• Title/Summary/Keyword: AI-based

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Integrated Data Safe Zone Prototype for Efficient Processing and Utilization of Pseudonymous Information in the Transportation Sector (교통분야 가명정보의 효율적 처리 및 활용을 위한 통합데이터안심구역 프로토타입)

  • Hyoungkun Lee;Keedong Yoo
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.23 no.3
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    • pp.48-66
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    • 2024
  • According to the three amended Laws of the Data Economy and the Data Industry Act of Korea, systems for pseudonymous data integration and Data Safe Zones have been operated separately by selected agencies, eventually causing a burden of use in SMEs, startups, and general users because of complicated and ineffective procedures. An over-stringent pseudonymization policy to prevent data breaches has also compromised data quality. Such trials should be improved to ensure the convenience of use and data quality. This paper proposes a prototype system of the Integrated Data Safe Zone based on redesigned and optimized pseudonymization workflows. Conventional workflows of pseudonymization were redesigned by applying the amended guidelines and selectively revising existing guidelines for business process redesign. The proposed prototype has been shown quantitatively to outperform the conventional one: 6-fold increase in time efficiency, 1.28-fold in cost reduction, and 1.3-fold improvement in data quality.

Real-World Application of Artificial Intelligence for Detecting Pathologic Gastric Atypia and Neoplastic Lesions

  • Young Hoon Chang;Cheol Min Shin;Hae Dong Lee;Jinbae Park;Jiwoon Jeon;Soo-Jeong Cho;Seung Joo Kang;Jae-Yong Chung;Yu Kyung Jun;Yonghoon Choi;Hyuk Yoon;Young Soo Park;Nayoung Kim;Dong Ho Lee
    • Journal of Gastric Cancer
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    • v.24 no.3
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    • pp.327-340
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    • 2024
  • Purpose: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. Materials and Methods: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). Results: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. Conclusions: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

Class-Agnostic 3D Mask Proposal and 2D-3D Visual Feature Ensemble for Efficient Open-Vocabulary 3D Instance Segmentation (효율적인 개방형 어휘 3차원 개체 분할을 위한 클래스-독립적인 3차원 마스크 제안과 2차원-3차원 시각적 특징 앙상블)

  • Sungho Song;Kyungmin Park;Incheol Kim
    • The Transactions of the Korea Information Processing Society
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    • v.13 no.7
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    • pp.335-347
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    • 2024
  • Open-vocabulary 3D point cloud instance segmentation (OV-3DIS) is a challenging visual task to segment a 3D scene point cloud into object instances of both base and novel classes. In this paper, we propose a novel model Open3DME for OV-3DIS to address important design issues and overcome limitations of the existing approaches. First, in order to improve the quality of class-agnostic 3D masks, our model makes use of T3DIS, an advanced Transformer-based 3D point cloud instance segmentation model, as mask proposal module. Second, in order to obtain semantically text-aligned visual features of each point cloud segment, our model extracts both 2D and 3D features from the point cloud and the corresponding multi-view RGB images by using pretrained CLIP and OpenSeg encoders respectively. Last, to effectively make use of both 2D and 3D visual features of each point cloud segment during label assignment, our model adopts a unique feature ensemble method. To validate our model, we conducted both quantitative and qualitative experiments on ScanNet-V2 benchmark dataset, demonstrating significant performance gains.

Methodology for Generating UAV's Effective Flight Area that Satisfies the Required Spatial Resolution (요구 공간해상도를 만족하는 무인기의 유효 비행 영역 생성 방법)

  • Ji Won Woo;Yang Gon Kim;Jung Woo An;Sang Yun Park;Gyeong Rae Nam
    • Journal of Advanced Navigation Technology
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    • v.28 no.4
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    • pp.400-407
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    • 2024
  • The role of unmanned aerial vehicles (UAVs) in modern warfare is increasingly significant, making their capacity for autonomous missions essential. Accordingly, autonomous target detection/identification based on captured images is crucial, yet the effectiveness of AI models depends on image sharpness. Therefore, this study describes how to determine the field of view (FOV) of the camera and the flight position of the UAV considering the required spatial resolution. Firstly, the calculation of the size of the acquisition area is discussed in relation to the relative position of the UAV and the FOV of the camera. Through this, this paper first calculates the area that can satisfy the spatial resolution and then calculates the relative position of the UAV and the FOV of the camera that can satisfy it. Furthermore, this paper propose a method for calculating the effective range of the UAV's position that can satisfy the required spatial resolution, centred on the coordinate to be photographed. This is then processed into a tabular format, which can be used for mission planning.

