• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.337-351
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• 2018

This paper discusses 5G IOT, Augmented Reality, Cloud Computing, Big Data, Future Autonomous Driving Vehicle technology, and presents 5G utilization of Pyeongchang Winter Olympic Games and Jeju Smart City model. The reason is that 5G is the main artery of the 4th industry.5G is the fourth industrial aorta because 5G is the core infrastructure of the fourth industrial revolution. In order for the AI, autonomous vehicle, VR / AR, and Internet (IoT) era to take off, data must be transmitted several times faster and more securely than before. For example, if you send a stop signal to LTE, which is a communication technology, to a remote autonomous vehicle, it takes a hundredth of a second. It seems to be fairly fast, but if you run at 100km / h, you can not guarantee safety because the car moves 30cm until it stops. 5G is more than 20 gigabits per second (Gbps), about 40 times faster than current LTE. Theoretically, the vehicle can be set up within 1 cm. 5G not only connects 1 million Internet (IoT) devices within a radius of 1 kilometer, but also has a speed delay of less than 0.001 sec. Steve Mollenkov, chief executive officer of Qualcomm, the world's largest maker of smartphones, said, "5G is a key element and innovative technology that will connect the future." With 5G commercialization, there will be an economic effect of 12 trillion dollars in 2035 and 22 million new jobs We can expect to see the effect of creation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.23-30
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• 2021

In this study, we present a comparative analysis of major autoencoder(AE)-based anomaly detection methods for quality determination in the manufacturing process and a new anomaly discrimination criterion. Due to the characteristics of manufacturing site, anomalous instances are few and their types greatly vary. These properties degrade the performance of an AI-based anomaly detection model using the dataset for both normal and anomalous cases, and incur a lot of time and costs in obtaining additional data for performance improvement. To solve this problem, the studies on AE-based models such as AE and VAE are underway, which perform anomaly detection using only normal data. In this work, based on Convolutional AE, VAE, and Dilated VAE models, statistics on residual images, MSE, and information entropy were selected as outlier discriminant criteria to compare and analyze the performance of each model. In particular, the range value applied to the Convolutional AE model showed the best performance with AUC PRC 0.9570, F1 Score 0.8812 and AUC ROC 0.9548, accuracy 87.60%. This shows a performance improvement of an accuracy about 20%P(Percentage Point) compared to MSE, which was frequently used as a standard for determining outliers, and confirmed that model performance can be improved according to the criteria for determining outliers.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.2
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• pp.73-78
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• 2022

Since Traditional Korean medicine (TKM) doctors use various knowledge systems during treatment, diagnosis results may differ for each TKM doctor. However, it is difficult to explain all the reasons for the diagnosis because TKM doctors use both explicit and implicit knowledge. In this study, an upgraded random forest (RF)-based evaluation tool was proposed to extract clinical knowledge of TKM doctors. Also, it was confirmed to what extent the professor's clinical knowledge was delivered to the trainees by using the evaluation tool. The data used to construct the evaluation tool were targeted at 106 people who visited the Sasang Constitutional Department at Kyung Hee University Korean Medicine Hospital at Gangdong. For explicit knowledge extraction, four TKM doctors were asked to express the importance of symptoms as scores. In addition, for implicit knowledge extraction, importance score was confirmed in the RF model that learned the patient's symptoms and the TKM doctor's constitutional determination results. In order to confirm the delivery of clinical knowledge, the similarity of symptoms that professors and trainees consider important when discriminating constitution was calculated using the Jaccard coefficient. As a result of the study, our proposed tool was able to successfully evaluate the clinical knowledge of TKM doctors. Also, it was confirmed that the professor's clinical knowledge was delivered to the trainee. Our tool can be used in various fields such as providing feedback on treatment, education of training TKM doctors, and development of AI in TKM.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.105-118
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• 2022

The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.50-50
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• 2022

