• Journal of Korean Orthopaedic Sports Medicine
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• v.11 no.1
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• pp.44-50
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• 2012

Purpose: The purpose of this study is to analyze the relationship between acute anterior cruciate ligament (ACL) injury and bone bruise using the survey for location and incidence of bone bruise. Materials and Methods: From Jan. 2006 to Feb. 2010, 87 knees from who had complaint a traumatic knee pain were diagnosed as acute ACL tear using MRI evaluation. Associated injury, location and incidence of bone bruise were analyzed using MRI. The location of bone bruise on the MRI was classified as medial, central and lateral area on anteroposterior and lateral view of femur and tibia. The bone bruise was classified with Costa Paz classification. Results: Bone bruise of injury during daily living activity were located at medial area on coronary view and anterior area on sagittal view of distal femur, at medial area on coronary view and anterior area on sagittal view of proximal tibia (p=0.024, p=0.021, p=0.025 and p=0.029, respectively). Bone bruise of injury during sports activity were located at lateral area on coronary view and central area on sagittal view of distal femur, at lateral area on coronary view and posterior area on sagittal view of proximal tibia (p=0.014, p=0.015, p=0.018 and p=0.017, respectively). Bone bruise patterns due to traffic accident were inconclusive (p=0.264, p=0.254, p=0.229 and p=0.267, respectively). Conclusion: Injury mechanism of acute ACL injury from activities of daily living or sports activities compared to that of traffic accident showed a more consistent bone bruise patterns. Special attention to acute ACL tear must be paid in case of bone bruise at lateral tibial plateau and lateral femoral condyle.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.481-495
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• 2022

This study was designed to identify the actual state of naturalized plants and invasive alien species that cause disturbances to the ecosystem, plants which are introduced after forest restoration, and explore the implications resulting from the project. Onsite examination included 29 sites which have been subjected to forest restoration by the Korea Forest Service. Once these were chosen, activity took place twice a year in the spring (May-June) and in the summer (August-September) in 2020 and 2021. Areas not relevant to the project sites were excluded from this activity so that we could identify the plants that could be understood to have been introduced or brought into the site after the actual forest restoration. And the correlation was analyzed, between the naturalized flora within the project sites and the working types applied to the site through confirmation of completion of the restoration project. The naturalized plants appearing on the entire site cover a total of 109 taxa, which includes 29 families, 80 genera, 108 species and 1 subspecies, while invasive plants included 3 families, 7 genera and 8 species. The number of classifications and the naturalization rate gradually decreased over time, after the project. While there was no significant difference between the number of classification groups and the naturalization rate for naturalized plants between project sites, given the number of taxa of naturalized plants, organized by type of damage, there were relatively more naturalized plants that appeared in the severed section of the Baekdudaegan Mountain Range, as well as at quarry and facility sites. Seeding apparently results in naturalization rates as high as 15.545%, on average, based on comparisons of naturalization rates by sowing, seeding, planting, herb planting, and sod pitching channels, all of these being methods of vegetation for planting/greening of bareland and slopes within the project areas. With no seeding, it was 9.167%, higher than the average. As for other vegetation, there was no significant difference depending on application of the working type. This means that unlike the plants subjected to planting, the working type of seed planting which makes it difficult to identify whether a certain plant is a naturalized plant greatly affects the introduction of naturalized plants to the restoration sites, even when using herb planting and sod pitching to control plants and results. Therefore the study suggests that there be inspection by experts of seeds when sowing within restoration sites. The results of this study suggest good practices that will help to direct effective vegetation restoration and follow-up management.

• 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 Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Journal of Korean Orthopaedic Sports Medicine
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• v.9 no.1
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• pp.48-57
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• 2010

Purpose: To compare the incidence and risk factors for osteoarthritis after anterior cruciate ligament (ACL) reconstruction between two groups using bone-patellar tendon-bone (BPTB) and hamstring tendon (HT) autograft. Materials and Methods: 53 cases of ACL reconstruction using patellar tendon and 40 cases using hamstring tendon were followed up at least 8 years. Radiographic evaluation was done according to the Kellgren and Lawrence's classification. Clinical functional testing (Lysholm Knee Scores, the Tegner activity scores) and laxity testing (Lachman, pivot shift tests), and the instrumented laxity testing with $Telos^{(R)}$ were all examined in relation to the development of osteoarthritis. Results: Radiographic osteoarthritic changes were detected in 24 patients (45.3%) in BPTB group and 14 patients (35.0%) in HT group. Accompanying meniscal injury (BPTB p<0.001; HT p=0.091), intervals from the injury to reconstruction of > 12 months (BPTB p=0.037; HT p=0.021), and patient's age at reconstruction of > 25 years (BPTB p=0.003; HT p=0.048) were found to be significant independent predictors of osteoarthritis. However, no statistically significant correlations were found between the development of osteoarthritis and the clinical outcome or the radiographic stability in both groups. Conclusion: Although ACL reconstruction using BPTB or HT autograft had good clinical results at an average follow-up of 10 years, considerable incidence of radiographic osteoarthritic changes were noted. Various factors such as accompanying meniscal injury, protracted time from injury to reconstruction, more than 25 years old at the time of reconstruction were related to radiographic osteoarthritic changes.

