• Journal of Korean Library and Information Science Society
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• v.52 no.4
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• pp.229-253
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• 2021

The service strategy of the public library needs to be established based on the users who are the actual beneficiaries of the services, including the perspective of changes in the social environment. This study investigated and analyzed the users' perceptions of the library functions and services currently provided and the demand for future services, targeting public library users in the Busan area. The data were collected through a questionnaire, and the respondents were 733 public library users in Busan. The main result is that first, the role and function of the public library that Busan users consider most important was still in material collection and provision. Second, in the information service type, the demand for cultural/lifelong learning program service was the highest, and in the service program, the demand for new IT technology experience and education was the highest. Third, as a result of ISA analysis of information service type, material provision service and information literacy education service were types to be maintained, and reading related service was type to be managed intensively. Fourth, in the analysis of service demand by age, those aged 41 to 50 years old were the generation with the highest demand in all types except for the information literacy education service type, and the demand for information literacy education was the highest among the elderly generation over 61 years old. And the user group in the western part of Busan was higher than the user group in other regions in demand for almost all service types. The results of this study can be used as basic data when establishing strategies to optimize community public library services for users.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.43-57
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• 2022

Advances in measurement techniques have reduced measurement costs and enhanced safety resulting in less uncertainty. For example, an acoustic doppler current profiler (ADCP) based suspended sediment concentration (SSC) measurement technique is being accepted as an alternative to the conventional data collection method. In Korean rivers, horizontal ADCPs (H-ADCPs) are mounted on the automatic discharge monitoring stations, where SSC can be measured using the backscatter of ADCPs. However, automatic discharge monitoring stations and sediment monitoring stations do not always coincide which hinders the application of the new techniques that are not feasible to some stations. This work presents and analyzes H-ADCP-SSC models for 9 discharge monitoring stations in Korean rivers. In application of the Gaussian mixture model (GMM) to sediment-related variables (catchment area, particle size distributions of suspended sediment and bed material, water discharge-sediment discharge curves) from 44 sediment monitoring stations, it is revealed that those characteristics can distinguish sediment monitoring stations regionally. Linking the two results, we propose a protocol determining the H-ADCP-SSC model where no H-ADCP-SSC model is available.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.228-239
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• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.834-841
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• 2022

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.370-376
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• 2022

Smart ships related studies are being conducted in various fields owing to the development of technology, and an engine room patrol robot that can patrol the unmanned engine room is one such study. A patrol robot moves around the engine room based on the information learned through artificial intelligence and checks the machine normality and occurrence of abnormalities such as water leakage, oil leakage, and fire. Study on engine room patrol robots is mainly conducted on machine detection using artificial intelligence, however study on movement and control is insufficient. This causes a problem in that even if a patrol robot detects an object, there is no way to move to the detected object. To secure maneuverability to quickly identify the presence of abnormality in the engine room, this study experimented with whether a patrol robot can determine the shortest path by applying the A* algorithm. Data were obtained by driving a small car equipped with LiDAR in the ship engine room and creating a map by mapping the obtained data with SLAM(Simultaneous Localization And Mapping). The starting point and arrival point of the patrol robot were set on the map, and the A* algorithm was applied to determine whether the shortest path from the starting point to the arrival point was found. Simulation confirmed that the shortest route was well searched while avoiding obstacles from the starting point to the arrival point on the map. Applying this to the engine room patrol robot is believed to help improve ship safety.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• Journal of The Korean Association of Information Education
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• v.26 no.2
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• pp.105-120
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• 2022

In addition to the rapid changes in the times caused by the pandemic, the revision of the new curriculum coincides with the change in the proportion of the three elements of learners, society, and subjects that make up the curriculum. In particular, along with the proportion of 'social' in the curriculum, the scope of the word 'educational community' has increased, and the allowable range of curriculum restructuring centered on it has expanded. In order for the intended direction of education to be properly established in the new curriculum, various educational method studies are needed to cultivate newly emerged competencies and literacy. In this study, after selecting the contents and goals of the convergence curriculum based on various criteria for subject selection, the data science program was designed by reconstructing Living Lab's PDIE methodology. As an evaluation factor for this, we tried to analyze the effectiveness of 'creativity', 'problem-solving ability', 'communication ability', 'collaboration ability' among future competencies emphasized in the curriculum. As a result of the study, it was effective in improving creative and communication skills, and this study focuses on verifying the effectiveness of School Living Lab, suggesting the necessity of post-research that expands the application space of research and diversifies the role of educational community subjects.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.383-390
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• 2021

Recently, the drone industry is rapidly expanding as it is suggested that it will be used in various fields. As the size of the drone market grows, interest in drone-related certificates is also increasing. However, the current drone-related qualification system and education system are insufficient. Thus this study, analyzed the necessity of drone training, the features of functional games, and the effectiveness of educational training using AR through related technical studies to solve the practical difficulties of drone educational training. Later, drone educational training game contents using AR were divided into practice mode and test mode based on the drone national qualification course practical test, and the result screen was displayed at the end of the curriculum so that players could learn by level and evaluate the results on their own. In addition, constructed a hybrid processing system and network and AR operation system for response rate and response speed, implemented drone training game contents utilizing AR based on the design contents. It is expected that the use of game content using AR presented in this paper for drone training will further alleviate environmental difficulties and improve the sense of immersion in play, which will lead to a more effective drone educational training experience.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1743-1747
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• 2022

Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.