Lock-Jo Koo

Simulation framework for the verification of PLC programs in automobile industries

The objective of this study is to propose a framework of virtual plant models for the verification of PLC logic through modeling and simulation. The proposed virtual plant model consists of three types of object: the virtual device model (object model), the intermediary transfer model (functional model), and the PLC program & HMI (dynamic model). A virtual device model consists of a physical part, which is used to represent the properties of a real device and a logical part, which is used to manage the devices operation. For the fidelity of the virtual plant model, an intermediary transfer model controls the virtual device through a PLC program and sends information on the virtual devices state to the supervisory control model. Moreover, the PLC program and HMI are used for constructing communication environments similar to a real manufacturing line. For the implementation of the proposed virtual plant model, this study employs an I/O model based on the formalism of Automata and Discrete Event Systems Specifications (DEVS). As a result of the application to a car assembly line, 18 error codes are detected through the manual mode and have been revised. Finally, we can confirm there is no sequential error in the PLC program by checking the time chart. Moreover, the bottleneck and ramp-up/down times are reduced when a manufacturing system/line is built.

Seed Point Detection of Multiple Cancers Based on Empirical Domain Knowledge and K-means in Ultrasound Breast Image

The objective of this paper is to remove noises of image based on the heuristic noises filter and to automatically detect seed points of tumor region by using K-MEANS in breast ultrasound. The proposed method is to use 4 different kinds of process. First process is the pixel value which indicates the light and shade of image is acquired as matrix type. Second process is an image preprocessing phase that is aimed to maximize a contrast of image and to prevent a leak of personal information. The next process is the heuristic noise filter which is based on the opinion of medical specialist and it is applied to remove noises. The last process is to detect a seed point automatically by applying K-MEANS algorithm. As a result, the noise is effectively eliminated in all images and an automated detection is possible by determining seed points on each tumor.

A SUGGESTION OF SIMULATION FRAMEWORK FOR VERIFING PLC PROGRAM & APPLICATION IN AUTOMOTIVE PRODUCTION LINE

Proposed in this paper is a framework of virtual plant model for verification of PLC program by conducting the virtual plant modeling and simulation. The proposed virtual plant model consists of three types of objects: the virtual device model (object model), the intermediary transfer model (functional model) and PLC program (dynamic model). For the implementation of the proposed virtual plant model, this paper employs the I/O model which is based on Automata and Discrete Event System Specifications (DEVS) formalism. By applying an example of the automotive assembly line, nine errors in the PLC code are found and have been revised. With the proposed approach, it will be helpful to reduce the ramp up or down time of the automated manufacturing system.

PLC Control Logic Error Monitoring and Prediction Using Neural Network

This paper reviews monitoring and error prediction of PLC-program using Neural Network. In the PLC-device controlled manufacturing line, PLC-program holds place of underlying component. It becomes controlling mechanism. The level of automation in the production line relies on control mechanism practiced. In the modern manufacturing, PLC devices can handle whole production line given that structured and smart PLC-program is executed. In other words, PLC-program can manage whole process structure consisting set of procedures. We present a method to monitor PLC-program and PLC error prediction it using neural network. The neural network method being predictive in nature, it rigorously can monitor process signals from sensors, sensed during operation of PLC devices or execution of PLC-program. Subsequently, a neural network algorithm practiced for the analysis of signals. In this way, thorough monitoring of PLC-program can find possible errors from temporal parameters (e.g. Voltage, bias etc). In addition, possible alterations in program and irregularities can be minimized. That can result, easily to use in fault detection, maintenance, and decision support in manufacturing organization. Similarly, it can lessen down-time of machines and prevent possible risks.

Ladder Diagram generation using generic model of discrete event system

This paper describes plant modeling using DES (discrete event system) in generic model, and designing the controller for PLC-based manufacturing system. The tested model through simulation can be automatically transformed into LD (Ladder Diagram) program analyzing the controller elements. In addition, through the simulation of devices and controllers, possible logical errors can be minimized. The contribution of this work is to generate small and medium sized PLC-programs and reduce down-time and ramp-up time.

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back‐propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back‐propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.