• Journal of Adhesion and Interface
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• v.7 no.4
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• pp.24-27
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• 2006

International standards claim the best possible reliability in industrial manufacturing processes. This is also essential for the application with manual applicators. The application of adhesives with manual applicators is one of the most frequently used application techniques. The range of application reaches from the building of prototypes in the automobile industry over the use in single or small-batch manufacturing up to applications in crafts enterprises. Conventional manual applicators for adhesives and sealants don't fulfill the demands in international standards for the best possible reliability. Only the worker is able to control the quality and the quantity of the bond. A velocity-controlled manual applicator solves these restrictions. Special sensors and micro controllers calculate the flow-rate, the velocity and the location of the manual applicator. This leads to stable and repeatable application processes which are claimed in international standards. The location of the bond can be compared with the nominal value, so that it is possible to check the quality of the bond during application. Furthermore there is the potential to document the data of the manufacturing process.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.189-200
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• 2004

In this paper, we introduce a new soft computing technique that dwells on the ideas of combining fuzzy rules in a fuzzy system with polynomial neural networks (PNN). The PNN is a flexible neural architecture whose structure is developed through the modeling process. Unfortunately, the PNN has a fatal drawback in that it cannot be constructed for nonlinear systems with only a small amount of input variables. To overcome this limitation in the conventional PNN, we employed one of three principal soft computing components such as a fuzzy system. As such, a space of input variables is partitioned into several subspaces by the fuzzy system and these subspaces are utilized as new input variables to the PNN architecture. The proposed soft computing technique is achieved by merging the fuzzy system and the PNN into one unified framework. As a result, we can find a workable synergistic environment and the main characteristics of the two modeling techniques are harmonized. Thus, the proposed method alleviates the problems of PNN while providing superb performance. Identification results of the three-input nonlinear static function and nonlinear system with two inputs will be demonstrated to demonstrate the performance of the proposed approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.949-956
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• 2014

Recently, with the development of hardware and software technology related with image information delivery, the demand for various multimedia service has increased. But, the process of treating, sending, and storing image signals generates image degradation by various external causes. The main cause of image degradation is noise. image is mostly damaged by AWGN (additive white Gaussian noise). Therefore, there have been active researches on noise elimination. This paper, to reduce the effects of AWGN added to the image, suggests a noise-eliminating algorithm which is excellent in low-frequency and high-frequency characteristics in space. And, this paper, through simulation techniques, compared the result of the suggested algorithm with those of the existing methods. And, to evaluate the performance of it, PSNR (peak signal to noise ratio) was used.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.535-540
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• 2007

The hand recognition technique is widely used to the economic input method for user interfacing environment. However, the most of the techniques have the restricted with recognize that only a fixed background and a regular pattern. This restriction character has brought a difficult problem to apply at variable application class and to process restriction environment. In this paper, we propose a virtual input system that extracting about hand region information that is not restricted by background image and also that can be followed the location information. This research could be used some system demanded by input of a person such as control system at airport and pavilion and economic virtual input system in class of industrial computer and various kinds control-system.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1941-1954
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• 1999

This paper presents a design and implementation of CRs(CHILL Run-time support System) to adapt the concurrent processing facilities of CHILL(CCITT High Level Language) which had recommended by ITU-T(International Telecommunication Union Telecommunication Standardization Sector). Because the CHILL provides more various concurrent processing facilities that other concurrent programming language, a design and implementation on CRS can give us real effects to gain the major functionalities and the techniques of the concurrent processing. In this paper, we design the interface rules of concurrent functions to conform with the CHILL compiler. We use the concurrent processing primitives as the library style to be invoked by procedure calls, and implement the start-up routine of the CHILL program, the context switching routine, and the CHILL process control parts to control be execution of the CHILL processes concurrently.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.4
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• pp.279-285
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• 2019

This study proposes an enhanced switching pattern that can improve energy transfer efficiency in an active cell-balancing circuit using a multiwinding transformer. This balancing circuit performs cell balancing by transferring energy stored in a specific cell with high energy to another cell containing low energy through a multiwinding transformer. The circuit operates in flyback and buck-boost modes in accordance with the energy transfer path. In the conventional flyback mode, the leakage inductance of the transformer and the stray inductance component of winding can transfer energy to an undesired path during the balancing operation. This case results in cell imbalance during the cell-balancing process, which reduces the energy transfer efficiency. An enhanced switching pattern that can effectively perform cell balancing by minimizing the amount of energy transferred to the nontarget cells due to the leakage inductance components in the flyback mode is proposed. Energy transfer efficiency and balancing speed can be significantly improved using the proposed switching pattern compared with that using the conventional switching pattern. The performance improvements are verified by experiments using a 1 W prototype cell-balancing circuit.

• Journal of Semiconductor Engineering
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• v.2 no.2
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• pp.130-135
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• 2021

Recently, deep neural networks (DNNs) are actively used for action control so that an autonomous system, such as the robot, can perform human-like behaviors and operations. Unlike recognition tasks, the real-time operation is essential in action control, and it is too slow to use remote learning on a server communicating through a network. New learning techniques, such as reinforcement learning (RL), are needed to determine and select the correct robot behavior locally. In this paper, we propose an energy-efficient DNN processor with a LUT-based processing engine and near-zero skipper. A CNN-based facial emotion recognition and an RNN-based emotional dialogue generation model is integrated for natural HRI system and tested with the proposed processor. It supports 1b to 16b variable weight bit precision with and 57.6% and 28.5% lower energy consumption than conventional MAC arithmetic units for 1b and 16b weight precision. Also, the near-zero skipper reduces 36% of MAC operation and consumes 28% lower energy consumption for facial emotion recognition tasks. Implemented in 65nm CMOS process, the proposed processor occupies 1784×1784 um2 areas and dissipates 0.28 mW and 34.4 mW at 1fps and 30fps facial emotion recognition tasks.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.314-319
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• 2002

The Automation and Control Group at the University of Brasilia is developing an automatic welding station based on an industrial robot and a controllable welding machine. Several techniques were applied in order to improve the quality of the welding joints. This paper deals with the implementation of a laser-based computer vision system to guide the robotic manipulator during the welding process. Currently the robot is taught to follow a prescribed trajectory which is recorded a repeated over and over relying on the repeatability specification from the robot manufacturer. The objective of the computer vision system is monitoring the actual trajectory followed by the welding torch and to evaluate deviations from the desired trajectory. The position errors then being transfer to a control algorithm in order to actuate the robotic manipulator and cancel the trajectory errors. The computer vision systems consists of a CCD camera attached to the welding torch, a laser emitting diode circuit, a PC computer-based frame grabber card, and a computer vision algorithm. The laser circuit establishes a sharp luminous reference line which images are captured through the video camera. The raw image data is then digitized and stored in the frame grabber card for further processing using specifically written algorithms. These image-processing algorithms give the actual welding path, the relative position between the pieces and the required corrections. Two case studies are considered: the first is the joining of two flat metal pieces; and the second is concerned with joining a cylindrical-shape piece to a flat surface. An implementation of this computer vision system using parallel computer processing is being studied.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.