A Time Series Forecasting Model with the Option to Choose between Global and Clustered Local Models for Hotel Demand Forecasting (호텔 수요 예측을 위한 전역/지역 모델을 선택적으로 활용하는 시계열 예측 모델)

  • Keehyun Park;Gyeongho Jung;Hyunchul Ahn
    • The Journal of Bigdata
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    • v.9 no.1
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    • pp.31-47
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    • 2024
  • With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

A Research on the Women's Costume on the Bigdata of Movie Napoleon

  • Weolkye KIM;Sangwon LEE
    • International Journal of Advanced Culture Technology
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    • v.12 no.3
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    • pp.21-28
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    • 2024
  • The public can access movies more easily than any other cultural genre. The film's costumes convey the social, political, and cultural climate of that time period. Additionally, it subtly conveys the message of the movie, including the intentions of the director and the characters. Filmmakers can now use fact-based materials to plan their films, and audiences can now watch costume in movies with objective standards, particularly in period dramas, thanks to the advancements in over-the-top (OTT) services. The 77th British Academy costume Award went to the movie Napoleon because of how much emphasis it placed on the outfit. Ninety-five percent of the costume was made by experts in military uniforms and costumery. In contrast to the previous aristocratic and exaggerated Rococo costume, Napoleonic clothing had a natural and common-class character. A natural-shaped Chemise dress composed of light, reflective material first appeared in the Directoire era, just after the French Revolution. Chemise dresses made of a variety of materials gained popularity during the Empire era. With Napoleon taking the throne and Josephine becoming the empress, the vibrant court culture resurfaced during the Empire era. The silk was embellished with gold thread and embroidery, train dangling forms, and different types of sleeves appeared in Empire styles. They wore Pellisse and shawls under the coat. The hair style had long, ancient hair and was adorned with fillets. They also wore straw hats, bonnets, and caps. Long gloves and parasols were also popular accessories, as were pearl or colored jewelry necklaces, earrings, bracelets, and rings. During the Empire era, tiaras were fashionable. Shoes were either low-heeled pumps or sandals. The movie uses Chemise and Empire costumes, which are versatile enough to be used in a range of settings and eras. When it came to details, the type of sleeve was employed without regard to time, such as when using those from an earlier or later period. Since jewelry was worn more often than not in that era, practically every character has earrings on their necklaces. Nearly exact replicas of the coronation costume can be found in paintings by Jacques-Louis David. The red trains, Josephine's Empire dress, the crown, the Tiara, and the costumes of every character in attendance were all clearly identifiable in terms of form and color. To further aid viewers in understanding and enhancing the film's overall coherence, a scene featuring David drawing the coronation was added. Overall, there were differences in that the historical costumes were accurately recreated, the materials and details were utilized without restriction, and some of the costumes were designed with modern materials or accessories that were used more than the historical costumes. This section appears to have been written to highlight the beauty of the characters' personalities or settings. There is a limitation to this study in that it only looked at aristocratic clothing, which includes Josephine's. We will concentrate on male clothing in future research.

Development of Machine Learning Model Use Cases for Intelligent Internet of Things Technology Education (지능형 사물인터넷 기술 교육을 위한 머신러닝 모델 활용 사례 개발)

  • Kyeong Hur
    • Journal of Practical Engineering Education
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    • v.16 no.4
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    • pp.449-457
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    • 2024
  • AIoT, the intelligent Internet of Things, refers to a technology that collects data measured by IoT devices and applies machine learning technology to create and utilize predictive models. Existing research on AIoT technology education focused on building an educational AIoT platform and teaching how to use it. However, there was a lack of case studies that taught the process of automatically creating and utilizing machine learning models from data measured by IoT devices. In this paper, we developed a case study using a machine learning model for AIoT technology education. The case developed in this paper consists of the following steps: data collection from AIoT devices, data preprocessing, automatic creation of machine learning models, calculation of accuracy for each model, determination of valid models, and data prediction using the valid models. In this paper, we considered that sensors in AIoT devices measure different ranges of values, and presented an example of data preprocessing accordingly. In addition, we developed a case where AIoT devices automatically determine what information they can predict by automatically generating several machine learning models and determining effective models with high accuracy among these models. By applying the developed cases, a variety of educational contents using AIoT, such as prediction-based object control using AIoT, can be developed.

