A decision-making approach for semi-decentralized rail transit control system

2018 ◽  
Author(s):  
Yida Guo ◽  
Cheng Zhang ◽  
Shaofeng Lu
Keyword(s):  
Decision Making ◽  
Control System ◽  
Rail Transit

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

2021 ◽  
pp. 1-15
Author(s):  
Qinyu Mei ◽  
Ming Li
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Control System ◽  
Convolutional Neural Network ◽  
Force Control ◽  
Control Experiment ◽  
Deep Convolutional Neural Network ◽  
Flexible Cable ◽  
Cable Force ◽  
And Training

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.

Redisign of the Air Traffic Control System-Moving People through Distributed Management

1978 ◽  
Vol 22 (1) ◽  
pp. 485-485
Author(s):  
John G. Kreifeldt
Keyword(s):  
Decision Making ◽  
Control System ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Negative Impact ◽  
Air Traffic ◽  
Critical Function ◽  
Congressional Hearings ◽  
The Future ◽  
Traffic Control System

The present national Air Traffic Control system is a ground-centralized, man intensive system which through design allows relatively little meaningful pilot participation in decision making. The negative impact of this existing design can be measured in delays, dollars and lives. The FAA's design plans for the future ATC system will result in an even more intensive ground-centralized system with even further reduction of pilot decision making participation. In addition, controllers will also be removed from on-line decision making through anticipated automation of some or all of this critical function. Recent congressional hearings indicate that neither pilots nor controllers are happy or sanguine regarding the FAA's design for the future ATC system.

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

2018 ◽  
Author(s):  
Suranga C. H. Geekiyanage ◽  
Dan Sui ◽  
Bernt S. Aadnoy
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Control System ◽  
Data Analysis ◽  
Quality Management ◽  
Data Quality ◽  
Incomplete Data ◽  
Laboratory Scale ◽  
Data Quality Management ◽  
Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

An Automated Conflict-alert Function for an Air Traffic Control System

1980 ◽  
Vol 33 (3) ◽  
pp. 475-481
Author(s):  
P. Bertolazzi ◽  
M. Lucertini
Keyword(s):  
Decision Making ◽  
Control System ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Air Traffic ◽  
False Alarms ◽  
Major Purpose ◽  
Number Of False Alarms ◽  
Traffic Control System

The major purpose of an air traffic control system is to ensure the separation of two or more aircraft flying in the same airspace, with an efficiency that can be expressed in terms of capacity and cost. As air traffic grows in numbers it becomes necessary to reduce the workload of the controllers by relieving them of many monitoring tasks, and eventually some decision-making tasks, through computerized automation. In this context many developments tend to build up an efficient conflict-alert subsystem.The problem of conflict-alert in the air needs strategic tools, to make collision unlikely or even impossible, and tactical tools to detect impending collisions. The latter detect potentially hazardous aircraft encounters and alert the controller in time to warn the pilots (if necessary) and should obviously provide this capability with a minimal number of false alarms and no increase in workload.

scholarly journals Modernizing Conceptions of Valuation and Cognitive-Control Deployment in Adolescent Risk Taking

2019 ◽  
Vol 29 (1) ◽  
pp. 102-109 ◽  
Author(s):  
Kathy T. Do ◽  
Paul B. Sharp ◽  
Eva H. Telzer
Keyword(s):  
Decision Making ◽  
Control System ◽  
Cognitive Control ◽  
Computational Model ◽  
Risk Taking ◽  
Expected Value ◽  
Risky Decision Making ◽  
Risky Decision ◽  
Adolescent Risk ◽  
Risk Taking Behavior

Heightened risk taking in adolescence has long been attributed to valuation systems overwhelming the deployment of cognitive control. However, this explanation of why adolescents engage in risk taking is insufficient given increasing evidence that risk-taking behavior can be strategic and involve elevated cognitive control. We argue that applying the expected-value-of-control computational model to adolescent risk taking can clarify under what conditions control is elevated or diminished during risky decision-making. Through this lens, we review research examining when adolescent risk taking might be due to—rather than a failure of—effective cognitive control and suggest compelling ways to test such hypotheses. This effort can resolve when risk taking arises from an immaturity of the control system itself, as opposed to arising from differences in what adolescents value relative to adults. It can also identify promising avenues for channeling cognitive control toward adaptive outcomes in adolescence.

A Concept for Intelligent Tool Exchange System for Industrial Manipulators

2016 ◽  
Vol 251 ◽  
pp. 158-163
Author(s):  
Jordan Mężyk
Keyword(s):  
Decision Making ◽  
Control System ◽  
Production Line ◽  
Intelligent System ◽  
Technical Level ◽  
Level Control ◽  
Exchange System ◽  
Industrial Manipulators ◽  
Basic Level ◽  
Task Level

For years now, the industrial manipulators have substituted human in many types of works during the manufacturing process. With robots the production rate increases, as does the quality of the product. The application of industrial manipulators increases the safety in the factories which in turn leads to savings. However, there are several difficulties in introduction of robotics into the production line. One of them is usually large cost of purchase and implementation of the robot. The proposed concept of the intelligent system for tool exchange is to address the stated issue. The aim of the system is to provide a mechanism for quick tool exchange supported by a decision making module that will constitute the self-reconfigurable industrial manipulator that is able to assess the situation at the production line, recognize the product and make decision on the operations to be performed, their sequence and which tool to use to perform that operation. Such a system of manipulator equipped with functions for reconfiguration and adaptation will be a solution for all applications where the production assortment changes quickly and is made in short series, and in particular for SMEs, which cannot afford purchase of several robots. They would buy one instead and let it perform variable tasks.The article briefly presents the concept of the hardware part of the exchange system, which includes both construction and the electronic modules for the tools, and concentrates on the concept of software expert system and database that allows intelligent decision making. The software control of the system is divided into four levels: the basic level, technical level, task level and procedure level. The basic level defines the movement of the robot such as: positioning, rotation, approach to the position etc. This level uses to control system of the applied manipulator and the data contained in the electronic modules of the tools. The basic level reflects the typical low-level control of the manipulator. The technical level defines the technical movement of the robot, small procedures related to the calibration of the tools, operation with the tool magazine (picking up and putting away) and related to tool maintenance – locking, unlocking, power supply, air supply and similar. The task level defines the operations related to the task performed on the object. Depending on the defined task the proper tool is selected (e.g. gripper, camera or a laser scanner) and, based on the data collected from sensors, performs the operation (e.g. scans the surface of the object). The highest level of control is totally independent on the hardware control system of the manipulator and the hardware of the robotic stand. Using the available databases and the recognition systems (e.g. cameras) makes decisions on what type of object appears on the production line and what operation is to be performed. The operations are defined as metacode that is interpreted and performed by lower control levels structurally bound to the control system of the manipulator.

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