• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.1
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• pp.1-16
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• 2024

Alvin Toffler mentioned that it is important for future society to keep pace with synchronization and that time deviations can hinder social development. As we experience the new normal era of untact, we have experienced an increase in non-face-to-face contact and accelerated digital transformation. Amid these rapid changes, we can maintain the need for synchronization or change in space. Therefore, we would like to study what kind of settlements people create and choose. We looked at the metaverse as an object that could indirectly find out about this, and used the content called "Animal Crossing" to collect data related to the spatial form of the metaverse. Sampling utilized a judgment sampling method during non-probability sampling to alleviate differences due to the progress of the game. The collected data was classified according to floor plan and location type and briefly organized through descriptive statistics. After matching each facility by use, data was constructed by setting coordinates for each cluster and listing them. This data was interpreted graphically on the coordinate plane for each cluster, and Euclidean analysis was performed to analyze the relationships between clusters and residential choice using a Euclidean matrix. As a result of the analysis, it could be interpreted that efficiency was pursued by arranging similar functions in close proximity. Nevertheless, when choosing a residence, it was interpreted that the intention was to create a community through arrangement adjacent to residents rather than efficiency or convenience. Due to the differences between the metaverse and the real world, it is expected that there will be limitations in equating it with reality. However, through the space expressed in the virtual world by people who are far away from the constraints of reality, we can indirectly know the wishes that we have not been able to express due to our lack of awareness.

• Korean Journal of Radiology
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• v.24 no.9
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• pp.860-870
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• 2023

Objective: The intra-parotid facial nerve (FN) can be visualized using three-dimensional double-echo steady-state water-excitation sequence magnetic resonance imaging (3D-DESS-WE-MRI). However, the clinical impact of FN imaging using 3D-DESS-WE-MRI before parotidectomy has not yet been explored. We compared the clinical outcomes of parotidectomy in patients with and without preoperative 3D-DESS-WE-MRI. Materials and Methods: This prospective, non-randomized, single-institution study included 296 adult patients who underwent parotidectomy for parotid tumors, excluding superficial and mobile tumors. Preoperative evaluation with 3D-DESS-WE-MRI was performed in 122 patients, and not performed in 174 patients. FN visibility and tumor location relative to FN on 3D-DESS-WE-MRI were evaluated in 120 patients. Rates of FN palsy (FNP) and operation times were compared between patients with and without 3D-DESS-WE-MRI; propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to adjust for surgical and tumor factors. Results: The main trunk, temporofacial branch, and cervicofacial branch of the intra-parotid FN were identified using 3D-DESS-WE-MRI in approximately 97.5% (117/120), 44.2% (53/120), and 25.0% (30/120) of cases, respectively. The tumor location relative to FN, as assessed on magnetic resonance imaging, concurred with surgical findings in 90.8% (109/120) of cases. Rates of temporary and permanent FNP did not vary between patients with and without 3D-DESS-WE-MRI according to PSM (odds ratio, 2.29 [95% confidence interval {CI} 0.64-8.25] and 2.02 [95% CI: 0.32-12.90], respectively) and IPTW (odds ratio, 1.76 [95% CI: 0.19-16.75] and 1.94 [95% CI: 0.20-18.49], respectively). Conversely, operation time for surgical identification of FN was significantly shorter with 3D-DESS-WE-MRI (median, 25 vs. 35 min for PSM and 25 vs. 30 min for IPTW, P < 0.001). Conclusion: Preoperative FN imaging with 3D-DESS-WE-MRI facilitated anatomical identification of FN and its relationship to the tumor during parotidectomy. This modality reduced operation time for FN identification, but did not significantly affect postoperative FNP rates.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.400-405
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• 2024

When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.479-488
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• 2018

This study was conducted to investigate factors affecting low body weight of solitary elderly people by grasping the illness and health behavior of elderly living alone. Using the raw data of the community health survey from 2014, the data of 922 elderly living alone who are 65 years or older were used for the final analysis. Data were analyzed by technical analysis statistics, The Rao Scott $x^2$ test, and logistic regression analysis using the composite analysis module of the SPSS/WIN 22.0 program. The results of this study showed that elderly living alone had a high percentage of chronic diseases as well as poor health behaviors such as smoking, drinking, lack of exercise, poor eating habits, difficulties in dentistry due to dentures, and poor subjective health status. Result of logistic regression analysis the risk probability of low-weight due to gender of the elderly living alone is as follows. Risk of low body weight because of smoking was 3.004 times among occasional smokers in women and the elderly, while risk of low body weight due to walking amount is less than 3 days the possibility that the person walking is low is 1.420 times significantly higher. When feeling subjective stress, the possibility of low body weight was 2.220 times greater for male elderly and 1.282 times for female elderly. The probability of low body weight for a person with a poor subjective health level was 3.633 times for male elderly and 1.590 times for female elderly. Based on the results of this study, it is necessary to establish appropriate nursing intervention and management strategies to improve health behavior of low body weight elderly living alone. It is also necessary to conduct additional studies considering various variables such as physical, psychological, and social characteristics of low-weight elderly individuals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.262-268
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• 2017

