• Safety and Health at Work
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• v.1 no.2
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• pp.140-148
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• 2010

Objectives: Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods: 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results: In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion: We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.1-7
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• 2015

As a measure of health, the percentage of body fat has been utilized for many ergonomist, physician, athletic trainers, and work physiologists. Underwater weighing procedure for measuring the percentage of body fat is popular and accurate. However, it is relatively expensive, difficult to perform and requires large space. Anthropometric techniques can be utilized to predict the percentage of body fat in the field setting because they are easy to implement and require little space. In this concern, the purpose of this study was to find a regression model to easily predict the percentage of body fat using the anthropometric circumference measurements as predictor variables. In this study, the data for 10 anthropometric circumference measurements for 252 men were analyzed. A full model with ten predictor variables was constructed based on subjective knowledge and literature. The linear regression modeling consists of variable selection and various assumptions regarding the anticipated model. All possible regression models and the assumptions are evaluated using various statistical methods. Based on the evaluation, a reduced model was selected with five predictor variables to predict the percentage of body fat. The model is : % Body Fat = 2.704-0.601 (Neck Circumference) + 0.974 (Abdominal Circumference) -0.332 (Hip Circumference) + 0.409 (Arm Circumference) - 1.618 (Wrist Circumference) + $\epsilon$. This model can be used to estimate the percentage of body fat using only a tape measure.

• Journal of Power Electronics
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• v.4 no.2
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• pp.102-111
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• 2004

This paper is concerned with a deadbeat current control implementation of shunt-type three-phase active power filter (APF). Although the one-dimensional deadbeat control method can attain time-optimal response of APF compensating current, one sampling period is actually required fur its settling time. This delay is a serious drawback for this control technique. To cancel such a delay and one more delay caused by DSP execution time, the desired APF compensating current has to be predicted two sampling periods ahead. Therefore an adaptive predictor is adopted for the purpose of both predicting the control error of two sampling periods ahead and bringing the robustness to the deadbeat current control system. By adding the adaptive predictor output as an adjustment term to the reference value of half a source voltage period before, settling time is made short in a transient state. On the other hand, in a steady state, THD (total harmonic distortion) of the utility grid side AC source current can be reduced as much as possible, compared to the case that ideal identification of controlled system could be made.

• Journal of the Korean Society of Safety
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• v.18 no.3
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• pp.154-160
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• 2003

Uncertain time delay happens when the process reads the sensor data and sends the control input to the plant located at a remote site in distributed control system. As in the case of data network using TCP/IP, VDN that integrates both device network and data network has uncertain time delay. Uncertain time delay can cause degradation in performance and stability of distributed control system based on VDN. This paper first investigates the transmission characteristic of VDN and suggests a control scheme based on the Smith's predictor to minimize the effect of uncertain varying time delay. The validity of the proposed control scheme is demonstrated with real-time velocity control of DC servo motor located in remote site.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Mass Spectrometry Letters
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• v.10 no.3
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• pp.93-101
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• 2019

Algorithms and software for predicting tandem mass spectra have been developed in recent years. In this work, we explore how distinct in silico $MS^2$ spectra are predicted for isomers, i.e. compounds having the same formula and similar molecular structures, to differentiate between them. We used the CFM-ID 2.0/3.0 predictor with regard to (a) test compounds, whose experimental mass spectra had been randomly sampled from the MassBank of North America (MoNA) collection, and to (b) the most widespread isomers of test compounds searched in the PubChem database. In the first validation test, in silico mass spectra constitute a reference library, and library searches are performed for test experimental spectra of "unknowns". The searches led to the true positive rate (TPR) of ($46-48{\pm}10$)%. In the second test, in silico and experimental spectra were interchanged and this resulted in a TPR of ($58{\pm}10$)%. There were no significant differences between results obtained with different metrics of spectral similarity and predictor versions. In a comparison of test compounds vs. their isomers, a statistically significant correlation between mass spectral data and structural features was observed. The TPR values obtained should be regarded as reasonable results for predicting tandem mass spectra of related chemical structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.3
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• pp.133-141
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• 2007

