• Journal of Nutrition and Health
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• v.28 no.1
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• pp.71-83
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• 1995

The purpose of the study was to develop generalized equations for estimating stature and weight for the nonambulatory elderly persons. Height weight recumbent knee height total ann length, midarm, waist and calf circumferences, triceps and subscapular skinfolds were measured from over 60 years old 315 ambulatory elderly. The equations to predict stature and weight were derived from participants in the validation sample and were applied to the participants in the cross-validation to test the accuracy and validity of equations. Stature and weight were significantly and negatively associated with age of women and similar patterns observed in men but associated to a slight degree. Knee height and total arm length were highly correlated with stature but the majority of the variances in stature was accounted for by knee height for both the men and women. In men, waist circumference was the most significantly correlated with weight and am, calf circumferences and so forth. But in women arm circumference was the highest then waist and calf circumference in order. The possible predictor variables to estimate of stature were knee height total arm length and age for both elderly men and women. Predictor variables to estimate of weight were recumbent measures of waist am, calf circumferences and knee height for both sexes. Inclusion of skinfold thickness measurements did not improve the prediction power of estimation for weight. When both equations developed from the present study and Chumlea's study were applied to cross-valida-tions samples, the equations derived from present study showed better accuracy and validity. The presentation of prediction equations using two, three, or four recommended measurements allows the selection of an equation based upon the measurements that are possible to collect on an individual basis.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.13-20
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• 2022

It's proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It's for the coefficient of determination (R²) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.206-214
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• 2020

This study examined the correlation between the respiratory pattern change (RPC) and abdominal mobility (AM) according to the positive result of the subjective lumbar instability factor (SLIF) in people with chronic low back pain (CLBP). Thirty-six adults with CLBP participated in this study. Twenty-eight items of the SLIFs were examined, and the subjects were divided into three groups according to the positive response numbers (PRN). After the change lists were scored, three RPC scores [costo-diaphragmatic RPC (CDRPC), breath hold change (BHC), and total RPC (TRPC)] were obtained. The abdominal mobility (AM) was measured between the maximal inspiration and exhalation at the xiphoid (AM1) and the 10^th rib (AM2) level of the trunk. The results showed that the RPC score and AM were compared according to the positive response number of SLIF, and the relationship between them was analyzed. A positive correlation was observed between the SLIF positive response number and CDRPC score, BHC score, and total RPC score, and a negative correlation was observed between the SLIFs positive response number and AM1 and AM2. Based on the results of this study, the combination of SLIF positive responses can be a predictor of non-physiological respiratory pattern changes in people with CLBP. Clinically, this prediction is expected to help save time for screening and improve the efficiency of therapy.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.1-14
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• 2002

The most important issue in providing multimedia traffic on a mobile computing environments is to guarantee the mobile host(client) with consistent QoS(Quality of Service). However, the QoS negotiated between the client and network in one cell may not be honored due to client mobility, causing hand-offs between cells. In this paper, a call admission control mechanism is proposed to provide consistent QoS guarantees for multimedia traffics in a mobile computing environment. Each cell can reserve fractional bandwidths for hand-off calls to its adjacent cells. It is important to determine the right amount of reserved bandwidth for hand-off calls because the blocking probability of new calls may increase if the amount of reserved bandwidth is more than necessary. An adaptive bandwidth reservation based on an MPP(Mobility Pattern Profile) and a 2-tier cell structure has been proposed to determine the amount of bandwidth to be reserved in the cell and to control dynamically its amount based on its network condition. We also propose a call admission control based on this bandwidth reservation and "next-cell prediction" scheme using an MPP. In order to evaluate the performance of our call admission control mechanism, we measure the metrics such as the blocking probability of our call admission control mechanism, we measure the metrics such as the blocking probability of new calls, dropping probability of hand-off calls, and bandwidth utilization. The simulation results show that the performance of our mechanism is superior to that of the existing mechanisms such as NR-CAT1, FR-CAT1, and AR-CAT1.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.8A
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• pp.1329-1337
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• 2001

