• Journal of the Korean Regional Science Association
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• v.38 no.3
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• pp.19-39
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• 2022

The aim of this study is to compare and analyze structural characteristics of smart city industry focused on Korea and Spain. Structural characteristics of industries were compared focusing on share, penetration, impact path and network clustering of smart industries. Research data used input-output tables established by Korea and Spain in 1995 and 2015, and industries were reclassified into 8 and 25 industries. The analysis model is the Smart SPIN Model. The key finding as follows: It was analyzed that there are differences in the structure and characteristics of the smart city industry between Korea and Spain. Firstly, It is analyzed that Korea has a larger share and penetration rate of IT manufacturing than Spain. On the other hands, Spain has a higher share and penetration rate in the IT service and knowledge service sectors than Korea. Secondly, Korea had many production paths for the IT service and the knowledge service. On the other hands, Spain included more production paths in the IT manufacturing sector. Thirdly, as a result of network analysis, Korea's smart industry has a characteristic that it is difficult to develop independently because it is dependent on traditional industries. In Spain, most of the smart industries were included in one industrial cluster, and it was analyzed to have an independent form. In conclusion, It was found that Korea has the industrial characteristics of a smart city based on IT manufacturing. Spain has the characteristics of smart city industry based on IT service and knowledge service. The results of this study are expected to provide basic data on the direction of smart city promotion and the establishment of smart city policies in Korea.

• Journal of Korea Water Resources Association
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• v.55 no.2
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• pp.121-133
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• 2022

The drought resulting from insufficient rainfall compared to the amount in an ordinary year can significantly impact a broad area at the same time. Another feature of this disaster is hard to recognize its onset and disappearance. Therefore, a reliable and fast way of predicting both the suffering area and the amount of water shortage from the upcoming drought is a key issue to develop a countermeasure of the disaster. However, the available drought scenarios are about 50 events that have been observed in the past. Due to the limited number of events, it is difficult to predict the water shortage in a case where the pattern of a natural disaster is different from the one in the past. To overcome the limitation, in this study, we applied the four RCP climate change scenarios to the water balance model and the annual amount of water shortage from 360 drought events was estimated. In the following chapter, the deep neural network model was trained with the SPEI values from the RCP scenarios and the amount of water shortage as the input and output, respectively. The trained model in each sub-basin enables us to easily and reliably predict the water shortage with the SPEI values in the past and the predicted meteorological conditions in the upcoming season. It can be helpful for decision-makers to respond to future droughts before their onset.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Korean Public Health Nursing
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• v.8 no.2
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• pp.35-56
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• 1994

This article was intended to survey whether Roy' Adapation model ('Roy Model') can be applied to family health assessment and to study whether application of the Roy Model to a Korean family is feasible. under the Roy Model, a family is viewed as an adaptation system having a series of process of input. process, feedback, and output. Further, the Roy Model indicates that a family contains Physiolosical, self-concept. role function and interdependent mode in respect of internal or external stimuli. In the event where the family health assessed, the adaptation mode of that family must be assess at the first stage. Then, the focal, contextual, residual stimuli affecting the family must be assessed. In 1984 Hanson suggested four types of family adaptation mode based upon the Roy Model and thereby enhanced the possibility for family health assessment. In order survey whether the Roy Model can be applied to the Korean family, the author of this article contracted adults of 169 who live in 'A' city to make open questions regarding family and then analyzed responses from them by utilizing Roy model. This study categorized family Adaptation mode based upon the' four types of family adaptation mode developed by Hanson. As a result of this study, family adaptation mode was categorized into 117 concepts. Those 117 concepts are consisted or Physiolosical mode of 47. self­concept mode of 56, role function mode of 9 and interdependent mode of 5. Further. stimuli affecting family were classified based upon Roy's definition as to three types of stimuli. Stimuli on a family are comprised focal stimuli concept of 19, contextual stimuli concepts of 19, one residual stimuli concept. this result implies that the Roy's Model can be applied to Korean family. Physiological mode shows meaning of survival. while self-concept mode reflects meaning of growth and emphasizes harmony among the family based on the familism. The role function mode shows continuity rather control of family member. By contrast, interdependent mode shows interaction with community to which the family belongs. but the degree of interaction does not appear too high. The analysis of family stimuli led this study to conclude that troubles within a family. changes in family structure and diease of family member generate stimuli. However, an application of the Roy Model contains the following problems: First, Roy argued that the family adaptation mode should be assessed at the first level family health assessment and then stimuli affecting family adaptation should be adaptation assessed at the second stage. To the belief of the author of this article. however, for checking family adaptation level. focal, contextual, residual stimuli should be confirmed by assessing stimuli at first stage. Then, the family adaptation mode in respect of such stimuli should be assessed. The rationale for this is that the family adaptation level is determined depending on degree of strength of focal. contextual. residual stimuli. Second. Whall (1991) raised a question 'Does one assess family adaptation mode and intervene in the stimuli?' 'Likewise, assessment of the family adaptation should be made in the following manner in order for family health to be enhanced. Third. Roy believes that additional stimuli (such as contextual and residual) are same as internal process (including nurturance. support, and socialization). However, the basis for this Roy's belief is not too clear. In spite of these problems which the author indicated above, it can be concluded that the Roy Model can serve as a good device for an assessment of family health and that the Roy Model can be applied to a Korean family. Finally, further research of family adaptation theory and family nursing theory is required for a development of these theories.

