• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.458-470
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• 2018

As the requirements of web apps become complex and rapidly changing, the maintenance of web apps becomes more important. However, web apps have a problem that more often than not there is not enough documentation to understand and maintain them. Thus, their effective maintenance requires models that represent their internal behavior occurring when they dynamically generate web pages. Previous works identify web components (such as JSPs and Servlets) as participants in the behavior but not web resources (such as images, CSS files, and JavaScript files). Moreover, they do not identify dependency relations between web components and web resources. This paper dynamically analyzes Java web apps to extract such dependency relations, which are included in our graph model for page generation. Case studies using open-source web apps show the applicability of the proposed approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.821-826
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• 2010

The door-lock switch is one of the most important part in the front-load washer. As an safety system, it keeps the door locked while the front-load washer or the dish washer is working and generating heat and spin. The door of front-load washer needs the emotionality as well as the safety because the consumers always get feels when using it. In this paper, We study about emotional factors in the door of front-load washer and analyze the difference of emotionality as the structure of the door-lock switch changed. For this, we analyze the force graph by using the software and the jig which can measure the force while the door is opened or closed.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.549-554
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• 2016

ConceptNet is a common sense knowledge base which is formed in a semantic graph whose nodes represent concepts and edges show relationships between concepts. As it is difficult to make knowledge base integrity, a knowledge base often suffers from incompleteness problem. Therefore the quality of reasoning performed over such knowledge bases is sometimes unreliable. This work presents neural tensor networks which can alleviate the problem of knowledge bases incompleteness by reasoning new assertions and adding them into ConceptNet. The neural tensor networks are trained with a collection of assertions extracted from ConceptNet. The input of the networks is two concepts, and the output is the confidence score, telling how possible the connection between two concepts is under a specified relationship. The neural tensor networks can expand the usefulness of ConceptNet by increasing the degree of nodes. The accuracy of the neural tensor networks is 87.7% on testing data set. Also the neural tensor networks can predict a new assertion which does not exist in ConceptNet with an accuracy 85.01%.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.263-270
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• 2021

Reinforcement learning(RL) is the method to find an optimal policy through training. and it is one of popular methods for solving lifesaving and disaster response problems effectively. However, the conventional reinforcement learning method for disaster response utilizes either simple environment such as. grid and graph or a self-developed environment that are hard to verify the practical effectiveness. In this paper, we propose the design of a disaster response RL environment which utilizes the detailed property information of the disaster simulation in order to utilize the reinforcement learning method in the real world. For the RL environment, we design and build the reinforcement learning communication as well as the interface between the RL agent and the disaster simulation. Also, we apply the dimension reduction method for converting non-image feature vectors into image format which is effectively utilized with convolution layer to utilize the high-dimensional and detailed property of the disaster simulation. To verify the effectiveness of our proposed method, we conducted empirical evaluations and it shows that our proposed method outperformed conventional methods in the building fire damage.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.129-136
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• 2019

There are a variety of entities in natural language such as people, organizations, places, and products. These entities can have many various meanings. The ambiguity of entity is a very challenging task in the field of natural language processing. Entity Linking(EL) is the task of linking the entity in the text to the appropriate entity in the knowledge base. Pairwise based approach, which is a representative method for solving the EL, is a method of solving the EL by using the association between two entities in a sentence. This method considers only the interdependence between entities appearing in the same sentence, and thus has a limitation of global interdependence. In this paper, we developed an Entity2vec model that uses Word2vec based on knowledge base of RDF type in order to solve the EL. And we applied the algorithms using the generated model and ranked each entity. In this paper, to overcome the limitations of a pairwise approach, we devised a pairwise approach based on comprehensive interdependency and compared it.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.31-38
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• 2021

In this paper, in order to obtain the optimization of the RNN model used for sentiment analysis, the correlation of each model was studied by observing the trend of loss and accuracy according to hyperparameter tuning. As a research method, after configuring the hidden layer with LSTM and the embedding layer that are most optimized to process sequential data, the loss and accuracy of each model were measured by tuning the unit, batch-size, and embedding size of the LSTM. As a result of the measurement, the loss was 41.9% and the accuracy was 11.4%, and the trend of the optimization model showed a consistently stable graph, confirming that the tuning of the hyperparameter had a profound effect on the model. In addition, it was confirmed that the decision of the embedding size among the three hyperparameters had the greatest influence on the model. In the future, this research will be continued, and research on an algorithm that allows the model to directly find the optimal hyperparameter will continue.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.117-124
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• 2021

Among growth disorders, 'Short Stature' can be improved through rapid diagnosis and treatment, and for that, it is important to detect early'Short Stature'. It is recommended to measure the height steadily for early detection of 'Short Stature' and checking the kid's growth process, but existing height measurement methods have problems such as time and space limitations, cost occurrence, and difficulty in keeping records. So in this paper, we proposed an 'Development of Kid Height Measurement Application based on Image using computer vision' method using smart phones, a medium that is highly accessible to people. In images taken through a smartphone camera, the kid's height is measured using algorithms from OpenCV, a computer vision library, and the measured heights were printed on the screen through 'a comparison graph with the standard height by gender and age' and 'list by date', made possible to check the kid's growth process. It is expected to measure height anytime, anywhere without time and space limitations and costs through this proposed method, and it is expected to help early detection of 'Short Stature' and other disorder through steady height measurement and confirmation of growth process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• Archives of Plastic Surgery
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• v.49 no.3
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• pp.462-470
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• 2022

Background Reactive oxygen species cause serious damage to the physiological function of tissues. Determination of total antioxidant capacity of skin tissue is one of the determinants of damaged tissue function. Mast cells (MCs) are one of the groups of cells that are invited to the site of injury. The healing process begins with the rapid release of various types of MCs' intermediate factors at the site of injury. Bone marrow mesenchymal stem cell (BMMSC) production and secretion have been shown to regenerate the skin. The aim of this research was to evaluate the wound-healing and antioxidant effects of BMMSCs per MCs. Methods Fifty-four albino Wistar male rats were divided into three groups: (1) nonsurgery, (2) surgery, and (3) surgery + BMMSCs. Groups 2 and 3 were operated with a 3 × 8 cm flap and in group 3, cell injections (7 × 10⁹ cell injection at the time of surgery) were performed. After days 4, 7, and 15, percentage of the surviving tissue, histological characteristics, superoxide dismutase (SOD) activity, and amount of malondialdehyde (MDA) were measured in the groups. For results, Graph Pad Prism 8 software was used, and data were analyzed and compared by analysis of variance and Tukey test. Results BMMSCs' application decreased the amount of MDA, increased SOD activity and survival rate of the flaps, and improved the histological characteristics. Conclusion This study revealed the protective effects BMMSCs alongside MCs against oxidative stress on the survival of the flaps. However, for clinical use, more research is needed to determine its benefits.