• Journal of the Microelectronics and Packaging Society
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• v.31 no.3
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• pp.42-49
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• 2024

In the electromagnetic wave shielding material market, superior shielding performance in the high-frequency range, along with flexibility and durability, has emerged as critical requirements. The need for high-performance EMI (Electromagnetic Interference) films to address electromagnetic wave interference issues is growing, particularly in various industrial sectors such as smart electronic devices, automotive electronic systems, and communication equipment. In this study, a trimodal silver paste was developed and fabricated into an EMI film, with its performance evaluated. The developed silver paste, utilizing a modified epoxy binder, exhibited properties suitable for screen printing processes. The film demonstrated excellent shielding performance, with an average attenuation of -99 dB in the high-frequency range of the 5G spectrum (26.5 GHz to 40 GHz), and a shielding effectiveness of -90.3 dB at 33.6 GHz. Flexibility and durability tests showed that the film maintained its flexibility even at a curvature radius of 1 mm. In the bending cycle test, the resistance increased by approximately 25.5% from 0.51 Ω to 0.64 Ω after 10,000 cycles in the outer bending scenario, while in the inner bending scenario, the resistance decreased by about 3.6%, indicating reduced resistance to compressive stress.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of the Korean Society of Radiology
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• v.10 no.2
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• pp.109-115
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• 2016

Despite of increasing the use of the digital imaging device in the radiology area, the setting on the optimal irradiation conditions are insufficient. In this study, the exposure dose and image quality by exposure condition of digital radiography device were compared. The exposure doses were obtained by adjusting the exposure condition as 5 steps respectively based on the exposure conditions that are currently used of CR and DR radiography devices. The acquired image has been assessed by 20 medical image professors using the assessment method of the Japanese Society for Tuberculosis Prevent. As a result, in the case of the CR system, the better image quality was obtained in the condition of 120 kVp and 1.5 mAs~2.4 mAs (quality score 91~95.5 points) than standard exposure condition(110 kVp, 3.2 mAs, 86 points). And exposure dose was evaluated as low with $61.3{\sim}98.4{\mu}Gy$ than standard condition($105.11{\mu}Gy$). In DR system, however, the image quality score was higher as 97~98.6 points in the lower tube voltage range (112 kVp, 2.4~3.2 mAs) condition than the standard exposure condition (125 kVp, 3.2 mAs, 91 points). In addition, the exposure dose was $61.5-77.2{\mu}Gy$ lower than standard condition($93{\mu}Gy$). In addition, the exposure dose was low as $61.5-77.2{\mu}Gy$ than standard condition($93{\mu}Gy$). With the results of this study, we confirmed that it is possible to reduce the patient exposure dose with the same image quality by adjusting the optimal exposure condition of digital device.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• The Journal of Korean Society for Radiation Therapy
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• v.19 no.1
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• pp.19-26
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• 2007

Purpose: To compare of the accuracy among various measurement procedures of HDR Brachytherapy, and to evaluate the clinical suitability and usefulness of alternative PMMA (polymethylmethacrylateplastics: $C_5H_8O_2$) plate phantom without any additional cost due to the purchase of measuring apparatus. Materials and Methods: We made a comparative study on three types of measuring systems: well type chamber, source calibration jig, and PMMA plate phantom. Farmer type chamber was used for source calibration jig method and PMMA plate phantom method. Measurement was done 5 times each in comparison with the measurement values from manufacturer. Measurement results from experiment were compared with that from the manufacturer which is offered with the source whenever a source is substituted by a new one and evaluate the accuracy of source activity. Results: As a consequence of Ir-192 source measurement using well type chamber, source calibration jig and PMMA plate phantom, RMS (Root Mean Square) values for the relative error are 0.6%, 1.57%, 2.1%, respectively, compared with the data from manufacturer. And the mean errors with standard deviation are given $-0.2{\pm}0.5%$, $0.97{\pm}1.23%$, $-0.89{\pm}1.87%$ respectively. Conclusion: From the results shown by the three types of measurement system (well type chamber, source calibration jig, and PMMA plate phantom), the measurement with well type chamber produced the best accuracy. It turns out that we can also use the alternative system of PMMA plate phantom clinically without purchasing any additional particular apparatus since the system does not exceed the recommendation of AAPM (American Association of Physicists in Medicine), which requires the error range of within ${\pm}5%$.

• Journal of Bio-Environment Control
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• v.4 no.1
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• pp.59-67
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• 1995

This study was conducted to develope the automatic insulation system which control inside temperature of the greenhouse. For this purpose, the double- wall greenhouse and system which could automatically supply and discharge styrene pellets were constructed and abrasion of the pellets, blower ability, insulating property, transmittance and shading effect were analyzed by the experiments. The results obtained from this study can be summarized as follows : 1. It took an hour and fifteen minutes to supply and discharge about 2㎥ pellets in the experimental greenhouse. However, it is possible to reduce the operation time by proper selection of the blower and exhaust port, and by proper control of the supply and return pipe. 2. It was founded that the indirect delivery way was more profitable than the direct one in the supply and return of pellets. 3. When the transmittance was measured between 10 a.m. and 3 p.m., the average light transmissivity rate was 67%. 4. In winter nighttime, the inside temperature of the double- wall greenhouse with out the pellets was higher than the outside temperature by 3.4$^{\circ}C$ on an average. However, the inside temperature of the double - wall greenhouse with insulated area 73% was higher than the outside by one 6.6$^{\circ}C$ on an average, and the inside temperature of the greenhouse with insulated area 100% was higher than outside one by 13.5$^{\circ}C$ on an average. Therefore, it was proved that the insulating ability of the double - wall greenhouse in nighttime was excellent. 5. When the outside temperature was 36.9$^{\circ}C$ on an average, the inside temperature of the double- wail greenhouse with insulated area 100% was 3$0^{\circ}C$ on an average. As the inside temperature was lower than the outside one by 7$^{\circ}C$ on an average, we could know that the shading effects of the double- wall greenhouse were excellent in summer daytime.

