scholarly journals Research on Intelligent Control and Integration Technology for Visual Inspection of TEC Components

2021 ◽  
Vol 2101 (1) ◽  
pp. 012032
Author(s):  
Zhishan Cai ◽  
Jiangwei Chen ◽  
Shaobin Yan ◽  
Shizhan Li ◽  
Tingdi Liao
Keyword(s):  
Intelligent Control ◽  
Visual Inspection ◽  
Surface Defects ◽  
Real Time Control ◽  
Electrical Control ◽  
Time Control ◽  
Learning Platform ◽  
Integration Mechanism ◽  
Control Image ◽  
Posture Adjustment

Abstract Technical schemes such as uniform TEC component feeding, posture adjustment, multi-station positioning, and communication between upper and lower machines were designed. The opto-electromechanical integration mechanism was integrated by software to realize the detection of TEC component surface defects, and qualified TEC components were separated from unqualified TEC components by blow sieving. Adopt ARM MCU for real-time control; Image recognition adopts the machine vision deep learning platform based on linux to learn and recognize the TEC components, and sends the results to the lower computer for sieving. The accuracy of triggering the camera to take pictures is 99.9%, which provides a guarantee for capturing and processing images accurately. The accuracy of blow sieving is over 99.9%, which can meet the sieving requirements. The electrical control system of the whole machine can meet the motion control requirements of detection.

Fuzzy Logic-Based Intelligent Control System for Active Ankle Foot Orthosis

2015 ◽  
pp. 248-279 ◽  
Author(s):  
M. Kanthi
Keyword(s):  
Control System ◽  
Data Acquisition ◽  
Intelligent Control ◽  
Control Unit ◽  
Real Time Control ◽  
Gait Patterns ◽  
Ankle Foot Orthosis ◽  
Intelligent Control System ◽  
Time Control ◽  
Foot Orthosis

The Ankle Foot Orthosis (AFO) is an orthotic device intended to assist or to restore the movements of the ankle foot complex in the case of pathological gait. Active AFO consists of sensor, controller, and actuator. The controller used in the conventional AFO to control the actuator does not use the property of synchronization of the feet. This chapter deals with development of a fuzzy-based intelligent control unit for an AFO using property of symmetry in the foot movements. The control system developed in LabVIEW provides real-time control of the defective foot by continuously monitoring the gait patterns. The input signals for the control system are generated by the sensor system having gyroscope. DC motor is used as an actuator. The data acquisition for Gait Analysis is done using National Instrument's data acquisition system DAQ6221 interfaced with a gyro-sensor.

Fuzzy Logic-Based Intelligent Control System for Active Ankle Foot Orthosis

Fuzzy Systems ◽  
2017 ◽  
pp. 1203-1236
Author(s):  
M. Kanthi
Keyword(s):  
Control System ◽  
Data Acquisition ◽  
Intelligent Control ◽  
Control Unit ◽  
Real Time Control ◽  
Gait Patterns ◽  
Ankle Foot Orthosis ◽  
Intelligent Control System ◽  
Time Control ◽  
Foot Orthosis

The Ankle Foot Orthosis (AFO) is an orthotic device intended to assist or to restore the movements of the ankle foot complex in the case of pathological gait. Active AFO consists of sensor, controller, and actuator. The controller used in the conventional AFO to control the actuator does not use the property of synchronization of the feet. This chapter deals with development of a fuzzy-based intelligent control unit for an AFO using property of symmetry in the foot movements. The control system developed in LabVIEW provides real-time control of the defective foot by continuously monitoring the gait patterns. The input signals for the control system are generated by the sensor system having gyroscope. DC motor is used as an actuator. The data acquisition for Gait Analysis is done using National Instrument's data acquisition system DAQ6221 interfaced with a gyro-sensor.

Development of the Open Architecture Platform for Controller

2011 ◽  
Vol 268-270 ◽  
pp. 626-630
Author(s):  
Jun Mei Xi ◽  
Gao Hua Liao
Keyword(s):  
Intelligent Control ◽  
Fast Response ◽  
High Accuracy ◽  
Open Architecture ◽  
Real Time Control ◽  
Velocity Error ◽  
Time Control ◽  
Control Interface ◽  
Signal Wire ◽  
Machine Interface

At present，the open arehitecture controller and the field bus for motion control is a hotspot at home and abroad in the field of numerieal control system.This paper analyses the advantage of the typieal open arehitecture controller and improves.The open movement contro1 Platform based on BUS,which uses the open numrerieal control arehiteehire, that is，in order to control the system intelligently, We use a signal wire or optical fibre which connects all the drivers and the I/O port to transmit signals. In the system，all the control is operated by the up-pc machine，and the intelligent control nod is only used for communicating and translating the instruction.The control system uses Visual C# to empolder the up-pc software, and uses C to empolder the intelligent control interface. Experiments show that the system with the velocity error of less than 5%, the transition time of less than 0.1s, position error of less than 1mm. It can achieve not only the customization of the human-machine interface, and the parameterization of the real-time control parts, but also the exact travel path, the fast response, high accuracy positioning and other performance.

