• Smart Structures and Systems
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• v.14 no.2
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• pp.129-144
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• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.842-850
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• 2021

Real Time Simulation (RTS) has long been used in the nuclear power industry for operator training and engineering purposes. And, Online Simulation (OLS) is based on RTS and with connection to the plant information system to acquire the measurement data in real time for calibrating the simulation models and following plant operation, for the purposes of analyzing plant events and providing indicative signs of malfunctioning. An OLS system has been developed to support PWR operations for CPR1000 plants. The OLS system provides graphical user interface (GUI) for operators to monitor critical plant operations for preventing faulty operation or analyzing plant events. Functionalities of the OLS system are depicted through the maneuvering of the GUI for various OLS functional modules in the system.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.127-133
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• 2002

Most structures are constantly influenced by repeated weathering phenomena and load action during a period of its utilization, and their component materials become superannuated and their design performance becomes slowly lost. Therefore, it is thought that it is necessary to develop the online monitoring system that can make a great contribution to the maintenance management and disaster prevention of the building structure by sensing any slight change of the entire structure regardless of its inside and outside. Especially, this study was intended to explain the entire system of the online structure by interpreting welded built-up H-beams, of structural steel members having many advantages in terms of the qualities of structural materials, through limit state design, and presenting the basic plan to apply it to the structure.

• Smart Structures and Systems
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• v.19 no.3
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• pp.259-268
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• 2017

System identification of structures is one of the important aspects of structural health monitoring. The accuracy and efficiency of identification results is affected severely by measurement noises, especially when the structure system is large, such as bridge structures, and when online system identification is required. In this paper, the least square estimation (LSE) method is used combined with the substructure approach for identifying structural parameters of a cable-stay bridge with large degree of freedoms online. Numerical analysis is carried out by first dividing the bridge structure into smaller substructures and then estimates the parameters of each substructure online using LSE method. Simulation results demonstrate that the proposed approach is capable of identifying structural parameters, however, the accuracy and efficiency of identification results depend highly on the noise sensitivities of loading region, loading pattern as well as element size.

• ETRI Journal
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• v.43 no.3
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• pp.459-470
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• 2021

This paper presents a low-cost prototype for monitoring online the maximum power produced by a domestic photovoltaic (PV) system using Internet of Things (IoT) technology. The most common tracking algorithms (P&O, InCond, HC, VSS InCond, and FL) were first simulated using MATLAB/Simulink and then implemented in a low-cost microcontroller (Arduino). The current, voltage, load current, load voltage, power at the maximum power point, duty cycle, module temperature, and in-plane solar irradiance are monitored. Using IoT technology, users can check in real time the change in power produced by their installation anywhere and anytime without additional effort or cost. The designed prototype is suitable for domestic PV applications, particularly at remote sites. It can also help users check online whether any abnormality has happened in their system based simply on the variation in the produced maximum power. Experimental results show that the system performs well. Moreover, the prototype is easy to implement, low in cost, saves time, and minimizes human effort. The developed monitoring system could be extended by integrating fault detection and diagnosis algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3970-3990
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• 2021

In view of the problems of insufficient data collection and processing capability of multi-source heterogeneous equipment, and low visibility of equipment status at the ship block construction site. A data collection method for ship block construction equipment based on wireless sensor network (WSN) technology and a data processing method based on edge computing were proposed. Based on the Browser/Server (B/S) architecture and the OneNET platform, an online monitoring system for ship block construction equipment was designed and developed, which realized the visual online monitoring and management of the ship block construction equipment status. Not only that, the feasibility and reliability of the monitoring system were verified by using the intelligent tire frame system as the application object. The research of this project can lay the foundation for the ship block construction equipment management and the ship block intelligent construction, and ultimately improve the quality and efficiency of ship block construction.

• Journal of Arbitration Studies
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• v.18 no.3
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• pp.91-116
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• 2008

E-Commerce constitutes an important part of all commercial activities. Online Alternative Dispute Resolution(Online ADR) or Online Dispute Resolution(ODR) is a new method of dispute, resolution which, is provided online. Most Online ADR services are alternatives to litigation. In this respect, they are the online transposition of the methods developed in the ADR movement such as negotiation, mediation and arbitration. But there are also online courts which are really normal courts in which the contesting parties communicate essentially online. This paper deals with the current operation of Online ADR and the ways to, activate it. They include (1) die establishment of legal stability regarding Online ADR, (2) the enhancement of system security in providing Online ADR services, (3) the introduction of Online ADR service platform for providing the various services through single window on a national, or global basis, and (4) the introduction of Online ADR online monitoring system for systematic dispute resolution services.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.821-826
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. Accordingly, this paper deals with Basic system and Online system. Basic system comprised of spindle rotational speed, feed rates, thrust, torque and flank wear measured tool microscope. Online system comprised of spindle rotational speed, feed rates, AE signal, flank wear area measured computer vision. On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.