A finite element model is developed for the extended Boussinesq equations that is capable of simulating the dynamics of long and short waves. Galerkin weighted residual method and the introduction of auxiliary variables for 3rd spatial derivative terms in the governing equations are used for the model development. The Adams-Bashforth-Moulton Predictor Corrector scheme is used as a time integration scheme for the extended Boussinesq finite element model so that the truncation error would not produce any non-physical dispersion or dissipation. This developed model is applied to the problems of solitary wave propagation. Predicted results is compared to available analytical solutions and laboratory measurements. A good agreement is observed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.2C
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• pp.160-169
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• 2002

An ATM-PON (Passive Optical Network) system consists of an OLT (Optical Line Termination), multiple ONUs (Optical Network Units) and the optical fiber which has a PON (Passive Optical Network)configuration with a passive optical splitter. To avoid cell collisions on the upstream transmission, an elaborate procedure called as ranging is needed when a new ONU is installed. The ONU can send upstream cells according to the grant provided by the OLT after the procedure. To prevent collisions being generated by the variation of several factors, OLT must performs continuously the cell phase monitoring. It means that the OLT predicts the expected arrival time, monitors the actual arrival time for all upstream cells and calculates the error between the times. Accordingly, TC (Transmission Convergence) chip in the OLT needs a predictor which predicts the time that the cell will arrive for the current grant. In this paper, we implement the predictor by using shift registers of which the length is equivalent to the equalized round trip delay. As each register consists of 8 bit, OLT can identify which ONU sends what type of cell (ranging cell, user cell, idle cell, and mini-slot). Also, TC chip is designed to calculate the effective bandwidth for all ONUs by using the function of predictor. With the time simulation and the measurement of an implemented optical board, we verify the operation of the predictor.

• Journal of Technologic Dentistry
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• v.17 no.1
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• pp.26-40
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• 1995

A survey has been performed for the dental technicians and office workers in small cities of Julla buk-do on theis problem of cough, phlegm, wheezing, nasal cattarrh & cold, and breathlessness by using SUN-81-AL survey form which is a guletionaire on respiratory symptons The results of the analysis are as follaus. 1. The complaint on cough was made by 15 dental technicians(21.4% and by 10 office workerr(16.7%). Dental technicians showed higher complaint on cough than office workers. The predietor variable for cough was the working hours for dental technicians and the period of smoking for the office sorkess. 2. The complaints on phelgm was made by 34 dental technicians(48.6%) and by 9 office workers(15.0%). The predictor variable on phelgm was the working hour for dental techniume and the period of smoking for the office workers. There was no statistically significant difference between two group on their complaint level. 3. The complaint on the breathlessnesr was made by 24 dental technicians(34.3%) and by 22 office workers(36.7%). The predictor variable on breathlessness was the period of smoking for dental technicians and the working hour for office workers. 4. The wmplaing on nasal catarrh & cold wax made by 29 dental technicians(41.4%) and 22 office workers(36.7%). The predictr variable on nasal catarrh & cold was the working hour for dental workerr, and the perird of smoking for the office workers. 5. The complaint on wheezing was made by 9 dental technicians(12.9%) and 8 office workers(13.3%). The primary predictor variable on wheezing was the working hour for both groups, and the secondary predictor variable was the period of smoking. 6. The complaint on the chest and lung dislase was made by 12 dental technicians(17.1%) and 4 office workers(6.7%) dental technicians showed bigher complaint. on chest and lung disease than the office workers. Bronchitis was the higher frequency illuess reported from both of the groups among chest and lung disease. 7. In conclusion, the predictor variable on respiratory illness was the working hour for dental techniciane, and the period of smoking for the office workers 8. 25 dental technicians(35.7%) and 9 office workers(15.0%) selected air pllution as the most urgent problem that working environment, has had. There was statistically significant difference between two groups(P<0.001)