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

• YAKHAK HOEJI
• /
• v.60 no.2
• /
• pp.85-91
• /
• 2016

Drying of granules for tablet formulation is one of the important unit operations. The loss on drying method is traditionally used for this purpose. However, it is a time-consuming method, requiring at least 1 h. Moreover, it is ineffective in monitoring the moisture content of granules during the drying process. In this study, online real-time monitoring of moisture content during the drying process was successfully performed using near-infrared (NIR) spectroscopy. NIR spectra were collected during 15 different drying batches for developing a reliable NIR spectroscopic method. Such a large number of batches were used to develop a more robust partial least squares (PLS) model. NIR spectra collected from 12 batches were used for developing the model that was validated by predicting the moisture content of the samples in the remaining 3 batches. The standard errors of predictions (SEPs) in the measurement of batch 1, batch 2, and batch 3 were 0.52%, 0.57%, and 0.56%, respectively. The online NIR spectroscopic method developed in this study was reliable and accurate in monitoring the moisture content during the drying process.

• KSBB Journal
• /
• v.28 no.1
• /
• pp.24-30
• /
• 2013

Dissolved oxygen, pH and cell concentration have been online monitored in cultivation processes with Escherichia coli by using a $MABOOMS^{TM}$ (microplate-based bioreactor with optical online monitoring systems). Fluorescent sensing membranes containing Ru ${(dpp)_3}^{2+}$ or HPTS were prepared with GA sol-gel matrix and coated into a well of a 24-well microplate. Fluorescence intensity was measured and correlated to the dissolved oxygen or pH. Cell concentrations were also online monitored by measuring optical reflectance at 650 nm. A well of a 24-well microplate could also be divided into 4 parts, each of which was coated with fluorescent sensing membranes for the detection of dissolved oxygen or pH. The 24-well microplate coated with fluorescent sensing membranes or a 4-divided sensing membrane. was used to online monitor the dissolved oxygen, pH and cell concentration during E. coli cultivations. The online monitoring results showed the characteristics of cell growth in cultivation processes very well.

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

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.6
• /
• pp.737-745
• /
• 2008

Recent developments of process and measurement technology bring much interest to the online monitoring of process operations such as milling, grinding, broaching, etc. The objective of online monitoring systems is to detect process changes as early as possible. This is helpful in protecting facilities against unexpected failures and then preventing unnecessary loss. This paper investigates, when the process monitoring data are obtained as a profile, the monitoring performances of a statistical $T^2$-statistic and a feedforward neural network by using a wavelet transform. Numerical experiments using cutting force data presented by Axinte show that the proposed wavelet based $T^2$-test has an acceptable power in detecting profile changes. However, its operating characteristic is very sensitive to autocorrelation. On the contrary, compared with $T^2$-test, the neural network has more stable performance in the presence of autocorrelation. This indicates that an adaptive feature to analyze noises should be incorporated into the wavelet based $T^2$-test.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.11 no.2
• /
• pp.103-111
• /
• 2002

Out of all metal-cutting process, 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. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by 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. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.22 no.2
• /
• pp.292-297
• /
• 2013

Nowadays, a strategy of the self-optimizing machining process is an imperative approach to improve the product quality and increase productivity of manufacturing systems. This paper presents a concept of self-optimizing forming system that allows the forming system automatically to adjust the forming parameters online for guarantee the product quality and avoiding the machine stop. An intelligent monitoring system that has the functions of observation, evaluation and diagnostic is developed to evaluate the pully quality during forming process. Any abnormal variation of forming machining parameters could be detected and adjusted by an intelligent control system aiming to maintain the machining stability and the desired product quality. This approach is being practiced on the pully forming machine for evaluating the efficiency of the proposed strategy.

• Structural Engineering and Mechanics
• /
• v.47 no.3
• /
• pp.361-381
• /
• 2013

A Bayesian probabilistic method is proposed for online estimation of the process noise and measurement noise parameters for Kalman filter. Kalman filter is a well-known recursive algorithm for state estimation of dynamical systems. In this algorithm, it is required to prescribe the covariance matrices of the process noise and measurement noise. However, inappropriate choice of these covariance matrices substantially deteriorates the performance of the Kalman filter. In this paper, a probabilistic method is proposed for online estimation of the noise parameters which govern the noise covariance matrices. The proposed Bayesian method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in an online manner. By utilizing the estimated noise parameters, reliable state estimation can be accomplished. Moreover, the proposed method does not assume any stationarity condition of the process noise and/or measurement noise. By removing the stationarity constraint, the proposed method enhances the applicability of the state estimation algorithm for nonstationary circumstances generally encountered in practice. To illustrate the efficacy and efficiency of the proposed method, examples using a fifty-story building with different stationarity scenarios of the process noise and measurement noise are presented.

• ETRI Journal
• /
• v.43 no.1
• /
• pp.141-151
• /
• 2021

Several methods exist for detecting hacking programs operating within online games. However, a significant amount of computational power is required to detect the illegal access of a hacking program in game clients. In this study, we propose a novel detection method that analyzes the protected memory area and the hacking program's process in real time. Our proposed method is composed of a three-step process: the collection of information from each PC, separation of the collected information according to OS and version, and analysis of the separated memory information. As a result, we successfully detect malicious injected dynamic link libraries in the normal memory space.

• ETRI Journal
• /
• v.28 no.4
• /
• pp.461-474
• /
• 2006

This project aims to design and develop a prototype for an agent that support online collaborative learning. Online collaborative learning, which has emerged as a new form of education in the knowledge-based society, is regarded as an effective method for improving practical and highly advanced problem-solving abilities. Collaborative learning involves complicated processes, such as organizing teams, setting common goals, performing tasks, and evaluating the outcome of team activities. Thus, a teacher may have difficulty promoting and evaluating the entire process of collaborative learning, and a system may need to be developed to support it. Therefore, to promote interaction among learners in the process of collaborative learning, this study designed an extensible collaborative learning agent (ECOLA) for an online learning environment.