• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.45-48
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• 2022

IoT 기술의 발전 및 확산으로 다양한 도메인에서 서로 다른 특성의 시계열 데이터가 수집되고 있다. 이에 따라 단일 목적으로 수집된 시계열 데이터만 아니라, 다른 목적으로 수집된 시계열 데이터들 또한 통합하여 분석활용하려는 수요 또한 높아지고 있다. 본 논문은 파편화된 시계열 데이터들을 선택하여 통합한 후 딥러닝 모델을 생성하고 활용할 수 있는 해시함수 기반 학습 모델 관리 플랫폼을 설계하고 구현하였다. 특정되지 않은 데이터들을 기반하여 모델을 학습하고 활용할 경우 생성 모델이 개별적으로 어떤 데이터로 어떻게 생성되었는지 기술되어야 향후 활용에 용이하다. 특히 시계열 데이터의 경우 학습 데이터의 시간 정보에 의존적일 수밖에 없으므로 해당 정보의 관리도 필요하다. 본 논문에서는 이러한 문제를 해결하기 위해 해시 함수를 이용해서 생성된 모델을 계층적으로 저장하여 원하는 모델을 쉽게 검색하고 활용할 수 있도록 하였다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.1082-1083
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• 2023

우리나라의 수출입 대부분은 해상을 통해 이루어지고 있으나 항만의 물류 창고는 데이터 네트워크를 통한 유기적인 화물의 출입과 현황관리가 부족한 실정이다. 이는 부족한 데이터 네트워크 인프라와 CCTV에 의한 아날로그 영상 데이터에 의존하는 기존 시스템의 한계로 인해 기인하는 바가 크다. 이에 IP 카메라와 엣지 디바이스의 영상분석에 의한 개별 화물 창고의 디지털 현황 분석 기반을 구축하고 분산된 개별 화물 창고의 데이터를 클라우드에 위치한 중앙 집중 데이터 분석 시스템을 구축하여 유연한 개별 화물 창고 관리와 지속적인 모니터링 기반을 제공한다. 사용자 인터페이스는 웹 기반으로 구축하여 항만 화물 관계자에게 편의성과 위치에 구애받지 않는 서비스를 제공한다. 이 과정에서 사설 IoT 네트워크를 통한 최소한의 시공비용으로 항만 내 인터넷 데이터 네트워크를 구축하여 향후 항만 내 다양한 데이터 서비스를 위한 초석을 제공한다.

• Journal of the FoodService Safety
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• v.4 no.2
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• pp.42-47
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• 2023

The FoodTech industry has been developed with the rise of start-up by using AI, big data, robotics, biotechnology. In addition, sustainable development is more important with the trend of population growth, aging, and climate change. We investigated the impact of FoodTech on the foodservice industry with the cases of the global and domestic companies. The technology of AI, IoT, blockchain, robotics, automation systems are widely used to improve food safety and hygiene while as the use of diagnostic biomarkers such as blood or DNA, digital platform and app, and AI-based solutions are used in the field of personalized nutrition. With the expand of FoodTech in foodservice industry, the competencies that the managers need to develop include understanding technology, resource management, self-development, work ethics, problem-solving, and communication, therefore the support of the related education and training is required.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.139-161
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• 2024

The Fourth Industrial Revolution has spurred widespread adoption of AI-based services, driving global interest in AI semiconductors for efficient large-scale computation. Text mining research, historically using LDA, has evolved with machine learning integration, exemplified by the 2021 BERTopic technology. This study employs BERTopic to analyze AI semiconductor-related patents and research data, generating 48 topics from 2,256 patents and 40 topics from 1,112 publications. While providing valuable insights into technology trends, the study acknowledges limitations in taking a macro approach to the entire AI semiconductor industry. Future research may explore specific technologies for more nuanced insights as the industry matures.

