• Journal of Power System Engineering
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• v.2 no.2
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• pp.73-80
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• 1998

The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

• Tribology and Lubricants
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• v.21 no.6
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• pp.272-277
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• 2005

Continuously variable transmission (CVT) of toroidal type has a elliptical shape of contact zone under the elastohydrodynamic lubrication (EHL) condition, where the power is transmitted only by shearing the lubricant. Due to the small contact area of elliptical shape, the traction of the shear behaviors of lubricant over the contact zone is under extremely high contact pressure over 1.0GPa. During the power transmission by shearing the fluid, many kinds of mechanical movements occur such as squeezing, sliding, rolling and spin. Among the movements, the spin effect that is the most undesirable contact behavior in transmitting the power frequently makes significant abnormal wear damage. In this work, the analysis of elliptical contact of EHL with spin effect is performed, which will give very useful information to understand the traction behaviors in toroidal type of CVT system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.4
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• pp.76-83
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• 2001

In this study, the static and dynamic characteristics of turning process was modelled and the analytic realization of regen-erative chatter mechanism was discussed. In this regard, we have discussed on the comparative assessment of recursive times series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision turning operation. In this study, simulation and experimental work were performed to show the malfunction behaviors. For this purpose, new Recursive Extended Instrument Variable Method(REIVM) was adopted for the on-line system identification and monitoring of a machining process. Also, we can apply REIVE algorithms in real process for the detection of chatter frequency and dynamic property and analyze the stability lobe of the system by changing a parameter of cutting dynamics in regenerative chatter mechanics, if it is stable or unstable, Also, The stability lobe of chatter was analysed.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.310-315
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• 2001

The most general feature of contact zone among the mechanical components is elliptical circle. In particular, continuously variable transmission (CVT) of toroidal type has elliptical shape of contact zone under the elastohydrodynamic lubrication condition, where the power is transmitted by the shearing the efluid. Due to the traction of the shear behaviors of lubricant over the small elliptical contact zone, high power of torque is transmitted. During the power transmission, many kinds of mechanical movements occur such as squeezing, sliding, rolling and spinning. The spinning effect that is not common contact behavior in tribological components frequently makes significant abnormal wear damage. In this work, the analysis of elliptical contact of elastohydrodynamic lubrication with spin effect is performed, which will give very useful information to understand the traction behaviors in toroidal type of CVT system.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.107-114
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• 2023

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.111-121
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• 2011

Intelligent video analysis systems require techniques which can predict accidents and provide alarms to the monitoring personnel. In this paper, we present an abnormal behavior analysis technique based on adaptive background generation. More specifically, abnormal behaviors include fence climbing, abandoned objects, fainting persons, and loitering persons. The proposed video analysis system consists of (i) background generation and (ii) abnormal behavior analysis modules. For robust background generation, the proposed system updates static regions by detecting motion changes at each frame. In addition, noise and shadow removal steps are also were added to improve the accuracy of the object detection. The abnormal behavior analysis module extracts object information, such as centroid, silhouette, size, and trajectory. As the result of the behavior analysis function objects' behavior is configured and analyzed based on the a priori specified scenarios, such as fence climbing, abandoning objects, fainting, and loitering. In the experimental results, the proposed system was able to detect the moving object and analyze the abnormal behavior in complex environments.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.61.1-61.1
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• 2015

Nanoscale semiconductor plasma processing has become one of the most challenging issues due to the limits of physicochemical fabrication routes with its inherent complexity. The mission of future and emerging plasma processing for development of next generation semiconductor processing is to achieve the ideal nanostructures without abnormal profiles and damages, such as 3D NAND cell array with ultra-high aspect ratio, cylinder capacitors, shallow trench isolation, and 3D logic devices. In spite of significant contributions of research frontiers, these processes are still unveiled due to their inherent complexity of physicochemical behaviors, and gaps in academic research prevent their predictable simulation. To overcome these issues, a Korean plasma consortium began in 2009 with the principal aim to develop a realistic and ultrafast 3D topography simulator of semiconductor plasma processing coupled with zero-D bulk plasma models. In this work, aspects of this computational tool are introduced. The simulator was composed of a multiple 3D level-set based moving algorithm, zero-D bulk plasma module including pulsed plasma processing, a 3D ballistic transport module, and a surface reaction module. The main rate coefficients in bulk and surface reaction models were extracted by molecular simulations or fitting experimental data from several diagnostic tools in an inductively coupled fluorocarbon plasma system. Furthermore, it is well known that realistic ballistic transport is a simulation bottleneck due to the brute-force computation required. In this work, effective parallel computing using graphics processing units was applied to improve the computational performance drastically, so that computer-aided design of these processes is possible due to drastically reduced computational time. Finally, it is demonstrated that 3D feature profile simulations coupled with bulk plasma models can lead to better understanding of abnormal behaviors, such as necking, bowing, etch stops and twisting during high aspect ratio contact hole etch.

• Journal of Digital Contents Society
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• v.16 no.1
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• pp.25-32
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• 2015

With the increased frequency of crime such as assaults and sexual violence, the reliance on CCTV in arresting criminals has increased as well. However, CCTV, which should be monitored by human labor force at all times, has limits in terms of budget and man-power. Thereby, the interest in intelligent security system is growing nowadays. Expanding the techniques of an objects behavior recognition in previous studies, we propose a system to detect forms of violence between 2~3 objects from images obtained in CCTV. It perceives by detecting the object with the difference operation and the morphology of the background image. The determinant criteria to define violent behaviors are suggested. Moreover, provable decision metric values through measurements of the number of violent condition are derived. As a result of the experiments with the threshold values, showed more than 80% recognition success rate. A future research for abnormal behaviors recognition system in a crowded circumstance remains to be developed.

• Nutrition Research and Practice
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• v.13 no.6
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• pp.488-497
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• 2019

BACKGROUND/OBJECTIVES: There are various factors that affect metabolic abnormalities related to obesity. The purpose of this study is to analyze the differences in dietary intakes and body compositions of obese women according to metabolic risks and to classify them as metabolically healthy obese (MHO) or metabolically abnormal obese (MAO). SUBJECTS/METHODS: This study was conducted on 59 obese Korean women aged 19 to 60 years. NCEP-ATPIII criteria were applied and the women classified as MHO (n = 45) or MAO (n = 14). Body composition of each subject was measured by using dual-energy x-ray absorptiometry (DEXA). Three-day food records were used to analyze dietary intake. Eating habits and health-related behaviors were determined through questionnaires. Indirect calorimetry was used to measure resting metabolic rate and respiratory rate. RESULTS: The average age of the subjects was 43.7 years. The analysis of body composition according to phenotype revealed significantly higher body fat mass (P < 0.05), arm fat mass (P < 0.05), and android fat mass (P < 0.05), as measured by DEXA, in the MAO group than in the MHO group. There was no significant difference in the dietary intake of the two groups. However, eating behaviors differed. Compared to the MHO group, the MAO women had a shorter meal time (less than 10 minutes), a preference of oily foods, and a tendency to eat until full. Therefore, the eating habits of MHO women were more positive than those of MAO women. CONCLUSIONS: The results suggest that fat distribution in each body region affects various metabolic abnormalities. A high level of arm fat mass in obese Korean women may increase metabolic risk. In addition, eating habits of obese Korean women are considered to be environmental factors affecting the metabolic phenotype of obese Korean women.

• Smart Structures and Systems
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• v.30 no.6
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.