• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.8
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• pp.309-315
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• 1986

We proposed a preliminary Data Flow Machine Model(DNL1) operating on the basis of Node Label. In this model, all the PMs(Processing Modules) were synchronized with the content of LC(Level Counter) and were not implemented dy the processing cability on conditional nodes. This paper presents an architecture of a concurrent multiprocessor system which was developed from DNL1 with two additional types of memories, CF(Control Flag) and ETF (Enabled Token Flag). The CF memory holds the control condition flag ('1' or '0') to be referenced to when a node is fired and the ETF represents the firability of a certain node. Firable nodes are fetched to the PU(Processing Unit) and processed. This Data Flow system can be extended hierarchically by a network of simple modules. The principle working elements of the machine are a set of PMs, each of which performs the execution of the data flow procedures held in a local memory, NTM(Node Token Memory) within the PM.

• 전기의세계
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• v.22 no.1
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• pp.9-19
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• 1973

This paper is for the supplementary studies of the theoretical treaties on the machine dynamics of the amplidyne generator under the influencies of the armature reaction. The author has already shown the time-domain expression of the dynamic relations of the machine with balanced control winding, under this operating condition. In this paper, analytical and experimental studies of a test machine are intended to supplement the theories derived in the previous work, entitled "On the dynamics and the demagnetization effect of the amplidyne generator with auxiliary feedback compensating winding". FACOM 230 digital computer is incorporated for processing of a series of experimental data. The machine dynamics are then numerically analyzed with the aid of the computer. The virtual machine responses to stepwise inputs are compared with the computer output to confirm the influence of the armature reaction effect on to the machine dynamics. dynamics.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.247-260
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• 2021

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims to provide richer expressiveness and incorporate a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate the viewing of connections and ideas on a database. The described structure of the data is often represented in an entity–relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes, and relationships. This paper attempts to examine the basic ER modeling notions in order to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships, and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.438-443
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• 2005

Handy paint rollers with simple or no patterns are generally used to transcribe its design to a wall just after painting. However, the types of the patterns are limited to several conventional ones, so that interior planners' or decorators' demands are gradually tending to getting attractive roller designs. In order to obtain abundant kinds of the roller designs, a new advanced 3D machining method should be established for cylindrical models. In this paper, a post-processor that can generate suitable NC data is proposed for multi-axis NC machine tools with a rotary unit. The 3D machining system with the post-processor is also presented for an attractive interior decorating. The machining system allows us to easily transcribe the relief designs from on a flat model to on a cylindrical model. The effectiveness of the proposed 3D machining system using the post-processor is demonstrated through some machining experiments.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.3
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• pp.75-78
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• 2008

Tire molds are manufactured by aluminum casting, direct five-axis machining, and electric discharging machining. Master models made of chemical wood are necessary if aluminum casting is used. They are designed with a three-dimensional computer-aided design system and milled by a five-axis machine. In this paper, a method for generating and machining a tire surface model is proposed and demonstrated. The groove surfaces, which are the main feature of the tire model, are created using a parametric design concept. An automatically programmed tool-like descriptive language is presented to implement the parametric design. Various groove geometries can be created by changing variables. For convenience, groove surfaces and raw cutter location (CL) data are generated in two-dimensional drawing space. The CL data are mapped to the tread surface to obtain five-axis CL data to machine the master model. The proposed method was tested by actual milling using the five-axis control machine. The results demonstrate that the method is useful for manufacturing a tire mold.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.2
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• pp.67-74
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• 2003

This paper presents the development of the Inspection machine for SMD mounted on cream solder of PCB. There are mounting errors of SMD such as misalignment, missing part, wrong orientation, wrong polarity and so on. The main hardware of the system consists of a machine vision part and a motion control part. Operating software has been developed in GUI environment to help user convenience. The Inspection Jobs consist of two procedures, that is, creation of the inspection reference data and automatic inspection. The Inspection reference data has a tree structure of linked list including PCB information, blocks, components, windows, and inspection methods. This paper presents versatile inspection methods which include a section length method, a projection method and histogram method. Therefore, user can choose the suitable procedure for various components. Finally, the automatic Inspection procedure using the reference data checks the mounting errors of components.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.130-133
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• 2020

Smart pig farm which is based on IoT has been widely adopted by many pig farmers. In order to achieve optimal control of smart pig farm, the relation between environmental conditions and performance metric should be characterized. In this study, the relation between multiple environmental conditions including temperature, humidity and various performance metrics, which are daily gain, feed intake, and MSY, is analyzed based on data obtained from 55 real pig farm. Especially, based on preprocessing of data, various regression based machine learning algorithms are considered. Through performance evaluation, we show that the performance can be predicted with high precision, which can improve the efficiency of management.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1039-1042
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• 1995

In real time application such as motion control, it is hard to find the application of non-causal filtering due to its need for future position data, even though it shows wide usage in off-line digital signal processing. Recently, some of motion control areas such as learning and repetitive control use non-causal filtering technique in their application. these kinds of zero-lag non-causal filter application are very usful not only to reduce the machine vibration, but also to increase control accuracy with comparatively less work. In this paper, genuine method to implement zero-lag non-causal filter in a CNC is introduced. Also the variation of this implementation for the learning operation is suggested to give the NC better control performance for a specific job. By adopting the new NC architecture call Soft-NC, all these implementions are made possible here, and especially large memory requirement which hinders their usage for many years is no longer barrier in their real world application.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.