The Journal of The Korea Institute of Intelligent Transport Systems
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v.22
no.5
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pp.1-18
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2023
With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.
Because active interactions occur among vegetation, hydrology, and geomorphology in riparian systems, any changes in one of these factors can significantly affect the other two. In this study, we evaluated these interactions at four sites (two in Gajeong and two in Hahan) along the Seomjin-gang River that was substantially devastated by an extreme flood in 2020. We examined the relationship between the riparian vegetation and the hydraulic characteristics of the flood using remote sensing, hydraulic modeling, and field surveys combined. The evaluation results showed that the floods caused a record-breaking rise of up to 43.1 m above sea level at the Yeseong-bridge stage gauge station (zero elevation 27.4 m) located between the Gajeong and Hahan sites, with the shear stress being four times higher in Hahan than in Gajeong. Additionally, the water level during the flood was estimated to be a maximum of 1 m higher depending on the location in the presence of riparian plants. Furthermore, both sites underwent extensive biological damage due to the flood, with 78-80% loss in vegetation, with preferential damage observed in large willow species, compared to Quercus acutissima. The above findings imply that all plant species exhibit different vulnerabilities towards extreme floods and do not induce similar behavior towards events causing a disturbance. In conclusion, we developed strategies for effectively managing riparian trees by minimizing flood hazards that could inevitably cause damage.
The purpose of this study is to analyze the achievements of employment support by the government support specialization project. The data used in this study are based on the comparison of 5 - year employment support field and the operation results of the program until 2014-2018. The results of the study are as follows. First, the overall employment rate of the university has been continuously increased. Especially, the employment rate of the specialization department has been higher than the employment rate of the non - specialization department. Second, as a result of the analysis of the employment capacity strengthening index and the learning capacity strengthening index, it showed a steady increase in each year and contributed to the cultivation of customized talents required by the local society and the national industry. Third, as a result of analyzing the satisfaction of students who are business users, it was confirmed that the business reflecting the demands of the consumers was realized. Fourth, the continuous improvement of the business and the reflux have made the infrastructure of the employment support project more advanced and the system of supporting employment of the university systematically established. In conclusion, the result of the employment support project according to the specialization program showed excellent results and it is necessary to complement theses results when establishing related business plan in the future.
The Journal of the Convergence on Culture Technology
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v.9
no.4
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pp.303-308
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2023
The purpose of this study is to provide a practical guide for establishing BSC that can be practically applied by SMEs. To this end, a case study was conducted to establish a performance evaluation system through a field-required Balanced Scorecard (BSC) for company J, a tent pole manufacturer, and to provide a management strategy system map. As a survey method, the requirements of the ordering organization were organized through a comparison of the BSC-related proposal requests in the first stage. The BSC establishment method was organized through the arrangement of the second stage result report. The 3rd stage BSC derived KPI indicators for SMEs for each of the 4 perspectives. A corporate vision was derived through a 4-step SWOT analysis. A strategy map was developed through 5-step field-required KPI, weight setting, and BSC. The 6-step final strategy system was also drawn up. As a result of the study, the four perspectives of the BSC were reconstructed by department. That is, the financial (financial) perspective is from the executives' perspective, the customer's perspective is from the sales department's perspective, the internal process perspective is from the design department/production quality department's perspective, and the learning/innovation perspective is from the management department's perspective. In addition, a total of 11 CSFs and a total of 49 KPIs of J company were derived. The limitation of the study is that the final strategy system through the company's BSC has only been carried out, and it needs to be linked with the company's compensation system in the future.
Journal of the Korea Academia-Industrial cooperation Society
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v.17
no.11
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pp.746-754
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2016
Smart healthcare systems, a convergent industry based on information and communications technologies (ICT), has emerged from personal health management to remote medical treatment as a distinguished industry. The smart healthcare environment provides technology to deliver vital information, such as pulse rate, body temperature, health status, and so on, from wearable devices to the hospital network where the physician is located. However, since it deals with the patient's personal medical information, there is a security issue for personal information management, and the system may be vulnerable to cyber-attacks in wireless networks. Therefore, this study focuses on a key-development and device-management system to generate keys in the smart environment to safely manage devices. The protocol is designed to provide safe communications with the generated key and to manage the devices, as well as the generated key. The security level is analyzed against attack methods that may occur in a healthcare environment, and it was compared with existing key methods and coding capabilities. In the performance evaluation, we analyze the security against attacks occurring in a smart healthcare environment, and the security and efficiency of the existing key encryption method, and we confirmed an improvement of about 15%, compared to the existing cipher systems.
