• 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 health informatics and statistics
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• v.42 no.4
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• pp.309-316
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• 2017

Objectives: Radon and its progeny pose environmental risks as a carcinogen, especially to the lungs. Investigating factors affecting indoor radon concentrations and models thereof are needed to prevent exposure to radon and to reduce indoor radon concentrations. The purpose of this study was to identify factors affecting indoor radon concentration and to construct a comprehensive model thereof. Methods: Questionnaires were administered to obtain data on residential environments, including building materials and life style. Decision tree and structural equation modeling were applied to predict residences at risk for higher radon concentrations and to develop the comprehensive model. Results: Greenery ratio, impermeable layer ratio, residence at ground level, daily ventilation, long-term heating, crack around the measuring device, and bedroom were significantly shown to be predictive factors of higher indoor radon concentrations. Daily ventilation reduced the probability of homes having indoor radon concentrations ${\geq}200Bq/m^3$ by 11.6%. Meanwhile, a greenery ratio ${\geq}65%$ without daily ventilation increased this probability by 15.3% compared to daily ventilation. The constructed model indicated greenery ratio and ventilation rate directly affecting indoor radon concentrations. Conclusions: Our model highlights the combined influences of geographical properties, groundwater, and lifestyle factors of an individual resident on indoor radon concentrations in Korea.

• Journal of Technology Innovation
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• v.29 no.4
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• pp.1-30
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• 2021

Due to the recent development of system semiconductors, technical innovation for the electric devices of the automobile industry is rapidly progressing. In particular, the electric device of automobiles is accelerating technology development competition among automobile parts makers, and the development cycle is also changing rapidly. Due to these changes, the importance of strategic planning for R&D is further strengthened. Due to the paradigm shift in the automobile industry, the Product-Technical Roadmap (P/TRM), one of the R&D strategies, analyzes technology forecasting, technology level evaluation, and technology acquisition method (Make/Collaborate/Buy) at the planning stage. The product-technical roadmap is a tool that identifies customer needs of products and technologies, selects technologies and sets development directions. However, most companies are developing the product-technical roadmap through a qualitative method that mainly relies on the technical papers, patent analysis, and expert Delphi method. In this study, empirical research was conducted through simulations that can supplement and strengthen the product-technical roadmap centered on the automobile industry by fusing Gartner's hype cycle, cumulative moving average-based data preprocessing, and deep learning (LSTM) time series analysis techniques. The empirical study presented in this paper can be used not only in the automobile industry but also in other manufacturing fields in general. In addition, from the corporate point of view, it is considered that it will become a foundation for moving forward as a leading company by providing products to the market in a timely manner through a more accurate product-technical roadmap, breaking away from the roadmap preparation method that has relied on qualitative methods.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.6
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• pp.639-646
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• 2022

Energy harvesting technologies that can convert wasted various energy into usable electrical energy have been widely investigated to overcome the limitation of batteries for the powering of IoT sensors and small electronic devices. Hybrid energy harvesting is known as a technology that enhances the output power of single energy harvesting device by housing two or more various energy harvesting mechanisms. In this study, we introduce a hybrid MME (Magneto-Mechano-Electric) generator coupled with the triboelectric effect. Through FEA modeling, four triboelectric materials, including PI (Polyimide), PFA(Teflon), Cu, and Al, were selected and compared with the expected triboelectric potentials. The effect of surface morphology was investigated as well. Among various combination of triboelectric materials and surface morphologies, PFA-Al combination with the surface morphology having nano-scale square projections showed highest output potential under triboelectrification. It is also experimentally confirmed that output voltage and power of the hybrid MME generator with triboelectric material combinations.

• Information Systems Review
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• v.22 no.2
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• pp.77-99
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• 2020

Smart speaker is a device that provides an interactive voice-based service that can search and use various information and contents such as music, calendar, weather, and merchandise using artificial intelligence. Since AI technology provides more sophisticated and optimized services to users by accumulating data, early smart speaker manufacturers tried to build a platform through aggressive marketing. However, the frequency of using smart speakers is less than once a month, accounting for more than one third of the total, and user satisfaction is only 49%. Accordingly, the necessity of strengthening the user experience of smart speakers has emerged in order to acquire a large number of users and to enable continuous use. Therefore, this study analyzes the user experience of the smart speaker and proposes a method for enhancing the user experience of the smart speaker. Based on the analysis results in two stages, we propose ways to enhance the user experience of smart speakers by model. The existing research on the user experience of the smart speaker was mainly conducted by survey and interview-based research, whereas this study collected the actual review data written by the user. Also, this study interpreted the analysis result based on the smart speaker user experience dimension. There is an academic significance in interpreting the text mining results by developing the smart speaker user experience dimension. Based on the results of this study, we can suggest strategies for enhancing the user experience to smart speaker manufacturers.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.243-250
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• 2024

Biomimetic robots, which emulate characteristics of biological entities such as birds or insects, have the potential to offer a tactical advantage in surveillance and reconnaissance in future battlefields. To effectively utilize these robots, it is essential to develop technologies that emulate the wing flapping of birds or the movements of cockroaches. However, this effort is complicated by the challenges associated with securing the necessary hardware and the complexities involved in software development and validation processes. In this paper, we presents the design and implementation of a multi-HILS based biomimic robot software validation testbed using modeling and simulation (M&S). By employing this testbed, developers can overcome the absence of hardware, simulate future battlefield scenarios, and conduct software development and testing. However, the multi-HILS based testbed may experience inter-device communication delays as the number of test robots increases, significantly affecting the reliability of simulation results. To address this issue, we propose the data distribution service priority (DDSP), a priority-based middleware. DDSP demonstrates an average delay reduction of 1.95 ms compared to the existing DDS, ensuring the required data transmission quality for the testbed.

