• Korean Journal of Forensic Psychology
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• v.12 no.2
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• pp.121-149
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• 2021

Statement Validity Analysis (SVA) is utilized in criminal investigations and the court to assess the credibility of given statements. During this procedure, the criteria for Criteria-Based Content Analysis (CBCA) are used to evaluate whether statements include the characteristics reflecting actual experiences about the event in question. Various studies had been conducted on the efficacy (classification rates) of CBCA criteria, yet the consistency of the findings was not investigated. In the current study, a meta-analysis was conducted with Korean CBCA studies reported from 2004 to 2020 (a total of fourteen studies). As a result, the total score of CBCA was found to successfully discriminate truth and fabrication. A significant positive (+) effect size was found with four criteria (3, 4, 10, and 12), all of which are classified as cognitive criteria. However, contrary to the underlying assumption for CBCA, criterion 18, classified as one of the motivational criteria, showed a significant negative (-) effect size. Meanwhile, moderator analyses were possible for eleven criteria (2~9, 12, 13, 15) and the results showed the significant effects of potential moderator variables such as the gender and status of the participants, study types and designs, number of raters, and publication status. The current results suggests that more careful attention is required to each criterion-especially the cognitive criteria-rather than the total CBCA score as well as the possible moderator effects in order to assess truthfulness of the statements. The implication, limitations, and suggestions for future studies were discussed.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.11-22
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• 2023

This study was established to provide basic information necessary for ecological management to restore the naturalness of black locust (Robinia pseudoacacia L.) plantations located in the mountains of Gyeongbuk, Korea. Using vegetation data collected from 200 black locust stands, vegetation types were classified using the TWINSPAN method, the spatial arrangement status according to the environmental gradient was identified through DCA analysis, and a synoptic table of communities was prepared based on the diagnostic species determined by determining community fidelity (Φ) for each vegetation type. The vegetation types were classified into seven types, namely, Quercus mongolica-Polygonatum odoratum var. pluriflorum type, Castanea crenata-Smilax china type, Clematis apiifolia-Lonicera japonica type, Rosa multiflora-Artemisia indica type, Quercus variabilis-Lindera glauca type, Ulmus parvifolia-Celtis sinensis type, and Prunus padus-Celastrus flagellaris type. These types usually reflected differences in complex factors such as altitude, moisture regime, successional stage, and disturbance regime. The mean relative importance value of the constituent species was highest for black locust(39.7), but oaks such as Quercus variabilis, Q. serrata, Q. mongolica, Q. acutissima, and Q. aliena were also identified as important constituent species with high relative importance values, indicating their potential for successional trends. In addition, the total percent cover of constituent species by vegetation type, life form composition, species diversity index, and indicator species were compared.

• 한국노년학
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• v.30 no.3
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• pp.779-791
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• 2010

This study was to examine the differential impacts of social experiences and conditions on health among men and women aged 65 years or older, using data of the "2004 Survey on living Status of the Korean Elderly". The outcome variables were any disability, self-rated health, multiple morbidity, and self-rated quality of life. Multiple Classification Analysis was used to test the differential exposure to social factors contributes to gender difference in health. Gender differences in vulnerability of each individual socioeconomic, psycho-social, and behavioral factors for health were assessed by comparing logit coefficients in men and women. I found that gender difference in exposure to social factors contribute to inequalities in health between older men and women, however, gender inequalities remained after controlling for differential exposure except in case of quality of life. In addition, gender differences in health were further explained by differential vulnerabilities to social factors between men and women. Findings of this study may affirm the importance of further and deeper investigation of gender differences in health in later life. Gender sensitive approach in health planning and polices for the elderly is also suggested.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Korean Society of Forest Science
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• v.86 no.1
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• pp.105-111
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• 1997

Root dynamics of a pine stand at Hongcheon, Korea was assayed with two kinds of soil samplers which had been tentatively manufactured to renovate the routine soil sampler, Oakfield soil sampler. Root-mixed soil samples were collected on December of 1995, March, May, August and December of 1996 within each randomly selected 8 plots. The amount of roots collected by the two kinds of soil samplers were not significantly different at the 5% level, which indicated that the renovated sampler was more desirable to be used since the sampler showed efficiencies in time for collection and quantification than the routine sampler. The quantities of total root in 100g soils were 469mg on December of 1995, and 352mg, 473mg, 461mg, 522mg on the following March, May, August and December, respectively. That is, total amount of roots showed the smallest in Spring and reached maximum in early Winter, although the differences were not significant among each season. By the way, the alive roots and dead roots showed significant differences among season, the alive roots took about 90% from May to early December while they decreased down to some 65% from late December to March. The roots of Pinus densiflora S. et Z. took about 46% of total roots although the species comprised 70% of crown layer, and the ratio of fine-roots of the species were higher than that of other species. By the way, the dynamics of total roots and that of alive roots were quite different. Thus, the study for root dynamics such as fine roots which take a major role for mycorrhizae formation or nutrient uptake should not be inferred from the data of total root dynamics but be investigated in detail by dividing them into each class.