• Economic and Environmental Geology
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• v.42 no.6
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• pp.575-589
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• 2009

Groundwater has been extracted for irrigation in Sacheon-Hadong area, which is close to the South Sea. We analyzed chemical components of groundwater to examine the effects of seawater intrusion and agricultural activities in the study area. Most groundwater samples displayed the Na/Cl concentration ratios similar to that of seawater (0.55) with an increasing tendency of electrical conductivity ($227-7,910\;{\mu}S/cm$) towards the coast. In addition, statistical interpretation of the cumulative frequency curves of Cl and $HCO_3$ showed that 30.1% of the groundwater samples were highly affected by seawater intrusion. Groundwaters in the study area mostly belonged to the Ca-Cl and Na-Cl type, demonstrating that they were highly influenced by seawater intrusion and cation exchange. The result of oxygen-hydrogen isotope analysis demonstrated slightly higher $\delta^{18}O$ ((-8.53)-(-6.13)‰) and ${\delta}D$ ((-58.7)-(-43.7)‰) comparing to mean oxygen-hydrogen isotope ratios in Korea. As a result of nitrogen isotope analysis, the $\delta^{15}N-NO_3$ values ((-0.5)-(19.1)‰) indicate two major sources of nitrate pollution (organic nitrogen in soil and animal and human wastes) and mixed source of the two. However, denitrification may partly contribute as a source of nitrogen. According to factor analysis, four factors were identified among which factor 1 with an eigenvalue of 6.21 reflected the influence of seawater intrusion. Cluster analysis indicated the classification of groundwater into fresh, saline, and mixed ones.

• Journal of Home Health Care Nursing
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• v.6
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• pp.33-45
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• 1999

The purpose of this study was to develop a basic framework and criteria for evaluation of quality care provided to patients with the attributes of disease in the home care nursing field, and to provide measurement tools for home health care in the future. The study design was a developmental study for evaluation of hospital-based HCN(home care nursing) in Korea. The study process was as follows: a home care nursing study team of College of Nursing. Yonsei University reviewed the nursing records of 47 patients who were enrolled at Yonsei University Medical Center Home Care Center in March, 1995. Twenty-five patients were insured at that time, were selected from 47 patients receiving home care service for study feasibility with six disease groups; Caesarean Section (C/S), simple nephrectomy, Liver cirrhosis(LC), chronic obstructive pulmonary disease(COPD), Lung cancer or cerebrovascular accident(CVA). In this study, the following items were selected : First step : Preliminary study 1. Criteria and items were selected on the basis of related literature on each disease area. 2. Items were identified by home care nurses. 3. A physician in charge reviewed the criteria and content of selected items. 4. Items were revised through preliminary study offered to both HCN patients and discharged patients from the home care center. Second step : Pretest 1. To verify the content of the items, a pretest was conducted with 18 patients of which there were three patients in each of the six selected disease groups. Third step : Test of reliability and validity of tools 1. Using the collected data from 25 patients with either cis, Simple nephrectomy, LC, COPD, Lung cancer, or CVA. the final items were revised through a panel discussion among experts in medical care who were researchers, doctors, or nurses. 2. Reliability and validity of the completed tool were verified with both inpatients and HCN patients in each of field for researches. The study results are as follows: 1. Standard for discharge with HCN referral The referral standard for home care, which included criteria for discharge with HCN referral and criteria leaving the hospital were established. These were developed through content analysis from the results of an open-ended questionnaire to related doctors concerning characteristic for discharge with HCN referral for each of the disease groups. The final criteria was decided by discussion among the researchers. 2. Instrument for measurement of health statusPatient health status was measured pre and post home care by direct observation and interview with an open-ended questionnaire which consisted of 61 items based on Gorden's nursing diagnosis classification. These included seven items on health knowledge and health management, eight items on nutrition and metabolism, three items on elimination, five items on activity and exercise, seven items on perception and cognition, three items on sleep and rest, three items on self-perception, three items on role and interpersonal relations, five items on sexuality and reproduction, five items on coping and stress, four items on value and religion, three items on family. and three items on facilities and environment. 3. Instrument for measurement of self-care The instrument for self-care measurement was classified with scales according to the attributes of the disease. Each scale measured understanding level and practice level by a Yes or No scale. Understanding level was measured by interview but practice level was measured by both observation and interview. Items for self-care measurement included 14 for patients with a CVA, five for women who had a cis, ten for patients with lung cancer, 12 for patients with COPD, five for patients with a simple nephrectomy, and 11 for patients with LC. 4. Record for follow-up management This included (1) OPD visit sheet, (2) ER visit form, (3) complications problem form, (4) readmission sheet. and (5) visit note for others medical centers which included visit date, reason for visit, patient name, caregivers, sex, age, time and cost required for visit, and traffic expenses, that is, there were open-end items that investigated OPD visits, emergency room visits, the problem and solution of complications, readmissions and visits to other medical institution to measure health problems and expenditures during the follow up period. 5. Instrument to measure patients satisfaction The satisfaction measurement instrument by Reisseer(1975) was referred to for the development of a tool to measure patient home care satisfaction. The instrument was an open-ended questionnaire which consisted of 11 domains; treatment, nursing care, information, time consumption, accessibility, rapidity, treatment skill, service relevance, attitude, satisfaction factors, dissatisfaction factors, overall satisfaction about nursing care, and others. In conclusion, Five evaluation instruments were developed for home care nursing. These were (1)standard for discharge with HCN referral. (2)instrument for measurement of health status, (3)instrument for measurement of self-care. (4)record for follow-up management, and (5)instrument to measure patient satisfaction. Also, the five instruments can be used to evaluate the effectiveness of the service to assure quality. Further research is needed to increase the reliability and validity of instrument through a community-based HCN evaluation.

