• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.

• Sleep Medicine and Psychophysiology
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• v.24 no.1
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• pp.46-54
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• 2017

Objectives: Domestic violence is related to many psychiatric diseases, such as depression, anxiety disorder, and PTSD. Heart rate variability (HRV) is an index of autonomic control of the heart and is related to cardiovascular and emotional disorders. Although there have been some studies on the effects of domestic violence on women's mental health, relatively little information is available on HRV in this population. The aim of this study is to investigate demographic data, psychological features, and HRV in female victims of domestic violence and difference between Korean and foreign female victims. Methods: A total of 210 female victims of domestic violence (166 Korean women and 44 foreign women) were recruited for this study. Psychological symptoms were measured using the Hamilton Rating Scale for Anxiety (HAM-A), Hamilton Rating Scale for Depression (HAM-D), and Impact of Event Scale-Revised (IES-R). HRV measures were assessed by time-domain and frequency-domain analyses. Results: The mean score of HAM-A was 13.81, that of HAM-D was 12.92, and that of IES-R was 33.61 ; there were no significant differences between Korean and foreign women in these measures. In HRV time domain analyses, approximate entropy (ApEn) was significantly increased in foreign women compared to the Korean women. The square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD) was significantly decreased in foreign women compared to Korean women. There were no significant differences in the other HRV variables between Korean and foreign women. Conclusion: Female victims of domestic violence in Korea are associated with depression, anxiety, and PTSD symptoms. The physiologic factors of a female victim's nationality could be related to higher ApEn and lower RMSSD in foreign female victims. These findings have important implications for future study to study the relationships among ethnic and environmental factors and HRV variables.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of The Korean Association For Science Education
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• v.40 no.5
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• pp.515-529
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• 2020

'Scientific inquiry experiments', which was newly created subjects in the 2015 revised curriculum, was expected in the aspect of learning science and developing core competences through science practices. Based on changed view of evaluation, assessments of a practice-centered subject 'Scientific inquiry experiments' should be try to conducted in various ways, but many challenges were reported. In this study, through analysis of current status of assessment of the subject, we intended to find the way of conducting and supporting 'Scientific inquiry experiments'. We collected assessment materials and explanatory description about them from 25 teachers who taught 'Scientific inquiry experiments' in 2018 and 2019. And we analyzed the cases with framework which were consisted with three main categories: elements, standards, methods of assessments. Also, we investigated how the results of assessment were utilized. For the validity, we requested verification of the results of our data analysis to experts of science education and science teachers. From them, we also collected their opinions about our analysis. As a result of the study, teachers assessed some elements of inquiry skills such as 'analysis and interpreting the data', 'conducting inquiry' more than others which were closely related to what subject-matter the teachers used to organized inquiry program with. In the aspect of domain of assessments, though cognitive domain and affective domain as well as skills were evaluated, we also found that the assessment of those domains had some limitation. In terms of standard of assessment, the goals of assessment were presented in most cases, but there were relatively few cases which had the specific criteria and the stepwise statements of expected performance of students. The time and subject of the assessment were mainly post-class and teachers, and others such as in-class assessments, peer-assessments were used only in specific contexts. In all cases, the results of assessments used for calculating students' grade, but in some cases, we could observe that the results used for improving teaching and feedback for students. Based on these results, we discussed how to support the assessments of 'Scientific inquiry experiments'.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.1
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• pp.36-41
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• 2013

In this paper time series wave data are simulated using wave spectrum with random phases of the wave signal. The simulated wave signals are used to study the effect of the sampling rate on the ocean wave characteristics. Effect of sampling rate on wave data which include extreme wave such as freak waves are examined and various wave characteristics including abnormality index (AI), kurtosis of wave profile and maximum wave height are examined. Various wave heights are decreased as the sampling rate decreases. The zero-th moment of the wave spectrum does not affect much on the sampling rate but the second moment are greately affected on the sampling rate. The error due to the sampling rate is decreases as the wave period increases. The error in significant wave height based on the wave spectrum $H_s$ is smaller than that on the time domain method $H_{1/3}$. AI index and kurtosis of wave profile do not deviate much from the exact date as long as the sampling rate is greater than 1 Hz. Ocean wave measurement with the sampling frequency higher than 1 Hz will result the error less than 5% in estimating the height of extreme waves.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.744-750
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• 2013

This study is to collect a basic data of how Cardiopulmonary Resuscitation (CPR) procedure can influence to cardiac arrest patient with and without the Depth Device during the average transport time period. The data has achieved by comparing result sheet of CPR procedure by hands only versus with Depth Device by twenty 1st and 2nd class Emergency Medical Technician (EMT) from five different fire stations in city of Chong-Ju, and twenty Emergency Rescue major students who completed the BLS provide course. The experiment participators experienced loss of compression depth and rate increase over time. However, the CPR procedure with Depth Device shows that both EMT and students to allow maintaining both the compression depth and rate. The experiment leaves a positive result for CPR operators and considers being valuable domain for cardiac arrest patient.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.15-23
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• 2016

