• 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.

• 한국임상약학회지
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• 제19권2호
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• pp.120-130
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• 2009

The objective of this study was to analyze and to improve therapeutic drug monitoring(TDM) service of vancomycin in a local hospital. Patients with TDM service between September 2005 and December 2008 were included and the data were collected for vancomycin use and components of TDM. During that period, 421 cases of TDM service of vancomycin in 236 patients were retrospectively reviewed. The first dosages of vancomycin were appropriate in 135(57.2%) patients and administration of vancomycin was discontinued in 126(53.4%) patients due to therapeutic failure or adverse drug reaction. MRSA was identified in 191(80.9%) patients and 135(70.7%) samples for the identification were sputum. According to the TDM reports, 232(55.1%) serum samples were obtained at the steady-state conditions and 55.5% of the samples that were drawn before the steady-state was due to the physician's inappropriate knowledge about the steady-state. Based on the time of vancomycin administration, 35.8% of the samples were not obtained at the recommended sampling time. For the patients in general wards, the most common reason for the incorrect samples was routine serum sampling by the laboratory medicine phlebotomists between 6 and 8 a.m. except sunday. In contrast, samples drawn by nurses or physicians at inappropriate time were the most common reason for the incorrect samples with patients in the intensive care units. Physicians accepted 68.5% of the recommendations for vancomycin dosage and administration. In conclusion, TDM service of vancomycin needs to be improved in inappropriate sampling time and vancomycin dosage. For solving these problems, current team made of TDM pharmacists and physicians of laboratory medicine can be expanded to include a physician of infectious diseases, nurses and laboratory medicine phlebotomists as new members. Through the TDM service of vancomycin by the new team, we can settle the problems and make the guideline for the scientific controversies associated with therapeutic monitoring of vancomycin.

• Nuclear Engineering and Technology
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• 제36권6호
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• pp.528-539
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• 2004

It is essential in commercial reactors that the safety limits imposed on the fuel pellets and fuel clad barriers, such as the linear power density (LPD) and the departure from nucleate boiling ratio (DNBR), are not violated during reactor operations. In order to accurately monitor the safety limits of current reactor states, a detailed three-dimensional (3D) core power distribution should be estimated from the in-core detector signals. In this paper, we propose a calculation methodology for detailed 3D core power distribution, using in-core detector signals and core monitoring constants such as the 3D Coupling Coefficients (3DCC), node power fraction, and pin-to-node factors. Also, the calculation method for several core safety parameters is introduced. The core monitoring constants for the real core state are promptly provided by the core design code and on-line MASTER (Multi-purpose Analyzer for Static and Transient Effects of Reactors), coupled with the core monitoring program. through the plant computer, core state variables, which include reactor thermal power, control rod bank position, boron concentration, inlet moderator temperature, and flow rate, are supplied as input data for MASTER. MASTER performs the core calculation based on the neutron balance equation and generates several core monitoring constants corresponding to the real core state in addition to the expected core power distribution. The accuracy of the developed method is verified through a comparison with the current CECOR method. Because in all the verification calculation cases the proposed method shows a more conservative value than the best estimated value and a less conservative one than the current CECOR and COLSS methods, it is also confirmed that this method secures a greater operating margin through the simulation of the YGN-3 Cycle-1 core from the viewpoint of the power peaking factor for the LPD and the pseudo hot pin axial power distribution for the DNBR calculation.

• 정보보호학회논문지
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• 제29권6호
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• pp.1403-1412
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• 2019

터빈, 배관 및 저장탱크와 같은 물리장치들의 경우 노후화뿐만 아니라 제어장치에 대한 사이버공격으로 인해 PLC(Programmable Logic Controller)와 같은 제어시스템의 보호 및 상태감시기능이 동작하지 않는 경우, 피해파급력이 크고, 가동 중지 시 그 비용 손실 또한 매우 크다. 가동 중인 물리장치의 작동을 중지하지 않고 간접적으로 상태감시를 함으로써 가용성을 유지하기 위한 방안으로써 온도, 가속도, 전류 등을 간접적으로 감지하고, 데이터들을 Influx DB에 저장하여 실시간으로 감시하는 시스템을 설계 및 구현한다. 실제 구현된 시스템으로부터 데이터를 얻고 이를 이용하여 이상상태를 감지할 수 있음을 검증하였다. 간접적 실시간 감시시스템의 범용화를 통해 데이터를 축적해 활용하면, 추가비용 없이 가동을 중지하지 않고 사용할 수 있을 뿐만 아니라 미리 고장을 예측하고 필요한 경우에만 조치를 취하는 고장예지기술, 이상상태를 이중으로 감시하는 신뢰도 높은 건전성 관리 기술을 통해 유지보수비용과 위험도를 대폭적으로 감소시키고, 보안위협에 대한 대비가 가능하다.

