. Fourth, in the period after the global financial crisis, the quality index has the highest abnormal return.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.53-69
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• 2003

Many process variables in modem manufacturing processes have influence on quality of products with complicated relationships. Therefore, it is necessary to control multiple quality variables in order to monitor abnormal signals in the processes. This study proposes an integrated procedure of self-organizing map (SOM) neural network and case-based reasoning (CBR) for multivariate process control. SOM generates patterns of quality variables. The patterns are compared with the reference patterns in order to decide whether their states are normal or abnormal using the goodness-of-fitness test. For validation, it generates artificial datasets consisting of six patterns, normal and abnormal patterns. Experimental results show that the abnormal patterns can be detected effectively. This study also shows that the CBR procedure enables to keep Type 2 error at very low level and reduce Type 1 error gradually, and then the proposed method can be a solution fur multivariate process control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.328-332
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• 1996

The compressor is one of important elements in refrigerator cycle and play an important role of refrigeration efficiency and quality. Therefore it is very important to monitor state of normal and abnormal compressor. In this research, technic of AE is used for monitoring abnormal rotary compressor and an AE parameter which is a most proper parameter to monitor the abnormal state of compressor is determined by signal processing, Finally, the monitoring result of rotary compressor is agreement with the result of life test.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.135-145
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• 2022

To improve audit quality, certain Chinese auditing firms have added a third-level review by an additional signing auditor to the general evaluation by a signing auditor team consisting of an engagement auditor and a partner. Nonetheless, our research-based on 36,033 firm-year observations from 2004 to 2019 reveals that compared to the general review system, auditor teams under the three-level review system are less likely to issue modified audit opinions when abnormal financial conditions arise. This finding suggests that, while larger auditor teams' knowledge, experience, and information advantages can theoretically sharpen their judgment, their performance is more susceptible to interference from divergent opinions, the diffusion of responsibility, and lower energy invested by individual auditors, ultimately impairing their judgment regarding the audited enterprises' abnormal financial conditions. That is, the three-level review system, which aims to improve audit quality, actually worsens audit quality. This conclusion remains valid after the problems of heteroscedasticity and endogeneity are addressed by using firm-level cluster robust standard errors and two-stage regression. We hope that our research will draw the attention of auditing firms, prompting them to reconsider the rationality of the three-level review system.

• Journal of Sensor Science and Technology
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• v.29 no.4
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• pp.249-254
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• 2020

It is difficult to control children who exhibit negative behavior in dental clinics. Various methods are used for preventing pediatric dental patients from being afraid and for eliminating the factors that cause psychological anxiety. However, when it is difficult to apply this routine behavioral control technique, sedation therapy is used to provide quality treatment. When the sleep anesthesia treatment is performed at the dentist's clinic, it is challenging to identify emergencies using the current breath detection method. When a dentist treats a patient that is under the influence of an anesthetic, the patient is unconscious and cannot immediately respond, even if the airway is blocked, which can cause unstable breathing or even death in severe cases. During emergencies, respiratory instability is not easily detected with first aid using conventional methods owing to time lag or noise from medical devices. Therefore, abnormal breathing needs to be evaluated in real-time using an intuitive method. In this paper, we propose a method for identifying abnormal breathing in real-time using an intuitive method. Respiration signals were measured using a 3M Littman electronic stethoscope when the patient's posture was supine. The characteristics of the signals were analyzed by applying the signal processing theory to distinguish abnormal breathing from normal breathing. By applying a short-time Fourier transform to the respiratory signals, the frequency range for each patient was found to be different, and the frequency of abnormal breathing was distributed across a broader range than that of normal breathing. From the wavelet transform, time-frequency information could be identified simultaneously, and the change in the amplitude with the time could also be determined. When the difference between the amplitude of normal breathing and abnormal breathing in the time domain was very large, abnormal breathing could be identified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.266-274
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• 2020

This paper reports a means of improving the abnormal noise of light tactical vehicles (LTVs) by applying a vehicle fitting type generator (hereinafter called generator). LTVs are classified as having generators, and there are no differences in the noise level. On the other hand, quality improvement was performed in response to unpleasant noise felt by the user (hereinafter called abnormal noise) during vehicle operation. To improve the quality, the generator mounting structure and the phenomenon of the vehicle in the problem were identified. Through this, it was confirmed that the noise caused by the generator installation was the rattle noise. Rattle noise at the engine driving system is normally caused by the transfer of irregular torque generated by the engine power stroke and the backlash by the spline-serration fitting structure between the engine coupler and rotor assembly in a generator. Therefore, this study established an improvement plan to apply a damper coupler to solve the cause of the abnormal noise. Regarding the improved establishment method, the improvement effect was confirmed from the influence of the irregular torque of the engine, noise level, dynamic characteristics analysis, and the endurance test of the parts.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.863-868
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• 2008

This paper investigates the abnormal noise of polygon mirror scanner motor due to the mechanical contact. In the high speed polygon mirror scanner motor the vibration of polygon mirror scanner motor is one of the main sources of abnormal noise, because structure-borne noise due to the vibration is bigger than aerodynamic noise, especially when the rotating part contacts the stationary parts. This research determines the main harmonics of structure-borne noise by using sound quality evaluation. It also develops an experimental set-up to measure the mechanical contact and vibration of polygon mirror scanner motor simultaneously. This paper also show that mechanical contact between rotating shaft and stationary sleeve is one of the dominant vibration sources of structure-borne noise which cause the abnormal noise of the high speed polygon mirror scanner motor by using the developed experimental set-up.

• The Journal of Korean Obstetrics and Gynecology
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• v.35 no.3
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• pp.74-87
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• 2022

Objectives: This study was performed to analyze the domestic study trends on abnormal uterine bleeding treated with Korean medicine. Methods: We searched the studies of abnormal uterine bleeding treated with Korean medicine via searching four Korean web databases. After that, we analyzed 15 studies which were selected according to the selection and exclusion criteria. Results: All 15 selected studies were case reports, and the total number of patients included was 33. Herbal medicine was used in all patients, and Igwiseungyang-tang-gami and Jeonsaenghwalhyol-tang-gami were the most commonly used. Other Korean medicine treatment such as acupuncture and moxibustion were performed. Acupuncture point most frequently used in acupuncture treatment were 三陰交 (SP6), 太衝 (LR3), 合谷 (LI4), and in moxibustion treatment were 關元 (CV4), 中脘 (CV12), 氣海 (CV6). In all studies, uterine bleeding was reduced after treatment. Conclusions: This study shows that Korean medicine could be helpful in treating abnormal uterine bleeding. In order to increase the basis for clinical use in the future, high quality of additional studies should be conducted.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.