A Layered, Multi-Agent System for Intelligent Control

1992 ◽  
Vol 4 (2) ◽  
pp. 152-158
Author(s):  
Takuya Ishioka ◽  
◽  
Morikazu Takegaki
Keyword(s):  
Real Time ◽  
Intelligent Control ◽  
Time Constraints ◽  
Multi Agent System ◽  
Real Time Control ◽  
Agent Model ◽  
Intelligent Control System ◽  
Time Control ◽  
Rule Set ◽  
Multi Agent

We propose a layered, multi-agent model as a method for establishing an intelligent control system. The model handles each task, which constitutes control processing, as a group of agents operating autonomously, and organizes the agents in a layered structure from the viewpoint of real-time constraints. This enables real-time control processing and knowledge information processing simultaneously. We will describe a method for building the layered multiagent model using a concurrent rule set. The concurrent rule set can be processed by extending the expert shell and can realize the layered agent model in various computer environments through the use of the shell.

Fuzzy Real-time Advanced Shell for Intelligent Control with Fuzzy Algorithm Compiler

1996 ◽  
Vol 8 (1) ◽  
pp. 49-57
Author(s):  
Yoichiro Maeda ◽  
Keyword(s):  
Real Time ◽  
Intelligent Control ◽  
High Speed ◽  
Fuzzy Reasoning ◽  
User Program ◽  
Real Time Control ◽  
Fuzzy Algorithm ◽  
Shell System ◽  
Hard Time ◽  
Time Control

In general, an expert system which supports the conventional fuzzy reasoning has relatively high user interface functions like the rule editor by multi-windows and others. However, it has suffered from some problems, because it has a hard time linking with a user program as it is rule-driven on a shell or because its reasoning speed is slow, as the shell system tends to be huge, and it is, therefore, not suited to real-time control. Under these circumstances, the present author and others have developed a user-program-initiated fuzzy shell FRASH (Fuzzy Real-time Advanced SHell) which is used for intelligent control in real time. This paper is intended mainly to describe the basic configuration of FRASH and its expression functions. This shell has a number of features, such as a high-speed reasoning engine in the form of a library, a function for online-tuning of all the data in the reasoning, an off-line editor function based on multi-windows, a function for displaying reasoning states, a capability to express fuzzy frames which can define fuzzy numbers as slot values, a fuzzy algorithm compiler based on macro-descriptions, and so on. In addition, with a view to confirming the basic functions of the shell, a simple simulation was carried out by taking up vehicle tracking and overtaking control as an example. By this simulation, it was demonstrated that the fuzzy reasoning function of FRASH is sufficiently fast for mechatronic control in real time and that the fuzzy frame and fuzzy algorithm functions are effective in describing macrolevel decision-making problems.

scholarly journals Advanced Intelligent Control through Versatile Intelligent Portable Platforms

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3644
Author(s):  
Luige Vladareanu
Keyword(s):  
Computer Vision ◽  
Decision Support ◽  
Intelligent Control ◽  
Process Mining ◽  
Real Time Control ◽  
Time Control ◽  
Intelligent Sensors ◽  
Intelligent Decision Support Systems ◽  
Intelligent Communication ◽  
Adaptive Sensor Networks

Deep research and communicating new trends in the design, control and applications of the real time control of intelligent sensors systems using advanced intelligent control methods and techniques is the main purpose of this research. The innovative multi-sensor fusion techniques, integrated through the Versatile Intelligent Portable (VIP) platforms are developed, combined with computer vision, virtual and augmented reality (VR&AR) and intelligent communication, including remote control, adaptive sensor networks, human-robot (H2R) interaction systems and machine-to-machine (M2M) interfaces. Intelligent decision support systems (IDSS), including remote sensing, and their integration with DSS, GA-based DSS, fuzzy sets DSS, rough sets-based DSS, intelligent agent-assisted DSS, process mining integration into decision support, adaptive DSS, computer vision based DSS, sensory and robotic DSS, are highlighted in the field of advanced intelligent control.

A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice
Keyword(s):  
Real Time ◽  
Linear Time ◽  
Treatment Plan ◽  
Control Architecture ◽  
Real Time Control ◽  
Time Representation ◽  
Time Control ◽  
Continuous Support ◽  
Definition Of ◽  
Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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