• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.31-40
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• 2023

Various policies and services are being implemented in Korea and other countries, such as the expansion of convenience facilities for mobility support, the provision of special means of transportation, and the establishment of public transportation route plans and fare policies based on data and AI-based movement pattern analysis to ensure the mobility rights of the weak in transportation. However, A research is still needed to improve service convenience in order to more conveniently use the desired means of transportation in a necessary situation from the viewpoint of the transportation vulnerable. This study examines the policies and services for the promotion of mobility for the transportation disadvantaged, and presents a MyData-based service model for mobility support for the transportation disadvantaged. In the proposed service model, the transportation-disabled person can freely choose and use the means of transportation according to individual circumstances, and receive the same transportation welfare voucher benefits provided by the state or government. The proposed service model defines the MyData platform that supports the safe collection and use of personal data, the authentication of traffic welfare recipients based on MyData, and the payment function for fee settlement after using the service as key components. In this research, the service satisfaction from the user's point of view was investigated by implementing the proposed service model and providing a demonstration service for the transportation vulnerable in Daejeon.

• Quality Improvement in Health Care
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• v.28 no.1
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• pp.45-54
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• 2022

Purpose: The study aims to identify the theme list and priority criteria of patient safety theme reports in South Korea. Methods: The survey was conducted twice, and the importance of each criterion and theme was measured on a nine-point scale using the Delphi technique by a panel of 19 patient safety experts. The criteria included severity, universality, preventability, and organizational-social impact. Descriptive statistics such as frequency, percentage, mean, standard deviation, median, and interval quartile range were used to analyze the data. Results: The parameters were assigned a weighted average of 35% for severity, 20% for universality, 30% for preventability, and 15% for organizational-social impact, respectively. The final top three rankings were surgery safety, blood transfusion safety, and medication safety. In addition to expert opinion, for the theme that is selected based on the priority ranking, one to five sub-topics can be derived from the theme based on the priority ranking, societal demands, or the yearly priority list of patient safety incidents. Conclusion: It is recommended that the official patient safety center distribute the report in the form of a summary that can be utilized nationwide at medical institutions, government institutions, and other places. Updates, as well as accumulated theme reports, will serve as the baseline data for the proposal of the system and for the policy designed to implement and improve institutions' safety practices as a standard of domestic patient safety practice guidelines.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.78-88
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• 2022

Objectives: To develop the smart sensor to protect worker's health from chemical exposure by adopting ICT (Information and Communications Technology) technologies. Methods: To develope real-time chemical exposure monitoring system, IoT (Internet of Things) sensor technology and regulations were reviewed. We developed and produced smart sensor. A smart sensor is a system consisting of a sensor unit, a communication unit, and a platform. To verify the performance of smart sensors, each sensor has been certified by the Korea Laboratory Accreditation Scheme (KOLAS). Results: Chemicals (TVOC; Total Volatile Organic Compounds, Cl₂: Chlorine, HF: Hydrogen fluoride and HCN: Hydrogen cyanide) were selected according to a priority logic (KOSHA Alert, acute poisoning statistics, literature review). Notifications were set according to OEL (occupational exposure limit). Sensors were selected based on OEL and the capabilities of the sensors. Communication is designed to use LTE (Long Term Evolution) and Wi-Fi at the same time for convenience. Electronic platform were applied to build this monitoring system. Conclusions: Real-time monitoring system for OEL of hazardous chemicals in workplace was developed. Smart sensor can detect chemicals to complement monitoring of traditional workplace environmental monitoring such as short term and peak exposure. Further research is needed to expand the scope of application, improve reliability, and systematically application.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.189-196
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• 2022

Skyline query is a scheme for exploring objects that are suitable for user preferences based on multiple attributes of objects. Existing skyline queries return search results as batch processing, but the need for real-time search results has increased with the advent of interactive apps or mobile environments. Online algorithm for Skyline improves the return speed of objects to explore preferred objects in real time. However, the object navigation process requires unnecessary navigation time due to repeated comparative operations. This paper proposes a Pre-processing Online Algorithm for Skyline Query (POA) to eliminate unnecessary search time in Online Algorithm exploration techniques and provide the results of skyline queries in real time. Proposed techniques use the concept of range-limiting to existing Online Algorithm to perform pretreatment and then eliminate repetitive rediscovering regions first. POAs showed improvement in standard distributions, bias distributions, positive correlations, and negative correlations of discrete data sets compared to Online Algorithm. The POAs used in this paper improve navigation performance by minimizing comparison targets for Online Algorithm, which will be a new criterion for rapid service to users in the face of increasing use of mobile devices.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.129-140
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• 2021

The next generation mBcN should be built to cooperate with the wireless network to support hyper-speed and hyper-connectivity. In this paper, we propose a network architecture for the cooperation mBcN and 5G commercial network and architecture of the cooperative security gateway required for the cooperation. The proposed cooperative security gateway is between gNB and UPF to support LBO, SFC, and security. Our analysis shows that the proposed architecture has several advantages. First of all, user equipment connected with the mBcN can be easily connected through the 5G commercial radio network to the mBcN. Second, the military application traffic can be transmitted to mBcN without going through the 5G core network, reducing the end-to-end transmission delay without causing the traffic load on the 5G core network. In addition, the security level of the military application can effectively be maintained because the user equipment can be connected to the cooperative security gateway, and the traffic generated by the user equipment is transmitted to the mBcN without going through the 5G core network. Finally, we demonstrate that LBO, SFC, and security modules are essential functions of the proposed gateway in the 5G test-bed environment.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.107-114
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• 2022

In this paper, availabilities of student-evaluations of team activities in the computer science basic classes were analysed. For the purpose, correlation analysis was conducted to investigate the relationships among peer-evaluation, self-evaluation, and academic achievement, and it was found that there was a statistically significant positive correlation among them. Moreover, the gap between peer-evaluation scores and self-evaluation scores was analyzed. When a one-sample t-test was performed, it was found that the gap was very significant. However, the size of the gap was not different between the two classes. That is, regardless of grade level, the students' self-evaluation scores tended to be on average higher than the evaluation scores received from peers. Finally, when analyzing the relationship between the gap in peer-evaluation and self-evaluation scores and academic achievement, there was no significant correlation between the gap in scores and academic achievement. In other words, there was no difference in the tendency of evaluation for students with high or low academic achievement. The results of the analysis shows the availability of student-evaluations of team activities in the evaluation of team-based instruction. The high correlation between self-evaluation and peer-evaluation indicates the objectivity of student-evaluation. Although it is clear that the self-evaluation score is higher on average than the score received from peers, it is more useful in terms of objectivity because it does not vary according to grade, subject, or academic achievement.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.363-370
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• 2022

Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.3
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• pp.39-47
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• 2022

In this paper, we proposed a thickness measurement method of concrete slab using GPR, and the verification of the suggested algorithm was carried out through real-scale experiment. The thickness measurement algorithm developed in this study is to set the relative dielectric constant based on the unique shape of parabola, and time series data can be converted to thickness information. GPR scanning were conducted in four types of slab structure for noise reduction, including finishing mortar, autoclaved lightweight concrete, and noise damping layer. The thickness obtained by GPR was compared with Boring data, and the average error was 1.95 mm. In order to investigate the effect of finishing materials on the slab, additional three types of finishing materials were placed, and the following average error was 1.70 mm. In addition, sampling interval from device, the effect of radius on the shape of parabola, and Boring error were comprehensively discussed. Based on the experimental verification, GPR scanning and the suggested algorithm have a great potential that they can be applied to the thickness measurement of finishing mortar from concrete slab with high accuracy.