• 한국지능시스템학회논문지
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• 제20권4호
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• pp.483-488
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• 2010

센서시스템을 이용한 상시감시는 발전소의 효율적인 운전과 안전을 담보하는 데 필수적이다. 상시감시기술을 구현하기 위해서는 우선 센서로부터 전송된 신호로부터 발전소 운전파라미터의 참값을 예측하는 모델 즉 Auto-association (AA) 모델을 확보하는 것이 중요하다. 이를 위해 본 논문에서는 Support Vector Regression (SVR)과 Partial Least Square Regression (PLSR)을 이용하는 방안을 각각 제시한다. 이렇게 해서 구축된 모델은 모니터해야 할 파라미터가 많을 때에도 쉽게 적용할 수 있다. 실제 발전소에서 수집된 데이터셋을 이용하여 AA 모델링의 정확도 및 민감도를 비교한 결과, 정확도 면에서는 SVR이 우수한 반면 민감도 면에서는 PLSR이 다소 나은 것으로 나타났다.

• 한국정밀공학회지
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• 제30권5호
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• pp.480-485
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• 2013

Enhancing energy efficiency of machine tools causes substantial impacts on the manufacturing industries, to cope with the competitive introduction of the total energy management strategies. Real-time energy monitoring is essential to identify energy consumption patterns of the machine tools and correlate them with the energy management strategy. Integrated analysis of machine tool's operation status and the corresponding energy usage is most important to accurately evaluate the energy efficiency under the various machining process environments. This paper proposes a system architecture to realize the online energy monitoring system and the embedded energy monitoring approach interconnected with the CNC kernel. The shop-floor operation management system is presented to integrate the proposed online energy monitoring approach.

• Educational Technology International
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• 제20권2호
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• pp.223-255
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• 2019

The purpose of this study is to draw implications for designing online tools to support monitoring in collaborative learning. For this purpose, eighteen research papers that explored learner dashboards and group awareness tools were analyzed. The driving questions for this analysis related to the information and outcomes that must be monitored, whose performance they represent, and who monitors the extent of learning. The analytical frameworks used for this study included the following: three modes of co-regulation in terms of who regulates whose learning (self-regulation in collaborative learning, other regulation, and socially shared regulation) and four categories of dashboard information to determine which information is monitored (information about preparation, participation, interaction, and achievements). As a result, five design implications for learner dashboards that support monitoring were posited: a) Monitoring tools for collaborative learning should support multiple targets: the individual learner, peers, and the entire group; b) When supporting personal monitoring, information about the individual and peers should be displayed simultaneously to allow direct comparison; c) Information on collaborative learning achievements should be provided in terms of the content of knowledge acquired rather than test scores; d) In addition to information related to interaction between learners, the interaction between learners and learning materials can also be provided; and e) Presentation of the same information to individuals or groups should be variable.

• KSBB Journal
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• 제28권1호
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• pp.31-35
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• 2013

In this work a $MABOOMS^{TM}$ has been employed to cultivate microorganisms and investigated the effects of culture medium components on cell growth. A 24-well microplate coated with 4-divided fluorescent sensing membranes was used to monitor the dissolved oxygen, pH and cell concentration during cultivations. Fluorescence intensity for dissolved oxygen or solution pH and reflectance for cell concentration was online monitored by using the $MABOOMS^{TM}$. The online monitoring results showed the effects of culture medium components on cell growth in cultivation processes very well.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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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.

• Smart Structures and Systems
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• 제21권5호
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• pp.537-548
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• 2018

A tunnel deformation monitoring system is developed with the use of fiber Bragg grating (FBG) sensing technique, aiming at providing continuous monitoring of railway tunnel deformation in the long term, and early warning for the rail service maintainers and authorities to avoid catastrophic consequences when significant deformation occurs. Specifically, a set of FBG bending gauges with the ability of angle measurement and temperature compensation is designed and manufactured for the purpose of online monitoring of tunnel deformation. An overall profile of lateral tunnel displacement along the longitudinal direction can be obtained by implementing an array of the FBG bending gauges interconnected by rigid rods, in conjunction with a proper algorithm. The devised system is verified in laboratory experiments with a test setup enabling to imitate various patterns of tunnel deformation before the implementation of this system in an in-service high-speed railway (HSR) tunnel.

• Smart Structures and Systems
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• 제24권3호
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• pp.319-332
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• 2019

Structural health monitoring (SHM) is of great importance to super high-rise buildings. The Shanghai Tower is currently the tallest building in China, and a complete SHM system was simultaneously constructed at the beginning of the construction of the tower. Due to the variety of sensor types and the large number of measurement points in the SHM system, an online automatic structural health assessment method with few computations and no manual intervention is needed. This paper introduces a structural health assessment method for the Shanghai Tower that uses the coefficients of an autoregressive (AR) time series model as structural state indicators. An analysis of collected data indicates that the coefficients of the AR model are affected by environmental factors, and the principal component analysis method is used to remove the influence of environmental factors. Finally, the control chart method is used to track the changes in structural state indicators, and a plan for online automatic structure health state evaluation is proposed. This method is applied to long-term acceleration and inclination data from the Shanghai Tower and successfully identifies the changes in the structural state. Overall, the structural state indicators of the Shanghai Tower are stable, and the structure is in a healthy state.

• Educational Technology International
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• 제24권1호
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• pp.53-80
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• 2023

This study investigates faculty support for quality assurance in online education, and offers suggestions for its improvement based on feedback from Instructional Design (ID) staff working at a public university in the U.S. Qualitative research using semi-structured interviews was conducted with seven ID staff in order to examine their perceptions regarding faculty support related to quality assurance in online education. The results of the data analysis indicate that four types of faculty support-quality assurance reviews using Quality Matter (QM) standards, templates, individual consultations with ongoing support, and monitoring-were offered for faculty. Faculty support for quality assurance in online education could be improved by developing specific quality assurance standards, recruiting external experts, examining learning effects, developing a quality assurance management system, and sharing documents among ID staff. This study highlights the necessity of quality assurance in online education and provides cases of faculty support in a real higher education setting.

• 생태와환경
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• 제40권2호
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• pp.193-200
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• 2007

수질자동측정시스템에서 운영하는 온라인 TOC 자동측정장치의 측정값에 대한 불확도를 산출하였다. 본 연구에서 개발한 모델식을 이용하여 ISO 불확도 규정을 바탕으로 요소별 불확도 및 표준합성불확도 그리고 확장불확도를 산출하였다. 온라인 TOC 자동측정장치는 검량선을 이용한 농도산출, 측정감도 변동, 매질효과 및 채수지점에 따라 가장 많은 불확도를 가지는 것으로 사료된다. 환경부에서 운영중인 수질자동측정시스템에 설치된 온라인 TOC자동측정장치를 이용하여 4가지 불확도 요소별 불확도 측정실험을 진행한 결과 매질효과에 의한 불확도가 가장 큰 영향을 주는 것으로 나타났다. 매질효과에 의한 불확도는 주입한 퇴적물의 양이 증가할수록 기여율이 함께 증가하였다.

• Smart Structures and Systems
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• 제16권2호
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.