• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.19 no.2
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• pp.16-22
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• 2011

It is recognized that the control surface fault is detected by monitoring the value of the coefficients due to the control surface deviation. It is found out the control surface stuck position by comparing the trim value with the reference value. To detect and isolate the fault, two mixed methods apply to the real-time parameter estimation and similarity measure. If the scatter of aerodynamic coefficients for the fault and normal are closing nearly, fault decision is difficult. Applying similarity measure to decide for fault or not, it makes a clear and easy distinction between fault and normal. Low power processor is applied to the real-time parameter estimator and computation of similarity measure.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1181-1190
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• 2006

Statistical analyses for spatial data are important features for various types of fields. Spatial data are taken at specific locations or within specific regions and their relative positions are recorded. Lattice data are synoptic observation covering an entire spatial region, like cancer rates corresponding to each county in a state. Until now, the echelon analysis has been applied only to univariate spatial data. As a result, it is impossible to detect the hotspots on the multivariate spatial data In this paper, we expand the spatial data to time series structure. And then we analyze them on the time space and detect the hotspots. Echelon dendrogram has been made by piling up each multivariate spatial data to bring time spatial data. We perform the structural analysis of temporal spatial data.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.325-331
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• 2023

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.

• Korean Journal of Clinical Laboratory Science
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• v.46 no.2
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• pp.64-67
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• 2014

Vancomycin-resistant Enterococci (VRE) are a leading cause of a nosocomial infection. While seven glycopeptide resistance genotypes have been found in Enterococci, vanA and vanB are the most common resistance genotypes. Aims of this study were to detect antibiotic susceptibilities of 23 Enterococcus spp, which broke out in a university hospital by the disk diffusion test, to investigate specific genes of vanA and vanB by conventional and real-time PCR. PCR for vanA and vanB was performed on 23 Enterococci, all 23 were positive for vanA type. This study reports the validation of a simple and rapid VRE detection method that can be easily incorporated into the daily routine of a clinical laboratory. Early detection of VRE strains, including those with susceptibility to Vancomycin, is of paramount clinical importance, as it allows a rapid initiation of strict infection control practices as well as a therapeutic guidance for a confirmed infection. The real-time PCR method is a rapid technique to detect vanA in Enterococci. It is simple and reliable for the rapid characterization of VRE.

• IE interfaces
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• v.14 no.1
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• pp.95-105
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• 2001

Previous work measurement methods need much time and effort of time study analysts because they have to measure required time through direct observations. In this study, we propose a method which efficiently measures standard times without involvement of human analysts using digital image processing techniques. This method consists of two main steps: motion representation step and cycle segmentation step. In motion representation step, we first detect the motion of any object distinct from its background by differencing two consecutive images separated by a constant time interval. The images thus obtained then pass through an edge detector filter. Finally, the mean values of coordinates of significant pixels of the edge image are obtained. Through these processes, the motions of the observed worker are represented by two time series data of worker location in horizontal and vertical axes. In the second step, called the cycle segmentation step, we extract the frames which have maximum or minimum coordinates in one cycle and store them in a stack, and calculate each cycle time using these frames. In this step we also consider methods on how to detect work delays due to unexpected events such as operator's escapement from the work area, or interruptions. To condude, the experimental results show that the proposed method is very cost-effective and useful for measuring time standards for various work environment.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.31-37
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• 1996

This Paper is concerned with effective signal identification method for tool breakage and micro chipping using discrete wavelet transform of feed motor current in milling operations. The wavelet transform uses an analyzing waveletfunction which is localized in both frequency and time domain to detect subtle time localized changes in input signals. The changing pattern of wavelet coefficient is continuously compared to detect tool breakage and micro chipping over one spindle revolution. The results indicate that the wavelet transform can identify tool failure with much greater sensi- tivity than the time domain monitoring and frequency domain monitoring such as FFT. Experimental results are presented to support the proposed scheme.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.2
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• pp.93-101
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• 2016

This paper presents the results obtained using time-series-based methods for structural damage assessment. The methods are applied to a wind turbine blade structure subjected to fatigue loads. A 9 m CX-100 (carbon experimental 100 kW) blade is harmonically excited at its first natural frequency to introduce a failure mode. Consequently, a through-thickness fatigue crack is visually identified at 8.5 million cycles. The time domain data from the piezoelectric active-sensing techniques are measured during the fatigue loadings and used to detect incipient damage. The damage-sensitive features, such as the first four moments and a normality indicator, are extracted from the time domain data. Time series autoregressive models with exogenous inputs are also implemented. These features could efficiently detect a fatigue crack and are less sensitive to operational variations than the other methods.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.279-286
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• 2000

The objective of this research is to detect the papaya fruits on tree in an orchard. The detection of papaya on natural background is difficult because colors of fruits and background such as leaves are similarly green. We cannot separate it from leaves by color information. Therefore, this research will use shape information instead. First, we detect an interested object by detecting its boundary using edge detection technique. However, the edge detection will detect every objects boundary in the image. Therefore, shape description technique will be used to describe which one is the interested object boundary. The good shape description should be invariant in scaling, rotating, and translating. The successful concept is to use Fourier series, which is called "Fourier Descriptors". Elliptic Fourier Descriptors can completely represent any shape, which is selected to describe the shape of papaya. From the edge detection image, it takes a long time to match every boundary directly. The pre-processing task will reduce non-papaya edge to speed up matching time. The deformable template is used to optimize the matching. Then, clustering the similar shapes by the distance between each centroid, papaya can be completely detected from the background.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.188-194
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• 2000

This study is about fault detection and fault area detection of LV circuit in Cab Cubicle system which have control of train to keep safety in High Speed Train. LV circuit is operated with diagnosis system like safety system. In this paper, we suggest a design and an implementation method to detect fault or to detect fault area automatically about LV circuit. The implemented system is tested successfully after implementation of some function. We expect reduction to diagnosis area or repair time by fault area module