• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1276-1291
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• 2014

In the field of visible light communication (VLC), light-emitting diodes (LEDs) are used for transmitting data via visible light. In this study, we analyze pulse dual slope modulation (PDSM) as a means of delivering information in VLC. PDSM involves the modulation of symmetrical slope pulses to encode binary 0s and 1s, and owing to the moderately increasing and decreasing pulse shapes that are created, this method enables more spectral efficiency than the variable pulse position modulation (VPPM) technique currently adopted in IEEE 802.15.7. In particular, PDSM allows for the avoidance of intra-frame flicker by providing idle pulses in a spectrum-efficient way. A simple detection scheme is proposed for PDSMsignals, and its bit error rate (BER) is analyzed mathematically at varying slopes to validate the process through simulation. The BER performance of PDSM detection using dual sampling is compared to the performances of PDSM and VPPM using correlation detection. It is found that, when the probability of idle pulse transmission is less than 0.08 and higher than 0, the BER of dual sampling PDSM is lower than that of PDSM using correlation detection over the entire light intensity range.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1109-1114
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• 2015

This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9A
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• pp.920-927
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• 2006

There are three critical considerations in developing a PCM telemetry encoder to be installed in an artillery projectile. The first is the performance consideration, such as sampling rate and data transmission rate. The second is the size consideration due to the severely limited installation space in an artillery projectile and the last is the power consumption consideration due to limitations of the munition's power supply. To meet these three considerations, the best alternative is a one-chip solution. Using a commercially available TMS320F2812 DSP, we have implemented a 30-channel PCM telemetry encoder to process randomized data frames, composed of 16-channel analog data, 14-channel digital data and 2-frame synchronization channels per data frame at 10Mbps transmitting baud rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.8 no.1
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• pp.1-11
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• 1983

The object of this paper is to investigate a PCM telemetry system which is designed and constructed in individual modules for an airborne remote measurement system for the first time in Korea. The time division multiplexing PCM encoder has maximum allowable input channels 64 words/frame, 140kbits/sec bit rate. 200frames/sec and 10 bits/wod resolution. And the transmitting unit is designed for 2.2-2.3GHz(s-band) telemetry frequency band, PCM/FM modulation. The Study of airborne PCM telemtry system contribute to develop a device which can acquire various technical data of newly developing flying vehicles by remote measurement. The performance of the proposed system has been verified through a seguence of tests.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.2018-2020
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• 1998

In design of a digital current controller for a 3$\phi$ voltage-source (VS) PWM converter, its conventional model, i.e., stationary or synchronous reference frame model, is used in obtaining its discretized version. It introduces, however, inherent errors since the following practical problems are not taken into consideration: the characteristics of the space vector-based pulsewidth modulation (SVPWM) and the time delays in the process of sampling and computation. In this paper, the new hybrid reference frame model of the 3$\phi$ VS PWM converter is proposed considering these problems. In addition, the direct digital current controller based on this model is designed without any prediction or extrapolation algorithm to compensate the time delay. So the control algorithm is made very simple. The validity of the proposed algorithm is proved by the computer simulation results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.136-139
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• 2004

This thesis is realization of embedded system that of MPEG-2 down conversion using IDCT. A method for down conversion of MPEG compressed video is to perform low-pass filtering and sub-sampling after full decompression. However, this method is need large memory and high computational complexity. Recent research has been focussed on the down conversion in the DCT domain. But DCT method is reduced image qualify. The embedded system is require low complexity, and high speed algorithm. When applied to embedded system that down conversion method, DCT method is played average 29 frame per second, and better 25% than spatial-domain down conversion.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.405-414
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• 2012

In a stratified sample, the sampling frame is divided into non-overlapping groups or strata (e.g. geographical areas, age-groups, and genders). A sample is taken from each stratum, if this sample is a simple random sample it is referred to as stratified random sampling. In this paper, we study the bootstrap inference (including confidence interval) and test for a stratified population mean. We also introduce the bootstrap consistency based on limiting distribution related to the plug-in estimator of the population mean. We suggest three bootstrap confidence intervals such as standard bootstrap method, percentile bootstrap method and studentized bootstrap method. We also suggest a bootstrap test method computing the $ASL_{boot}$(Achieved Significance Level). The results of estimation are verified using simulation.