• Smart Media Journal
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• v.13 no.1
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• pp.45-51
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• 2024

Long Range Wide Area Network (LoRaWAN) is a widely adopted Internet of Things (IoT) protocol due to its high range and lower energy consumption. LoRaWAN utilizes Adaptive Data Rate (ADR) for efficient resource (e.g., spreading factor and transmission power) management. The ADR manages these two resource parameters on the network server side and end device side. This paper focuses on analyzing the ADR and Gaussian ADR performance of LoRaWAN. We have performed NS3 simulation under a static scenario by varying the antenna height. The simulation results showed that antenna height has a significant impact on the packet delivery ratio. Higher antenna height (e.g., 50 m) has shown an improved packet success ratio when compared with lower antenna height (e.g., 10 m) in static and mobility scenarios. Based on the results, it is suggested to use the antenna at higher allevation for successful packet delivery.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.707-721
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• 2022

In this study, a state-of-the-art review on urban inundation simulation technology was presented summarizing major achievements and limitations, and future research recommendations and challenges. More than 160 papers published in major domestic academic journals since the 2000s were analyzed. After analyzing the core themes and contents of the papers, the status of technological development was reviewed according to simulation methodologies such as physically-based and data-driven approaches. In addition, research trends for application purposes and advances in overseas and related fields were analyzed. Since more than 60% of urban inundation research used Storm Water Management Model (SWMM), developing new modeling techniques for detailed physical processes of dual drainage was encouraged. Data-based approaches have become a new status quo in urban inundation modeling. However, given that hydrological extreme data is rare, balanced research development of data and physically-based approaches was recommended. Urban inundation analysis technology, actively combined with new technologies in other fields such as artificial intelligence, IoT, and metaverse, would require continuous support from society and holistic approaches to solve challenges from climate risk and reduce disaster damage.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1308-1308
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• 2024

Computer vision techniques have been widely employed in automated construction management to enhance safety and prevent accidents at construction sites. However, previous research in the field of vision-based approaches has often overlooked small-sized construction tools. These tools present unique challenges in data collection due to their diverse shapes and sizes, as well as in improving model performance to accurately detect and classify them. To address these challenges, this study aimed to enhance the performance of vision-based classifiers for small-sized construction tools, including bucket, cord reel, hammer, and tacker, by leveraging synthetic images generated from a 3D virtual environment. Three classifiers were developed using the YOLOv8 algorithm, each differing in the composition of the training dataset: (i) 'Real-4000', trained on 4,000 authentic images collected through web crawling methods (1,000 images per object); (ii) 'Hybrid-4000', consisting of 2,000 authentic images and 2,000 synthetic images; and (iii) 'Hybrid-8000', incorporating 4,000 authentic images and 4,000 synthetic images. To validate the performance of the classifiers, 144 directly-captured images for each object were collected from real construction sites as the test dataset. The mean Average Precision at an IoU threshold of 0.5 (mAP_0.5) for the classifiers was 79.6%, 90.8%, and 94.8%, respectively, with the 'Hybrid-8000' model demonstrating the highest performance. Notably, for objects with significant shape variations, the use of synthetic images led to the enhanced performance of the vision-based classifiers. Moreover, the practical applicability of the proposed classifiers was validated through confidence scores, particularly between the 'Hybrid-4000' and 'Hybrid-8000' models. Statistical analysis using t-tests indicated that the performance of the 'Hybrid-4000' model would either matched or exceeded that of the 'Hybrid-8000'model based on confidence scores. Thus, employing the 'Hybrid-4000' model may be preferable in terms of data collection efficiency and processing time, contributing to enhanced safety and real-time automation and robotics in construction practices.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1473-1483
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• 2014

A public restroom is different from a household toilet in terms of location and a large number of sharing users. In addition, public restroom is usually messy and filthy. Recently, public toilet tends to be clearly managed than before, but it still has hygienic and clear problems. In this paper, we propose a machine-to-machine (M2M)-based public restroom management system to solve these problems, in which the system with a wireless communication device sends the status information of the toilet, such as blockage or trouble detected by a sensor, to the manager of the restroom at a remote location. In particular, we have developed a prototype management system for public restroom taking into account several system requirements, and verified the basic operations and performance of the management system. With the application of the system to public facilities, it will furnish users with more pleasant environments by restroom administrators who can respond effectively to the troubled toilet.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1256-1262
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• 2016

As the population of camping is increased in campsites and auto camp sites, the electrical equipment can safely supply electricity to users in a variety of electricity bill policy is required in the campsite. In this paper, we develop the vending machine for electricity that can control the outdoor electrical outlet from the management server using the Z-Wave WPAN and android mobile application. The developed vending machine for electricity consists of the management server, the controller, the outdoor outlet box, and the mobile application. The management server provides reservation and electricity bill payment to users. The management server controls the electrical outlet box through the controller to safely supply electricity to users. The controller that is a relay device between the management server and the switch controls switches based on Z-Wave mesh network. Outdoor electrical outlet box has 2 meter switches. We receive the relevant authorization to provide commercial electricity services using the outdoor electrical outlet box in the campsite.