• Korean journal of applied entomology
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• v.29 no.3
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• pp.209-215
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• 1990

Some preliminary studies were conducted to find out whether the levee-burning could justifiable for the suppression of insect pests, particularly the smaller brown planthopper (Laodelphax striatellus F.). Density surveys on pests and their enemies (mostly spiders) were carried out upto the mid May at an experimental paddy field located in Suwon after of it's levee $(72\times1m)$ was burned on Feb. 20, 1987. Results were discussed in relation to density recovering of both pests and their possible enemies (spiders) and summarized as below. Not a single individual of any pest or enemy was found from the levee upto sometime after the levee-burning. Grasses started to grow more vigorously in burned ares than in unburned upto about 60 days after the burning. And densities of both pest and enemies grew higher in burned areas than in unburned from about 75 days after the burning (in Early may). It is suspected that all individuals of pests and enemies fond from the burned areas could have immigrated from the surrounding areas. If levee-burning was carried out in much wider areas, much longer time would be needed to recover the density of both pests and enemies to the center region of the burning. Wingless spiders would require even longer time than winged pest species to re-establish in the center region of the widely burned field. Pirata subpiraticus, the most abundant spider species in Korean paddy fields, starts to move about and searches for food at above $9^{\circ}C$ which is somewhat lower than the critical temperature for the pest species. Thus P. subpiraticus would require more food than other pest species early in the spring, and therefore, it would have lower probability to survive than pest species particularly in burned areas. Experiments for pest suppression with levee-burning would better be carried on in much wider areas, and its justification seems to be discussed after man other disciplines related to both pests and their natural enemies were throughly studied together with their density surveys. However, according to the present point of vie, the opinion that levee-burning is helpful for controlling pests which over winter on levee areas could not be justifiable.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.331-367
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• 2008

This study aims to validate a web-based neuropsychological testing tool developed by Kwak(2007) and to suggest solutions to potential problems that can deteriorate its validity. When it targets a wider range of subjects, a web-based neuropsychological testing tool is challenged by high drop-out rates, lack of motivation, lack of interactivity with the experimenter, fear of computer, etc. As a possible solution to these threats, this study aims to redesign the user interface of a web-based attention testing tool through three phases of study. In Study 1, an extensive analysis of Kwak's(2007) attention testing tool was conducted to identify potential usability problems. The Heuristic Walkthrough(HW) method was used by three usability experts to review various design features. As a result, many problems were found throughout the tool. The findings concluded that the design of instructions, user information survey forms, task screen, results screen, etc. did not conform to the needs of users and their tasks. In Study 2, 11 guidelines for the design of web-based attention testing tools were established based on the findings from Study 1. The guidelines were used to optimize the design and organization of the tool so that it fits to the user and task needs. The resulting new design alternative was then implemented as a working prototype using the JAVA programming language. In Study 3, a comparative study was conducted to demonstrate the excellence of the new design of attention testing tool(named graphic style tool) over the existing design(named text style tool). A total of 60 subjects participated in user testing sessions where their error frequency, error patterns, and subjective satisfaction were measured through performance observation and questionnaires. Through the task performance measurement, a number of user errors in various types were observed in the existing text style tool. The questionnaire results were also in support of the new graphic style tool, users rated the new graphic style tool higher than the existing text style tool in terms of overall satisfaction, screen design, terms and system information, ease of learning, and system performance.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1332-1344
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• 2004

Upon development of information super-highway and multimedia-related technoiogies in recent years, more efficient technologies to transmit, store and retrieve the multimedia data are required. Among such technologies, firstly, it is common that the semantic-based image retrieval is annotated separately in order to give certain meanings to the image data and the low-level property information that include information about color, texture, and shape Despite the fact that the semantic-based information retrieval has been made by utilizing such vocabulary dictionary as the key words that given, however it brings about a problem that has not yet freed from the limit of the existing keyword-based text information retrieval. The second problem is that it reveals a decreased retrieval performance in the content-based image retrieval system, and is difficult to separate the object from the image that has complex background, and also is difficult to extract an area due to excessive division of those regions. Further, it is difficult to separate the objects from the image that possesses multiple objects in complex scene. To solve the problems, in this paper, I established a content-based retrieval system that can be processed in 5 different steps. The most critical process of those 5 steps is that among RGB images, the one that has the largest and the smallest background are to be extracted. Particularly. I propose the method that extracts the subject as well as the background by using an Image, which has the largest background. Also, to solve the second problem, I propose the method in which multiple objects are separated using RGB channel selection techniques having optimized the excessive division of area by utilizing Watermerge's threshold value with the object separation using the method of RGB channels separation. The tests proved that the methods proposed by me were superior to the existing methods in terms of retrieval performances insomuch as to replace those methods that developed for the purpose of retrieving those complex objects that used to be difficult to retrieve up until now.