• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.4
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• pp.466-476
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• 1986

This paper proposes a system that translates the machine independent intermediate micro-programming language(IML) into microcode, using the register allocation algorithm, the microinstruction format and the field information for the target machine emulation on a microprogrammable host machine. The IML, which is for PDP-8 emulation on a microprogrammable hypothsetical 16 bit host machine, is microcoded by the proposed system, and the validity of the algorithm in the proposed system is verified by executing a test program of the target machine on the emulator.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.136-141
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• 2023

The continuous shrinking of transistors in integrated circuits leads to difficulties in improving performance, resulting in the emerging transistors such as nanosheet field-effect transistors. In this paper, we propose a TCAD-machine learning framework of nanosheet FETs to model the current-voltage characteristics. Sentaurus TCAD simulations of nanosheet FETs are performed to obtain a large amount of device data. A machine learning model of I-V characteristics is trained using the multi-layer perceptron from these TCAD data. The weights and biases obtained from multi-layer perceptron are implemented in a PSPICE netlist to verify the accuracy of I-V and the DC transfer characteristics of a CMOS inverter. It is found that the proposed machine learning model is applicable to the prediction of nanosheet field-effect transistors device and circuit performance.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.503-510
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• 2010

This paper proposes the use of fuzzy applications to a 4-machine and 10-bus system to check stability in open conditions. Fuzzy controllers and the excitation of a synchronous generator are added. Power system stabilizers (PSSs) are added to the excitation system to enhance damping during low frequency oscillations. A fuzzy logic power system stabilizer (PSS) for stability enhancement of a multi-machine power system is also presented. To attain stability enhancement, speed deviation ($\Delta\omega$) and acceleration ($\Delta\varpi$) of the Kota Thermal synchronous generator rotor are taken as inputs to the fuzzy logic controller. These variables have significant effects on the damping of generator shaft mechanical oscillations. The stabilizing signals are computed using fuzzy membership functions that are dependent on these variables. The performance of the fuzzy logic PSS is compared with the open power system, after which the simulations are tested under different operating conditions and changes in reference voltage. The simulation results are quite encouraging and satisfactory. Similarly, the system is tested for the different defuzzification methods, and based on the results, the centroid method elicits the best possible system response.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.802-811
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• 1996

Farmers need alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds located in the seedline between crop plants and there are no selective heribicides for some crop/weed situations. Since hand labor is costly , an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Currently no such system exists for removing weeds located in the seedline between crop plants. The goal of this project is to build a real-time , machine vision weed control system that can detect crop and weed locations. remove weeds and thin crop plants. In order to accomplish this objective , a real-time robotic system was developed to identify and locate outdoor plants using machine vision technology, pattern recognition techniques, knowledge-based decision theory, and robotics. The prototype weed control system is composed f a real-time computer vision system, a uniform illumination device, and a precision chemical application system. The prototype system is mounted on the UC Davis Robotic Cultivator , which finds the center of the seedline of crop plants. Field tests showed that the robotic spraying system correctly targeted simulated weeds (metal coins of 2.54 cm diameter) with an average error of 0.78 cm and the standard deviation of 0.62cm.

• 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

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3B
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• pp.287-296
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• 2011

The white stick mainly used for visually impaired person has difficulty in providing location information and effective countermeasures for emergency situations encountered during walking as well as detecting floating obstacles on the ground. In this paper, we propose a machine-to-machine based intelligent walking assistance system for safe and convenient walking of the visually impaired. The proposed system consists of a walking assistance stick used by the visually impaired and a server supporting multiple stick users in remote places through mobile communication networks. The stick equipped with ultrasonic sensors, GPS(global positioning system) receiver and vibrator not only detects floating obstacles, but also offers stick users with present location identification utilizing a text-to-voice conversion technology. Besides providing geographic information, the server notifies the emergency locations of users to guardian and aid agency, and it provides log information during walking such as the place, time and the number of accidents. Test results with a developed prototype system have shown that the system properly performs the functions and satisfies overall system performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.307-309
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• 2016

This paper present about the system for sensing and controlling a wireless power transfer system using bluetooth protocol in robot, healthcare, smart-grid, and autonomous car. Recently a variety of applications using the Internet of Things (Internet of Things) and machine to machine (Machine to Machine) have been raised in many industries. To do this, it requires the fusion technology which is constituted with control, computing and networking. Embedded system is centered existing control system and Cyber Physical System(CPS) is the systems which was converged of a computing technologies using a wired or wireless network. CPS was adopted in the future government-led technology in the United States and Europe and is being pursued in cooperation with institutes, industries, and academia. In this paper, we implement and describe a technique for controlling the system for transmitting power wirelessly by sensing method using the matching of CPS technology concepts.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.65-72
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• 2020

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

• Nuclear Engineering and Technology
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• v.47 no.5
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• pp.624-632
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• 2015

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

• Journal of the Korean Society of Safety
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• v.30 no.5
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• pp.108-113
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• 2015

Plastic injection moulding machine is widely used for many industrial field. It is classified into mandatory safety certification machinery in Industrial Safety and Health Act because of its high hazard. In order to prevent industrial accidents by plastic injection moulding machine, it is necessary for designer to identify hazardous factors and assess the failure modes to mitigate them. This study tabulates the failure modes of main parts of plastic injection moulding machine and how their failure has affect on the machine being considered. Failure Mode & Effect Analysis(FMEA) method has been used to assess the hazard on plastic injection moulding machine. Risk and risk priority number(RPN) has been calculated in order to estimate the hazard of failures using severity, probability and detection. Accidents caused by plastic injection moulding machine is compared with the RPN which was estimated by main regions such as injection unit, clamping unit, hydraulic and system units to find out the most dangerous region. As the results, the order of RPN is injection unit, clamping unit, hydraulic unit and system units. Barrel is the most dangerous part in the plastic injection moulding machine.