The PLC: a logical development

Programmable Logic Controllers (PLCs) have been used to control industrial processes and equipment for over 40 years, having their first commercially recognised application in 1969. Since then there have been enormous changes in the design and application of PLCs, yet developments were evolutionary rather than radical. The flexibility of the PLC does not confine it to industrial use and it has been used for disparate non-industrial control applications . This article reviews the history, development and industrial applications of the PLC

Comparative Study of PID Based VMC and Fuzzy Logic Controllers for Flyback Converter

In this paper performance of flyback converter by using PID controller and Fuzzy controller are studied, compared and analyzed. The above study is done for 200W, 230V A.C input 48V DC output. Design of fuzzy controller is based on the heuristic knowledge of converter behaviour, and tuning of fuzzy inference requires some expertise to minimize unproductive trial and error. The design of PID control is based on the frequency response of the converter. For the DC-DC converters, the performance of the fuzzy controller was superior in some respects to that of the PID controller. The fuzzy controller is easily to develop, they cover a wide range of operating conditions, and they are more readily customizable in natural language terms. Simulation is done in Matlab environment to show the performance variations of above mentioned converters using both Fuzzy & PID controllers

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

In recent years, researchers have conducted many studies on the design and control of prosthesis devices that take the place of a missing limb. Functional ability of prosthesis hands that mimic biological hand functions increases depending on the number of independent finger movements possible. From this perspective, in this study, six different finger movements were given to a prosthesis hand via bioelectrical signals, and the functionality of the prosthesis hand was increased. Bioelectrical signals were recorded by surface electromyography for four muscles with the help of surface electrodes. The recorded bioelectrical signals were subjected to a series of preprocessing and feature extraction processes. In order to create meaningful patterns of motion and an effective cognitive interaction network between the human and the prosthetic hand, fuzzy logic classification algorithms were developed. A five-fingered and 15-jointed prosthetic hand was designed via SolidWorks, and a prosthetic prototype was produced by a 3D printer. In addition, prosthetic hand simulator was designed in Matlab/SimMechanics. Pattern control of both the simulator and the prototype hand in real time was achieved. Position control of motors connected to each joint of the prosthetic hand was provided by a PID controller. Thus, an effective cognitive communication network established between the user, and the real-time pattern control of the prosthesis was provided by bioelectrical signals

The Digital Divide in Process Safety: Quantitative Risk Analysis of Human-AI Collaboration

Digital technologies have dramatically accelerated the digital transformation in process industries, boosted new industrial applications, upgraded the production system, and enhanced operational efficiency. In contrast, the challenges and gaps between human and artificial intelligence (AI) have become more and more prominent, whereas the digital divide in process safety is aggregating. The study attempts to address the following questions: (i)What is AI in the process safety context? (ii)What is the difference between AI and humans in process safety? (iii)How do AI and humans collaborate in process safety? (iv)What are the challenges and gaps in human-AI collaboration? (v)How to quantify the risk of human-AI collaboration in process safety? Qualitative risk analysis based on brainstorming and literature review, and quantitative risk analysis based on layer of protection analysis (LOPA) and Bayesian network (BN), were applied to explore and model. The importance of human reliability should be stressed in the digital age, not usually to increase the reliability of AI, and human-centered AI design in process safety needs to be propagated

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it