최근 광대역 통신망을 무선 영역까지 확장하고자 하는 노력의 일환으로 무선 ATM 시스템에 관한 연구가 활발히 이루어지고 있다. 무선 ATM에서는 셀의 반경이 작아져서 핸드오버가 자주 일어나게 되는데 이는 QoS 저하를 야기하며, 핸드오버를 위한 채널이 부족하면 호가 절단되는 현상을 야기하게 된다. 이를 방지하기 위하여 본 논문에서는 QoS 보장이 용이하고, 채널 할당을 효율적으로 할 수 있는 핸드오버 방식을 제안한다. 대부분의 사용자는 일정한 패턴을 따라 이동함을 이용하여 이 패턴에 따라 핸드오버에 필요한 채널을 예약하고, 단말의 이동 시 신속한 핸드오버를 수행하게 된다. 제안한 알고리즘을 사용할 경우 핸드오버 호의 절단율을 줄일 수 있고, 채널 사용도 효율적으로 이루어진다.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.412-415
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• 2011

본 논문에서는 정적 무선 센서 네트워크상의 클러스터링 기법인 EECS(Energy Efficient Clustering Scheme)의 노드와 Base Station간의 거리를 고려한 head 선출 과정에 노드의 이동성 및 미래 위치 예측을 융합하여 확장한 새로운 동적환경상의 클러스터링 기법 EECS-M(Energy Efficient Clustering Scheme in Mobile wireless sensor networks)을 제안한다. 실험을 통하여 EECS-M이 동적 환경상의 LEACH-M, WCA 및 정적 환경상의 EECS, LEACH 클러스터링 알고리즘들에 비해 life time 및 life time 대비 네트워크의 잔여 에너지 측면에서 성능향상을 가진다는 것을 보인다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.255-258
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• 2001

In this paper, DC and small signal characteristics with different physical parameters are expected for p-HEMTs (Pseudomorphic High Electron Mobility Transistors) with different temperatures ranging from 300K to 623K which are widely used for a low noise and/or ultra high frequency device. A device of 0.2$\times$200 ${\mu}{\textrm}{m}$$^2$dimension having very low noise has been chosen to extract the experimental data. Theoretical prediction has been obtained using a simulaor(HELENA) which needs experimental input data extracted from reverse engineering process. From the results, relation between structural parameters and temperature dependency of electrical characteristics are qualitatively explained to use in the design of descrete and integrated circuits to guarantee the optimal operation of the system.

• Journal of KSNVE
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• v.9 no.3
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• pp.587-592
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• 1999

Piston slap is one of the sources producing engine block surface vibration and mechanical noise. To analyze piston slap-induced vibration, a realistic but simple model is proposed and verified experimentally. A piston is modeled by 3 degree of freedom system and an impact point between piston skirt and cylinder wall by 2 degree of freedom system. Numerical simulation estimates impact forces of piston in cylinder, and the engine block surface vibration response is predicted by the convoluton of the impact forces with measured impulse responses. Experimental verification on the predicted response has been also performed by using a commercial 4-cylinder diesel engine. the predicted and experimental vibration responses confirm that the suggested model is practically useful.

• Transactions of Materials Processing
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• v.21 no.6
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• pp.366-371
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• 2012

Atomistic simulations have become useful tools for exploring new insights in materials science, but the length and time scale that can be handled with atomistic simulations are seriously limiting their practical applications. In order to make meaningful quantitative predictions, atomistic simulations are necessarily combined with higher-scale modeling. The present research is thus concerned with the development of a multi-scale model and its application to the prediction of the mechanical properties of body-centered cubic(BCC) iron with an emphasis on the coupling of atomistic molecular dynamics with meso-scale discrete dislocation dynamics modeling. In order to achieve predictive multi-scale simulations, it is necessary to properly incorporate atomistic details into the meso-scale approach. This challenge is handled with the proposed hierarchical information passing strategy from atomistic to meso-scale by obtaining material properties and dislocation mobility. Finally, this fundamental and physics-based meso-scale approach is employed for quantitative predictions of the mechanical response of single crystal iron.