• Corrosion Science and Technology
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• v.15 no.5
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• pp.217-225
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• 2016

Linear polarization resistance (LPR) testing of soils has been used extensively by a number of water utilities across Australia for many years now to determine the condition of buried ferrous water mains. The LPR test itself is a relatively simple, inexpensive test that serves as a substitute for actual exhumation and physical inspection of buried water mains to determine corrosion losses. LPR testing results (and the corresponding pit depth estimates) in combination with proprietary pipe failure algorithms can provideauseful predictive tool in determiningthe current and future conditions of an asset. Anumber of LPR tests have been developed on soil by various researchers over the years1), but few have gained widespread commercial use, partly due to the difficulty in replicating the results. This author developed an electrochemical cell that was suitable for LPR soil testing and utilized this cell to test a series of soil samples obtained through an extensive program of field exhumations. The objective of this testing was to examine the relationship between short-term electrochemical testing and long-term in-situ corrosion of buried water mains, utilizing an LPR test that could be robustly replicated. Forty-one soil samples and related corrosion data were obtained from ad hoc condition assessments of buried water mains located throughout the Hunter region of New South Wales, Australia. Each sample was subjected to the electrochemical test developed by the author, and the resulting polarization data were compared with long-term pitting data obtained from each water main. The results of this testing program enabled the author to undertake a comprehensive review of the LPR technique as it is applied to soils and to examine whether correlations can be made between LPR testing results and long-term field corrosion.

• Korean Journal of Community Nutrition
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• v.7 no.4
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• pp.495-505
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• 2002

A study was performed to develop as a screening tool the Simple Evaluation Questionnaire for Screening the Over-weight-type Dietary Pattern in 30 to 49 Year Old Adults. We used the data from the 30 to 49 year old subjects who participated in the three surveys - the health behavior survey, the dietary habit survey and the food intake survey - as the National Health and Nutrition Survey 1998. The 3,598 adults were classified into to two body fatness groups of normal (including underweight) and overweight (including obese) on the basis of their relative body weight (RBW) When comparing variables between the two groups, significant differences were found in gender, education, job, employment status, perceived health status, sadness / depression state, stress level, age, number of diseases, age when overweightedness started, maximum body weight, sleep length, drinking pattern (yes/no) , amount of alcoholic drinks, frequency of intoxication or drunkeness, amount of alcoholic drinks when drunk, intensity of exercise, frequency of exercise, exercise duration, skipped meals, small meals and drug supplements. In terms of food intake, there were significant differences in the daily food intake in terms of breakfast, dinner, daily kimchi and dairy products. In terms of mealtimes, we found differences in the amount of cooked rice at breakfast, kimchi at lunch, soup / kuk at dinner, fresh vegetables for snacks, fried foods for snacks between breakfast and lunch, and fruits /juices for snacks between lunch and dinner. After developing questions with indicators and analyzing the indicators by logistic regression analysis three times, we chose 10 questions for a simple evaluation of dietary patterns for the overweight-type category in order to give one point each. Among them we selected two questions to add one additional point and one question to add two additional points. The average scores of the overweight and normal groups, as shown by the developed questionnaire, were $5.97 \pm 2.36 \pm 7.36 \pm 2.21$, respectively. A score of seven points was selected as the cut-off point. We examined the sensitivity, specificity and positive predictive value of the questionnaire to the results of 67%, 59% and 62%, respectively.

• The korean journal of orthodontics
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• v.46 no.5
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• pp.331-342
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• 2016

Objective: To evaluate the accuracy and reliability of the diagnostic tools available for assessing maxillary transverse deficiencies. Methods: An electronic search of three databases was performed from their date of establishment to April 2015, with manual searching of reference lists of relevant articles. Articles were considered for inclusion if they reported the accuracy or reliability of a diagnostic method or evaluation technique for maxillary transverse dimensions in mixed or permanent dentitions. Risk of bias was assessed in the included articles, using the Quality Assessment of Diagnostic Accuracy Studies tool-2. Results: Nine articles were selected. The studies were heterogeneous, with moderate to low methodological quality, and all had a high risk of bias. Four suggested that the use of arch width prediction indices with dental cast measurements is unreliable for use in diagnosis. Frontal cephalograms derived from cone-beam computed tomography (CBCT) images were reportedly more reliable for assessing intermaxillary transverse discrepancies than posteroanterior cephalograms. Two studies proposed new three-dimensional transverse analyses with CBCT images that were reportedly reliable, but have not been validated for clinical sensitivity or specificity. No studies reported sensitivity, specificity, positive or negative predictive values or likelihood ratios, or ROC curves of the methods for the diagnosis of transverse deficiencies. Conclusions: Current evidence does not enable solid conclusions to be drawn, owing to a lack of reliable high quality diagnostic studies evaluating maxillary transverse deficiencies. CBCT images are reportedly more reliable for diagnosis, but further validation is required to confirm CBCT's accuracy and diagnostic superiority.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.6
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• pp.132-141
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• 2013