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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v.28
no.4
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pp.158-177
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2023
An estuary is a transitional water area that links the land and sea through rivers and streams, transporting various components from the land to the sea, which plays an important role in determining primary productivity in the coastal environment, and this coastal ecosystem captures a huge amount of carbon into biomass, known as blue carbon, which mitigates climate change as a potential carbon reservoir. This study examined the variation of mean grain size and organic carbon content of the surface sediments for 6 years and analyzed their relationship in the western and southern estuarine areas (Han River Estuary, Geum River Estuary, Yeongsan River Estuary, Seomjin River Estuary, and Nakdong River Estuary) and the East Sea upwelling area. During the sampling period (2015 to 2020), seasonal variation of both properties was not observed, because their variations might be controlled by diverse oceanographic environments and hydrographic conditions within each survey area. However, despite the synoptic problem of all samples, the positive relationship was obtained between the averages of mean grain size and organic carbon content, which clearly distinguishes each survey area. The unique positive relationship in all estuarine areas implies that the same process by sediment clay particles is important in the organic carbon accumulation. However, additional important factor may be expected in the organic carbon accumulation in the East Sea upwelling area. Further necessary data (sedimentation rate, dry bulk density etc) should be required for the estimation of carbon stock to evaluate the major estuaries in Korea as potential carbon reservoirs in the coastal environment.
Exercises using a Balance Board (BB) are effective in developing balance, strengthening core muscles, and improving physical fitness and concentration. In particular, the Smart Balance Board (SBB), which integrates with various digital content, provides appropriate feedback compared to traditional balance boards, maximizing the effectiveness of the exercise. However, most systems only offer visual and auditory feedback, failing to evaluate the impact on user engagement, interest, and the accuracy of exercise postures. This study proposes an Immersive Smart Balance Board (I-SBB) that utilizes multiple sensors to enable training with various feedback mechanisms and precise postures. The proposed system, based on Arduino, consists of a gyro sensor for measuring the board's posture, a communication module for wired/wireless communication, an infrared sensor to guide the user's foot placement, and a vibration motor for tactile feedback. The board's posture measurements are smoothly corrected using a Kalman Filter, and the multi-sensor data is processed in real-time using FreeRTOS. The proposed I-SBB is shown to be effective in enhancing user concentration and engagement, as well as generating interest, by integrating with diverse content.
The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.
Ultrasound transducers are an essential component of combined photoacoustic and ultrasound imaging systems and play an important role in image evaluation. However, ultrasound transducers are opaque; therefore, light must bypass the ultrasound transducer to reach the target point to produce a photoacoustic image. Providing different paths for the optical and acoustic signals results in a complicated system design, increasing the system volume. To overcome these problems, an optically Transparent Ultrasound Transducer (TUT) was developed. Unlike conventional opaque ultrasound transducers, optically TUT can be fabricated by a variety of manufacturing methods and they are suitable for use with specific piezoelectric elements and serve various purposes. In this study, a comparative analysis of the results of using Lithium Niobate (LNO), Lead Magnesium Niobate-Lead Titanate (PMN-PT), and Polyvinylidene Difluoride (PVDF), which are materials used in piezoelectric element-based TUT. LNO is a piezoelectric element widely used in TUT, and PMN-PT has been actively studied recently with a higher transmission and reception rate than LNO. Existing TUT have lower ultrasound resolution than photoacoustic resolution, but they have recently been manufacturing focused TUT with high ultrasound resolution using PVDF. A comparative analysis of the production results of these TUT was performed.
A various technology of remote sensing and image analysis are applied to study landscape changes and their influencing factors in stream corridors. We developed a method to detect landscape changes over time by calculating the optical index using multispectral images taken from satellites at various time points, calculating the threshold to delineate the boundaries of water bodies, and creating binarized maps into land and water areas. This method was applied to the upstream reach of the weirs in the Geumgang River to track changes in the sandbar formed by the opening of the weir gate. First, we collected multispectral images with a resolution of 10 m × 10 m taken from the Sentinel-2 satellite at various times before and after the opening of the dam in the Geumgang River. The normalized difference water index (NDWI) was calculated using the green light and near-infrared bands from the collected images. The Otsu's threshold of NDWI calculated to delineate the boundary of the water body ranged from -0.0573 to 0.1367. The boundary of the water area determined by remote sensing matched the boundary in the actual image. A map binarized into water and land areas was created using NDWI and the Otsu's threshold. According to these results of the developed method, it was estimated that a total of 379.7 ha of new sandbar was formed by opening the three weir floodgates from 2017 to 2021 in the longitudinal range from Baekje Weir to Daecheong Dam on the Geumgang River. The landscape detection method developed in this study is evaluated as a useful method that can obtain objective results with few resources over a wide spatial and temporal range.
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