• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.1-9
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• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.796-801
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• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.34-39
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• 2006

This paper introduces a network security situation awareness tool using a traffic pattern map which facilitates recognizing a current network status by extracting and analyzing predetermined traffic features and displaying an abnormal or harmful traffic which deteriorates network performance. The traffic pattern-map consists of $26{\times}26$ intersections, on which the occupancy rate of the port having maximum occupancy is displayed as a bar graph. In general, in case of the Internet worm, the source address section on the traffic pattern map is activated. In case of DDoS the destination address section is activated.

• Journal of the Korean Ceramic Society
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• v.34 no.7
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• pp.699-702
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• 1997

The cross sectional image of thin oxide failure of MOS device could be observed by Emission Microscope and Focused Ion Beam at the weak point. The oxide failures in low electric field was associated with the presence of a particle or abnormal pattern. The failures occuring at medium field are related to a pit of Si substrate. The pits could be originated from the microdefects of Cz Si wafer.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.85-93
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• 2011

In this paper, we analyzed the function elements of the Integrated meter reading system based on PLC which is working in Mok-dong, Seoul and studied how to improve the vulnerability. Also we propose an efficient method for the estimation of abnormal life status through frequency domain processing of utility meter readings. We found out that even after removing the high-frequency components from the raw meter data, the shape of the graph still maintains the original graph characteristics. The graph of the inverse transformed data has simpler and smoother curve than the original graph pattern. The original graph is not good to be used in deciding whether the residence's life pattern is normal or not. We could find out that the graph which is processed frequency signal has simple and intuitive graph pattern.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• Journal of the Korean Wood Science and Technology
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• v.44 no.6
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• pp.903-912
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• 2016

Morphological and anatomical evaluation of grafted P. merkusii have been undertaken to obtain the information about compatible and incompatible symptoms of 18 years old grafts based on morphological observation and microscopic analysis. Samples of compatible and incompatible grafts were obtained from previous research conducted by the Silviculture Departement Team in 1994. Result showed that compatible grafts have normal stem form and secondary growth (diameter growth), but some abnormality symptoms like undulated pattern of annual growth rings, phloem thickening and abnormality resin ducts in inner and middle parts of the union area occurred. Incompatible ones showed abnormality of the stem form, cortex-bark necrosis and swelling in the union area. Microscopic observation showed abnormality of all parts of the union, undulated pattern of annual growth rings, phloem thickening, abnormal resin ducts, low numbers and discontinuity of vascular elements in the union area.

• Korean Journal of Veterinary Service
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• v.36 no.2
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• pp.127-132
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• 2013

An 11-year-old poodle bitch was presented for investigation of multicentric mammary masses. Abdominal sonography and radiography demonstrated abnormal enlargement of uterus and ovaries. Blood analysis revealed high progesterone concentration. The ovariohysterectomy and mastectomy were performed. Histopathologically, the mammary masses revealed complex carcinoma-tubulopapillary carcinoma with papillary pattern and tubule pattern. In the uterus, cystic endometrial hyperplasia was observed. Scattered inflammatory cells were observed in the endometrial stroma and mucinous material was protruded from endometrial surface. Also, in the ovaries, bilateral ovary granulosa cell tumor was detected. The bitch made a complete recovery following the ovariohysterectomy and mastectomy. This case was a very rare multiple tumor occurrence with bilateral ovary granulosa cell tumor and mammary complex carcinoma. High progesterone concentration was characterized clinically in the bitch.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.86-96
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• 2015

A lot of research works in the BPM area focuses on the development of new techniques in process mining. Even though the application of process mining to analyze real life process logs is important, only few case studies are available. Thus, in this paper, we conduct a case study on how to analyze a real life process log which comes from a Korean company in the heavy industry area. We analyze a customer service process that consists of a series of activities to enhance the level of customer satisfaction. In this case study, five research questions are derived based on collected questions from the company. Then we focus on bottleneck analysis, basic performance analysis and pattern analysis that are selected in order to answer the research questions. The analysis shows some abnormal behaviors in the process and possible ways to improve current processes are suggested.

• Journal of Biomedical Engineering Research
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• v.16 no.1
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• pp.95-100
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• 1995

In this study, we have evaluated the effect of amplitude and frequency perturbation of EGG signal for single vowels associated with laryngeal pathology. The normal EGG signal was properly characterized by an autoregressive model which has an optimal order of ninth using the parametric method. This can be analyzed by determining the transfer function. Perturbations in the fundamental pitch and in the peak amplitude of EGG signal measured with a four-electrode system using the modulation/demodulation techniques were investigated for the purpose of developing a decision criteria for the laryngeal function analysis. The abnormal EGG signal has nonperiodic and unstable characteristics. It can be discriminated by the calculation of opening and closing time of glottis using the EGG signal. In case of normal and abnormal subjects, m$\pm$0.5*sd was discriminating line for frequency perturbation and m$\pm$2*sd for normal amplitude perturbations, respectively. Also, The normal and abnormal cases of the subjects can be discriminated effectively using the pattern of attractor derived with Hilbert transform of EGG signal.