• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.63-76
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• 2018

The purpose of this study was to investigate the effect of contact type textile electrode structure on heart activity signal acquisition for smart healthcare. In this study, we devised six contact type textile electrodes whose electrode size and configuration were manipulated for measuring heart activity signals using computerized embroidery. We detected heart activity signals using a modified lead II and by attaching each textile electrode to the chest band in four healthy male subjects in a standing static posture. We measured the signals four times repeatedly for all types of electrodes. The heart activity signals were sampled at 1 kHz using a BIOPAC ECG100, and the detected original signals were filtered through a band-pass filter. To compare the performance of heart activity signal acquisition among the different structures of the textile electrodes, we conducted a qualitative analysis using signal waveform and size as parameters. In addition, we performed a quantitative analysis by calculating signal power ratio (SPR) of the heart activity signals obtained through each electrode. We analyzed differences in the performance of heart activity signal acquisition of the six electrodes by performing difference and post-hoc tests using nonparametric statistic methods on the calculated SPR. The results showed a significant difference both in terms of qualitative and quantitative aspects of heart activity signals among the tested contact type textile electrodes. Regarding the configurations of the contact type textile electrodes, the three-dimensionally inflated electrode (3DIE) was found to obtain better quality signals than the flat electrode. However, regarding the electrode size, no significant difference was found in performance of heart signal acquisition for the three electrode sizes. These results suggest that the configuration method (flat/3DIE), which is one of the two requirements of a contact type textile electrode structure for heart activity signal acquisition, has a critical effect on the performance of heart activity signal acquisition for wearable healthcare. Based on the results of this study, we plan to develop a smart clothing technology that can monitor high-quality heart activity without time and space constraints by implementing a clothing platform integrated with the textile electrode and developing a performance improvement plan.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.249-257
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• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.471-492
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• 2017

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.422-435
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• 2005

Compared with analog broadcasting, digital broadcasting supports technical background to provide personalize the TV watching environment by offering broadcasting services that can adapt to viewers' preferences. However, current digital broadcasting shows limited services such as reservation recording, simple program guiding with an electronic program guide (EPG) on a personal video recorder system, and primitive data broadcasting by broadcasters. Therefore, the purpose of this paper is to suggest a new broadcasting environment which gives a person facility and a difference fur watching TV by serving enhanced personalized services. For that reason, we propose an intelligent broadcasting system which can minimize viewer's actions, and enhanced broadcasting services which are based on understanding of the semantics of broadcasting contents. To implement the system, agent technology as well as the MPEG-7 and TV-Anytime Forum (TVAF) are employed. For content-level services, real-time content filtering and personalized video skimming are designed and implemented. To verify the usefulness of the proposed system, we demonstrate it with a test-bed on which content-level personalized services are implemented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.1
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• pp.124-132
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• 2002

In this paper, we present a vision algorithm and method for input image improvement and preprocessing of dented and raised characters on the sidewall of tires. we define optical condition between reflect coefficient and reflectance by the physical vector calculate. On the contrary this work will recognize the engraved characters using the computer vision technique. Tire input images have all most same grey levels between the characters and backgrounds. The reflectance is little from a tire surface. therefore, it's very difficult segment the characters from the background. Moreover, one side of the character string is raised and the other is dented. So, the captured images are varied with the angle of camera and illumination. For optimum Input images, the angle between camera and illumination was found out to be with in 90$^{\circ}$. In addition, We used complex filtering with low-pass and high-pass band filters to improve input images, for clear input images. Finally we define equation reflect coefficient and reflectance. By doing this, we obtained good images of tires for pattern recognition.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.227-236
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• 2003

Leaks in underground pipelines can cause social, environmental and economical problems. One of relevant countermeasures against leaks is to find and repair of leak points of the pipes. Leak noise is a good source to identify the location of leak points of the pipelines. Although there have been several methods to detect the leak location with leak noise, such as listening rods, hydrophones or ground microphones, they have not been so efficient tools. In this paper, acoustic emission (AE) sensors and accelermeters are used to detect leak locations which could provide all easier and move efficient method. Filtering, signal processing and algorithm of raw input data from sensors for the detection of leak location are described. A 120m-long pipeline system for experiment is installed and the results with the system show that the algorithm with the AE sensors and accelerometers offers accurate pinpointing of leaks. Theoretical analysis of sound wave propagation speed of water in underground pipes, which is critically important in leak locating, is also described.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.151-157
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• 2019

Recently, monitoring respiration of people has been of interest using non-invasive method. Among the vital signals usually used for indicating health status, non-invasive and portable device based monitoring respiratory status is practically useful and enable one to promptly deal with abnormal physical status. This paper proposes the approach to real-time detection of apnea signal based on Short-Time-Fourier-Transform(STFT). Contrary to the analysis of a signal in frequency domain using Fast-Fourier Transform, this paper employs Short-time-Fourier-Transform so that frequency response can be analyzed in short time interval. The respiratory signal is acquired using UWB radar sensor that enables one to obtain respiration signal in contactless way. Detection of respiratory status is carried out by analyzing frequency response, and classification of respiratory status can be provided. In particular, STFT is employed to analyze respiratory signal in real-time, leading to effective analysis of the respiratory status in practice. In the case of existence of noise in the signal, appropriate filtering process is employed as well. The proposed method is straightforward and is workable in practice to analyze the respiratory status of people. To evaluate the proposed method, experimental results are provided.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.19-23
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• 2024

Patients with amputations of both hands need prosthetic hands that serve both cosmetic and functional purposes, and research on prosthetic hands using electromyography of remaining muscles is active, but there is still the problem of high cost. In this study, an artificial prosthetic hand was manufactured and its performance was evaluated using low-cost parts and software such as a surface electromyography sensor, machine learning software Edge Impulse, Arduino Nano 33 BLE, and 3D printing. Using signals acquired with surface electromyography sensors and subjected to digital signal processing through Edge Impulse, the flexing movement signals of each finger were transmitted to the fingers of the prosthetic hand model through training to determine the type of finger movement using machine learning. When the digital signal processing conditions were set to a notch filter of 60 Hz, a bandpass filter of 10-300 Hz, and a sampling frequency of 1,000 Hz, the accuracy of machine learning was the highest at 82.1%. The possibility of being confused between each finger flexion movement was highest for the ring finger, with a 44.7% chance of being confused with the movement of the index finger. More research is needed to successfully develop a low-cost prosthetic hand.