To support the telematics and infotainment services, vehicle-to-everything (V2X) communication requires a robust and reliable network. To do this, the 3rd Generation Partnership Project (3GPP) has recently developed V2X communication. For reliable communication, accurate channel estimation should be done. However, because vehicle speed is very fast, radio channel is rapidly changed with time. Therefore, it is difficult to accurately estimate the channel. In this paper, we propose the new linear minimum mean square error (LMMSE) channel interpolation scheme based on the Long Term Evolution (LTE) sidelink system in vehicle-to-vehicle (V2V) environments. In our proposed reduced decision error (RDE) channel estimation scheme, LMMSE channel estimation is applied in the pilot symbol, and then in the data symbol, smoothing and LMMSE channel interpolation scheme is applied. After that, time and frequency domain averaging are applied to obtain the whole channel frequency response. In addition, the LMMSE equalizer of the receiver side can reduce the error propagation due to the decision error. Therefore, it is possible to detect the reliable data. Analysis and simulation results demonstrate that the proposed scheme outperforms currently conventional schemes in normalized mean square error (NMSE) and bit error rate (BER).

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.4
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• pp.398-405
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• 2001

The rivet joint has typical structural feature that can be initiation site for the fatigue crack due to the combination of local stress concentration around rivet hole and the moisture trapping. From a viewpoint of structural assurance, it is crucial to evaluate the size of crack around the rivet holes by appropriate nondestructive evaluation techniques. Lamb wave that is one of guided waves, offers a more efficient tool for nondestructive inspection of plates. The neural network that is considered to be the most suitable for pattern recognition has been used by researchers in NDE field to classify different types of flaws and flaw sizes. In this study, clack size evaluation around the rivet hole using the neural network based on the back-propagation algorithm has been tarried out by extracting some features from the ultrasonic Lamb wave for A12024-T3 skin panel of aircraft. Special attention was paid to reduce the coupling effect between the transducer and the specimen by extracting some features related to time md frequency component data in ultrasonic waveform. It was demonstrated clearly that features extracted from the time and frequency domain data of Lamb wave signal were very useful to determine crack size initiated from rivet hole through neural network.

• Journal of Society of Occupational Therapy for the Aged and Dementia
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• v.12 no.2
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• pp.57-65
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• 2018

Objective : The purpose of this study was to compare the demographic characteristics, health and quality of life between general adults and adults living with dementia. Method : The data were collected using raw data of the 2016 community health survey and compared between 2,592 adults living with dementia patients and 225,840 general adults. health were assessed for sleep time, stress level, depression, and subjective health status, and quality of life was measured by EQ-5D. Result : In comparison of demographic characteristics, age and family number of adults living with dementia patients were significantly higher than general adults (p<.001), income and eduation levels were low (p<.001), and marital status was higher rate of living with spouse (p<.05). In comparison of health status, adults living with dementia patients were significantly longer in sleep time than the general adults (p<.001), and stress level was higher (p<.001), the percentage of experience of depression was higher (p<.001), and the subjective health status was worse (p<.001). Adults living with dementia patients were significantly lower in quality of life total score and all sub-domain than general adults (p<.001). Conclusion : Based on the results of this study, it is necessary to seek ways to improve the health and quality of life of dementia patients' families.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.222-234
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• 2021

Airborne-pests can be introduced into Korea from overseas areas by wind, which can cause considerable damage to major crops. Meteorological models have been used to estimate the wind trajectories of airborne insects. The objective of this study is to analyze the effect of input settings on the prediction of areas where airborne pests arrive by wind. The wind trajectories were predicted using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The HYSPLIT model was used to track the wind dispersal path of particles under the assumption that brown plant hopper (Nilaparvata lugens) was introduced into Korea from sites where the pest was reported in China. Meteorological input data including instantaneous and average wind speed were generated using meso-scale numerical weather model outputs for the domain where China, Korea, and Japan were included. In addition, the calculation time intervals were set to 1, 30, and 60 minutes for the wind trajectory calculation during early June in 2019 and 2020. It was found that the use of instantaneous and average wind speed data resulted in a considerably large difference between the arrival areas of airborne pests. In contrast, the spatial distribution of arrival areas had a relatively high degree of similarity when the time intervals were set to be 1 minute. Furthermore, these dispersal patterns predicted using the instantaneous wind speed were similar to the regions where the given pest was observed in Korea. These results suggest that the impact assessment of input settings on wind trajectory prediction would be needed to improve the reliability of an approach to predict regions where airborne-pest could be introduced.