• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.24-27
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• 1999

IMBE(Improved Multi-Band Excitation) vocoders exhibit good performance at low data rates. The major drawback to IMBE coders is their large computational requirements. In this paper, thus, we propose a new pitch search method that preserves the quality of the IMBE vocoder with reduced complexity. The basic idea is to skip unnecessary range of the pitch searching by using the quantization error. Applying the proposed method to the IMBE vocoder, we can get approximately $45.88\%$ processing time reduction and there is no difference in voice quality between conventional IMBE and proposed IMBE.

• The Korean Journal of Health Service Management
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• v.4 no.2
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• pp.33-46
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• 2010

In this study, we investigated web contents accessibility of 60 hospital web sites in Korea. The eight evaluation criteria were used for estimating the web contents accessibility of the web sites. These criteria were as follows: providing an alternative text, providing caption for moving picture, providing a skip navigation, usage of pop-up windows, usage of a summary or a caption tag for data table, providing a page title, providing a label for online form, and usage of java scripts. K-WAH 3.0 was used for estimating five evaluation criteria. According to Internet web contents accessibility guideline 1.0, we estimated the rest three evaluation criteria manually and described good or bad examples for the evaluation results technically. The results show that the web accessibility of hospital web sites is generally insufficient and the constant interests in improvement for accessibility are urgently needed.

• The Journal of Korean Medicine
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• v.31 no.3
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• pp.28-33
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• 2010

Objective: To investigate the situation of health-related main factors of Korean middle school adolescents. Methods: Data were collected via a self-reporting questionnaire from 2,254 students (650 boys and 1,604 girls), and their sleeping, exercise, dietary pattern, stress, physical problems, and taking of food supplements were analyzed. Results: Korean middle school students sleep about 7 hours per day, and they exercise less than 4 hours per week. Around 13% of the students skip breakfast nearly every day. 35% of the students were under severe stress, and 38% have reported at least one symptom of physical distresses including gastrointestinal disorders or headaches. 31% of the students took a functional supplement, especially one with vitamins as the most favored one, and ginseng and herbal drugs coming second and third. Conclusion: This result first reports a general feature of health-associated factors in middle school students. This study in the future will be basic information to develop medical supports for adolescents using traditional Korean medicine.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.123-125
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• 2011

울산항은 1996년 9월부터 입출항 선박의 항행안전을 위해 VTS 시스템을 설치운영하고 있으나 많은 선박통항량과 위험화물 운송선박의 잦은 운항으로 여러 가지 위험요소가 상존하고 있는 개항장이다. VTS 시스템은 레이더의 자동물표추적장치에 의한 데이터, PORT-MIS의 선박관련 데이터 등 많은 정보들이 분산 처리되어 관제사에게 제공되고 있으나 최근 선박에 설치 운영되고 있는 선박자동식별장치(AIS)에 의해 선박의 정보들이 더욱 신속하고 정확하게 처리되는 것으로 평가되고 있다. 그러나 인위적인 과실에 의한 AIS의 오류정보들과 원활하지 못한 데이터통신에 의한 데이터 누락현상에 의해 VTS 시스템 운용에 막대한 영향을 초래하고 있다. 이러한 인위적인 과실에 의한 AIS의 오류 데이터는 PSC 검사관들의 적극적인 개선의지로 정책적인 계도작업을 수행하고 있으므로 점차 개선될 것으로 기대된다. 따라서 본 연구에서 AIS의 원활하지 못한 통신망에 의한 데이터 누락 현상에 의한 VTS 시스템에서의 영향을 조사 분석하고 이에 따른 개선 방안을 제시하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1231-1233
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• 2011

WBAN(Wireless Body Area Network)과 같은 스트림 데이터의 환경에서는 데이터가 아닌 질의들이 등록되어 있고 데이터들이 끊임없이 시스템에 도착한다. 때문에 도착한 데이터에 대해서 처리할 수 있는 질의만을 찾아 해당 질의들만을 수행하도록 해서 시스템의 질의 부담을 덜어주는 방법이 필요하다. 기존의 단순하고 단편적인 질의의 문제점을 해결하고자 본 연구에서는 Interval Skip List 자료 구조와 시간기반 윈도우를 이용하여 효율적인 실시간 모니터링 시스템을 구현하였다. 특히 산소포화도 생체 센서들로부터 연속적으로 전송되는 스트림 데이터에 대해 다양한 조건을 포함하는 질의들이 실행 되는데 이러한 실시간 모니터링 질의들을 효율적으로 식별하기 위한 질의 인덱스를 설계하였다.

• Bulletin of the Korean Chemical Society
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• v.30 no.9
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• pp.2061-2065
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• 2009

Two simple algorithm modifications are made to the THRASH data retrieval program with the aim of improving analysis speed for complex Fourier transform ion cyclotron resonance (FTICR) mass spectra. Instead of calculating the least-squares fit for every charge state in the backup charge state determination algorithm, only some charge states are pre-selected based on the plausibility values obtained from the FT/Patterson analysis. Second, a modification is made to skip figure-of-merit (FOM) calculations in the central m/z region between two neighboring peaks in isotopic cluster distributions, in which signal intensities are negligible. These combined modifications result in a significant improvement in the analysis speed, which reduces analysis time as much as 50% for ubiquitin (8.6 kDa, 76 amino acids) FTICR MS and MS/MS spectra at the reliability (RL) value = 0.90 and five pre-selected charge states with minimal decreases in data analysis quality (Table 3).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.1-14
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• 2006

Sort-last parallel rendering using a cluster of GPUs has been widely used as an efficient method for visualizing large- scale volume datasets. The performance of this method is constrained by load balancing when data parallelism is included. In previous works static partitioning could lead to self-balance when only task level parallelism is included. In this paper, we present a load balancing scheme that adapts to the characteristic of volume dataset when data parallelism is also employed. We effectively combine the hierarchical data structures (octree and BSP tree) in order to skip empty regions and distribute workload to corresponding rendering nodes. Moreover, we also exploit a 3D clustering method to determine visibility order and save the AGP bandwidths on each rendering node. Experimental results show that our scheme can achieve significant performance gains compared with traditional static load distribution schemes.

• Journal of the Korean Society of School Health
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• v.34 no.3
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• pp.179-189
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• 2021

Purpose: The purpose of the study was to investigate health behavior changes in Korean adolescents before and during the COVID-19 pandemic. Methods: A descriptive cross-sectional design was employed. Data were obtained from the 15~16th (2019~2020) Korea Youth Risk Behavior Web-based Survey. Overall, the data of 112,251 students, 57,303 before the COVID pandemic and 54,948 during the pandemic, were included. The data were analyzed using rao-scott 𝝌²-test, t-test, ANCOVA and logistic regression analysis. Results: While sitting time for study decreased, sitting time for leisure increased in the 2020 group compared to the 2019 group. The 2020 group was more likely to skip breakfast and have a higher BMI than the 2019 group. The 2020 group was less likely to consume fruit and engage in moderate and vigorous physical activities than the 2019 group. The 2020 group was less likely to engage in CC, EC, and HTP current smoking and be exposed to secondhand smoke at home, school, and public places than the 2019 group. The 2020 group was more likely to be satisfied with sleep, but less likely to experience stress, depression, suicidal ideation, suicidal plans, and suicidal attempts than the 2019 group. Conclusion: It is necessary to reduce sedentary time, encourage physical activities, manage smoking and drinking rates continuously, and establish a network system to prevent psychological loneliness and isolation for adolescents, which requires participation of experts from the community as a whole.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.