• Korean Journal of Optics and Photonics
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• v.27 no.1
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• pp.18-24
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• 2016

To effectively utilize a flash and predict its effects on an infrared device, it is essential to know the infrared characteristics of the flash source. In this paper, a study of the IR characteristics of flash light sources is carried out. The IR characteristics of three flash sources, of which two are combustive and the other is explosive, are measured with an IR characteristic measurement system over the middle- and long-wavelength infrared ranges. From the measurements, the radiances over the two IR ranges and the radiative temperatures of the flashes are extracted. The IR radiance of flash A is found to be the strongest among the three, followed by those of sources C and B. It is also shown that the IR radiance of flash A is about 10 times stronger than that of flash B, even though these two sources are the same type of flash with the same powder. This means that the IR radiance intensity of a combustive flash source depends only on the amount of powder, not on the characteristics of the powder. From the measured radiance over MWIR and LWIR ranges for each flashes, the radiative temperatures of the flashes are extracted by fitting the measured data to blackbody radiance. The best-fit radiative temperatures (equivalent to black-body temperatures) of the three flash sources A, B, and C are 3300, 1120, and 1640 K respectively. From the radiance measurements and radiative temperatures of the three flash sources, it is shown that a combustive source radiates more IR energy than an explosive one; this mean, in turn, that the effects of a combustive flash on an IR device are more profound than those of an explosive flash source. The measured IR radiances and radiative temperatures of the flash sources in this study can be used to estimate the effects of flashes on various IR devices, and play a critical role for the modeling and simulation of the effects of a flash source on various IR devices.

• Progress in Medical Physics
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• v.27 no.1
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• pp.31-36
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• 2016

The paper discusses radiation dose of dual energy CT on which copper modulation layer, is mounted in order to improve diagnostic performance of the dual energy CT. The radiation dose is estimated using MCNPX and its results are compared with that of the conventional dual energy CT system. CT X-ray spectra of 80 and 120 kVp, which are usually used for thorax, abdominal, head, and neck CT scans, were generated by the SPEC78 code and were used for the source specification 'SDEF' card for MCNPX dose modeling. The copper modulation layer was located 20 cm away from a source covering half of the X-ray window. The radiation dose was measured as changing its thickness from 0.5 to 2.0 mm at intervals of 0.5 mm. Since the MCNPX tally provides only normalized values to a single particle, the dose conversion coefficients of F6 tally for the modulation layer-based dual energy CBCT should be calculated for matching the modeling results into the actual dose. The dose conversion coefficient is $7.2*10^4cGy/output$ that is obtained from dose calibration curve between F6 tally and experimental results in which GAFCHORMIC EBT3 films were exposed by an already known source. Consequently, the dose of the modulation layer-based dual energy cone beam CT is 33~40% less than that of the single energy CT system. On the basis of the results, it is considered that scattered dose produced by the copper modulation layer is very small. It shows that the modulation layer-based dual energy CBCT system can effectively reduce radiation dose, which is the major disadvantage of established dual energy CT.

• The Journal of Information Systems
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• v.24 no.2
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• pp.23-52
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• 2015

Purpose This study aims to understand the role of digital knowledge in accepting the green technology. This study combined digital option theory with the second version of the Unified Theory of Acceptance and Use of Technology (UTAUT2). Contrary to other studies in which the UTAUT2 is used to explain IT adoption behavior, we look at the relationship between IT and the UTAUT2 from a new angle, incorporating an important aspect of IT, that is, digitized knowledge richness, as a determinant of the UTAUT2. Design/methodology/approach Grounded in the UTAUT2, a content analysis was conducted to investigate novel constructs dedicated to explaining green technology adoption. In this study, an amended version of the UTAUT2 specific to green technology is offered that better explains the green technology adoption behavior of consumers. Using the items identified by content analysis, we developed a questionnaire with 36 survey items. We measured all the items on a seven-point Likert-type scale. We randomly selected 402 survey respondents from a set of panel data. After a pilot study, we analyzed the main survey data by using PLS 2.0M3 and SPSS 20.0, and employed structural equation modeling to test the hypotheses. Findings The results suggest that the UTAUT2 was found to be extendable to technologies other than conventional IT. Social influence is more significant than conventional utilitarian and hedonic-based constructs such as those utilized in the UTAUT and UTAUT2 in explaining adoption behavior in the context of green technologies. The hypothesized connection between digitized knowledge richness and adoption intention was supported by the results of studies on the role of IT in formation of attitudes toward eco-friendly production. The results also indicate that digital knowledge can also encourage people to try green technology when they learn that their peers are already using the technology successfully.