The energy consumption of buildings is approximately 20.5% of the total energy consumption, and the interest in energy efficiency and low consumption of the building is increasing. Several studies have performed energy analysis and evaluation. Energy analysis and evaluation are effective when applied in the initial design phase. In the initial design phase, however, the energy performance is evaluated using general level information, such as glazing area and surface area. Therefore, the evaluation results of the detailed design stage, which is based on the drawings, including detailed information of the materials and facilities, will be different. Thus far, most studies have reported the analysis and evaluation at the detailed design stage, where detailed information about the materials installed in the building becomes clear. Therefore, it is possible to improve the accuracy of the energy environment analysis if the energy environment information generated during the life cycle of the building can be established and accurate information can be provided in the analysis at the initial design stage using a probability / statistical method. On the other hand, historical data on energy use has not been established in Korea. Therefore, this study performed energy environment analysis to construct the energy environment historical data. As a result of the research, information classification system, information model, and service model for acquiring and providing energy environment information that can be used for building lifecycle information of buildings are presented and used as the basic data. The results can be utilized in the historical data management system so that the reliability of analysis can be improved by supplementing the input information at the initial design stage. If the historical data is stacked, it can be used as learning data in methods, such as probability / statistics or artificial intelligence for energy environment analysis in the initial design stage.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.1-23
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• 2009

The Survey on the Industrial Research and Development(R&D) is the primary source of information on R&D performed by Korea industrial sector. The results of the survey are used to assess trends in R&D expenditures. Government agencies, corporations, and research organizations use the data to investigate productivity determinants, formulate tax policy, and compare individual company performance with industry averages. Recently, Korea Industrial Technology Association(KOITA) has collected the data by complete enumeration. Koita has, currently, considered sample survey because the number of R&D institutions in industry has been dramatically increased. This study develops survey design for the industrial research and development(R&D) statistics by introducing a sample survey. Companies are divided into 8 groups according to the amount of R&D expenditures and firm size or type. We collect the sample from 24 or 8 sampling strata and compare the results with those of complete enumeration survey. The estimates from 24 sampling strata are not significantly different to the results of complete enumeration survey. We propose the survey design as follows: Companies are divided into 11 groups including the companies of which R&D expenditures are unknown. All large companies are included in the survey and medium and small companies are sampled from 70% and 3%. Simple random sampling (SRS) is applied to the small company partition since they show uniform distribution in R&D expenditures. The independent probability proportionate to size (PPS) sampling procedure may be applied to those companies identified as 'not R&D performers'. When respondents do not provide the requested information, estimates for the missing data are made using imputation algorithms. In the future study, new key variables should be developed in survey questionnaires.

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.531-546
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• 2018

Air quality forecasting system with Asian dust emissions was developed in East Asia, and $PM_{10}$ forecasting performance of chemical transport model with Asian dust emissions was validated and evaluated. The chemical transport model (CTM) with Asian dust emission was found to supplement $PM_{10}$ concentrations that had been under-estimated in China regions and improved statistics for performance of CTM, although the model were overestimated during some periods in China. In Korea, the prediction model adequately simulated inflow of Asian dust events on February 22~24 and March 16~17, but the model is found to be overestimated during no Asian dust event periods on April. However, the model supplemented $PM_{10}$ concentrations, which was underestimated in most regions in Korea and the statistics for performance of the models were improved. The $PM_{10}$ forecasting performance of air quality forecasting model with Asian dust emissions tends to improve POD (Probability of Detection) compared to basic model without Asian dust emissions, but A (Accuracy) has shown similar or decreased, and FAR (False Alarms) have increased during 2017.Therefore, the developed air quality forecasting model with Asian dust emission was not proposed as a representative $PM_{10}$ forecast model in South Korea.

• Radiation Oncology Journal
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• v.18 no.1
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• pp.1-10
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• 2000

