• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.519-526
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• 2013

ECG signal has the feature that is repeated in a cycle of P, Q, R, S, and T waves and is sampled at a high sampling frequency in general. By using the feature of periodic ECG signals, maximizing compression efficiency while minimizing the loss of important information for diagnosis is required. However, the periodic characteristics of such amplitude and period is not constant by measuring time and patients. Even though measured at the same time, the patient's characteristics display different periodic intervals. In this paper, an adaptive multi-level coding is provided by coding adaptively the dominant and non-dominant signal interval of the ECG signal. The proposed method can maximize the compression efficiency by using a multi-level code that applies different compression ratios considering information loss associated with the dominant signal intervals and non-dominant signal intervals. For the case of long time measurement, this method has a merit of maximizing compression ratio compared with existing compression methods that do not use the periodicity of the ECG signal and for the lossless compression coding of non-dominant signal intervals, the method has an advantage that can be stored without loss of information. The effectiveness of the ECG signal compression is proved throughout the experiment on ECG signal of MIT-BIH arrhythmia database.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.4 s.316
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• pp.28-35
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• 2007

Airbone laser altimeters have been utilized for 3D topographic mapping of the earth, moon, and planets with high resolution and accuracy, which is a rapidly growing remote sensing technique that measures the round-trip time emitted laser pulse to determine the topography. The traveling time from the laser scanner to the Earth's surface and back is directly related to the distance of the sensor to the ground. When there are several objects within the travel path of the laser pulse, the reflected laser pluses are distorted by surface variation within the footprint, generating multiple echoes because each target transforms the emitted pulse. The shapes of the received waveforms also contain important information about surface roughness, slope and reflectivity. Waveform processing algorithms parameterize and model the return signal resulting from the interaction of the transmitted laser pulse with the surface. Each of the multiple targets within the footprint can be identified. Assuming each response is gaussian, returns are modeled as a mixture gaussian distribution. Then, the parameters of the model are estimated by LMS Method or EM algorithm However, each response actually shows the skewness in the right side with the slowly decaying tail. For the application to require more accurate analysis, the tail information is to be quantified by an approach to decompose the tail. One method to handle with this problem is proposed in this study.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.813-818
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• 2020

Recently, with the rapid increase in technology and users of smart devices, the smart watch market has grown, and its utility and usability are continuously expanding. The strengths of smartwatches are wearable portability, application immediacy, data diversity and real-time capability. Despite these strengths, smartwatches have limitations such as battery limitations, display and user interface size limitations, and memory limitations. In addition, there is a need to supplement developers and standard devices, operating system standard models, and killer application modules. In particular, monitoring and application of user's biometric information is becoming a major service for smart watches. The biometric information of such a smart watch generates a large amount of data in real time. In order to advance the biometric information service, stable peer-to-peer transmission of sensing data to a remote smartphone or local server storage must be performed. We propose a synchronization method to ensure wireless remote peer-to-peer transmission stability in a smart watch system. We design a wireless peer-to-peer transmission process based on this synchronization method, analyze asynchronous transmission process and proposed synchronous transmission process, and propose a transmission efficiency method according to an increase in transmission amount.

• Journal of Broadcast Engineering
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• v.13 no.2
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• pp.179-187
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• 2008

In this paper, we proposed the qualify metric based on SVC and the subjective quality. The proposed quality metric is for a general purpose. It means we can use it for any video sequences regardless of its temporal and spatial characteristics. The Quality of Service(QoS) is one of the important issues in heterogeneous environment which has diverse restrictions such as limited network bandwidth and limited display resolution. Scalable Video Coding(SVC) is the efficient video coding skill in heterogeneous environment. Because SVC can be adapted to various quality bitstreams using three scalabilities(spatial, temporal, and SNR) from one bitstream which has full scalability. To maximize the QoS in this environment, we should consider the subjective quality which is the viewer response. And also we should consider temporal and spatial characteristics of video sequence because the subjective quality is affected by temporal and spatial characteristics of video sequence. To verify the efficiency of the proposed method, we perform subjective assessments. The experimental results show that the proposed method has high correlation with subjective quality. The proposed method can be a decision tool of SVC birstream extraction.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.45-51
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• 2021