• 전자통신동향분석
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• 제33권1호
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• pp.101-110
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• 2018

Based on the main technologies of the 4th Industrial Revolution, industries including the smart home, transportation, agriculture, factory, energy, and medical care industries are rapidly developing. Disaster management technologies and services based on state-of-the-art convergence technologies are being widely applied for the purposes of public safety. State-of-the-art scientific technologies including the Internet of Things (IoT) are expected to offer alternative solutions to pending issues of disaster and safety. Particularly in disaster management, a "prevention activity"to avoid and control disasters in advance is essential, and thus disaster prevention and safety monitoring technologies based on hyper-connected intelligence are fundamental for society during the 4th Industrial Revolution. IoT technologies are being actively applied and utilized in various fields to prevent social and natural disasters. In this article, we introduce the development trends of disaster prevention and safety monitoring technologies based on IoT technologies.

• 제어로봇시스템학회논문지
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• 제6권6호
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• pp.486-491
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• 2000

The dicing process cuts a semiconductor wafer to lengthwise and crosswise direction by using a rotating circular diamond blade. But inferior goods are made under the influence of several parameters in dicing such as blade, wafer, cutting water and cutting conditions. This paper describes a monitoring algorithm using neural network in order to find out an instant of vibration signal change when bad dicing appears. The algorithm is composed of two steps: feature extraction and decision. In the feature extraction, five features processed from vibration signal which is acquired by accelerometer attached on blade head are proposed. In the decision, back-propagation neural network is adopted to classify the dicing process into normal and abnormal dicing, and normal and damaged blade. Experiments have been performed for GaAs semiconductor wafer in the case of normal/abnormal dicing and normal/damaged blade. Based upon observation of the experimental results, the proposed scheme shown has a good accuracy of classification performance by which the inferior goods decreased from 35.2% to 6.5%.

• 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.

• 소성∙가공
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• 제16권1호
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• pp.54-60
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• 2007

Optimum injection molding condition for a box mold was searched by the Response Surface Analysis(RSA) with the aid of process monitoring system(PMS). Process variables on the control panel of the injection molding machine such as barrel temperatures, screw speed profile, holding pressures, etc. cannot guarantee the uniformity of the material variables directly related with the state of the product in the mold cavity. In order to make sure the state of the resin in the cavity, pressures and temperatures in the cavity, runner and nozzle were monitored in the experiment with the PMS. To accomplish the consistency of the molding process, dependent variables such as the switchover point and holding time were searched with the PMS. With a proper objective function about deflection of the box-type product, the optimum injection molding condition was obtained.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 제2회 학술대회 논문집 일렉트렛트 및 응용기술전문연구회
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• pp.36-39
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• 2000

This paper presents the technology of condition diagnosis & life estimation on insulation system of the traction motor. In the non-destructive methods for diagnosis of coil insulation state, residual dielectric strength is estimated by the D-map which consist of the partial discharge quantity Q and average degradation degree $\Delta$. In the operating history of machine, the N-Y life estimation method is based on the stop-starting numbers and operating times with considering each degradation factor by the thermal, electrical and heat-cycle stress. With the on-line conditioning monitoring on the currents of traction motors, detecting the abnormal operating state due to bearing faults, stator or armature faults, eccentricity related faults and broken rotor bars can be performed.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.572-580
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• 2013

Power metering and monitoring system is a basic element of Smart Grid technology. This paper proposes a new Non-Intrusive Load Monitoring (NILM) method for a residential buildings sector using the measured total active power consumption. Home electrical appliances are classified by ON/OFF state models, Multi-state models, and Composite models according to their operational characteristics observed by experiments. In order to disaggregate the operation and the power consumption of each model, an algorithm which includes a switching function, a truth table matrix, and a matching process is presented. Typical profiles of each appliances and disaggregation results are shown and classified. To improve the accuracy, a Time Lagging (TL) algorithm and a Permanent-On model (PO) algorithm are additionally proposed. The method is validated as comparing the simulation results to the experimental ones with high accuracy.