Robotic Drilling Systems(RDSs) set the standard for the factory automation systems in aerospace manufacturing. With the benefits of cost effective drilling and predictive maintenance, RDSs can provide greater flexibility in the manufacturing process. The system can be easily adopted to manage very complex and time-consuming processes, such as automated fastening hole drilling processes of large aircraft sections, where it would be difficult accomplished by workers following teaching or conventional guided methods. However, in order to build an RDS based on a CAD model, the precise calibration of the Tool Center Point(TCP) must be performed in order to define the relationships between the fastening-hole target and the End Effector(EEF). Based on the kinematics principle, the robot manipulator requires a new method to correct the 3D errors between the CAD model of the reference coordinate system and the actual measurements. The system can be called as a successful system if following conditions can be met; a. seamless integration of the industrial robot controller and the IO Level communication, b. performing pre-defined drilling procedures automatically. This study focuses on implementing a new technology called iGPS into the fastening-hole-drilling process, which is a critical process in aircraft manufacturing. The proposed system exhibits better than 100-micron 3D accuracy under the predefined working space. Based on the proposed EEF fastening-hole machining process, the corresponding processes and programs are developed, and its feasibility is studied.

• Resources Recycling
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• v.26 no.4
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• pp.56-61
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• 2017

This study investigated the feasibility of a statistical approach for soil recycling through the prediction of BaP, DahA and total PAH (${\Sigma}PAH$) concentrations from BaA concentration. As results of regression, excellent linear correlations ($R^2$ > 0.90) were observed between BaA and BaP (or DahA) concentrations. When a developed prediction equation was applied to other investigations as a validation study, good prediction results were obtained. The predictive model showed very good correlation between the measured and calculated BaP. From this equation, BaA was an apparently important hydrocarbon for the prediction of PAHs. This model might provide a potentially useful tool for the calculation of average BaP, DahA and ${\Sigma}PAH$ without additional tests.

• Journal of The Korean Society of Emergency Medicine
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• v.26 no.1
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• pp.29-37
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• 2015

Purpose: Stool exams are a useful tool for the early presumptive diagnosis of infectious bacterial diarrhea in the Emergency Department (ED). CT scans are often used to increase the physician's level of certainty and to facilitate patient triage by identifying the source of pain in most patients with an acute abdomen in the ED. This study was designed to investigate the correlation between stool exams and abdominal CT in patients with acute diarrhea visiting the ED. Methods: We conducted a retrospective study in the emergency department of a national university hospital from January 1, 2012 to June 30, 2013. The subjects consisted of 156 patients with acute diarrhea and abdominal pain who had stool exam results and abdominal CT findings. We divided the patients into three groups according to the stool exam results. Simultaneously, we evaluated their CT findings of the bowel and adjacent structures. Results: A total of 156 patients were enrolled. Frequency of abnormal CT findings showed statistically significant correlation with stool exams (p-value <0.001). Abnormal CT findings increased as WBCs and RBCs in stool increased (p-value <0.001). Conclusion: The stool exam was a statistically significant predictive variable in indirectly determining the severity of acute diarrhea and it showed correlation with the frequency of abnormal CT findings. It is suggested that stool exams can be used as a susceptible marker for predicting the probability of severe infectious colitis, and for making an early decision regarding close medical attention.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.462-471
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• 2021

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• The Journal of Information Systems
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• v.28 no.3
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• pp.249-276
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• 2019

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.323-330
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• 2020

Purpose: The mastoid region is ideal for studying sexual dimorphism due to its anatomical position at the base of the skull. This study aimed to determine sex in the Iranian population based on measurements of the mastoid process using different data mining algorithms. Materials and Methods: This retrospective study was conducted on 190 3-dimensional cone-beam computed tomographic (CBCT) images of 105 women and 85 men between the ages of 18 and 70 years. On each CBCT scan, the following 9 landmarks were measured: the distance between the porion and the mastoidale; the mastoid length, height, and width; the distance between the mastoidale and the mastoid incision; the intermastoid distance (IMD); the distance between the lowest point of the mastoid triangle and the most prominent convex surface of the mastoid (MF); the distance between the most prominent convex mastoid point (IMSLD); and the intersecting angle drawn from the most prominent right and left mastoid point (MMCA). Several predictive models were constructed and their accuracy was compared using cross-validation. Results: The results of the t-test revealed a statistically significant difference between the sexes in all variables except MF and MMCA. The random forest model, with an accuracy of 97.0%, had the best performance in predicting sex. The IMSLD and IMD made the largest contributions to predicting sex, while the MMCA variable had the least significant role. Conclusion: These results show the possibility of developing an accurate tool using data mining algorithms for sex determination in the forensic framework.