Temperature Prediction and Control of Cement Preheater Using Alternative Fuels (대체연료를 사용하는 시멘트 예열실 온도 예측 제어)

  • Baasan-Ochir Baljinnyam;Yerim Lee;Boseon Yoo;Jaesik Choi
    • Resources Recycling
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    • v.33 no.4
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    • pp.3-14
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    • 2024
  • The preheating and calcination processes in cement manufacturing, which are crucial for producing the cement intermediate product clinker, require a substantial quantity of fossil fuels to generate high-temperature thermal energy. However, owing to the ever-increasing severity of environmental pollution, considerable efforts are being made to reduce carbon emissions from fossil fuels in the cement industry. Several preliminary studies have focused on increasing the usage of alternative fuels like refuse-derived fuel (RDF). Alternative fuels offer several advantages, such as reduced carbon emissions, mitigated generation of nitrogen oxides, and incineration in preheaters and kilns instead of landfilling. However, owing to the diverse compositions of alternative fuels, estimating their calorific value is challenging. This makes it difficult to regulate the preheater stability, thereby limiting the usage of alternative fuels. Therefore, in this study, a model based on deep neural networks is developed to accurately predict the preheater temperature and propose optimal fuel input quantities using explainable artificial intelligence. Utilizing the proposed model in actual preheating process sites resulted in a 5% reduction in fossil fuel usage, 5%p increase in the substitution rate with alternative fuels, and 35% reduction in preheater temperature fluctuations.

Analysis of Food Tech Startups: A Case Study Utilizing the ERIS Model (푸드테크 스타트업 현황 분석 및 ERIS 모델 기반 성공 사례연구)

  • Sunhee Seo;Yeeun Park;Jae yeong Choi
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.19 no.4
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    • pp.161-182
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    • 2024
  • The study analyzed the rapidly growing food tech startup in South Korea, focusing on industry classification, core technological domains, investment stages, and growth trajectories. Utilizing the ERIS model, two innovative food tech startups, MyChef and CatchTable, were examined as case studies. Results revealed food tech startups are focusing on information technology and smart distribution technology-oriented solutions rather than traditional food production. This study also found that robotics and AI integration were key technology areas. Analyzing the emergence of food tech startups, investment stages, and cumulative investment amounts based on founding years revealed a trend of scaling operations through rounds of funding, especially after securing SERIES A and B funding. The period between 2014 and 2018 saw a dense concentration of food tech startup establishments, likely influenced by favorable conditions for technological innovation amid the Fourth Industrial Revolution. The high rate of strategic mergers and acquisitions and bankruptcy can be interpreted as the complexity inherent in the food tech industry. The case study of MyChef, which grew into HMR manufacturing, and Wad(CatchTable), which expanded into a restaurant reservation platform, derived the entrepreneurs, resources, industry, and strategic factors that served as success factors for food tech startups. This study has practical implications in that it provides entrepreneurs, investors, and policymakers in the food tech industry with insight and direction to develop strategies in line with market trends and technological changes and promote sustainable growth.

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Development and Application of a Scenario Analysis System for CBRN Hazard Prediction (화생방 오염확산 시나리오 분석 시스템 구축 및 활용)

  • Byungheon Lee;Jiyun Seo;Hyunwoo Nam
    • Journal of the Korea Society for Simulation
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    • v.33 no.3
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    • pp.13-26
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    • 2024
  • The CBRN(Chemical, Biological, Radiological, and Nuclear) hazard prediction model is a system that supports commanders in making better decisions by creating contamination distribution and damage prediction areas based on the weapons used, terrain, and weather information in the events of biochemical and radiological accidents. NBC_RAMS(Nuclear, Biological and Chemical Reporting And Modeling S/W System) developed by ADD (Agency for Defense Development) is used not only supporting for decision making plan for various military operations and exercises but also for post analyzing CBRN related events. With the NBC_RAMS's core engine, we introduced a CBR hazard assessment scenario analysis system that can generate contaminant distribution prediction results reflecting various CBR scenarios, and described how to apply it in specific purposes in terms of input information, meteorological data, land data with land coverage and DEM, and building data with pologon form. As a practical use case, a technology development case is addressed that tracks the origin location of contaminant source with artificial intelligence and a technology that selects the optimal location of a CBR detection sensor with score data by analyzing large amounts of data generated using the CBRN scenario analysis system. Through this system, it is possible to generate AI-specialized CBRN related to training and analysis data and support planning of operation and exercise by predicting battle field.