• 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 East-West Nursing Research
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• v.10 no.1
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• pp.11-26
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• 2004

The purpose of this study is to investigate the present conditions of nursing investment contents, its conversion process, and output in Oriental University Medical Center, Korea to get good qualified Oriental nursing result which is the ultimate purpose of the Oriental nursing management, and to develope a matrix of Oriental nursing management system on the basis of that project. The subjects for nursing investment and output contents were eighteen nursing directors in eleven Oriental University Medical Center and two hundred thirty-nine nurses with three years and over experience in Oriental medical center. The subjects for Oriental nursing organization, human affair management, and control function were nineteen Oriental medical center in Oriental University Medical Center, Korea. Data were collected from November, 2002 to February, 2003 with questionnaire. Data analysis was done by SPSS PC+ 12 program. Frequency, percentage, and minimum/maximum values were used for investment contents, and frequency and percentage were used for conversion process and output contents. 1. The input factors of oriental nursing management system The objective's western hospital career was over five years of one hundred and seventy-five(73.2%) persons. Nursing in-service education was performed in fourteen hospitals(77.8%). Two hundreds(83.7%) were pro to oriental nurse system. Only four hospitals(22.2%) had independent budget in nursing division. Nursing staff allocation to the bed was from 2.8:1 to 9.06:1 respectively, with a big gap of the rate following the hospitals. 2. The conversion factors of oriental nursing system 1) Oriental nursing system Oriental hospital nursing system was organized independently in ten hospitals among eighteen hospitals. The recruitment of nurses which was a vital role of the nursing division of the hospital was mostly(79%) opened. The education to develope nursing personnels was through in-service one in 97.4%. Education for oriental nursing and management was performed in 42.1%(eight hospitals) and that for reserves was done in 36.8%(seven hospitals). Administration for nursing education by nursing division was 68.5%(thirteen hospitals). The post education evaluation was performed by report submission in 36.8%(seven hospitals), by written examination in 26.3%, by questionnaires in 21.1%, and by lecture presentation in 15.8% subsequently. The directorial meeting for the nursing directors was attended by 84.2%(sixteen hospitals), and the meeting type was the medical executive and support division executive meeting in 55.6%(ten hospitals) and the personnel management in 39.6%(seven hospitals). 2) The actual conditions of oriental nursing personnel management The reason of working in oriental hospital was by voluntary in 67.1%(a hundred and sixty persons), by nursing department order in 28.0%(sixty-seven persons), and by others in 5.0%(twelve persons) respectively. The shift form was a three-shifts one in 94.7%(eighteen hospitals), a two-shift one in only one hospital. Duty assignment was functional in 52.6%(ten hospitals), team and functional in 26.3%(five hospitals) and no team alone. Promotion manual was present at 68.4%(thirteen hospitals) and the competency essentials comprised of performance evaluation in 79%, interview, written examination, training result, study result subsequently. No labor union existed in 79%(fifteen hospitals) 3) Oriental nursing preceptor system There were five oriental hospitals(27.7%) administering the preceptor utilization model, which showed lower rate than the twenty-two medical university hospitals in Seoul in which fifteen hospitals (72.7%) were having the system. To the question of necessity of oriental nurse system asked to the objectives of two hundred and thirty-nine with more than three year-experience in oriental hospital, two hundred persons(83.7%) answered positively. 4) The control of oriental nursing The evaluation results from the target hospitals were mostly not opened in 89.4% of oriental hospitals. Thirteen hospitals(68.3%) had evaluation system of direct managers and the next were three hospitals(15.8%) of direct managers and selves. There was one hospital(5.3% each) where fellows and superiors, fellows, and inferiors' evaluation was performed and no hospital where superiors, fellows, inferiors and selves, and superiors, fellows and selves' evaluation was performed. The QI activity of nursing was 42.1%(eight hospitals) for nursing service evaluation, 36.8% for survey of ECSI, 26.3% for survey of ICSI, 15.8% for medical visit rate, 10% for hospital standardization inspection in sequence. 3. The output factors of oriental nursing management system The job satisfaction appeared good in general, indicating very good in thirty-seven persons (15.7%), good in one hundred and fourteen persons (48.3%) and fair in eighty-five persons(36.0%).

