• Journal of IKEEE
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• v.23 no.4
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• pp.1122-1127
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• 2019

In this study, the bio-impedance of the human body is able to obtain a lot of information by monitoring the pathological and physiological conditions of clinical and biological tissues. The four electrode method system for biometrics measured the potential difference between two electrodes and the other two electrodes were used as electrodes for current flow. The newly developed dry gold electrode measured impedance from 1 Hz to 50 kHz and produced reproducible results. To verify the impedance measurement of the dry electrode, the pitting was performed using an equivalent circuit model of the bioelectrode skin, and the effectiveness was demonstrated through modeling. Fixed electrode types have a constant position of the electrodes attached during the measurement, so that a stable measurement can be obtained, thereby minimizing the error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1236-1243
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• 2010

The perimeter defense system was created and its characteristics were evaluated. It was designed to utilize the fiber sensing device, namely OTDR(Optical Time Domain Reflectometer) which has been used for the maintenance of the optical communication network. An OTDR was constituted by a pulse laser with the nature of 1310nm, +15dBm for the observation of 400 meter optical fence. The high-speed 32-bit processor(S3C2440) has applied to MPU(Main Processor Unit) which helps to improve the performance of OTDR algorithms. Consequently, the maximum error was 0.84 meter on the performance test of the 10km monitoring and the pass criteria of ${\pm}1m$ satisfied in all the sections. The alarm delay time was under 3 sec after detecting the disorder. For the case of secondary trespassing after primary trespassing, the optical switch was installed in OTDR to monitor the secondary trespassing and to measure the multi-point detection. Therefore, this paper shows that the detections of secondary trespassing and multi-point is possible by means of optical switch.

• Journal of Navigation and Port Research
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• v.33 no.1
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• pp.43-50
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• 2009

Recently, many maritime accidents have been increased and the collisions due to human error are given a great deal of proportions out of them We develop the Real-time Collision Risk Monitoring System (CRMS) for the navigational officers to cope with the emergency situation promptly and thus to reduce the probability of casualty. In this study, the risk of collision and grounding is evaluated by two kinds of method. The first method is based on Fuzzy algorithm, which evaluates the risk of collision between traffic ships. The second method is based on Environmental Stress (ES) Model, where the total risk of collision and grounding is evaluated by the environmental stress felt by human. The developed real-time CRMS has been installed to the ship handling simulator system and its capabilities have been tested through simulator experiments.

• Health Policy and Management
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• v.13 no.1
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• pp.98-115
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• 2003

Concerns about growing health insurance expenditures became a national Issue in 2001 when the National Health Insurance went into a deficit. Increases in spending for ambulatory care shared the largest portion of the problem. Methods and systems to control the spending should be developed and a system to measure case mix of providers is one of core components of the control system. The objectives of this article is to examine the feasibility of applying Korean Diagnosis Related Groups (KDRGs) to classify health insurance claims for ambulatory care and to identify problem areas of the classification. A database of 11,586,270 claims for ambulatory care delivered during January 2002 was obtained for the study, and the final number of claims analyzed was 8,319,494 after KDRG numbers were assigned to the data and records with an error KDRG were excluded from the study. The unit of analysis was a claim and resource use was measured by the sum of charges incurred during a month at a department of a hospital of at a clinic. Within group variance was assessed by th coefficient of variation (CV), and the classification accuracy was evaluated by the variance reduction achieved by the KDRG classification. The analyses were performed on both all and non-outlier data, and on a subset of the database to examine the validity of study results. Data were assigned to 787 KDRGs among 1,244 KDRGs defined in the classification system. For non-outlier data, 77.4％ of KDRGs had a CV of charges from tertiary care hospitals less than 100％ and 95.43％ of KDRGs for data from clinics. The variance reduction achieved by the KDRG classification was 40.80％ for non-outlier claims from tertiary care hospitals, 51.98％ for general hospitals, 40.89% for hospitals, and 54.99% for clinics. Similar results were obtained from the analyses performed on a subset of the study database. The study results indicated that KDRGs developed for a classification of inpatient care could be used for ambulatory care, although there were areas where the classification should be refined. Its power to predict tile resource utilization showed a potential for its application to measure case mix of providers for monitoring and managing delivery of ambulatory care. The issue concerning the quality of diagnostic information contained in insurance claims remains to be improved, and significance of future studies for other classification systems based on visits or episodes is guaranteed.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• Progress in Medical Physics
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• v.17 no.1
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• pp.24-31
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• 2006

