• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.429-440
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• 2008

As embedded system becomes more complex, software development becomes more important in the entire design process. Most embedded applications consist of multi -tasks, that are executed in parallel. So, dataflow model that expresses concurrency naturally is preferred than sequential programming language to develop multitask software. For the execution of multitasking codes, operating system is essential to schedule multi-tasks and to deal with the communication between tasks. But, it is needed to execute multitasking code without as when the target hardware platform cannot execute as or target platforms are candidates of design space exploration, because it is very costly to port as for all candidate platforms of DSE. For this reason, we propose the serialized multitasking code generation technique from dataflow specification. In the proposed technique, a task is specified with dataflow model, and generated as a C code. Code generation consists of two steps: First, a block in a task is generated as a separate function. Second, generated functions are scheduled by a multitasking scheduler that is also generated automatically. To make it easy to write customized scheduler manually, the data structure and information of each task are defined. With the preliminary experiment of DivX player, it is confirmed that the generated code from the proposed framework is efficiently and correctly executed on the target system.

• Safety and Health at Work
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• v.11 no.2
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• pp.178-186
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• 2020

Background: Cognitive impairment is a public health burden. Our objective was to investigate associations between work hours and cognitive function. Methods: Multi-Ethnic Study of Atherosclerosis (MESA) participants (n = 2,497; 50.7% men; age range 44-84 years) reported hours per week worked in all jobs in Exams 1 (2000-2002), 2 (2002-2004), 3 (2004-2005), and 5 (2010-2011). Cognitive function was assessed (Exam 5) using the Cognitive Abilities Screening Instrument (version 2), a measure of global cognitive functioning; the Digit Symbol Coding, a measure of processing speed; and the Digit Span test, a measure of attention and working memory. We used a prospective approach and linear regression to assess associations for every 10 hours of work. Results: Among all participants, associations of hours worked with cognitive function of any type were not statistically significant. In occupation-stratified analyses (interaction p = 0.051), longer work hours were associated with poorer global cognitive function among Sales/Office and blue-collar workers, after adjustment for age, sex, physical activity, body mass index, race/ethnicity, educational level, annual income, history of heart attack, diabetes, apolipoprotein E-epsilon 4 allele (ApoE4) status, birth-place, number of years in the United States, language spoken at MESA Exam 1, and work hours at Exam 5 (β = -0.55, 95% CI = -0.99, -0.09) and (β = -0.80, -1.51, -0.09), respectively. In occupation-stratified analyses (interaction p = 0.040), we also observed an inverse association with processing speed among blue-collar workers (adjusted β = -0.80, -1.52, -0.07). Sex, race/ethnicity, and ApoE4 did not significantly modify associations between work hours and cognitive function. Conclusion: Weak inverse associations were observed between work hours and cognitive function among Sales/Office and blue-collar workers.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.4
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• pp.313-332
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• 2014

With the increased creation, distribution, and use of image in context of the development of digital technologies and internet, research endeavors have accumulated drastically. As two dominant aspects of image retrieval have been considered content-based and concept-based image retrieval, concept-based image retrieval has been focused in the field of Library and Information Science. This study aims to systematically review the accumulated research of image retrieval from the perspective of LIS field. In order to achieve the purpose of this study, two data sets were prepared: a total of 282 image retrieval research papers from Web of Science, and a total of 35 image retrieval research from DBpia in Kore for comparison. For data analysis, systematic review methodology was utilized with bibliographic analysis of individual research papers in the data sets. The findings of this study demonstrated that two sub-areas, image indexing and description and image needs and image behavior, were dominant. Among these sub-areas, the results indicated that there were emerging areas such as collective indexing, image retrieval in terms of multi-language and multi-culture environments, and affective indexing and use. For the user-centered image retrieval research, college and graduate students were found prominent user groups for research while specific user groups such as medical/health related users, artists, and museum users were found considerably. With the comparison with the distribution of sub-areas of image retrieval research in Korea, considerable similarities were found. The findings of this study expect to guide research directions and agenda for future.

• Phonetics and Speech Sciences
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• v.12 no.3
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• pp.73-83
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• 2020

Measured values may differ between Multi-Dimensional Voice Program (MDVP), Praat, and Time-Frequency Analysis software (TF32), all of which are widely used in voice quality analysis, due to differences in the algorithms used in each analyzer. Therefore, this study aimed to compare the values of parameters of normal voice measured with each analyzer. After tokens of the vowel sound /a/ were collected from 35 normal adult subjects (19 male and 16 female), they were analyzed with MDVP, Praat, and TF32. The mean values obtained from Praat for jitter variables (J local, J abs, J rap, and J ppq), shimmer variables (S local, S dB, and S apq), and noise-to-harmonics ratio (NHR) were significantly lower than those from MDVP in both males and females (p<.01). The mean values of J local, J abs, and S local were significantly lower in the order MDVP, Praat, and TF32 in both genders. In conclusion, the measured values differed across voice analyzers due to the differences in the algorithms each analyzer uses. Therefore, it is important for clinicians to analyze pathologic voice after understanding the normal criteria used by each analyzer when they use a voice analyzer in clinical practice.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.237-242
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• 2016

