• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.40-47
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• 2021

In this study, the structural behavior by the details of the lap region with the headed bar was estimated through finite element analysis. To solve the finite element analysis of the anchorage region with complex contact conditions and nonlinear behavior, a quasi-static analysis technique by explicit dynamic analysis was performed. The accuracy of the finite element model was verified by comparing the experimental results with the finite element analysis results. It was confirmed that the quasi-static analysis technique well reflected the behavior of enlarged headed bar connection. As a result of performing numerical analysis using 21 finite element models with various development lengths and transverse reinforcement indexes, it was confirmed that the increase of development length and transverse reinforcement index improved the maximum strength and ductility. However, to satisfy the structural performance, it should be confirmed that both design variables(development length and transverse reinforcement index) must be enough at the design criteria. In the recently revised design standard(KDS 14 20 52 :2021), a design formula of headed bar that considers both the development length and the transverse reinforcing bar index is presented. Also the results of this study confirmed that not only the development length but also transverse reinforcing bars have a very important effect.

• Journal of the Korean Geotechnical Society
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• v.37 no.11
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• pp.119-129
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• 2021

The process of constructing the DB in the current geotechnical information DB system needs a lot of human and time resource consumption. In addition, it causes accuracy problems frequently because the current input method is a person viewing the PDF and directly inputting the results. Therefore, this study proposes building an automatic digital DB using AI (artificial intelligence) of boring logs. In order to automatically construct DB for various boring log formats without exception, the boring log forms were classified using the deep learning model ResNet 34 for a total of 6 boring log forms. As a result, the overall accuracy was 99.7, and the ROC_AUC score was 1.0, which separated the boring log forms with very high performance. After that, the text in the PDF is automatically read using the robotic processing automation technique fine-tuned for each form. Furthermore, the general information, strata information, and standard penetration test information were extracted, separated, and saved in the same format provided by the geotechnical information DB system. Finally, the information in the boring log was automatically converted into a DB at a speed of 140 pages per second.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.7-15
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• 2022

Since the direct and indirect damage caused by the fire in the underground utility tunnel will cause great damage to society as a whole, it is necessary to make efforts to prevent and control it in advance. The most of the fires that occur in cables are caused by short circuits, earth leakage, ignition due to over-current, overheating of conductor connections, and ignition due to sparks caused by breakdown of insulators. In order to find the cause of fire at an early stage due to the characteristics of the underground utility tunnel and to prevent disasters and safety accidents, we are constantly managing it with a detection system using image analysis and making efforts. Among them, a case of developing a fire detection system using CCTV-based deep learning image analysis technology has been reported. However, CCTV needs to be supplemented because there are blind spots. Therefore, we would like to develop a high-performance acoustic-based deep learning model that can prevent fire by detecting the spark sound before spark occurs. In this study, we propose a method that can collect sound in underground utility tunnel environments using microphone sensor through development and experiment of prototype module. After arranging an acoustic sensor in the underground utility tunnel with a lot of condensation, it verifies whether data can be collected in real time without malfunction.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.426-436
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• 2022

This study aimed to explore the role of models and reformulations as feedback to English writing in L2 learners' noticing and their writing. 92 participants were placed into three groups; a models group (MG), a reformulations group (RG), a control group (CG), involved in a three-stage writing task. In stage 1, they were asked to perform a 1st draft of writing, while taking notes on the problems they experienced. In stage 2, the MG was asked to compare their writing with a model text and the RG with a reformulated version of it. They were instructed to write down whatever they noticed in their comparison. The CG was asked to just read their writing. In stage 3, all the participants attempted subsequent revisions. The results indicated that all the participants noticed problematic linguistic features the most in a lexical category, and models and reformulations led to higher rate of noticing the problematic linguistic features reported in stage 1 and contributed to subsequent revisions. It was also revealed that the MG and RG significantly improved with their writings of MG and RG on the post-writing test. The findings imply that models and reformulations result in better performance in L2 writing and should be promoted in an English writing class.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.570-588
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• 2022

We scrutinized the acoustic outputs from the 70 shock wave generators of the 15 product models whose technical documents were available, among the 46 ballistic extracorporeal shock wave therapeutic devices of 11 domestic and 6 foreign manufacturers, approved by the Minster of Food & Drug Safety (Rep. Korea). We found that the acoustic Energy Flux Density (EFD), the most popular exposure parameter, was different by up to 563.64 times among shock wave generators at their minimum output settings and by up to 74.62 times at their maximum settings. In the same product model, the EFD was shown to vary depending on shock wave transmitters by up to 81.82 times at its minimum output setting and by up to 46.15 times at its maximum setting. The lowest EFD 0.013 mJ/mm² at the maximum output settings was much lower (2.1 %) than the maximum value 0.62 mJ/mm² at the minimum settings. The Large acoustic output differences (tens to hundreds of times)from the therapeutic devices approved for the same clinical indications imply that their therapeutic efficacy & safety may not be assured. The findings suggest the regulatory authority to revise her guideline to give clearer criteria for clinical approval and equality in performance, and recommend the authority to initiate a post-approval surveillance as well as a test in conformance between the data in technical documents and the real acoustic outputs clinically used.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.266-274
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• 2021

Ship officers are subject to excessive job stress, which has negative physical and psychological impacts and may adversely affect the smooth supply and demand of human resources. In this study, a mobile web application was developed as a tool for systematic job stress measurement and management of officers and verified through quality evaluation. Requirement analysis was performed by ship officers and staff in charge of human resources of shipping companies, and the results were reflected in the application configuration step. The application was designed according to the waterfall model, which is a traditional software development method, and functions were implemented using JSP and Spring Framework. Performance evaluation on the user interface, confirmed that proper input and output results were implemented, and the respondent results and the database were configured in the administrator interface. The results of evaluation questionnaires for quality evaluation of the interface based on ISO/IEC 9126-2 metric were significant 4.60 for the user interface and 4.65 for the administrator interface in a 5-point scale. In the future, it is necessary to conduct follow-up research on the development of data analysis system through utilization of the collected big-data sets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.325-333
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• 2021

With the rapidly changing era of the fourth industrial revolution, the utilization of IT technology is increasing. In addition, the demand for security authentication is increasing as shared services or IoT technologies are being developed as new business models. Security authentication is becoming increasingly important for all intelligent devices such as self-driving cars. However, most location-based security authentication technologies are being developed mainly with technologies that utilize server proximity or satellite location tracking, which limits the scope of their physical use. Location-based security authentication technology has recently been developed as a complementary replacement technology. In this study, we introduce location-based security authentication technology using cell broadcasting technology, which has a wider range of applications and is more convenient and business-friendly than existing location-based security authentication technologies. We also introduced application cases and business models related to this. In addition to the current status of technology development, we analyzed current changes in business models being employed. Based on our analysis results, this study draws the implication that technology diversification is necessary to improve the performance of innovative technologies. It is meaningful that it has found and studied advanced technologies other than existing location authentication methods and systems.