• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.217-224
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• 2019

The intelligent robot industry is a complex which encompasses all fields of science and technology, and its marketability and industrial impact are remarkable. Major countries in the world have been strengthening their policies to foster the intelligent robot industry, but discussions on liability issues and legal actions that are accompanied by the related big or small accidents are still insufficient. In this study, therefore, the patent law by artificial intelligence robots and the legislation for relevant legal actions at the criminal law level are presented. Patent law legislation by artificial intelligence robots should comply with the followings. First, the electronic human being other than humans ought to be given legal personality, which is the subject of patent infringement. Even if artificial intelligence has legal personality, legal responsibility will be varied depending on the judgment of whether the accident has occurred due to the malfunction of the artificial intelligence itself or due to the human intervention with malicious intention. Second, artificial intelligence as a subject of actors and responsibility should be distinguished strictly; in other words, the injunction is the responsibility of the intelligent robot itself, but the financial repayment is the responsibility of the owner. In the criminal law legislation, regulations for legal punishment of intelligent robot manufacturing companies and manufacturers should be prepared promptly in case of legal violation, by amending the scope of application of Article 47 (Penal Provisions) of the Intelligent Robots Development and Distribution Promotion Act. In this way, joint penal provisions, which can clearly distinguish the responsibilities of the related parties, should be established to contribute to the development of the fourth industrial revolution.

• Proceedings of the Korea Society for Simulation Conference
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• 1996.05a
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• pp.15-15
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• 1996

A shop floor control system (SFCS) is the central part of a CIM system used to control the activities of several pieces of manufacturing equipment (e.g., NC machines, robots, conveyors, AGVs, AS/RS). The SFCS receives orders and related process plans, and then performs selecting a specific process routing, allocating resources, scheduling the workpieces, downloading the processing instructions (e.g., RS-274 instructions for NC machines, VAL II programs for robot), monitoring the progress of activities, detecting and recovering from errors, and preparing reports on the status of the manufacturing system. Simulation has been utilized in discovering control policies used for resolving shop floor be control problems such as resource contentions, part dispatching, deadlock. The simulation model must be designed to respond to real-time data coming from a shop floor. However, to rapidly build a realtime simulation model of SFCS cannot be easily accomplished. This talk is to address an automatic program generator of discrete event simulation model for shop floor control from process plans and resource models. The program generator is capable of constructing complete discrete simulation models for multi-product and multi-stage flexible manufacturing systems.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.95-100
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• 2005

Making the best use of the features of the electro-rheological(ER) valve, a two-port pressure control valve using ER fluids is proposed and manufactured. The ER-Valve characteristics are evaluated by changing the intensity of the electric field and the number of electrode. In addition, the performance of the plate type ER-Valve is investigated by change the particle concentration of the ER fluid. As only with electrical signal change to the ER-Valve in which ER fluid flowing, ER fluid flow is controlled, so development of simple ER-Valves have been tried. The ER-Valves and pressure drop check method are considered to be applied to the fluid power control system. Using the minかnぉd pressure control valve, a one-link manipulator with FHA in robot system is driven. As a result, it is experimentally confirmed that the pressure control valve using ER fluids is applicable to use in driving actuator. If it applies characteristics of the ER fluids, it will be able to apply in the control system fir the ER Valve which occurs from industrial controller(PLC).

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.54-63
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• 2022

Among the various industrial robots, palletizing robots have received particular attention because of their higher productivity in accordance with technological progress. When designing a palletizing robot, the main components, such as the servo motors and reducers, should be properly selected to ensure its performance. In this study, a practical method for selecting the motors and reducers of a robot was proposed by performing the dynamic analysis of rigid-flexible multibody systems using ANSYS and ADAMS. In the first step, the links and frames were selected based on the structural analysis results obtained from ANSYS. Subsequently, a modal neutral file (MNF) with information on the flexible body was generated from the links and frames using modal analysis through ANSYS and APDL commands. Through a dynamic analysis of the flexible bodies, the specifications of the major components were finally determined by considering the required torque and power. To verify the effectiveness of the proposed method, the analysis results were compared with those of a rigid-body model. The comparison showed that rigid-flexible multibody dynamic analysis is much more useful than rigid body analysis, particularly for movements heavily influenced by gravity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.385-393
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• 2016

This paper presents the new position estimation method which contains the optical displacement sensor and the dead reckoning based position estimation algorithm for automation of hull cleaning robot. To evaluate feasibility of the proposed position estimation method on the hull cleaning robot, it was applied on the small scale robot model which has an identical drive method with the hull cleaning robot and then a set of the position estimation experiments were performed. The experimental results of the position estimation demonstrate that the estimated results with the optical displacement sensors is more accurate than used rotary encoder method. In addition, it continuously calculated the robot position quite close to the real robot driving path. In a follow-up study, the proposed position estimation method will be complemented and exploited on the actual hull cleaning robot by adding additional sensor modules that correct measurement errors.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.3
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• pp.274-290
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• 2019

This article links the change in regional manufacturing employment in Korea after the financial crisis to the geography of technological and trade shocks. We conceptualize the trade shock as the rapid growth in Korean imports from and exports to China and ASEAN countries. We then measure the exposure to technological shocks as the degree to which regions are specialized in routine tasks, which are susceptible to automation technologies. Results show that local labor markets specialized in routine tasks experience significant falls in manufacturing employment. Regions whose industrial structure exposes them to rising import competition experience sharp drop in manufacturing employment. We also found that export plays a major role in explaining the growth of regional manufacturing employment.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.62-66
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the learning control field was learning in robots doing repetitive tasks such as an assembly line works. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.1
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• pp.62-72
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• 1997

High flexibility and productivity using industrial robots are being achieved in manufacturing lines with off-line robot programmings. A good off-line programming system should have functions of robot modelling, trajectory planning, graphical teach-in, kinematic and dynamic simulations. Simulated results, however, can hardly be applied to on-line tasks until any calibration procedure is accompained. This paper proposes a visual calibration scheme in order to provide a calibration tool for our own off-line programming system of SCARA robots. The suggested scheme is based on the position-based visual servoings, and the perspective projection. The scheme requires only one camera as it uses saved kinematic data for three-dimensional visual calibration. Predicted images are generated and then compared with camera images for updating positions and orientations of objects. The scheme is simple and effective enough to be used in real time robot programming.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.38-47
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• 2001

In this paper, a new scheme of adaptive-neuro control system is presented to implement real-time control of robot manipulator using Digital Signal Processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of therir prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust perfor-mance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for the implementation of real-time control of robot system by the simulation and experi-ment.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.