• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.95-103
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• 2002

We know that the all of warfare system has been becoming complex and variety in warfield thru the Gulf-War. The all of warfare electronic systems is designed to inter-operate by networks in recently. Especially Warfare Equipment systems of Men-of-War(War ship) as like KDX(Korea Destroyer, Experimental), FF(Frigate), PCC(Costal Patrol Craft), Submarine are connected by Combat System Databus to the Command system(C2 System), so C2 system can control all of equipments in ship. In this view, the status of network(Combat System Databus) is very critical parameter in war field. So In this paper, we propose the method of Network Management System construction for War ship, and especially propose the architectural design of network management system for DX(Destroyer, Experimental) equipments using SNMP(Simple Network Management Protocol). And Link Utilization is monitored by simulation. 

• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.494-503
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• 2003

The IEEE 1394 High Speed Serial Bus is a versatile, high-performance, and low-cost method of promoting interoperability between all types of A/V and computing devices. IEEE 1394 provides two transfer modes: asynchronous mode for best effort service and isochronous mode for best effort service with bandwidth reservation. This paper shows the bus performance and compared the transfer odes first at the link level and then at the application level. For the application level performance, we analyze the bus systems with fixed and adaptive interfaces, applied between the upper layer and the 1394 layer, using polling systems. Also we verifies the analysis models with simulation studies. Based on our analysis, we conclude that the adaptive interface reduces the bus access time and so increases the bus utilization.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.444-448
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• 2005

This paper presents a developing procedure of the CAN-based real-time simulator for car body control, aiming at replacing the actual W/H (Wiring Harness) and J/B(Junction Box) couple eventually. The CAN protocol, as one kind of field-bus communication, defines the lowest 2 layers of the ISO/OSI standard, namely, the physical layer(PL) and the data link layer(DLL), for which the CSMA/NBA protocol is generally adopted. For CPU, two PIC18Fxx8x's are used because of their built-in integration of CAN controller, large internal FLASH memory (48K or 64K), and their costs. To control J/B's and actuators, 2 controller boards are separately implemented, between which CAN lines communicate through CAN transceivers MCP255. A power motor for washing windshield, 1 door lock motor, and 6 blink lamps are chosen for actuators of the simulator for the first stage. For the software architecture, a polling method is used for the fast global response time despite its slow individual response time. To improve the individual response time and to escape from some eventual trapped-function loops, High/Low ports of the CPU are simply used, which increases the stability of the actuator modules. The experimental test shows generally satisfactory results in normal transmitting / receiving function and message trace function. This simulator based on CAN shows a promising usefulness of lighter, more reliable and intelligent distributed body control approach than the conventional W/H and J/B couple. Another advantage of this approach lies in the distributed control itself, which gives better performance in hard real-time computing than centralized one, and in the ability of integrating different modules through CAN.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.57-60
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• 2003

IEEE 802.16 Wireless MAN standard specifies the air interface of fixed point-to-multipoint broadband wireless access systems providing multiple service. Among the service classes supported by the wireless MAN, the BE class is ranked on the lowest position in priority and is usually deployed by multicast and broadcast polling MAC scheme. In provisioning such BE service, the delay performance is influenced by a number of components including restrictions on resource request per SS, the number of request opportunities in upward frame, scheduling requests at BS, and contention resolution method. As candidate components of MAC function for BE service, we propose single and multiple request schemes (for controling the number of requests per SS), exhaustive and limited request schemes (for regulating the amount of grant per request) and FCFS, H-SMF, pure SMF, SS-wise Round Robin, and pure Round Robin (for scheduling requests at BS). Then, we construct MAC schemes by combining the above components and evaluate the delay performance exhibited by each MAC scheme using a simulation method. From numerical results, we investigate the effect of MAC components on average delay and delay variation and observe the dissonance on collision reduction in a resource - limited environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.371-384
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• 2019

Motion perception has been tremendously improved in neuroscience and computer vision. The baseline motion perception model is mediated by the dorsal visual pathway involving the cortex areas the primary visual cortex (V1) and the middle temporal (V5 or MT) visual area. However, few works have been done on the extension of neural models to improve the efficacy and robustness of motion perception of real sequences. To overcome shortcomings in situations, such as varying illumination and large displacement, an adaptive V1-MT motion perception (Ad-V1MTMP) algorithm enriched to deal with real sequences is proposed and analyzed. First, the total variation semi-norm model based on Gabor functions (TV-Gabor) for structure-texture decomposition is performed to manage the illumination and color changes. And then, we study the impact of image local context, which is processed in extra-striate visual areas II (V2), on spatial motion integration by MT neurons, and propose a V1-V2 method to extract the image contrast information at a given location. Furthermore, we take feedback inputs from V2 into account during the polling stage. To use the algorithm on natural scenes, finally, multi-scale approach has been used to handle the frequency range, and adaptive pyramidal decomposition and decomposed spatio-temporal filters have been used to diminish computational cost. Theoretical analysis and experimental results suggest the new Ad-V1MTMP algorithm which mimics human primary motion pathway has universal, effective and robust performance.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.27-36
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• 2010

In Web registration pages and online polling applications, CAPTCHA(Completely Automated Public Turing Test To Tell Computers and Human Apart) is used for distinguishing human users from automated programs. Text-based CAPTCHAs have been widely used in many popular Web sites in which distorted text is used. However, because the advanced optical character recognition techniques can recognize the distorted texts, the reliability becomes low. Image-based CAPTCHAs have been proposed to improve the reliability of the text-based CAPTCHAs. However, these systems also are known as having some drawbacks. First, some image-based CAPTCHA systems with small number of image files in their image dictionary is not so reliable since attacker can recognize images by repeated executions of machine learning programs. Second, users may feel uncomfortable since they have to try CAPTCHA tests repeatedly when they fail to input a correct keyword. Third, some image-base CAPTCHAs require high communication cost since they should send several image files for one CAPTCHA. To solve these problems of image-based CAPTCHA, this paper proposes a new CAPTCHA based on both image and text. In this system, an image and keywords are integrated into one CAPTCHA image to give user a hint for the answer keyword. The proposed CAPTCHA can help users to input easily the answer keyword with the hint in the fused image. Also, the proposed system can reduce the communication costs since it uses only a fused image file for one CAPTCHA. To improve the reliability of the image-text fusion CAPTCHA, we also propose a dynamic building method of large image dictionary from gathering huge amount of images from theinternet with filtering phase for preserving the correctness of CAPTCHA images. In this paper, we proved that the proposed image-text fusion CAPTCHA provides users more convenience and high reliability than the image-based CAPTCHA through experiments.

• 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.

• 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.