한국농어촌공사 산하의 농업용저수지 중 3786개소에 대한 수질조사를 '19년도에 실시한 결과, TOC 기준 4등급 초과 저수지 비율은 약 20%로써, 도심 근교 저수지에서 녹조현상 빈발로 인해 수질, 악취, 미관 등의 환경문제 개선 민원이 다수 발생하고 있다. 현재 녹조 발생 사후관리를 위해 주로 사용되고 있는 대형 조류제거선은 저수심 수변부에서의 적용성에 한계가 있고, Al 기반의 응집제를 사용하여 조류를 수거해서 폐기하고 있는 실정이다. (주)이엔이티는 농어촌연구원, (주)코레드, (주)삼호인넷과 함께 호소나 정체하천의 수변지역에 적용될 수 있는 저에너지형 유해조류 포집시스템 개발과, 수거된 조류부산물을 무독화하여 농업재료로 재활용하는 방안을 연구하고 있다. 저수지나 정체수역의 녹조는 바람, 수면유동 등에 의해 수변에 집적되는 특성이 있어, 인공지능 기술로 녹조현상을 감시하여 조류 밀집구간에 접근할 수 있는 자율이동식 수상이동장치를 개발 중이다. 수상이동장치는 조류포집장치를 탑재하기 위한 부력체, 원격 운전이 가능한 무인항법장치, 수변식생대 및 저수심지역 이동을 고려한 수차방식 추진체, 전체 장치의 전원 공급을 위한 고성능 배터리 등으로 구성하여 상세 도면 설계를 진행하고 있다. 조류포집장치에는 표층에 주로 분포하는 남조류를 선택 흡입하는 포집 부표를 적용하였고, Al계 응집제 사용을 배제한 분리막 실험을 통해 침지형 막분리조 및 가압형 농축조를 설계하였다. 유해조류 포집 및 농축은 수상에서 이동체에 탑재하여 이뤄지고, 육상에서는 자원 회수가 가능하도록 회분식 응집공정으로 구분하였다. 조류 밀집지역에서 수거된 조류의 무독화 및 농업재료 자원화 타당성 평가를 위해 특용 버섯균주를 활용한 시료별 분석항목을 선정하고 실험 매트릭스에 따라 실증실험을 수행하였다. 수거조류를 전처리하여 성분 및 발열량을 분석하고 버섯재배 전후의 마이크로시스틴 독소(LR, RR, LR)를 포함한 성분 분석을 수행하여, 고체연료, 비료 및 사료로 활용방안을 검토하였다. 무인자율이동 조류포집장치는 실증화 규모로 제작하여 기선정된 테스트베드에서 현장적용성 평가를 수행할 예정이다. 본 연구를 통해 개발된 유해조류 포집 시스템은 기존의 녹조제거 방안을 보완하여 정체수역의 생태계 복원 및 친수공간의 환경개선 등에 적용되며, 무독화가 입증된 유해조류의 농업재료 자원화 기술은 고부가 상품 개발 및 환경폐기물 감축에 활용될 것이다.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.333-351
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• 2023

This study investigates user behavior in library spaces through the lens of AI camera analytics. By leveraging the face recognition and tracking capabilities of AI cameras, we accurately identified the gender and age of visitors and meticulously collected video data to track their movements. Our findings revealed that female users slightly outnumbered male users and the dominant age group was individuals in their 30s. User visits peaked between Tuesday to Friday, with the highest footfall recorded between 14:00 and 15:00 pm, while visits decreased over the weekend. Most visitors utilized one or two specific spaces, frequently consulting the information desk for inquiries, checking out/returning items, or using the rest area for relaxation. The library stacks were used approximately twice as much as they were avoided. The most frequented subject areas were Philosophy(100), Religion(200), Social Sciences(300), Science(400), Technology(500), and Literature(800), with Literature(800) and Religion(200) displaying the most intersections with other areas. By categorizing users into five clusters based on space utilization patterns, we discerned varying objectives and subject interests, providing insights for future library service enhancements. Moreover, the study underscores the need to address the associated costs and privacy concerns when considering the broader application of AI camera analytics in library settings.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.