• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Asian Journal of Atmospheric Environment
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• v.5 no.4
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• pp.278-291
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• 2011

Korea has experienced dramatic development and has become highly industrialized and urbanized during the past 40 years, which has resulted in rapid economic growth. Due to the industrialization and urbanization, however, air pollutant emission sources have increased substantially. Rapid increases in emission sources have caused Korea to suffer from serious air pollution. An air pollutant emissions inventory is one set of essential data to help policymakers understand the current status of air pollution levels, to establish air pollution control policies and to analyze the impacts of implementation of policies, as well as for air quality studies. To accurately and realistically estimate administrative district level air pollutant emissions of Korea, we developed a Korean Emissions Inventory System named the Clean Air Policy Support System (CAPSS). In CAPSS, emissions sources are classified into four levels. Emission factors for each classification category are collected from various domestic and international research reports, and the CAPSS utilizes various national, regional and local level statistical data, compiled by approximately 150 Korean organizations. In this paper, we introduced for the first time, a Korean national emissions inventory system and release Korea's official 2007 air pollutant emissions for five regulated air pollutants.

• Journal of agricultural medicine and community health
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• v.43 no.2
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• pp.63-73
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• 2018

Objectives: The purpose of this study was to review the disaster preparation and response programs and the status of disaster preparation in public health center. Methods: In depth interview was performed in September 2017 using 5 open questions to the persons who are in charge of disaster response services in 5 public health centers of different levels in Korea. The questions included general characteristics of public health center, disaster programs and future issues. The research hired a quality method. Results: In general, the persons in charge recognized the cooperative agency of local government in disaster management. There were no disaster preparation programs developed by the public health centers. Most of the preparation were passive activities such as emergency support, crisis management on communicable disease and quarantine, participation in biological disaster response training, and education etc. The persons in charge emphasized necessity of disaster preparation programs. Conclusions: Disaster preparation and responsiveness is an evolving issue in public health centers in Korea. Medical support system and communicable disease management system are being set up in the national level. A comprehensive system covering health management, nutritional support, mental health, environment management of shelter, and volunteers supports on public health center level needs to be developed along with a easy-to-follow manual.

• The Journal of Korean Society for Radiation Therapy
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• v.17 no.2
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• pp.87-94
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• 2005

Purpose : Heavy metal use when producing the block from the workshop. At this time, production of heavy metal dust and fume gives risk in human. This like heavy metal to improve seriousness through measurement and analysis. And by the quest in solution is purpose of this thesis. Materials and Methods : Organization is Inductively Coupled Plasma Atomic Emission Spectrometer, and the object is Deajeon city 4 workshops in university hospital radiation oncology (Bismuth, Lead, Tin and cadmium). Method is the ppb the pumping it does at unit, comparison analysis. And the Calculation heavy metal standard level in air through heavy metal standard level in body and blood, so Heavy metal temporary standard set. Results : Subterranean existence room air quality the administration laws appointed Lead and Cadmium's exposure recommend that it is $3{\mu}g/m^3\;and\;2{\mu}g/m^3$. And Bismuth and Tin decides $7{\mu}g/m^3\;and\;6{\mu}g/m^3$ through standard level in air heavy metal and standard level in body and blood. Heavy metal measurement level of workshops in 4 university hospital Daejeon city compares with work existence and nonexistence. On work nonexistence almost measurement level is below the recommend level. But work existence case express high level. Also consequently in composition ratio of the block is continuous with the detection ratio. Conclusion : Worker's heavy metal contamination imbrued serious for solution founds basic part. In hospital may operation on local air exhauster and periodical efficiency check, protector offer, et al. And worker have a correct understanding part of heavy metal contamination, and have continuous interest, health control. Finally, learned society sphere administer to establishment standard level and periodical measurement. And it founds basic solution plan of periodical special health checkup.

• 한국체육학회지인문사회과학편
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• v.57 no.3
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• pp.345-356
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• 2018

Objective: The purpose of this study was to investigate the local stability of the lower extremity joints and muscle activation patterns of the lower extremity during walking between falling and non-falling group in the elderly women. Method: Forty women, heel strikers, were recruited for this study. Twenty subjects (age:72.55±5.42yrs; height:154.40±4.26cm; mass:57.40±6.21kg; preference walking speed:0.52±0.17m/s; fall frequency=1.70±1.26 times) had a history falls(fall group) within two years and Twenty subjects (71.90±2..90yrs; height:155.28±4.73cm; mass:56.70±5.241kg; preference walking speed: 0.56±0.13m/s) had no history falls(non-fall group). While they were walking on a instrumented treadmill at their preference speed for a long while, kinematic and EMG signals were obtained using 3-D motion capture and wireless EMG electrodes, respectively. Local stability of the ankle and knee joint were calculated using Lyapunov Exponent (LyE) and muscles activation and their co-contraction index were also quantified. Hypotheses were tested using one-way ANOVA and Mann-Whitey. Spearman rank was also used to determine the correlation coefficients between variables. Level of significance was set at p<.05. Results: Local stability in the knee joint adduction-abduction was significantly greater in fall group than non-fall group(p<.05). Activation of anterior tibials that acts on the foot segment dorsal flexion was greater in non-fall group than fall group(p<.05). CI between gastrocnemius and anterior tibials was found to be significantly different between two groups(p<.05). In addition, there was significant correlation between CI of the leg and LyE of the ankle joint flexion-extention in the fall group(p<.05). Conclusion: In conclusion, muscles that act on the knee joint abduction-adduction as well as gastrocnemius and anterior tibials that act on the ankle joint flexion-extention need to be strengthened to prevent from potential fall during walking.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.

