• Proceedings of the Korean Society of Marine Engineers Conference
• /
• 2005.06a
• /
• pp.281-286
• /
• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.212-216
• /
• 1996

Recently, with the development of NC and CNC machine tools and the high labor wage, the cutting process requires the high speed and automatic system which uses industrial robots and the flexible manufacturing system(FMS) that combines several machine tools. In this system, the whole system can be influenced by just one of the machin tools. So it needs to detect a problem and to solve it immediately In in-process state. The monitoring system through measuring the motor current with current sensor has been attracting the attention of lots of researchers view of its low cost and flexibility. By using the pattern discriminant with the detected three-phase-current signal, that is, $I_{RMS}$, a system which can monitor and analyze abnormal machining process condition of the workpiece during the machining will be able to be developed in this research.h.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.05a
• /
• pp.932-937
• /
• 2003

When oil pressure of damper is lost or relative stiffness of spring drops in vibration system, it can be fatally dangerous situation. A fault diagnosis method for vibration system using Support Vector Machine(SVM)is suggested in the paper. SVM is used to classify input data or applied to function regression. System status can be classified by judging input data based on optimal separable hyperplane obtained using SVM which learns normal and abnormal status. It is learned from the relationship of system state variables in term of spring, mass and damper. Normal and abnormal status are learned using phase plane as in put space, then the learned SVM is used to construct algorithm to predict the system status quantitatively

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.131-139
• /
• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.7
• /
• pp.3637-3641
• /
• 2016

To determine the prevalence of human papillomavirus (HPV) among women with atypical squamous cells of undetermined significance (ASC-US) referred to colposcopy and the implications for clinical management in low- and middle-income countries (LMIC), the present study was conducted. We included 200 women living in $Maring{\acute{a}}$/Brazil referred to colposcopy service between August 2012 and March 2013 due to an abnormal cytology from ASC-US until high-grade intraepithelial lesion (HSIL). HPV was detected and genotyped by polymerase chain reaction (PCR). The mean age was $36.8{\pm}10.5$ years, and women with and without ASC-US had similar mean ages ($37.4{\pm}11.5$ and $36.4{\pm}9.96$ years, respectively). The highest prevalence of ASC-US occurred at 20-24 years (40%). HPV-DNA was positive in 164 (82.0%) women.Of the 57 women with ASC-US, 30 (52.6%) were HPV-DNA-positive and 21 (70%) were high-risk HPV-positive (HR-HPV); the latter was similar to women without ASC-US (76.9%) but with other abnormal cytological findings present. Our data demonstrated that performing tests for HR-HPV can be used for management of women with ASC-US to support the decision of which women should be referred for an immediate or later colposcopy. The same conclusions can be applied to other LMICs for which HPV testing for primary screening has not been adopted.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.8
• /
• pp.731-737
• /
• 2014

This paper presents abnormal behavior detection in crowd within surveillance video. We have defined below two cases as a abnormal behavior; first as a sporadically spread phenomenon and second as a sudden running in same direction. In order to detect these two abnormal behaviors, we first extract the motion vector and propose a new descriptor which is combined MHOF(Multi-scale Histogram of Optical Flow) and DCHOF(Directional Change Histogram of Optical Flow). Also, binary classifier SVM(Support Vector Machine) is used for detection. The accuracy of the proposed algorithm is evaluated by both UMN and PETS 2009 dataset and comparisons with the state-of-the-art method validate the advantages of our algorithm.

• Anxiety and mood
• /
• v.10 no.2
• /
• pp.143-150
• /
• 2014

Objective : The subgenual cingulate cortex, a part of default-mode network, has been known to playa key role in the pathophysiology of depression. The previous studies have reported abnormal functional connectivity between the subgenual cingulate cortex and other brain regions in the patients with depression. The goal of this shldy was to explore the resting-state functional connectivity of the subgenual cingulate cortex between the patients with depression and healthy subjects. Methods : Twenty patients with major depression and age- and sex-matched 20 healthy subjects underwent 5-minute resting state fMRI scans. The functional connectivity map in each subject was acquired using seed-based correlation analysis with the seed located in the subgenual cingulate cortex (Talairach coordinates; x=-10, y=5, z=-10). The functional connectivity maps were calculated using AFNI and compared between the patient and healthy subject group via two-sample T-test using 3dttest++ in AFNI package. Results : Functional connectivity was decreased between the subgenual cingulate cortex and both sides of fusiform gyrus in depressed subjects. Connectivity was also decreased between the subgenual cingulate cortex and the left cerebellum in the patient group. There was no correlation between the severity of depression and the degree of functional connectivity between the subgenual cingulate cortex and the regions showing decreased functional connectivity. Conclusion : Decreased resting-state functional connectivity between the subgenual cingulate cortex and both sides of fusiform gyrus, and decreased connectivity between the subgenual cingulate cortex and the left cerebellum found in the patients with major depression in comparison to the healthy subjects might be related to abnormal emotional and cognitive processing of depressed patients.

• Journal of Biosystems Engineering
• /
• v.26 no.6
• /
• pp.563-570
• /
• 2001

To develop a stalk detaching system the effect of shape factor of red pepper affecting the performance of unfolding, arranging and cutting was analysed. The obtained results are as follows : By cutting experiment, it was found that the bending of stalk affected the cutting rate of stalk, and that the bending of body increased the amount of peppers that were expelled from the cutting guide by conveying brush. The ratios, 'bending length of a body/body length'and 'bending length of a stalk/stalk length', could be used as criteria far abnormality of body and stalk of peppers, respectively. As a result of experiment, it was concluded that mechanical treatment would be difficult for the peppers with indexes greater than 0.4 and 0.3 fur body bending and stack bending. respectively. So, these indexes were used as criteria for distinguishing abnormality from normality of peppers. In the unfolding unit, conveyance of peppers was impossible for both of normal and abnormal ones at the inclination angle of 10°, especially, at the frequency of 8.3 Hz peppers maintained stationary state. At the inclination angle of 20°, both of normal and abnormal peppers showed similar tendencies, but abnormal ones showed an accumulation trend gradually with increased feeding speed. In the arranging unit, conveyance of peppers was almost impossible for both of normal and abnormal ones at the inclination angle of 20°, showing almost no difference between the conveyances of normal and abnormal ones. In the case of the inclination angle of 30°, at the condition of the feeding speeds and frequency corresponding 0.06 m/s, 0.08 m/s and 8.3 Hz, respectively, the passing time of the abnormal peppers on the arranging plate increased rapidly.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2009.10a
• /
• pp.307-308
• /
• 2009

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.689-700
• /
• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.