Purpose : Although using the high energy Photon beam with conventional Parallel-opposed beams radiotherapy for nasopharyngeal carcinoma, radiation-induced xerostomia is a troublesome problem for patients. We conducted this study to explore a new parotid gland sparing technique in 3-D conformal radiotherapy (3-D CRT) in an effort to prevent the radiation-induced xerostomia. Materials and Methods : We peformed three different planning for four clinically node-negative nasopharyngeal cancer patients with different location of tumor(intracranial extension, nasal cavity extension, oropharyngeal extension, parapharyngeal extension), and intercompared the plans. Total prescription dose was 70.2 Gy to the isocenter. For plan-A, 2-D parallel opposing fields, a conventional radiotherapy technique, were employed. For plan-B, 2-D parallel opposing fields were used up until 54 Gy and afterwards 3-D non-coplanar beams were used. For plan-C, the new technique, 54 Gy was delivered by 3-D conformal 3-port beams (AP and both lateral ports with wedge compensator; shielding both superficial lobes of parotid glands at the AP beam using BEV) from the beginning of the treatment and early spinal cord block (at 36 Gy) was peformed. And bilateral posterior necks were treated with electron after 36 Gy. After 54 Gy, non-coplanar beams were used for cone-down plan. We intercompared dose statistics (Dmax, Dmin, Dmean, D95, DO5, V95, VOS, Volume receiving 46 Gy) and dose volume histograms (DVH) of tumor and normal tissues and NTCP values of parotid glands for the above three plans. Results : For all patients, the new technique (plan-C) was comparable or superior to the other plans in target volume isodose distribution and dose statistics and it has more homogenous target volume coverage. The new technique was most superior to the other plans in parotid glands sparing (volume receiving 46 Gy: 100, 98, 69$\%$ for each plan-A, B and C). And it showed the lowest NTCP value of parotid glands in all patients (range of NTCP; 96$\~$100$\%$, 79$\~$99$\%$, 51$\~$72$\%$ for each plan-A, B and C). Conclusion : We conclude that the new technique employing 3-D conformal radiotherapy at the beginning of radiotherapy and cone down using non-coplanar beams with early spinal cord block is highly recommended to spare parotid glands for node-negative nasopharygeal cancer patients.

• Korean Journal of Occupational Health Nursing
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• v.6 no.2
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• pp.110-127
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• 1997

The aim of this study is to present the basic datum for the promotion of effectiveness and improvement of nursery tasks, by grasping the situation of operating room nurses tasks and the degree of their fatigue. For these researches, 70 nurses out of the operating rooms of 3 university-affiliated hospitals-two in Seoul and one in Pusan, were chosen at random by a non-probability sampling method. These researches were done from April 14 to April 26 in 1997 by questionairing method. The questionaire was composed of 30 items, which asks the examinees of their physical, mental, and neurosensory symptoms, with 10 items respectively. The reliability of the research instrument was turned out very high with Cronbach's ${\alpha}=.9376$. The datum were electronically processed using Statistics Program for Social Sciences(SPSS). The analysis of datum in this study has a general character, in which the demosociological character and the special ex-officio character was calculated by frequency and percentage. The situation of tasks in operating rooms was calculated by frequency and percentage. The fatigue of operating room nurses was calculated by average and standard deviation. To compare the fatigue with regard to the character of operating room nurse's tasks, t-test and F-test(ANOVA) were used after the character of variations, and the variations at the level of P<.05 which might have some meaning was verified after the fact with Duncan's Multiple Range(DMR). The results of this research are as follows : 1) The nurses working in operating rooms show their fatigue in three fields-in Group I physical symptoms 3.28, in Group III neurosensory symptoms 2.85, and in Group II mental symptoms 2.73, which shows I Dominant type (general type). 2) They complain, in Group I they are "feeling the heavy legs," 3.28 and in Group II they "occasionally forget soon what to do," 3.09. and in Group III, they "feel lumbago," 3.47, which is the highest rate of the three. The highest rate results from the character of their tasks, in which they have to move rapidly the heavy appliances and do their jobs standing many hours, especially wearing heavy radiation protector. 3) As to transportation, subway using group feel the greatest fatigue, 3.18(F=4.315, P=.008). 4) As to department, nurses in the orthopedic's surgery part feel the greatest fatigue, 3.26(F=2.040, P=.050). 5) As to the change of physical symptoms, the group answering that they found physical abnormality after working in operating rooms show the greatest fatigue, 3.12(t=-3.13, P=.003). 6) At to the general circumstances, the group answering that they receive insufficient consideration on their physical abnormality in their department, show the greatest fatigue, 3.10(F=3.200, P=0.47). 7) As to the relation with superior officers, the group answering that their superior officer has an impetuous temperament, show great fatigue, 3.11(F=4.855, P=.011). 8) As to the time of feeling fatigue, the fatigue reaches the highest point 1~2 hours after operations, 3.04(F=2.703, P=.046). 9) When they feel fatigue after scrub nurse duties, they feel the greatest fatigue 2 hours after the duties, 3.09( F=2.841, P=.038). 10) As to the operation instruments, when they use complex instruments borrowing from the outside in addition to the basic instruments, their fatigue becomes the greatest, 3.09(F=7.831, P=.000). 11) As to the kind of operation, when they participate in orthopedic's surgery operations, they feel the greatest fatigue, 3.18(F=4.362, P=.000). With the above results, it is proved that the degree of operating room nurses' fatigue is considerably high. So it may be concluded that the measure for lessening the fatigue should be find immediately, not on the level of personal matters but on the level of hospital nursing administration.