• 전기저널
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• 9호통권69호
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• pp.5-8
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• 1982

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권1호
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• pp.25-32
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• 2020

다양한 분야에서 활용되는 상황인지 시스템은 상황정보를 획득하기 위한 추상화 과정에서 규칙 기반의 인공기능 기술이 기존에 사용되었다. 그러나 서비스에 대한 사용자의 요구사항이 다양해지고 사용되는 데이터의 증대로 규칙이 복잡해지면서 규칙 기반 모델의 유지보수와 비정형 데이터를 처리하는데 어려움이 있다. 이러한 한계점을 극복하기 위해 많은 연구들에서는 상황인지 시스템에 기계학습 기술을 적용하였으며, 이러한 기계학습 기반의 모델을 상황인지 시스템에 사용하기 위해서는 주기적으로 학습 데이터를 제공해야 한다. 이에 기계학습 기반 상황인지 시스템에 대한 선행연구에서는 여러 개의 기계학습 모델을 적용하기 위한 학습 데이터 생성, 제공 등의 과정을 보였으나 제한된 종류의 기계학습 모델만을 적용 가능하여 확장성이 고려되어야 한다. 본 논문은 기계학습 기반의 상황인지 시스템의 확장성을 고려한 기계학습 모델의 학습 데이터 생성 방법을 제안한다. 제안하는 방법은 시스템의 확장성을 고려하여 기계학습 모델의 요구사항을 반영할 수 있는 학습 데이터 생성 모델을 정의하고 학습 데이터 생성 모듈을 바탕으로 각각의 기계학습 모델의 학습 데이터를 생성하는 것이다. 시스템의 확장성의 검증을 위해 실험에서는 노인의 건강상태 알림 서비스를 위한 심박상태 분석 모델을 대상으로 한 학습데이터 생성 스키마를 기반으로 학습데이터 생성 모델을 정의하고 실환경에서 정의된 모델을 S/W에 적용하여 학습데이터를 생성한다. 또한 생성된 학습데이터의 유효성을 검증하기 위해 사용되는 기계학습 모델에 생성한 학습데이터를 학습시켜 정확도를 비교하는 과정을 보인다.

• 산업경영시스템학회지
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• 제29권1호
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• pp.87-93
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• 2006

In order to implement Artificial Intelligence, various technologies have been widely used. Artificial Intelligence are applied for many industrial products and machine tools are the center of manufacturing devices in intelligent manufacturing devices. The purpose of this paper is to present the design of Decision Support Agent that is applicable to machine tools. This system is that decision whether to act in accordance with machine status is support system. It communicates with other active agents such as sensory and dialogue agent. The proposed design of decision support agent facilitates the effective operation and control of machine tools and provides a systematic way to integrate the expert's knowledge that will implement Intelligent Machine Tools.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.674-677
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• 2000

This paper describes a remote monitoring and control system using mobile internet environments through TCP/IP. The server program acts as the main interface between the machine system and the internet technology. The client program is to be distributed to the remote users such as PDA who want to monitor the machine status. A server system is designed to send information of the faults and status of application system to the client part. Therefore, The client parts can take measures and control the system remotely according to the error and faults of system. We developed the client- serversystem for remote control of a green house environment.

• 한국수산과학회지
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• 제55권5호
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• pp.638-644
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• 2022

The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

• 산업경영시스템학회지
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• 제43권2호
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• Journal of Mechanical Science and Technology
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• 제20권4호
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• pp.455-466
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• 2006

This study was an attempt to control and monitor Computerized Numerical Controller (CNC) machines anywhere and anytime for the development of a ubiquitous machine (u-machine). With a Personal Digital Assistant (PDA) phone, the machine status and machining data of CNC machines can be monitored in wired and wireless environments, including the environments of IMT2000 and Wireless LAN. Moreover, CNC machines can be controlled anywhere and anytime. The concept of the anywhere-anytime controlling and monitoring of a manufacturing system was implemented in this study for the purpose of u-manufacturing and u-machines. In this concept, the communication between the CNC controller and the PDA phone was successfully performed anywhere and anytime for the real-time monitoring and control of CNC machines. In addition, the interface between the CNC controller and the developed application module was implemented by Object linking and embedding for Process Control (OPC) and shared CNC memory. For communication, the design of a server contents module within the target CNC was based on a TCP/IP. Furthermore, the client contents module within the PDA phone was designed with the aid of embedded c++ programming for mobile communication. For the interface, the monitoring data, such as the machine status, the machine running state, the name of the Numerical Control (NC) program, the alarm and the position of the stage axes, were acquired in real time from real machines with the aid of the OPC method and by sharing the CNC memory. The control data, such as the start, hold, emergency stop, reserved start and reserved stop, were also applied to the CNC domain of the real machine. CNC machines can therefore be controlled and monitored in real time, anywhere and anytime. Moreover, prompt notification from CNC machines to mobile phones, including cellular phones and PDA phones, can be automatically realized in emergencies.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.27-30
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• 2019

Factory monitoring systems are gaining importance in wide areas of industry. Especially, there have been many efforts in implementation of vibration measurement and analysis for monitoring the status of rotating machines. In this paper, a digital signal processor (DSP) based monitoring system dedicated to the vibration monitoring and analysis on rotating machines is discussed. Vibration signals are acquired and processed for the continuous monitoring of the machine status. Time domain signals and fast Fourier transform (FFT) are used for vibration analysis. All of the signal processing procedures are done in the DSP to reduce the production and maintenance cost. The developed system could also provide remote and mobile monitoring capabilities to operator via internet connection. This paper describes the overview of the functional blocks of the implemented system. Test results based on signals from small-size single phase motors are discussed for monitoring and defect diagnosis of the machine status.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 Handout for 2000 Inter. Machine Tool Technical Seminar
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• pp.61-70
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• 2000

This paper described the current status of machine tool technology and its future trends with a particular emphasis on high-speed machining. People in machine tool industry have continuously sought to serve fast-changing manufacturing industry with economical machining solutins. At presents, it appears that more productivity gain is demanded to shorten time-to-market and machining requirements become more stringent. In this regard, this paper firstly addressed a high-speed spindle as a key element for the next-generation machine tools. The sequel to it apparently went to high-speed feed axes and final discussion included the problem of how to optimize overall system including servo function. Lastly a brief look to NC technology including machine intelligence was taken.

• 한국정밀공학회지
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• 제15권1호
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• pp.19-25
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• 1998

Remote fault diagnosis and maintenance system using general telecommunication network is necessary for an effective fault diagnosis and higher productivity of NC machine tools. In order to monitor machine tool condition and diagnose alarm states due to electrical and mechanical faults, a remote data communication system for monitoring of NC machine fault diagnosis and status is developed. The developed system consists of (1) remote communication module among NC's and host PC using PSTN. (2) 8 channels analog data sensing module, (3) digital I/O module for control or NC machine, (4) communication module between NC machine and remote data communication system via RS-232C, and (5) software man-machine interface. Diagnostic monitoring results generated through a successive type inference engine are displayed in user-friendly graphics. The validity and reliability of the developed system is verified to be a powerful commercial version on a vertical machining center through a series of experiments.