• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_1
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• pp.217-223
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• 2023

With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.569-574
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• 2005

This paper presents the core technology of Construction Business Process Automation to model and automate construction business processes. Business Process Reengineering (BPR) and Automation (BPA) have been recognized as one of the important aspects in construction business management. However, BPR requires a lot of efforts to identify, document, implement, execute, maintain, and keep track thousands of business processes to deliver a project. Moreover, existing BPA technologies used in existing Enterprise Resource Planning (ERP) systems do not lend themselves to effective scalability for construction business process management. Application of Workflow and Object Technologies would be quite effective in implementing a scalable enterprise application for construction community. This paper present the technologies and methodologies for automating construction business processes by addressing how: 1) Automated construction management tasks are developed as software components, 2) The process modeling is facilitated by dragging-and dropping task components in a network, 3) Raising business requests and instantiating corresponding process instances are delivered, and 4) Business process instances are executed by using workflow technology based on real-time simulation engine. This paper presents how the construction business process automation is achieved by using equipment reservation and cancellation processes simplified intentionally.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.310-313
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• 2022

In the pipeline for supplying purified water to each household, measurements such as flow meters and pressure meters are installed at important points to monitor in real time. The measured data acquired to the central control room through wireless or wired communication, but data may not be acquired due to intermittent communication failures. Since then, even if the communication network is restored, data during the failure period is not stored on the site, or even if it is stored, data cannot be automatically stored in the database. Low cost with universally installed in the field in order to address these data logging by developing a module is compatible with PLC, automatically would like to make sure that we can recover in the database.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.569-577
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• 2000

The position of tobacco leaves is affluence to the quality. To evaluate its quality, sample leaves was collected according to the position of attachment. In Korea, the position was divided into four classes such as high, middle, low and inside positioned leaves. Until now, the grade of standard sample was determined by human expert from korea ginseng and tobacco company. Many research were done by the chemical and spectrum analysis using NIR and computer vision. The grade of tobacco leaves mainly classified into 5 grades according to the attached position and its chemical composition. In high and low positioned leaves shows a low level grade under grade 3. Generally, inside and medium positioned leaf has a high level grade. This is the basic research to develop a real time tobacco leaves grading system combined with portable NIR spectrum analysis system. However, this research just deals with position recognition and grading using the color machine vision. The RGB color information was converted to HSI image format and the sample was all investigated using the bundle of tobacco leaves. Quality grade and position recognition was performed through well known general error back propagation neural network. Finally, the relationship about attached leaf position and its grade was analyzed.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.240-257
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• 2023

The industrial Internet of Things (IIoT) is characterized by intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. The heterogeneous IIoT devices require a high data rate, high reliability, high coverage, and low delay, thus posing a significant challenge to information security. High-performance edge and cloud servers are a good backup solution for IIoT devices with limited capabilities. However, privacy leakage and network attack cases may occur in heterogeneous IIoT environments. Cloud-based multi-party computing is a reliable privacy-protecting technology that encourages multiparty participation in joint computing without privacy disclosure. However, the default cloud selection method does not meet the heterogeneous IIoT requirements. The server can be dishonest, significantly increasing the probability of multi-party computation failure or inefficiency. This paper proposes a blockchain and smart contract-based optimized cloud node selection framework. Different participants choose the best server that meets their performance demands, considering the communication delay. Smart contracts provide a progressive request mechanism to increase participation. The simulation results show that our framework improves overall multi-party computing efficiency by up to 44.73%.

