• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.94-96
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• 2022

Plug & Play techniques have been proposed in various ways to overcome the complexity of sensors/drivers in IoT application development. IEEE1451 standard abstracts sensors/drivers into a data structure called TEDS. As a result, characteristics of the sensor/driver are unnecessary when connecting the sensor/driver to the host device. The method proposed by ETRI is a format in which device drivers of sensors/drivers are dynamically loaded and connected to hosts, and there is no particular abstraction data structure. Both schemes are located at both ends in terms of the abstraction unit of the sensor/driver. We present the problem based on this fact, and what optimized methods can exist in the middle of it. In this paper, we analyze existing Plug&Play techniques. Also, we specify abstraction units of sensors/drivers, and analyze them.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.94.4-94
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• 2002

Traditional control systems that consist of sensors, actuators and a controller centralized and connected with point-to-point links, have become distributed because of their performance limits and maintenance problems. Sensors and actuators are changed to smart devices having a processor and these devices and controllers are connected with fieldbuses such as Profibus, FIP, CAN, LonWorks and so on. Because they are distributed, it takes any delay to transmit data from sensor to controller and data from controller to actuator according to network characteristic. Also, the execution times of tasks in a node are not regular and depend on the node characteristic and the number of tasks and so on...

• Composites Research
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• v.28 no.4
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• pp.148-154
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• 2015

In order to detect damage in early stages and properly maintaining structures, the structural health monitoring technology is employed. In most cases, active-sensing SHM needs many piezoelectric (PZT) sensors and actuators. Thus, if there is a defect on PZT used for active-sensing SHM, the structural status could be misclassified. This study, for reliable SHM performance, investigated to detect defects of sensors by using the admittance-based sensor diagnostics. This study also introduced an algorithm that can diagnose sensor defects based only on data measured from the sensors in case that information about the changes in adhesive and environmental investigation, this study confirms that the proposed algorithm could be efficiently applied to real-world structures in which a significant temperature variation could take place.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.864-869
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• 2004

A novel concept of feedback loop design for modal test and model updating is proposed. This method uses the closed -loop natural frequency information for parameter modification to overcome the problems associated with the conventional method employing the modal sensitivity matrix. To obtain new modal information from closed-loop system, controllers should be effective in changing modal data while guaranteeing the stability of closed-loop system. It is very hard to guarantee the stability of the closed-loop system with non-collocated sensor and actuator set. Ill this research, we proposed a controller called mode-decoupling controller that can change a target mode as much as the designer wants guaranteeing the stability of closed-loop system. This controller can be computed just using measured open-loop modeshape matrix. A simulation based on time domain input/output data is performed to check the feasibility of proposed control method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.10
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• pp.955-961
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• 2004

A novel concept of feedback loop design for modal test and model updating is proposed. This method uses the closed-loop natural frequency information for parameter modification to overcome the problems associated with the conventional method employing the modal sensitivity matrix. To obtain new modal information from closed-loop system, controllers should be effective in changing modal data while guaranteeing the stability of closed-loop system. But it is very hard to guarantee the stability of the closed-loop system with non-collocated sensor and actuator set. In this research, we proposed a controller called mode-decoupling controller that can change a target mode as much as the designer wants guaranteeing the stability of closed-loop system. This controller can be computed Just using measured open-loop modeshape matrix. A simulation based on time domain input/output data is performed to check the feasibility of proposed control method.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.127-132
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• 2022

As the IoT technology is more developed, it is more important for the accuracy of IoT data. Since the IoT data supports a different formats and protocols, it is often happened that the IoT system is failed or the incorrect data is generated with the unreliable IoT devices(sensor, actuator). Because the abnormality of IoT device or the user situation is not detected correctly, this problem makes the user to be unsatisfied with the IoT system. This study proposes the decision methodology of IoT data consistency whether the IoT data is generated in normal range or not by using the mathematical functions('gradient descent function' and 'linear regression function'). It may be concluded that the gradient function method is suitable for the IoT data which the 'increasing velocity' is related with the next generated pattern(eg. sensor devices), the linear regression function method is suitable for the IoT data which the 'the difference from linear regression function' is related with the next generated pattern in case the data has a linear pattern(eg. water meter, electric meter).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.165-170
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• 2017

This paper presents an event-triggered model predictive controller for adaptive cruise control system with sampled and quantized-data. Unlike existing works, a longitudinal continuous-time model is used for the predictive control of the system. To efficiently utilize network resources, event-trigger scheme is employed, which allows limited sensor and actuator signal satisfying the condition that the measurement of errors is over the ratio of a trigger level. The proposed control gain is obtained by solving a convex problem satisfying several linear matrix inequalities at every sampling times. Simulation results are given to show the effectiveness of the proposed design method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.599-603
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• 2003

This paper present an error compensation system and On-Machine Measurement(OMM) system for improving the machining accuracy of ultra-precision lathe. The Fast-Tool-Servo(FTS) driven by a piezoelectric actuator is applied for error compensation system. The controller is implemented on the 32bit DSP for feedback control of piezoelectric actuator. The control system is designed to compensates three kinds of machining errors such as the straightness error of X-axis slide, the thermal growth error of the spindle. and the squareness between spindle and X-axis slide. OMM is preposed to measure the finished profile of workpiece on the machine-tool using capacitive sensor with highly accurate ruby tip probe guided by air bearing. The data acquisition system is linked to the CNC controller to get the position of each axis in real-time. Through the experiments, it is founded that the thermal growth of spindle and tile squareness error between spindle and X-axis slide influenced to machining error more than straightness error of X-axis slide in small travel length. These errors were simulated as a sinusoidal signal which has very low frequency and the FTS could compensate the signal less than 30 m. The implemented OMM system has been tested by measuring flat surface of 50 mm diameter and shows measurement error less than 400 mm

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.12
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• pp.1865-1870
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• 2010

We develop an IPMC actuator with self-sensing behavior based on an accurate neural network model (NNM). The supplied voltage and voltage signals measured at two determined points on both sides of the IPMC sheet are used as inputs to the NNM. A CCD laser displacement sensor is installed in the rig for accurate measurement of the IPMC tip displacement that is used as the training output of the proposed NNM. Consequently, the NNM model is used to estimate the IPMC tip displacement; the NNM parameters are optimized by the collected input/output training data. The effectiveness of the model for the IPMC actuator is then verified by modeling results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.