• Journal of Digital Convergence
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• v.13 no.8
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• pp.321-329
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• 2015

This study investigates effects of hardware and telecommunication and software service divided by ICT service on each 5 transportations to explore convergence of ICT and Transportation. Research models are production inducing effects, Added Value inducing effects of Demand-Driven model and Shortage cost effects of Supply-Driven model by using data for 2010~2012 of Input-Output Table. Results are that network and software service effects are more impact than hardware effects on transportations. Especially, hardware is impacted heavily on production inducing effect, telecommunications and software services has had a significant impact on the production inducing effect and Shortage cost effects. In addition, by each detail the transportation industries, packages and other transport and road transport is influenced greatly from ICT. On the other hand, rail and water transport are relatively lower impact by ICT, However, the effects of rail and water transport by ICT is grater than investment ratio of ICT. As a result, increasing investment in the ICT services could contribute to development of rail and water transport development.

• Advances in aircraft and spacecraft science
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• v.5 no.2
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• pp.225-237
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• 2018

The difficulties of satellite vibration testing are due to the commonly expressed qualification requirements being incompatible with the limited performance of the entire controlled system (satellite + interface + shaker + controller). Two features cause the problem: firstly, the main satellite modes (i.e., the first structural mode and the high and low tank modes) are very weakly damped; secondly, the controller is just too basic to achieve the expected performance in such cases. The combination of these two issues results in oscillations around the notching levels and high amplitude beating immediately after the mode. The beating overshoots are a major risk source because they can result in the test being aborted if the qualification upper limit is exceeded. Although the abort is, in itself, a safety measure protecting the tested satellite, it increases the risk of structural fatigue, firstly because the abort threshold has been already reached, and secondly, because the test must restart at the same close-resonance frequency and remain there until the qualification level is reached and the sweep frequency can continue. The beat minimum relates only to small successive frequency ranges in which the qualification level is not reached. Although they are less problematic because they do not cause an inadvertent test shutdown, such situations inevitably result in waiver requests from the client. A controlled-system analysis indicates an operating principle that cannot provide sufficient stability: the drive calculation (which controls the process) simply multiplies the frequency reference (usually called cola) and a function of the following setpoint, the ratio between the amplitude already reached and the previous setpoint, and the compression factor. This function value changes at each cola interval, but it never takes into account the sensor signal phase. Because of these limitations, we firstly examined whether it was possible to empirically determine, using a series of tests with a very simple dummy, a controller setting process that significantly improves the results. As the attempt failed, we have performed simulations seeking an optimum adjustment by finding the Least Mean Square of the difference between the reference and response signal. The simulations showed a significant improvement during the notch beat and a small reduction in the beat amplitude. However, the small improvement in this process was not useful because it highlighted the need to change the reference at each cola interval, sometimes with instructions almost twice the qualification level. Another uncertainty regarding the consequences of such an approach involves the impact of differences between the estimated model (used in the simulation) and the actual system. As limitations in the current controller were identified in different approaches, we considered the feasibility of a new controller that takes into account an estimated single-input multi-output (SIMO) model. Its parameters were estimated from a very low-level throughput. Against this backdrop, we analyzed the feasibility of an LQG control in cancelling beating, and this article highlights the relevance of such an approach.

• The Journal of Fisheries Business Administration
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• v.32 no.1
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• pp.15-39
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• 2001

The study, using the input-output analysis of 402 industrial sectors by Bank of Korea(BOK) and the resulting outcomes of price model, aims to evaluate the spillover effects the international fluctuations in crude oil prices have on the commodities prices and consequently, analyse the management and profitability of the offshore fisheries in Korea. At present, the fisher men are provided with tax-free oils for their fishing operations as specified under the Special Tax Treatment Control Law. However, the exhaustion of marine resources and new international fisheries agreements, which resulted in the loss of fishing grounds, made the stable catch even more unpredictable and the hike in the price of the international crude oil would have adverse effects on the fishing industry. The study revealed that the increasing rise in the price of crude oil would exert sweeping spillover effects on other industry sectors in general and accordingly, lead to a poorer performance by fisheries. The price spillover coefficients for the diesel oil was 0.6026, which would translate into the 42.6% increase in the prices of oil when the increase ratio of 73.3% for the base crude oil was applied based on the calculation methods employed in the study. This in turn increased the ratio of diesel oil required in the offshore fisheries from 23.3% to 16.6%, diminishing the ratio of current net profits to minus 2.0% from 4.2% otherwise. By fishing type, the Pair Trawl suffered current net profits loss most by ratio of minus 9.4% and other fisheries such as Coastal Stow Nets, Coastal Angling, Danish Sein also suffered ratio of 7% and more in the loss of current net profits. With the deteriorating fishing performance, coupled with the increasing international crude oil prices, it is urgently required that the authorities concerned deliberate in depth on such schemes as follows in efforts to secure stable fishing production. First, provision of large-scale storage facilities for oil is needed to timely adapt to the fluctuations in international crude oil prices. Secondly, in line with the stabilization of tax-free oil prices, duty levied on oils for fishing and tax collected from the refineries need to be tax-exempt. Thirdly, the beneficiaries from the provision of tax-free oil should be broadened, not limited to special fishing operation only. Fourth, investment in stabilization of the oil prices should be encouraged, possibly through funding from the formation of fisheries development funds underway.