• Journal of Korean Society of Forest Science
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• v.107 no.1
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• pp.96-107
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• 2018

This study analyzed the linkage between the Forestry sector Official Development Assistance (ODA) Project in South Korea and the Sustainable Development Goals (SDGs) of United Nations (UN), Suggested direction of ODA project focusing on the implementation of the SDGs. Forestry sector ODA project data in South Korea have collected from Economic Development Cooperation Fund (EDCF) statistical inquiry system developed by The Export-Import Bank of Korea. According to the analysis result, Forestry sector ODA project in South Korea have been actively implemented in the fields of forestry development, forestry policy and administration. In both fields, Korea Forest Service and Korea International Cooperation Agency (KOICA) carried out the most projects. The Forestry sector ODA project data in South Korea are classified technical development, capacity building, construction of infrastructure and afforestation based on their objectives and contents. SDGs emphasizes the importance of national implementation assessment and this study analyze linkage between ODA activity content in each classification item and 2016 Korea Forest Service Performance Management Plan indicator. Analyzed the 2016 Korea Forest Service Performance Management Plan indicator and SDGs target and SDGs indicator were identified. finally, SDGs goals were recognized. In conclusion, Forestry sector ODA project in South Korea are associated with the SDGs Goal 1 (No Poverty), Goal 2 (Zero Hunger), Goal 6 (Clean Water and Sanitation), Goal 13 (Climate Action), Goal 15 (Life on Land) and Goal 17 (Partnership for The Goals). Therefore, With the launch of the SDGs, This study analyzed the linkage among the Forestry sector ODA Project in South Korea, the 2016 Korea Forest Service Performance Management Plan and the SDGs. it presented the limitations of Forestry sector ODA Project in South Korea and made proposals for the implementation of the SDGs.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.2
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• pp.192-217
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• 2001

Object：This study was carried out to classify adolescents in runaway shelters by evaluating their psychopathology. And the ultimate purpose is to offer basic data for preventing adolescents‘ runaway and for diversifying runaway shelters suitable for the problem of individual adolescent. Method：128 adolescents who stay in the runaway shelters were asked to complete self-report qeustionnaires including basic sociodemographic data, Child Behavior Check List(CBCL), Minnesota Multiphasic Personality Inventory(MMPI), and Symptom Check List-90-Revised(SCL-90-R). Korean Wechsler Adult Intelligence Scale(K-WAIS)[or Korean Educational Developmental Institute-Wechsler Intelligence Scale for Children(KEDI-WISC)] and Bender-Gestalt test(BGT) were also done by clinical psychologists. Results：The most common age of the subjects were 15-year-old, and they dropped out their schools in the middle school most commonly. Mostly they were from middle class family and their parents' educational level were high school graduates. The first runaway episode was most common in the middleschool period, and their runaways were repeated. The most common frequency of runaways were more than 10 times. About 10% of them abused drugs and about 80% of them abused alcohol. One third of them had experiences of illegal problems and 10% of them engaged in sexual activity for money. 95 adolescents(83%) in CBCL, 42 adolescents(36%) in SCL-90-R, and 70 adolescents(69.3%) in MMPI showed clinical significance. In intelligence test, 22 adolescents(22%) were mentally retarded. In BGT, 35 adolescents(39.4%) manifested brain dysfunction signs. Conclusion：Runaway adolescents in the shelters have variable and severe psychopathology. Their psychopathology is classified as follows；The behavior disorder group, the mood disorder group with anxiety/depression, the somatic disorder group with somatic symptoms, and the psychosis group with possibility of severe psychopathology. Therefore it is very important to evaluate psychiatric problems of runaway adolescents, and specific therapeutic interventions according to their problems are required.