• The Journal of Korean Academy of Prosthodontics
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• v.54 no.3
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• pp.239-245
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• 2016

Purpose: The purpose of this study was to identify clinical complications in removable partial denture (RPD) with implant-supported surveyed prostheses, and to analyze the factors associated with the complications such as location of the implant, splinting adjacent prostheses, the type of retentive clasps, Kennedy classification, and opposing dentition. Materials and Methods: A retrospective clinical study was carried out for 11 patients (7 male, 4 female), mean age of 67.5, who received RPD with Implant-supported surveyed prostheses between 2000 and 2016. The mechanical complications of 11 RPDs and 37 supporting implant prostheses and the state of natural teeth and peripheral soft tissue were examined. Then the factors associated with the complications were analyzed. Results: The average of 3.4 implant-supported prostheses were used for each RPD. Complications found during the follow-up period of an average of 42.1 months were in order of dislodgement of temporary cement-retained prostheses, opposing tooth fracture/mobility, screw fracture/loosening, clasp loosening, veneer porcelain fracture, marginal bone resorption and mobility of implant, artificial tooth fracture. Complications occurred more frequently in anterior region compared to posterior region, non-splinted prostheses compared to splinted prostheses, surveyed prostheses applied by wrought wire clasp compared to other clasps, and natural dentition compared to other removable prostheses as opposing dentition. There were no significant differences in complications according to the Kennedy classification. Conclusion: All implant-assisted RPD functioned successfully throughout the follow-up. However, further clinical studies are necessary because the clinical evidences are still not enough to guarantee the satisfactory prognosis of implant-assisted RPD for long-term result.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1043-1060
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• 2023

An acceleration of climate change in recent years has led to increased attention towards 'blue carbon' which refers to the carbon captured by the ocean. However, our comprehension of marine ecosystems is still incomplete. This study classified and analyzed global marine eco-provinces using k-means clustering considering carbon cycling. We utilized five input variables during the past 20 years (2001-2020): Carbon-based Productivity Model (CbPM) Net Primary Production (NPP), particulate inorganic and organic carbon (PIC and POC), sea surface salinity (SSS), and sea surface temperature (SST). A total of nine eco-provinces were classified through an optimization process, and the spatial distribution and environmental characteristics of each province were analyzed. Among them, five provinces showed characteristics of open oceans, while four provinces reflected characteristics of coastal and high-latitude regions. Furthermore, a qualitative comparison was conducted with previous studies regarding marine ecological zones to provide a detailed analysis of the features of nine eco-provinces considering carbon cycling. Finally, we examined the changes in nine eco-provinces for four periods in the past (2001-2005, 2006-2010, 2011-2015, and 2016-2020). Rapid changes in coastal ecosystems were observed, and especially, significant decreases in the eco-provinces having higher productivity by large freshwater inflow were identified. Our findings can serve as valuable reference material for marine ecosystem classification and coastal management, with consideration of carbon cycling and ongoing climate changes. The findings can also be employed in the development of guidelines for the systematic management of vulnerable coastal regions to climate change.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Journal of Korean Academy of Nursing Administration
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• v.1 no.2
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• pp.388-407
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• 1995

This study is (a) to describe the history of Total Quality Management (TQM) generated in the industry, health care service, and nursing society ; (b) to define the concept, total quality management including the definition of quality ; (C) to explain the each principle of TQM theory developed by main theorists, E. Deming, J. Juran, and B. Crosby ; (d) to give the examples related to TQM implementation at the health care organization ; and (e) to mention the extent to which the health care organizations are able to evaluate their cultural organization toward TQM and have had the way to measure the effect of TQM implementation. TQM referred to Continuous Quality Improvement(CQI), Quality Improvement(QI), and Total Quality Improvement(TQI), was not recognized by experts in the United States industry, but by economists in Japan until the end of the 1970's. However, the United States' government led to introduce the principles of TQM to general industry as well as health care service area so that TQM became a main philosophy to manage the organizations in health care service. TQM is a structured, systematic process for creating organization-wide participation in planning and implementing continuous improvement in quality. E. Deming established the "Chain reaction in Quality" and the fourteen point of TQM. The Chain reaction in quality is to describe the relationship among the reduction of waste, rework, and delay, quality improvement, customer satisfaction, and productivity. There are fourteen points to explain the principles of TQM by E. Deming. Juran defined the "Quality Trilogy" to improve the level of quality in any organization. Quality Trilogy has three steps such as quality planning, quality control, and quality improvement for implementing the TQM projects. Crosby describes his TQM theory by establishing "Four Absolutes" and "Fourteen steps in TQM" implementation. Until now, most healthcare organizations have made efforts to organize the TQM task team and to implement TQM principles with various issues. There are three priorities to select the TQM issues : High-volume, High-risk, and Problem-prone. However, there is no absolute, credible measurement yet to evaluate the effects of TQM implementation in health care organization regardless of the classification of health care organizations, geographical background, and social influence. Thus, developing the evaluation way in terms of TQM is the foremost task in health service area. The most important thing for TQM implementation in the organization is to settle up the concept, cultural transformation from traditional management toward quality.

• Geophysics and Geophysical Exploration
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• v.23 no.4
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• pp.230-242
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• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.