• Journal of Korean Society of Forest Science
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• v.81 no.1
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• pp.40-52
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• 1992

The use of meteorological information is essential in the industrial society. More specialized weather services are required to perform better industrial activities including forestry. A topoclimatological technique, in this study, which makes use of empirical relationships between the topography and the weather in Cheju Island was applied to produce reasonable estimates of monthly air temperatures over remote land area where routine observations are rare. Altitude values of the 250m grid points were first read from a 1 : 25000 topographic map. The mean altitude and other valuable topographical variables were then determined for each $1km^2$ land area. Daily minimum, maximum and mean air temperature data were collected from 19 points in Cheju Island from June 1987 to September 1988. The data were analyzed and grouped into 36 sets by type of air temperature and by month. Each of data set was regressed to the topographical variables to delineate empirical relationships between the local air temperature and the site topography. The total of 36 regression equations were finally selected and the equations were used to calculate the monthly air temperature for each $1km^2$ land area. The outputs were presented in a fine-mesh grid map with a 6-level contour capability.

• 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 Distribution Research
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• v.15 no.4
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• pp.1-19
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• 2010

As having the movement of developing private brand (PB) goods, domestic big retailers are facing up with new problems. Thus, it is required studies of PB products, and how consumers recognize PB products as a consideration commodity set. Also, it is worthy in order that it gives us the important meaning on the marketing strategy with focusing on evaluating the differences between customers buying PB grocery goods with respect to demographic characteristics and purchasing behaviors. PB has some advantages for customers and retailers. However, according to AC Nielson's report (2005), Asian and emerging market has 1/5 sales relatively to Western countries. But we can assume that the emerging market has the most potential growth through this result. As a result from several other studies, it becomes necessary to not only increase the rate of selling composition of PB product temporarily, but also analyze the characteristics of customers using big retailers and segmenting customer groups to make PB product as a consideration commodity set for them. In addition, it is needed to have a variety of acts of marketing. From studies related to PB, there is a prejudice - cheap products have low quality - but, evaluation by customers who have used those products shows neutral stand, and there is a study representing that it is the most important to accumulate the belief between the retailers selling PB products and consumers using those for the accurate evaluation and intention on purchasing. Also, by the result from analyzing the characteristics of customers buying PB products, we could assume that higher income and higher education level, more preference on PB products. Especially, according to TNS's research, the primary targets of PB product are 30's who seeks value for money and planned spending habits, and 40's who have teenager children, and are interested in encouraging themselves. This paper used Probit model to analyze the characteristics of consumers. This model helps us to analyze with the variables representing the demographic characteristics of consumers (gender, age, educational level, occupation, income level, living area), and variables related to purchasing behavior (visiting frequency on big retailers, the average amount that they pay for goods in there, and check-up which brand made those goods). The method we used in this study is by man to man interview and survey on-line with the rate of 89% and 11% in Seoul and Gyunggi Province, respectively, for about one month from the beginning of February, 2008. As a result of this, under the assumption that people buy PB products more as long as they go shopping more, it was not meaningful for target groups which we pointed out as frequently visiting customers to be. Although, we have expected women buy more PB products than men do, gender doesn't mean anything for the result. And, it has inferred that married people buy more PB goods than singles do. It was also meaningless with variables related to occupation. Because housewives are often exposed to any kind of supermarket than workers are, we could not get any relatives. Moreover, we couldn't proof that younger generation prefer big retailers more than older people who 50~60's. Education levels doesn't affect on the purchase of PB product as well. Related to living area, the result is statistically not similar as we expected whether living in Seoul or not. It shows there is no relationship with the preference on retail brands and PB products, and it is similar with the study researched by TNS(2008) that customers tend to buy PB product impulsively no matter which brand it is and where they are even though their shopping place is the big market where customers are often using. Variables on which we had meaningful results are income level and living place. That is, customers who have 3,000,000~6,000,000 WON every month on average are more willing to buy PB products than other customers whose income is over 6,000,000 WON, and residents not living in Seoul prefer PB goods than those who are living in Seoul. To explain more about what we got, if there is only one condition about customer's visiting frequency on big retails, we could come up with this result that more exposed to PB products, more purchasing frequency. Consequently, it brings the important insight that large retailers have to prepare something to make customers visit them often to increase selling rate of PB products. To demonstrate the result of analyzing more, what is more efficient variables are demographically including marital status, income level, and residential area to buy items that affect the PB products and could include the frequency of visiting large markets by the purchase habits. Specifically, then, married couples rather than singles, middle-income customers than high-income customers, and local residents not living in Seoul than customers in Seoul are more likely to purchase PB goods. In addition, as long as a customer visits two times more, then the purchasing rate of PB products is to increase over 5.3%. Therefore, it seems that retailers are better to make a shopping place as fun and comfortable places. With overwhelming the idea that PB products are just cheap, one-time purchase goods, it is needed to increase the loyalty on those goods like NB products, try to make PB products as a consideration products set, and occur to sustainable sales. Especially, as suggested by this paper, it seems like it strongly needs to identify the characteristics of customers who prefer PB, to segment those customers, and to select the main target, and to do positioning with well-planned marketing strategies. Then, it is able to give us a meaningful point on marketing strategy by developing the field of PB study, identifying the difference of life style and shopping habits of customers.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."