• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.60-69
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• 1998

This paper proposes a speech synthesis method using Fuzzy VQ, and then study how to make choice of fuzziness value which optimizes (controls) the performance of FVQ in order to obtain the synthesized speech which is closer to the original speech. When FVQ is used to synthesize a speech, analysis stage generates membership function values which represents the degree to which an input speech pattern matches each speech patterns in codebook, and synthesis stage reproduces a synthesized speech, using membership function values which is obtained in analysis stage, fuzziness value, and fuzzy-c-means operation. By comparsion of the performance of the FVQ and VQ synthesizer with simmulation, we show that, although the FVQ codebook size is half of a VQ codebook size, the performance of FVQ is almost equal to that of VQ. This results imply that, when Fuzzy VQ is used to obtain the same performance with that of VQ in speech synthesis, we can reduce by half of memory size at a codebook storage. And then we have found that, for the optimized FVQ with maximum SQNR in synthesized speech, the fuzziness value should be small when the variance of analysis frame is relatively large, while fuzziness value should be large, when it is small. As a results of comparsion of the speeches synthesized by VQ and FVQ in their spectrogram of frequency domain, we have found that spectrum bands(formant frequency and pitch frequency) of FVQ synthesized speech are closer to the original speech than those using VQ.

• The Korean Journal of Archival Studies
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• no.51
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• pp.45-87
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• 2017

In recent years, there were some who have conducted research on deriving the social and historical meanings of records through the analysis of specific records collections. This trend is an attempt to pay attention to human actions in the society and to read the society again through the records created by such actions. In this study, I derive various meanings and values of these records through the analysis of the "Laos Renewable Energy Support Activities" collection. Moreover, I study how the collection was reconstructed by the Human and Memory Archives. The "Laos Renewable Energy Support Activities" is the personal record of the donor who led the project, and contains the process and results of the project. Through this collection, I was able to look at the life of the donor as a foreign aid activist in Laos and realized his values. Furthermore, through the business process record, I was able to discover the implications of climate change response overseas aid projects. In addition, I was able to look at the culture and environment of Laos through the eyes of the donor who has been residing there for a long time.

• Management & Information Systems Review
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• v.37 no.4
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• pp.237-256
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• 2018

This study focused on the concept of organizational learning as a prior variable of innovation activities, and reviewed the relationship between organizational learning, innovation and management performance. According to prior studies, the ability to perform these activities may be enhanced through organizational learning, as the success of the innovation requires activities to acquire and share knowledge within the organization. In other words, organizational learning is playing a role as a precursor to innovation. Therefore, in this study, the effects of organizational learning on management performance are to be verified through the mediation effect of product and innovation activities. Organizational learning provides various definitions and components for each scholar, but this study consisted of a series of knowledge acquisition, information distribution, information analysis and process memory using the framework of the learning ability analysis by Levitt and March(1988) and Huber(1991), Innovation was also divided into product innovation and process innovation, and measured with sub-variables such as presentation of new products and improvement activities to increase productivity. Management performance was measured as financial and non-financial performance. To verify the effects of the mediation, we used a three-step regression analysis procedure of Baron and Kenny(1986)'s and a sobel-test. Empirical studies show that organizational learning has a positive effect on management performance and that knowledge acquisition and information distribution, which are the early stages of learning activities in the lower variables, affect performance through product innovation. Based on the results of the above empirical study, the implications, limitations of the study and future research directions were presented.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.639-649
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• 2011

The purpose of this study aimed to analyze factors on motives to tree burial and choice conditions to tree burial site in order to suggest policy direction for the desirable settlement of tree burial. For those purpose, this study performed questionnaire, targeting 522 visitors of funeral hall all around Korea. As the result, the factors of motives to tree burial were extracted as follows: funeral ceremony progressed along with trees, simplicity, memorial site's easy insurance, environmental friendliness and consideration toward descendants. The factors on choice conditions to tree burial sites were extracted as follows: beauty of natural scenery, emotional mood as a memorial site, convenience, stability and economic feasibility. Based on the results of factor analysis, this study suggested policies related to motives to tree burial as follows: develop various types of tree burial sites, develop a funeral ceremony suitable for tree burial, come into wide use of tree burial as a social welfare service, develop tree burial methods capable of many burials, and improve professionalism to manage tree burial system. In addition, this study proposed related choice conditions to tree burial sites as follows: establish natural forest scenery, convert existing graveyards into tree burial sites, select easily accessible places for tree burial sites, form tree burial sites as places for both rest and memory, and reduce using fee of tree burial site.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.181-191
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• 2021

