• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.345-350
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• 2016

This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.19-27
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• 2015

Due to the advantages of fast read/write operation and low power consumption, SSD(Solid State Drive) is now widely adopted as storage device of smart phone, laptop computer, server, etc. However, the shortcomings of SSD such as limited number of write operations and asymmetric read/write operation lead to the problem of shortened life span of SSD. Therefore, the block replacement policy of SSD used as cache for HDD is very important. The existing solutions for improving the lifespan of SSD including the LARC scheme typically employ the LRU algorithm to manage the SSD blocks, which may increase the miss rate in SSD due to the replacement of frequently used block instead of rarely used block. In this paper we propose a novel block replacement scheme which considers the block reuse interval to effectively handle various data read/write patterns. The proposed scheme replaces the block in SSD based on the recency decided by reuse interval and age along with hit ratio. Computer simulation using workload trace files reveals that the proposed scheme consistently improves the performance and lifespan of SSD by increasing the hit ratio and decreasing the number of write operations compared to the existing schemes including LARC.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.696-701
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• 2016

Multi-stage deep drawing is a widely used industrial manufacturing process, and its applications are gradually expanding to both small products and large metallic products. The USB C-type socket used in smart phones, for example, is manufactured using oval multi-stage deep drawing. The socket is very small and slender and it requires precise manufacturing. The thickness distribution of the final product is guaranteed only if it is uniform throughout the overall process. Therefore, minimizing the height difference between long and short sidewalls after the first operation is important for this goal. An initial blank optimization was performed for an oval-type drawing process based on finite element simulations. The goal was to determine an initial blank geometry that can maintain uniform height and thickness after the first draw operation. The initial blank shape of the sheet metal was optimized, and the results show that it satisfied the conditions of minimal thickness reduction and even thickness distribution. The geometry from the optimized simulation was compared with experimental results, which showed good agreement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.582-590
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• 2016

The purpose of this study is to investigate the relationship between suicidal attitude and suicidal ideation of college students and also to provide the fundamental data in order to strengthen both the standard and the recommendation of media coverage in Korea. A total of 249 college students over the age of 18 years in B city and G do were enrolled for this study. The data were collected between June 9 and July 7 of 2014 and analyzed by using descriptive statistics, t-test, ANOVA, Pearson correlation coefficients, and multiple regression with SPSS 21.0. The mean score of a message of suicidal motivation reports 2 was $51.06{\pm}10.55$, which was the highest. The score of suicidal ideation was $10.41{\pm}12.88$. There were sign&ificant differences in suicidal thought with respect to school system, family income, pocket money, time of physical activity, smoking, and smart-phone usage in university students. It was shown that suicidal thoughts were significantly correlated with message of simple suicidal reports 1(r=.303, p<.001), message of suicidal motivation reports 2(r=.251, p<.001), and message of suicidal mourning man reports 3(r=.225, p<.001). As the suicidal attitude have a close association with reports of suicide, it is necessary that studies on a variety of factors influencing suicidal thoughts of students be repeated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.292-301
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• 2016

Because damages arising from the occurrence of foot-and-mouth disease (FMD) are very great, it is essential to make a preemptive diagnosis to cope with it in order to minimize those damages. The main symptoms of foot-and-mouth disease are body temperature increase, loss of appetite, formation of blisters in the mouth, on hooves and breasts, etc. in a cow or a bull, among which the body temperature check is the easiest and quickest way to detect the disease. In this paper, an algorithm to detect FMD from the hooves of cattle was developed and implemented for preemptive coping with foot-and-mouth disease, and a hoof check test is conducted after the installation of a high-resolution camera module, a thermo-graphic camera, and a temperature/humidity module in the cattle shed. Through the algorithm and system developed in this study, it is possible to cope with an early-stage situation in which cattle are suspected as suffering from foot-and-mouth disease, creating an optimized growth environment for cattle. In particular, in this study, the system to cope with FMD does not use a portable thermo-graphic camera, but a fixed camera attached to the cattle shed. It does not need additional personnel, has a function to measure the temperature of cattle hooves automatically through an image algorithm, and includes an automated alarm for a smart phone. This system enables the prediction of a possible occurrence of foot-and-mouth disease on a real-time basis, and also enables initial-stage disinfection to be performed to cope with the disease without needing extra personnel.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.391-397
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• 2017

Recently, digital image correlation (DIC) techniques have been used to measure dynamic deformation during tensile testing. The standard tensile test method measures the average displacement of the relevant specimen to calculate the true stress-strain curve. Therefore, the validity of the true stress curve is restricted to the stress incurred within the uniform stretching interval, i.e., the maximum stress corresponds to the starting point of the necking deformation. Alternatively, if DIC is used, the effective range of the strain and strain rate can be extended to the breaking point of the tensile specimen, because of the feasibility of measuring the local strain over the entire area of interest. Because of these advantages, many optical 3D measurement systems have been introduced and used in research and industry. However, the conventional 3D measurement systems are exceedingly expensive and time consuming. In addition, these systems have the disadvantage of a very large equipment size which makes their transport difficult. In this study, a 2D image correction method employing a 2D DIC measurement method in conjunction with a numerical analysis method is developed using a smartphone. The results of the proposed modified 2D DIC method yielded higher accuracy than that obtained via the 3D measurement equipment. In conclusion, it was demonstrated that the proposed 2D DIC and calibration methods yield accurate measurement results with low time costs.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.503-508
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• 2010

Recently, the advance of mobile devices has made various services possible beyond simple communication. One of services is the predicting the future path of users and providing the most suitable location based service based on the prediction results. Almost of these prediction methods are based on previous path data. Thus, calculating similarities between current location information and the previous trajectories for path prediction is an important operation. The collected trajectory data have a huge amount of location information generally. These information needs the high computational cost for calculating similarities. For reducing computational cost, the meaningful location based trajectory model approaches are proposed. However, most of the previous researches are considering only the physical information such as stay time and the distance for extracting the meaningful locations. Thus, they will probably ignore the characteristics of users for meaningful location extraction. In this paper, we suggest a meaningful location extracting and trajectory simplification approach considering the stay time, distance, and additionally interaction information of user. The method collects the location information using GPS device and interaction information between the user and the others. Using these data, the proposed method defines the proximity of the people who are related with the user. The system extracts the meaningful locations based on the calculated proximities, stay time and distance. Using the selected meaningful locations the trajectories are simplified. For verifying the usability of the proposed method, we collect the behavioral data of smart phone users. Using these data, we measure the suitability of meaningful location extraction method, and the accuracy of prediction approach based on simplified trajectories. Following these result, we confirmed the usability of proposed method.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.62-70
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• 2015

Recently the demand for projected capacitance touch screens is sharply growing especially for large screens for medical devices, PC monitors and TVs. Large touch screens in general need a controller of higher complexity. They usually have a larger number of driving and sensing lines, and hence it takes longer to scan one frame for touch detection leading to a low frame scan rate. In this paper, a novel touch screen control technique is presented, which scans each frame in two steps of simultaneous multi-channel driving. The first step is to drive all driving lines simultaneously and determine which sensing lines have any touch. The second step is to sequentially rescan only the touched sensing lines, and determine exact positions of the touches. This technique can substantially increase the frame scan rate. This technique has been implemented using an FPGA and an AFE board, and tested using a commercial 23-inch touch screen panel. Experimental results show that the proposed technique improves the frame scan rate by 8.4 times for the 23-inch touch screen panel over conventional methods.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.