• Annual Conference of KIPS
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• 2011.04a
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• pp.681-684
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• 2011

최근 IEEE 802.11 WLAN(Wireless Local Area Network)에서 실시간 멀티미디어 서비스가 증가하고 있다. WLAN 의 패킷 전송방식은 Unicast 또는 Broadcast 방식이 있다. Unicast 방식은 재전송을 포함하여 유실율이 적으나 사용자가 증가할수록 AP 에서 필요한 무선 자원의 크기가 증가한다. 무선 자원의 크기가 증가하면 AP 부하가 증가하여 서비스 수용에 한계가 있다. Broadcast 방식은 사용자 수에 상관 없이 무선 자원의 크기가 일정하나, 패킷 유실율이 높다. 본 논문에서는 이러한 문제점을 해결하기 위해 Broadcast 와 FEC(Forward Error Correction) Erasure Code 기반 기술을 적용하는 것을 제안한다. 방송 패킷을 이용 AP 의 부하를 줄이고, Reed Solomon Erasure Code 를 적용하여 패킷 유실 복구율을 높인다. 이러한 방법을 통하여 다수의 사용자에게 안정적인 실시간 멀티미디어 방송 서비스를 제공 할 수 있다. 제안한 방법을 검증하기 위해 Android Platform 에서 FEC 적용 유무에 따른 수신율을 측정하였다. 그 결과 유실율 30%미만인 Broadcast 환경에서 96.4% 이상의 수신율을 보였다.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.516-524
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• 2022

The construction industry is considered the most hazardous industry globally. Therefore, safety education is crucial for raising the safety awareness of construction workers working at construction sites and creating a safe working environment. However, the current safety education method and tools cannot provide trainees with realistic and practical experiences that might help better safety awareness in practice. A metaverse, a real-time network of 3D virtual worlds focused on social connection, was created for more interactive communication, collaboration, and coordination between users. Several previous studies have noted that the metaverse has excellent potential for improved safety education performance, but its required functions and practical applications have not been thoroughly researched. In order to fill the research gap, this paper reviewed the potential benefits of a metaverse based on the current research and suggested its application for safety education purposes. This paper scrutinized the metaverse's key functions, particularly its information and knowledge sharing function and reality capture function. Then, the authors created a metaverse prototype based on the two key functions described above. The main contribution of this paper is reviewing the potential benefits of a metaverse for safety education. A realistic and feasible metaverse platform should be developed in future studies, and its impact on safety education should be quantitatively verified.

• Annual Conference of KIPS
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• 2008.11a
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• pp.413-416
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• 2008

In the presence of multiple continuous queries, multi-query optimizing is a new challenge to process multiple stream data in real-time. So, in this paper, we proposed an approach to optimize multi-query of sliding window on network traffic data streams and do some comparisons to traditional queries without optimizing. We also detail some method of scheduling on different data streams, while different scheduling made different results. We test the results on variety of multi-query processing schedule, and proofed the proposed method is effectively optimized the data stream similar multi-queries.

• Annual Conference of KIPS
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• 2008.05a
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• pp.987-990
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• 2008

센서 데이터들을 실제 IT 환경 하에서 데이터 처리하기 위해서 네트워크 인프라 환경으로 USN(Ubiquitous Sensor Network) 환경이 많이 연구되어지고 있다. 이들 환경은 온도, 소리, 진동, 습도등 생활 주변의 정보를 수집하여 이에 발생된 데이터들을 특정 시스템에서 처리하여 주변상황 상태등을 인식 및 관리를 하는데 목적을 둔다. 기존 USN 기술은 주로 네트워킹 관점의 연구가 활발히 수행되어지고 있다. USN은 미래 유비쿼터스 사회를 위한 기반 인프라로 국가적으로 기술 개발을 추진 중에 있으며 다양한 시범 서비스를 추진 중에 있다. 최근에는 환경, 에너지 등의 미래 신 성장 산업 분야에서 환경문제를 해결할 수 있는 기술 개발을 전략적으로 추진 중에 있다. 본 논문에서는 다양한 건축자재 및 생활 용품에서 배출되고 실내공간, 사무실, 주거지 등 일반 환경에서 보편적으로 존재하는 인체유해물질인 포름알데히드(HCHO) 오염농도를 수집 할 수 있는 센서네트워크 환경을 구축하고 수집된 HCHO 센서 데이터들을 이용하여 주변 사람들에게 위험정보를 제시하는 한 시스템으로 ET-IT 융합 시스템인 실시간 모니터링 시스템을 제시한다.