With the development of deep learning, semantic segmentation methods are being studied in various fields. There is a problem that segmenation accuracy drops in fields that require accuracy such as medical image analysis. In this paper, we improved PSPNet, which is a deep learning based segmentation method to minimized the loss of features during semantic segmentation. Conventional deep learning based segmentation methods result in lower resolution and loss of object features during feature extraction and compression. Due to these losses, the edge and the internal information of the object are lost, and there is a problem that the accuracy at the time of object segmentation is lowered. To solve these problems, we improved PSPNet, which is a semantic segmentation model. The multi-scale attention proposed to the conventional PSPNet was added to prevent feature loss of objects. The feature purification process was performed by applying the attention method to the conventional PPM module. By suppressing unnecessary feature information, eadg and texture information was improved. The proposed method trained on the Cityscapes dataset and use the segmentation index MIoU for quantitative evaluation. As a result of the experiment, the segmentation accuracy was improved by about 1.5% compared to the conventional PSPNet.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.1-8
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• 2019

In this paper, we propose an improved facial skin color extraction method to solve the problem that facial surface is lost due to shadow or illumination in skin color extraction process and skin color extraction is not possible. In the conventional HSV method, when facial surface is brightly illuminated by light, the skin color component is lost in the skin color extraction process, so that a loss area appears on the face surface. In order to solve these problems, we extract the skin color, determine the elements in the H channel value range of the skin color in the HSV color space among the lost skin elements, and combine the coordinates of the lost part with the coordinates of the original image, To minimize the number of In the face detection process, the face was detected using the LBP Cascade Classifier, which represents texture feature information in the extracted skin color image. Experimental results show that the proposed method improves the detection rate and accuracy by 5.8% and 9.6%, respectively, compared with conventional RGB and HSV skin color extraction and face detection using the LBP cascade classifier method.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.233-238
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• 2008

Ambient Media (or ambient displays, Wisneski et al 1998) is one of newly emergent dynamic design objects, representing information through subtle changes of everyday objects and environments. Unlike the existing GUI-based media focusing on the situation that a user is concentrating the media, ambient media enable a user to be peripherally aware of the information without his/her concentration. In order to design ambient media, it is necessary to interpret the basic elements of dynamic changes in physical space and the proper method to apply them for peripherally representing information in ambient media. The aim of this study is to investigate dynamic design elements and the method to apply them for ambient media design in physical space. We explored and refined the type of the dynamic design elements, analyzing dynamic qualities of ambient media cases as well as various time based design fields. As a result, 4 dynamic elements - tempo, connectivity, intensity and rhythm- were proposed. By literature reviews related to peripheral awareness in psychology or ambient media, we analyzed the characteristics of dynamic design elements. Based on that, we developed a new ambient media, 'Cyber Pung-Kyung', and applied dynamic elements for designing the prototype system. The research outcome is expected to contribute designing ambient media which can represent information peripherally or centrally as needed. Understanding the dynamic design elements is helpful for designing various time based design outcomes as well.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.2
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• pp.26-33
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• 2009

New equipment that is useful for driving is developing every day. Navigation is one of the most popular equipment among them. Telematics market including navigation is getting bigger and bigger. However, traffic accident from using telematics equipment is also increasing. Drivers may lose glance using navigation, and driver's workload will also grow by driver's distraction. This thesis is base on the study about the influence on the drivers' workload by using the telematics equipment. Previous study of drivers' workload used psychological method and behavior test method, but it was less connection with telematics equipment. The main object of this thesis is measuring the workload according to the telematics usage by HMI (Human Machine Interface) in the virtual reality. Therefore, we developed GPS simulator, and made an experiment of whether using the navigation or not on the highway and an experiment of the location of navigation in downtown. The result of these experiments is that workload when driver used navigation was higher than when driver didn't use navigation. In addition, workload was different according to the location, and HUD (Head-Up Display) was especially higher than other locations but also its information delivery ability was the best.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.4
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• pp.427-435
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• 2008

In this paper, a stable marching-on in time(MOT) method with a time domain combined field integral equation(CFIE) is presented to obtain the transient scattering response from arbitrarily shaped three-dimensional conducting bodies. This formulation is based on a linear combination of the time domain electric field integral equation(EFIE) with the magnetic field integral equation(MFIE). The time derivatives in the EFIE and MFIE are approximated using a central finite difference scheme and other terms are averaged over time. This time domain CFIE approach produces results that are accurate and stable when solving for transient scattering responses from conducting objects. Numerical results with the proposed MOT scheme are presented and compared with those obtained from the conventional method and the inverse discrete Fourier transform(IDFT) of the frequency domain CFIE solution.