The major advantage of slip-ring technology in Spiral CT is that it facilitates continuous rotation of the x-ray tube, so that volume data can be acquired from a patient quickly. Not only for such a fast scan, but also for the dose reduction purpose, high signal-to-noise ratio and fast data acquisition system is required. In this study, we have built a multi-channel photodetector and multi-channel data acquisition system for CT application. The detector module consisted of CdWO4 crystal and Si photodiode in 16 channels. For the performance test of the preamplifier stage, both the transimpedance and switched integrator types are optimized for the photodetector modules. Switched integrator showed better noise performance in the limited bandwidth which is suitable for the current CT application. The control sequence for data acquisition and 20 bit ADC is designed with VHDL(Very High Speed Integrated Circuit Hardware Description Language) and implemented on FPGA(Field Programmable Gate Array) chip. Our Si photodiode detector module coupled to CdWO4 crystal showed comparable signal with other commercially available photodiode for CT. Switched integrator type showed higher SNR but narrower bandwidth compared to transimpedance preamplifier. Digital hardware is designed by FPGA, so that the control signal could be redesigned without hardware alteration.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.364-387
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• 2009

Female marriage immigrants have increased since the mid-1990s in Korea. Thus, their adaptation to Korean society has been a big social issue in Korea. There are a lot of academic researches on their adaptation to Korean society so far. We cannot sufficiently understand general main factors of their adaptation to Korean society because of methodological problems in the studies on the female marriage immigrants. Particularly, there are very few studies on female marriage immigrants' adaptation to their communities. This study analyzes determinants of female marriage immigrants' adaptation to their communities, using stepwise multi-regression. Data are collected from questionary survey on female marriage immigrants in Seoul, Gyeong-gi, Daegu, Gyeong-buk, Gwangju, Jeon-nam. Dependent variables are community life, spatial cognition and activities, and neighborhood relationship. Each dependent variable is analyzed with 30 independent variables through stepwise multi-regression. As a result, 16 positive determinants and 2 negative ones are selected. Positive determinants are resident identity, age, adaptation to home, number of Korean friends and same nationals' friends in Korea and so on. But, Korean language fluence is not selected as a significant factor. This is different from a general recognition. As exiting researches, the importance of social network and adaptation variables is also identified in this study.

• 재활복지
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• v.22 no.1
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• pp.157-168
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• 2018

The purpose of this study is to investigate the impact of semi-closed vocal training-based Vocal Aerobic Treatment on the voice improvement of soprano. Study subject was one soprano who appealed to the suffering of her voice problem due to vocal cord nodule. A study method of conducting pre/post acoustic evaluation and subjective voice evaluation to compare the measures was used; Vocal Aerobic Treatment was carried out twice a week for a total of 32 session. In the acoustic evaluation, MDVP (multi-dimensional voice program) and VRP (voice range profile) were used to evaluate the pitch, voice quality, and voice range; in the subjective voice evaluation, SVHI (singing voice handicap index) was used to assess voice satisfaction. As a result of the pitch evaluation, the soprano maintained a proper Fo. As a result of the voice quality evaluation, the jitter, shimmer, and the noise harmonic ratio numbers decreased compared to the numbers shown before the treatment. As a result of the voice range evaluation, the scope of the range was broadened, with the number of semitone increasing from 30 to 35. As for the subjective voice evaluation, the result of the total score obtained after the survey report divided by the number of questions showed a decrease from 3.6 to 0.6. The soprano herself reported of having a minor extent of a voice problem. The summary of the above results reflects that Vocal Aerobic Treatment is useful in the voice improvement of vocalists However, as this study is case research regarding the Vocal Aerobic Treatment effect on one soprano, further research on the treatment effect covering many other vocalists is necessary. Also, there is a need for follow-up studies regarding voice management and voice treatment program on not only the vocalists but also the voice users in many other professions.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1062-1072
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• 2006

Recently, new intelligent service frameworks, such as ubiquitous computing are proposed. So, the necessity of adaptive agent system has been increased. In this paper, we propose an intelligent service agent to help that ubiquitous computing system offer user suitable service in ubiquitous computing environment. In order to offer user suitable uT-service, an intelligent service agent mediates the gap between the context information in uT-service system, and user preference is reflected in it. Therefore, we focus on following three components; the first is suitable multi agent framework-agent communication analysis and applicable method of inference engine, the second is uT-ontologies to describe various context information-context information sharing between agents and context information understanding between agents, the third is learning method of user profile to apply in uT-service system. This approach enables us to build adaptive uT-service system to offer suitable service according to user preference.

• Journal of Advanced Navigation Technology
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• v.5 no.1
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• pp.1-10
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• 2001

Speech has a good characteristics of which car drivers busy to concern with miscellaneous operation can make use in convenient handling and manipulating of devices. By utilizing this, this works proposes a speaker verification method for protecting cars from being stolen and identifying a person trying to access critical on-line services. In this, continuant phonemes recognition which uses language information of speech and MLP(mult-layer perceptron) which has some advantages against previous stochastic methods are adopted. The recognition method, though, involves huge computation amount for learning, so it is somewhat difficult to adopt this in speaker verification application in which speakers should enroll themselves at real time. To relieve this problem, this works presents a solution that introduces speaker cohort models from speaker verification score normalization technique established before, dividing background speakers into small cohorts in advance. As a result, this enables computation burden to be reduced through classifying the enrolling speaker into one of those cohorts and going through enrollment for only that cohort.