• Proceedings of the KSME Conference
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• 2001.11a
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• pp.821-826
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. Accordingly, this paper deals with Basic system and Online system. Basic system comprised of spindle rotational speed, feed rates, thrust, torque and flank wear measured tool microscope. Online system comprised of spindle rotational speed, feed rates, AE signal, flank wear area measured computer vision. On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.947-951
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• 1997

Recently, the necessity of the detection of abnormal machining process is being emphasized in order to improve the machining accuracy and reduce the cost in unmanned operating system. The vibration by chatter generated in cutting processes within machine tools is a relative motion between tools and workpieces. So, if the chatter occurs, the surface roughness and accuracy of workpieces will be deteriorate and it leads to the rapid wear of tools. The author intended to use the I /sab/RMS (current of root mean square) of current sigals and the movimg C.V. (coefficient of variation) of each phase for the detection method of chatter.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.47-54
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• 2009

Spin coaters are the essential instruments in micro-fabrication processes, which apply uniform thin films to flat substrates. In this research, a spin coater diagnosis system is developed to detect the abnormal operation of TFT-LCD process in real time. To facilitate the real-time data acquisition and analysis, the circular-buffered continuous data transfer and the short-time Fourier transform are applied to the fault diagnosis system. To determine whether the system condition is normal or not, a steady-state detection algorithm and a frequency spectrum comparison algorithm using confidence interval are newly devised. Since abnormal condition of a spin coater is rarely encountered, algorithm is tested on a CD-ROM drive and the developed program is verified by a function generator. Actual threshold values for the fault detection are tuned in a spin coater in process.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.348-357
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• 2018

The topic of anomaly detection is one of the emerging research themes in computer vision, computer interaction, video analysis and monitoring. Observers focus attention on behaviors that vary in the magnitude or direction of the motion and behave differently in rules of motion with other objects. In this paper, we use this information and propose a system that detects abnormal behavior by using simple features extracted by optical flow. Our system can be applied in real life. Experimental results show high performance in detecting abnormal behavior in various videos.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.6
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• pp.16-21
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• 2005

The cutting characteristics of hardened steel(AISI 52100) by PCBN tools is investigated with respect to cutting force, workpiece surface roughness and tool flank wear by the vision system. Hard Owning is carried out with various cutting conditions; spindle rotational speed, depth of cut and feed rate. Backpropagation neural networks(BPNs) are used for detection of tool wear. The input vectors of neural network comprise of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output is the tool wear state which is either usable or failure. The detection of the abnormal states using BPNs achieves $96.8\%$ reliability even when the spindle rotational speed and feedrate are changed.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1171-1181
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• 2014

Different power quality (PQ) disturbance sources can have major impacts on the power supply grid. This study proposes, for the first time, an early warning approach to identifying PQ problems and providing early warning prompts based on the monitored data of PQ disturbance sources. To establish a steady-state power quality early warning index system, the characteristics of PQ disturbance sources are analyzed and summed up. The higher order statistics anomaly detection (HOSAD) algorithm, based on skewness and kurtosis, and hierarchical power quality early warning flow, were then used to mine limit-exceeding and abnormal data and analyze their severity. Cases studies show that the proposed approach is effective and feasible, and that it is possible to provide timely power quality early warnings for limit-exceeding and abnormal data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• Annual Conference of KIPS
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• 2023.05a
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• pp.2-4
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• 2023

Sequence data plays an important role in the field of intelligence, especially for industrial control, traffic control and other aspects. Finding abnormal parts in sequence data has long been an application field of AI technology. In this paper, we propose an anomaly detection method for sequence data using a diffusion model. The diffusion model has two major advantages: interpretability derived from rigorous mathematical derivation and unrestricted selection of backbone models. This method uses the diffusion model to predict and reconstruct the sequence data, and then detects the abnormal part by comparing with the real data. This paper successfully verifies the feasibility of the diffusion model in the field of anomaly detection. We use the combination of MLP and diffusion model to generate data and compare the generated data with real data to detect anomalous points.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.7
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• pp.3087-3091
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• 2014

Purpose: To determine the utility of digital rectal examination (DRE), serum total prostate specific antigen (tPSA) estimation, and transrectal ultrasound (TRUS) for the detection of prostate cancer (PCa) in men with lower urinary tract symptoms (LUTS). Materials and Methods: All patients with abnormal DRE, TRUS, or serum tPSA >4ng/ml, in any combination, underwent TRUS-guided needle biopsy. Eight cores of prostatic tissue were obtained from different areas of the peripheral prostate and examined histopathologically for the nature of the pathology. Results: PCa was detected in 151 (50.3%) patients, remaining 149 (49.7%) showed benign changes with or without active prostatitis. PCa was detected in 13 (56.5%), 9 (19.1%), 26 (28.3%), and 103 (74.6%) of patients with tPSA <4 ng/ml, 4-10 ng/ml, 10-20 ng/ml and >20 ng/ml respectively. Only 13 patients with PCa had abnormal DRE and TRUS with serum PSA <4 ng/ml. The detection rate was highest in patients with tPSA >20 ng/ml. The association between tPSA level and cancer detection was statistically significant (p<0.01). Among 209 patients with abnormal DRE and raised serum PSA, PCa was detected in 128 (61.2%). Conclusions: The incidence of PCa increases with increasing serum level of tPSA. The overall screening and detection rate can be further improved by using DRE, TRUS and TRUS-guided prostate needle biopsies.