The purpose of this study is to analyze the spatial structure of director Im Kwon-taek's film , which was released in 1997. The space of the film contains the character and characteristics of the characters, and allows us to understand the contemporary reality and external circumstances surrounding the characters. For this purpose, this study used Michel Foucault's concept of heterotopia. The concept of heterotopia defines the character of the era and provides implications for how capital, power, institutions and norms surrounding our lives are being visualized through space. Based on this understanding, this study first dealt with the theoretical considerations of Michel Foucault's concept of heterotopia and its meaning. And through this, we investigated the possibility that the space of the film can be defined as a heterotopia. Through the analysis of the film's dialogue, scenes, and editing, the space of the film was divided into a heterotopia of deviation, a heterotopia of resistance impossibility, and a heterotopia of boundaries. The meaning of the film obtained through this analysis is as follows. The woman in the film is passively represented and floating on the border of heterotopia. And the film represent history and memory at the same time, and presents a heterotopia as the arena of competition.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Dementia and Neurocognitive Disorders
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• v.22 no.3
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• pp.100-108
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• 2023

Background and Purpose: The efficacy and safety of GV1001 have been demonstrated in patients with moderate-to-severe Alzheimer's disease (AD). In this study, we aimed to further demonstrate the effectiveness of GV1001 using subscales of the Severe Impairment Battery (SIB), which is a validated measure to assess cognitive function in patients with moderate-to-severe AD. Methods: We performed a post hoc analysis of data from a 6 month, multicenter, phase 2, randomized, double-blind, placebo-controlled trial with GV1001 (ClinicalTrials.gov, NCT03184467). Patients were randomized to receive either GV1001 or a placebo for 24 weeks. In the current study, nine subscales of SIB-social interaction, memory, orientation, language, attention, praxis, visuospatial ability, construction, and orientation to name-were compared between the treatment (GV1001 1.12 mg) and placebo groups at weeks 12 and 24. The safety endpoints for these patients were also determined based on adverse events. Results: In addition to the considerable beneficial effect of GV1001 on the SIB total score, GV1001 1.12 mg showed the most significant effect on language function at 24 weeks compared to placebo in both the full analysis set (FAS) and per-protocol set (PPS) (p=0.017 and p=0.011, respectively). The rate of adverse events did not differ significantly between the 2 groups. Conclusions: Patients with moderate-to-severe AD receiving GV1001 had greater language benefits than those receiving placebo, as measured using the SIB language subscale.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Yeungnam Medical Science
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• v.14 no.2
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• pp.314-328
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• 1997

A cross-sectional study was performed to evaluate the effects of chronic exposure to low-dose solvent on neurobehavioral performance of 48 male workers exposed to organic solvents. A control group of 50 workers was selected from same factories. Each worker completed a medical and occupational questionnaire and four tests of Neurobehavioral Core Test Battery. These included Benton visual retention test, digit symbol, digit span, and pursuit aiming. Comparison of mean performance showed a significantly poorer performance on digit symbol, digit span, and pursuit aiming. In univariate analysis, age contributed to poor performance on Benton visual retention test and educational level was found to reduce the performance on symbol digit in both groups. Amount of alcohol intake was found to reduce the performance on digit symbol and smoking appeared to slow pursuit aiming in the exposure group. In multiple regression analysis, controlling for age, educational level, alcohol, and smoking, Solvent exposure was found to be associated with performance of digit span, and number of correct dot of pursuit aiming. Age on Benton visual retention, educational level on digit symbol, arid smoking on pursuit aiming were found to be a significant factors on each test items. This study suggest that short-term memory, and perception can be affected easily by chronic exposure of organic solvents which air concentration levels were under the Threshold Limit Value.