• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.127-137
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing a portable gas analyzer (K4B2), an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). A regression equation estimating the energy expenditure (EE) was derived by using data from the accelerometer and information on the participants. The recognition rate of our algorithm was 97.34%, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61%.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.396-404
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• 2014

The field of searching clothing, which is very difficult due to the nature of the informal sector, has been in an effort to reduce the recognition error and computational complexity. However, there is no concrete examples of the whole progress of learning and recognizing for cloth, and the related technologies are still showing many limitations. In this paper, the whole process including identifying both the person and cloth in an image and analyzing both its color and texture pattern is specifically shown for classification. Especially, deformable search descriptor, LBPROT_35 is proposed for identifying the pattern of clothing. The proposed method is scale and rotation invariant, so we can obtain even higher detection rate even though the scale and angle of the image changes. In addition, the color classifier with the color space quantization is proposed not to loose color similarity. In simulation, we build database by training a total of 810 images from the clothing images on the internet, and test some of them. As a result, the proposed method shows a good performance as it has 94.4% matching rate while the former Dense-SIFT method has 63.9%.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.39-48
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• 2016

This research aims to identify innovative companies' attention to the emotion and sensibility and its application by analyzing the contents of the corporate annual reports. Annual report is a good reference data because it describes not only various current products and services' annual activities and business performance but also corporate future direction. Sensibility is interpreted and used with various words internationally: various related terms such as sensibility, sense, emotion, feeling and affection are analyzed not only by the definition but also the interrelationship among them, and included for the contents analysis. To select the innovative companies, the researcher used 'Fast company' that is the economic journal and deducted the companies list via 'The world's 50 most innovative companies' in 2009 and 2014. Listed companies' 2009 and 2014 annual reports' contents were analyzed to identify the rate of the recognition and the application of sensibility to their business. Even though the quantitative result of the content analysis indicates not a strong relationship between corporate innovativeness and 'sensibility' qualitative result identifies companies are paying more attention to the 'sense' and 'feeling' during five years. In conclusion, the innovation that company pursues strategically is shifting from differentiation and the technological leadership to satisfying user experiences and the number of companies which express and measure user feeling and emotions are increasing.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.10
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• pp.712-720
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• 2001

Algorithms from the research of the layout migration were proposed in the paper. These are automatic recognition algorithm for the VLSI devices from it, graph based construction algorithm to maintain the constraints, dependencies, and design rule between the devices, and high speed compaction algorithm to reduce size of the VLSI area and reuse the design with compacted size for the new technology. Also, this paper describes that why proposed algorithms are essential for the era of the SoC (System on a Chip), design reuse, and IP DB, which are the big concerns in these days. In addition to introduce our algorithms, the benchmark showed that our performance is superior by 27 times faster than that of the commercial one, and has better efficiency by 3 times in disk usage.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.333-340
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• 2011

KJJVC(Korea-Japan Joint VLBI Correlator) was installed at the KJCC(Korea-Japan Correlation Center) and has been operated by KASI(Korea Astronomy and Space Science Institute) from 2009. KJNC is able to correlate the VLBI observed data through KVN(Korean VLBI Network), VERA(VLBI Exploration of Radio Astrometry), and JVN(Japanese VLBI Network) and its joint network array. And it is used exclusively as computer in order to process the observed data for the scientific purpose KJJVC used the VSI(VLBI Standard Interface) as the VLBI international standard at the data input-output specification between each component. Especially, for correlating the observed data, the data is transmitted with 1024Mbps speed between Mark5B high-speed playback and RVDB(Raw VLBI Data Buffer). The EMI(Electromagnetic lnterference), which is occurred by data transmission with high-speed, cause the data loss and the loss occurrence is frequently often for long transmission cable. Finally it will be caused the data recognition error by decreasing the voltage level of digital data signal. In this paper, in order to minimize the data loss by measuring the EMI noise level in transmission of the VSI specification, the 3 methods such as 1) RC filtering method, 2) lmpedance matching using Microstrip line, and 3) Signal buffering method using Differential line driver, were proposed. To verify the effectiveness of each proposed method, the performance evaluation was conducted by implementing and simulations for each method. Each proposed method was effectively confirmed as the high-speed data transmission of the VSI specification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.34-38
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• 2016

This study aims to develop qualification criteria for training and establishing an expert pool of 3D printing operation experts and 3D printing development specialists in the emerging 3D printing industry. We investigate the domestic and international 3D printing industry, as well as 3D printing- related training institutions and quantity of test examinees. Utilizing competency analysis, this study examines the competencies and educational content of both 3D printing operation expert as well as 3D printing development specialists. In addition, a survey was conducted on the issue of institutionalization of qualification criteria for 3D printing operation expert and 3D printer development specialists. This study further proposes evaluation methods for qualification criteria for both 3D printing operation expert and 3D printer development specialists based on simulations of both written and performance tests. Based on the findings, this study proposes instructional program outlines as well as comprehensive guidelines for training and qualification processes for 3D printing operation expert and 3D printer development specialists.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.713-722
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• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.234-240
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• 2021

In this paper, the mask-based speech enhancement is improved for effective speech recognition in noise environments. In the mask-based speech enhancement, enhanced spectrum is obtained by multiplying the noisy speech spectrum by the mask. The VoiceFilter (VF) model is used as the mask estimation, and the Spectrogram Inpainting (SI) technique is used to remove residual noise of enhanced spectrum. In this paper, we propose a combined loss to further improve speech enhancement. In order to effectively remove the residual noise in the speech, the positive part of the Triplet loss is used with the component loss. For the experiment TIMIT database is re-constructed using NOISEX92 noise and background music samples with various Signal to Noise Ratio (SNR) conditions. Source to Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI) are used as the metrics of performance evaluation. When the VF was trained with the mean squared error and the SI model was trained with the combined loss, SDR, PESQ, and STOI were improved by 0.5, 0.06, and 0.002 respectively compared to the system trained only with the mean squared error.

• Journal of the Korean Society for information Management
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• v.31 no.1
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• pp.251-275
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• 2014

This study conducted qualitative research to investigate the librarians' organizational commitment and relations between the commitment and their goal recognition in the university library context with several open-ended, deep interviews with university librarians in order to understand the relations from diverse perspectives. As a result, many participants showed low level of organizational commitment because of their hygiene factors, such as payment, incentives, relations with colleagues, and job conditions as well as motivators including characteristics of the work, sense of accomplishment, responsibility, promotion, and self-improvement. The two factors are considered to have direct impacts on their organizational commitment. Also, the research revealed that positive goal perception was related to motivators and, at the same time, hygiene factors. Accordingly, we concluded that librarians' perception of goals are closely related to both hygiene factors and motivators. We also identify that a need of a statistical study with a questionnaire survey to confirm the relations between the organizational commitment and goal perception for developing managerial measures for the organizational commitment, and further;for better library performance.