Automated analysis software was developed to measure the magnitude of the intrafractional and interfractional errors during breast radiation treatments. Error analysis results are important for determining suitable planning target volumes (PTV) prior to Implementing breast-conserving 3-D conformal radiation treatment (CRT). The electrical portal imaging device (EPID) used for this study was a Portal Vision LC250 liquid-filled ionization detector (fast frame-averaging mode, 1.4 frames per second, 256X256 pixels). Twelve patients were imaged for a minimum of 7 treatment days. During each treatment day, an average of 8 to 9 images per field were acquired (dose rate of 400 MU/minute). We developed automated image analysis software to quantitatively analyze 2,931 images (encompassing 720 measurements). Standard deviations ($\sigma$) of intrafractional (breathing motion) and intefractional (setup uncertainty) errors were calculated. The PTV margin to include the clinical target volume (CTV) with 95% confidence level was calculated as $2\;(1.96\;{\sigma})$. To compensate for intra-fractional error (mainly due to breathing motion) the required PTV margin ranged from 2 mm to 4 mm. However, PTV margins compensating for intefractional error ranged from 7 mm to 31 mm. The total average error observed for 12 patients was 17 mm. The intefractional setup error ranged from 2 to 15 times larger than intrafractional errors associated with breathing motion. Prior to 3-D conformal radiation treatment or IMRT breast treatment, the magnitude of setup errors must be measured and properly incorporated into the PTV. To reduce large PTVs for breast IMRT or 3-D CRT, an image-guided system would be extremely valuable, if not required. EPID systems should incorporate automated analysis software as described in this report to process and take advantage of the large numbers of EPID images available for error analysis which will help Individual clinics arrive at an appropriate PTV for their practice. Such systems can also provide valuable patient monitoring information with minimal effort.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.3
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• pp.178-184
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• 2014

In this study, monitoring method which is more economic and easier in maintenance comparing to existing monitoring system was scrutinized for application to sewer intercepting chamber of 10.2 ha basin area by estimating CSOs (Combined Sewer Overflows) quantity and quality with 2 rainfall events using electrical conductivity data and civil research model. The result showed that determination coefficient of flow estimation by EC (Electrical Conductivity) dilution ratio and observed data was over 0.86 for all cases and the accuracy of estimation was improved from 0.5 to 0.8 for determination coefficient ($R^2$) and from 54.1% to 68.5% for accumulation frequency of relative error by considering antecedent dry days and rainfall duration. CSOs water quality estimation results by civil research model showed that determination coefficients were 0.64~0.97 for BOD and 0.70~0.95 for SS.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.4
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• pp.1-9
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• 2009

In this paper, a small area 12-bit 300MSPS CMOS Digital-to-Analog Converter(DAC) is proposed for display systems. The architecture of the DAC is based on a current steering 6+6 segmented type, which reduces non-linearity error and other secondary effects. In order to improve the linearity and glitch noise, an analog current cell using monitoring bias circuit is designed. For the purpose of reducing chip area and power dissipation, furthermore, a noble self-clocked switching logic is proposed. To verify the performance, it is fabricated with $0.13{\mu}m$ thick-gate 1-poly 6-metal N-well Samsung CMOS technology. The effective chip area is $0.26mm^2$ ($510{\mu}m{\times}510{\mu}m$) with 100mW power consumption. The measured INL (Integrated Non Linearity) and DNL (Differential Non Linearity) are within ${\pm}3LSB$ and ${\pm}1LSB$, respectively. The measured SFDR is about 70dB, when the input frequency is 15MHz at 300MHz clock frequency.

• Journal of Biosystems Engineering
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• v.37 no.4
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• pp.225-232
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• 2012

Purpose: Accurate monitoring of information from various agricultural vehicles is one of the most important factors for appropriate management strategy of field operations. While there has been a number of study and design on applications of sensors and actuators for data acquisition and control system in tractor, incompatibility between various customized hardware and software has become a major obstacle to the universal deployment in real field operation. International standard for implementation of electronic control unit (ECU) in agricultural vehicles has becoming a mandatory requirement for inter-operation compatibility in the international trade of agricultural vehicle industries. The ISO 11783 standard is basically based upon well known communication technology designated using the controller area network (CAN) bus. While CAN bus could provide 1.0 Mbps of communication speed, the standard only recommended 250 kbps. Methods: This study presents the implementation and evaluation of ISO 11783 for tractor electronic control units (TECU)with a higher transmission rate from multiple ECU than 250 kbps. Throughput and loss rate of the developed prototype were calculated across manipulated bus load for laboratory experimental tests, and the maximum requirement of transmission rate by ISO 11873 was satisfied with lower than 60% of bus load. Results: Field tests with a TECU implemented to process messages from global positioning system (GPS) receiver resulted that the root mean square error of position information was lower than 4 m with 0.5 m/s as a travelling speed. Conclusions: Results of this study represent the utilization of the international standard ISO 11783 to providepractical developments in terms with the inter-operability of TECU.