• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3B
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• pp.112-122
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• 2008

Basing on Piconet, The Bluetooth System forms network and transmits data. There is one Master and maximum 7 Slave bluetooth devices in one piconet. A job scheduler performed by Master bluetooth device, gives the chance of data transmission to Slave bluetooth devices, which connected to Master, using polling method in piconet. The maximum data rate is 723.2 kb/s when it uses ACL link with DH5 packet type in a piconet which is constructed by two bluetooth devices. However, if there are one master and two slave devices in a piconet, then the maximum data rate is reduced to a half(361.6kb/s), because a master device has to support same data rate for all connected devices. And, there is the defect in scatternet when data transmission rate becomes low(Maximum rate: 302.2kb/s). This paper proposals the new ad-hoc network topology called "DoublePico"for overcome the low data transmission in scatternet which is constructed by piconets. The method of doublepico that represented in this paper makes high data transfer rate(Maximum rate: 457.57kb/s) in bluetooth ad-hoc networks.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Society of Disaster Information
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• v.14 no.3
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• pp.238-246
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• 2018

Purpose: The purpose of this study is to examine the directions for establishing a disaster safety village in rural areas where damage from a similar type of disaster occurs repeatedly by conducting the consciousness survey targeting at experts and disaster safety officials in a local government. Method: The risks of disaster in rural areas were examined and the concept and characteristics of disaster safety village which is a measure on the basis of Myeon (township) among the measures of village unit were examined in order to carry out this study. In addition, opinion polling targeting at officials-in-charge in the local government and survey targeting at experts in disaster safety and building village were conducted. Based on the findings, the directions for establishing a disaster safety village that fitted the characteristics of rural areas were examined. Result: The officials-in-charge in the local government answered that rural areas have a high risk of storm and flood such as heavy snowing, typhoon, drought, and heavy rain as well as forest fire, and it is difficult to draw voluntary participation of farmers for disaster management activities due to their main duties. They also replied that active support and participation of residents in rural areas are necessary for future improvement measures. The experts mostly replied that the problem of disaster safety village project is a temporary project which has low sustainability, and the lack of connections between the central government, local governments and residents was stressed out as the difficulties. They said that measures to secure the budget and the directions of project promotion system should be promoted by the central government, local governments and residents together. Conclusion: The results of this study are as follows. First, a disaster safety village should be established in consideration of the disaster types and characteristics. Second, measures to secure the budget for utilizing the central government fund as well as local government fund and village development fund should be prepared when establishing and operating a disaster safety village in rural areas. Third, measures to utilize a disaster safety village in rural areas for a long period of time such as the re-authorization system should be prepared in order to continuously operate and manage such villages after its establishment. Fourth, detailed measures that allow residents of rural areas to positively participate in the activities for establishing a disaster safety village in rural areas should be prepared.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.199-219
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• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.