• Journal of Korea Society of Industrial Information Systems
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• v.8 no.3
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• pp.32-41
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• 2003

In this paper, we implemented a looms management system which supports remote monitoring and scientific management of the looms. In the implemented system, the layout of the looms is placed in the user interface, and each loom's operating state and rate are automatically represented there. The implemented system has aggregate query processing functions for the looms existing in the selected area by the louse and it also has high level query processing functions to support the chart and pivot table; it can be used as a decision support system. The proposed system can detect temporal or persistent problems of the looms. Therefore, it can be used to raise the productivity and to reduce the cost in textile companies by coping with the situation properly.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.108-114
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• 2007

A new cutting force estimating approach and machining state monitoring examples are presented which uses a cylindrical displacement sensor built into the spindle. To identify the tool-spindle system dynamics with frequency up to 2 kHz, a home-built electro-magnetic exciter is used. The result is used to build an algorithm to extract the dynamic cutting force signal from the spindle error motion; because the built-in spindle sensor signal contains both spindle-tool dynamics and tool-workpiece interactions. This sensor is very sensitive and can measure broadband signal without affecting the system dynamics. The main characteristic is that it is designed so that the measurement is irrelevant to the geometric errors by covering the entire circumferential area between the target and sensor. It is also very simple to be installed. Usually the spindle front cover part is copied and replaced with a new one with this sensor added. It gives valuable information about the operating condition of the spindle at any time. It can be used to monitor cutting force and chatter vibration, to predict roughness and to compensate the form error by overriding spindle speed or feed rate. This approach is particularly useful in monitoring a high speed machining process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3946-3965
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• 2018

Network anomaly detection in Software Defined Networking, especially the detection of DDoS attack, has been given great attention in recent years. It is convenient to build the Traffic Matrix from a global view in SDN. However, the monitoring and management of high-volume feature-rich traffic in large networks brings significant challenges. In this paper, we propose a moving window Principal Components Analysis based anomaly detection and mitigation approach to map data onto a low-dimensional subspace and keep monitoring the network state in real-time. Once the anomaly is detected, the controller will install the defense flow table rules onto the corresponding data plane switches to mitigate the attack. Furthermore, we evaluate our approach with experiments. The Receiver Operating Characteristic curves show that our approach performs well in both detection probability and false alarm probability compared with the entropy-based approach. In addition, the mitigation effect is impressive that our approach can prevent most of the attacking traffic. At last, we evaluate the overhead of the system, including the detection delay and utilization of CPU, which is not excessive. Our anomaly detection approach is lightweight and effective.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.75-86
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• 2020

With the recent advancement of malware, the actors performing malicious tasks are often not processes. Malicious code injected into the process that is installed by default in the operating system works thread by thread in the same way as DLL / code injection. In this case, diagnosing and blocking the process as malicious can cause serious problems with system operation. This white paper lists the problems of how to use process-based monitoring information to identify and block the malicious state of a process and presents an improved solution.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.259-261
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• 2003

This paper suggests wireless diagnosis and monitoring system using SMS mode of mobile communication network for distribution transformer which could prevent electrical accident in the near future. Data are acquired by measuring the temperature of insulator oil in the distribution transformer and load current. Data acquisition of sensor using mobile communication network carried out filtering of sensor's output to optimize the size of send data Merit of this inspection method is that management, control and monitoring some transformers can be carried out using only one server. This inspection method will be the way of inspection to be worth spotlight in the near future because it is able to solve easily with the minimum facility inspection about state of transformer which is operating, to wide coverage about machine's wrong operation in other field.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1142-1147
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• 2002

For the evaluation of the DC motor noise and vibration, usually it is rely on human feeling because some kinds of noise are not definitely represented by measurement Instrument such as sound meter. But when we consider time signal of the noise and vibration. It is possible to represent them. And in this paper. it is suggested to study output current shape of the motor because it Is the source to make speed and torque variation of the motor. If the current shape is not stable. it makes operating state of the motor unstable and produces noise and vibration. By analyzing signal at time and frequency of noise and vibration and current shape. it is possible to automation of the noise and vibration measurement in the Production line.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.689-700
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• 2017

This paper presents an effective approach for wind turbine (WT) condition assessment based on the data collected from wind farm supervisory control and data acquisition (SCADA) system. Three types of assessment indices are determined based on the monitoring parameters obtained from the SCADA system. Neural Networks (NNs) are used to establish prediction models for the assessment indices that are dependent on environmental conditions such as ambient temperature and wind speed. An abnormal level index (ALI) is defined to quantify the abnormal level of the proposed indices. Prediction errors of the prediction models follow a normal distribution. Thus, the ALIs can be calculated based on the probability density function of normal distribution. For other assessment indices, the ALIs are calculated by the nonparametric estimation based cumulative probability density function. A Back-Propagation NN (BPNN) algorithm is used for the overall WT condition assessment. The inputs to the BPNN are the ALIs of the proposed indices. The network structure and the number of nodes in the hidden layer are carefully chosen when the BPNN model is being trained. The condition assessment method has been used for real 1.5 MW WTs with doubly fed induction generators. Results show that the proposed assessment method could effectively predict the change of operating conditions prior to fault occurrences and provide early alarming of the developing faults of WTs.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.5
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• pp.751-760
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• 2021

This study investigated the changes in water quality parameters and microbial colonies when ozone was applied to a semi-recirculating aquaculture system (semi-RAS) for the olive flounder Paralichthys olivaceus (500 g in average weight). Concentrations of ozone-produced oxidants (OPO) in rearing tanks were maintained at 0, 0.014, 0.025 mg/L as Cl₂ for 26 days. Except total ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, phosphate phosphorus, chemical oxygen demand, and total suspended solids decreased significantly with increasing OPO concentration in daily and weekly monitoring (P<0.05). Colony forming unit (CFU) counts of heterotrophic marine bacteria decreased in an OPO concentration-dependent manner. Overall reduction rates of microbial colonies in the treatments were 80% higher than those of the control (P<0.05). During the experiment, the OPO concentration-driven ozonation was reliably practiced without any adverse effects on the animals cultured in semi-RAS. Considering the biohazard, operating cost, and stability of ozonation, an OPO concentration of 0.014 mg/L would be sufficient to control water quality parameters and microbial colonies in a semi-RAS.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.