• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.04a
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• pp.70-77
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• 1996

Recently many efforts have been made to improve the efficiency of design and production of the structures using the automation system. But, this work has been progressed as independent or partial system. And, the study on the integrated system is not sufficient in application for practical problems yet. This paper deals with the fundamental concept of modeling system and application method on structural modeling. At first, the core of the integrated system is a shape modeling system that can represent the geometric and topological information. This system must be designed as an open system to be combined with each independent automation system. The appropriate concept to realize this system on structural modeling is object oriented modeling and this enables to integrate each automation system successfully, This concept was applied to automatic mesh generation. For shape modeling system, half-edge data structure that is being used in solid modeling was modified to handle the plate structure in the plane. And, to generate the triangular meshes, direct node connection method was used. And, as a result, the integrated system that generate the meshes of two dimensional structure automatically was realized. And, programmed by C++, these systems can be combined with other systems easily and have good reusability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1032-1037
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• 2006

The channel is one of the precious and limited resources in wireless networks. There are many researches on the channel management. Recently, the optimization problem of guard channels has been an important issue. In this paper, we propose an intelligent channel management technique based on the neural networks. An SRN channel allocation model is developed to generate the learning data for the neural networks and the performance analysis of system. In the proposed technique, the neural network is trained to generate optimal guard channel number g, using backpropagation supervised learning algorithm. The optimal g is computed using the neural network and compared to the g computed by the SRM model. The numerical results show that the difference between the value of 8 by backpropagation and that value by SRM model is ignorable.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.197-206
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• 2021

We compared two typical ensemble time scale algorithms; AT1 and Kalman filter. Four commercial atomic clocks composed of two hydrogen masers and two cesium atomic clocks provided measurement data to the algorithms. The allocation of relative weights to the clocks is important to generate a stable ensemble time. A 30 day-average-weight model, which was obtained from the average Allan variance of each clock, was applied to the AT1 algorithm. For the reduced Kalman filter (Kred) algorithm, we gave the same weights to the two hydrogen masers. We also compared the frequency stabilities of the outcome from the algorithms when the frequency offsets and/or the frequency drift offsets estimated by the algorithms were corrected or not corrected by the KRISS-made primary frequency standard, KRISS-F1. We found that the Kred algorithm is more effective to generate a stable ensemble time scale in the long-term, and the algorithm also generates much enhanced short-term stability when the frequency offset is used for the calculation of the Allan deviation instead of the phase offset.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1407-1423
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• 2020

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.353-368
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• 2023

The four wing unmanned aerial vehicle owns the characteristics of small size, light weight, convenient operation and well stability. But it is easily disturbed by external environmental factors during flight with these disadvantages of short endurance and poor attitude solving ability. For solving these problems, a microprocessor based on STM32 chip is designed and the overall development is completed by the resources such as built-in timer and multi-function mode general-purpose input/output provided by the master micro controller unit, together with radio receiver, attitude meter, barometer, electronic speed control and other devices. The unmanned aerial vehicle can be remotely controlled and send radio waves to its corresponding receiver, control the analog level change of its corresponding channel pins. The master control chip can analyze and process the data to send multiple sets pulse signals of pulse width modulation to each electronic speed control. Then the electronic speed control will transform different pulse signals into different sizes of current value to drive the motor located in each direction of the frame to generate different rotational speed and generate lift force. To control the body of the unmanned aerial vehicle, so as to achieve the operator's requirements for attitude control, the PID controller based on Kalman filter is used to achieve quick response time and control accuracy. Test results show that the design is feasible.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1255-1255
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• 2022

Recently, research on digital twins to generate digital information and manage construction in real-time using advanced technology is being conducted actively. However, in the construction industry, it is difficult to optimize and apply digital technology in real-time due to the nature of the construction industry in which information is constantly fluctuating. In addition, inaccurate information on the topography of construction projects is a major challenge for earthmoving processes. In order to ultimately improve the cost-effectiveness of construction projects, both construction quality and productivity should be addressed through efficient construction information management in large-scale earthworks projects. Therefore, in this study, a 3D digital map-based AR site management work support system for higher efficiency and accuracy of site management was proposed by using unmanned aerial vehicles (UAV) in wide earthworks construction sites to generate point cloud data, building a 3D digital map through acquisition and analysis of on-site sensor-based information, and performing the visualization with AR at the site By utilizing the 3D digital map-based AR site management work support system proposed in this study, information is able to be provided quickly to field managers to enable an intuitive understanding of field conditions and immediate work processing, thereby reducing field management sluggishness and limitations of traditional information exchange systems. It is expected to contribute to the improvement of productivity by overcoming factors that decrease productivity in the construction industry and the improvement of work efficiency at construction sites.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.2
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• pp.123-132
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• 2003

The quality certification test is usually conducted by a certifying organization to determine and guarantee the quality of software after the software development phase, commonly without the actual source code, but with by going against the product's manual. In this paper, we implement a Manual-based Automatic Test data generating tool: MaT, the test technique based on manual, that automatizes producing the test data from analysis data of software package and manual. The input data of MaT are the result of the analysis of software and manual. We propose 'menu-based test analysis model' in order to generate the input data. We believe that the proposed technique and tool he]p improving quality and reliability of the software.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.193-206
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• 2012

Keys play a fundamental role in all data models. They allow database systems to uniquely identify data items, and therefore, promote efficient data processing in many applications. Due to this, support is required to discover keys. These include keys that are semantically meaningful for the application domain, or are satisfied by a given database. We study the discovery of keys from SQL tables. We investigate the structural and computational properties of Armstrong tables for sets of SQL keys. Inspections of Armstrong tables enable data engineers to consolidate their understanding of semantically meaningful keys, and to communicate this understanding to other stake-holders. The stake-holders may want to make changes to the tables or provide entirely different tables to communicate their views to the data engineers. For such a purpose, we propose data mining algorithms that discover keys from a given SQL table. We combine the key mining algorithms with Armstrong table computations to generate informative Armstrong tables, that is, key-preserving semantic samples of existing SQL tables. Finally, we define formal measures to assess the distance between sets of SQL keys. The measures can be applied to validate the usefulness of Armstrong tables, and to automate the marking and feedback of non-multiple choice questions in database courses.

• Journal of Multimedia Information System
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• v.2 no.3
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• pp.287-294
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• 2015

We are living in epoch of information when Internet touches all aspects of our lives. Therefore, it provides a plenty of services each of which benefits people in different ways. Electronic Mail (E-mail), File Transfer Protocol (FTP), Voice/Video Communication, Search Engines are bright examples of Internet services. Between them Social Network Services (SNS) continuously gain its popularity over the past years. Most popular SNSs like Facebook, Weibo and Twitter generate millions of data every minute. Twitter is one of SNS which allows its users post short instant messages. They, 100 million, posted 340 million tweets per day (2012)[1]. Often big amount of data contains lots of noisy data which can be defined as uninteresting and unclassifiable data. However, researchers can take advantage of such huge information in order to analyze and extract meaningful and interesting features. The way to collect SNS data as well as tweets is handled by crawlers. Twitter crawler has recently emerged as a great tool to crawl Twitter data as well as tweets. In this project, we develop Twitter Crawler system which enables us to extract Twitter data. We implemented our system in Java language along with MySQL. We use Twitter4J which is a java library for communicating with Twitter API. The application, first, connects to Twitter API, then retrieves tweets, and stores them into database. We also develop crawling strategies to efficiently extract tweets in terms of time and amount.