Remote laboratory to support control theory

The Control Systems plays a vital role in the industry, which is the most essential application of the Electrical Engineering. The control concepts are present in most of the automation systems. The Control Systems theory is the key concept to achieve the automation and makes world faster. But, in reality the study of control engineering is decreased in the recent years, because of the difficulty in learning the concepts of the control theory. Most of the students feel difficult to understand theoretical concepts of control systems. The traditional teaching methodology is one way of teaching control systems concepts. Even though books are proper way of teaching control systems in a systematic way, we need additional tool to create interaction between the subject and the students. The teaching platform is worth to analyse the possibility to add or complement the way of standing with means able to add Real evidences. In another way, it is important that the provided lab experiment should be affordable. The teaching platform to support control theory has been introduced with set of experiments to create Real evidences, and manuals to carry out those experiments, slides to have a guidance and Graphical User Interface (GUI) to have an interaction with the control system is provided

The Development Of Optimization Methods For Knowledge Base Enrichment Processes

The paper presents the concept of approach to the research and evaluation of the processes of intellectual activity associated with the enrichment of the knowledge base. A feature of the research of the process dynamics is the need of simultaneous consideration of such diverse factors as the complexity of information perception, the presence of the deviations of the response from the standard in the process of reproduction and accounting of the test time.A significant influence on the methods of optimization of the knowledge base enrichment process is exerted by a considerable duration of the task learning process. This causes the use of the multifactor experimental design theory to accelerate the progress towards the optimum.The research results can be used in the development of technologies for efficient knowledge assimilation, automation of skills, and also in the development of expert systems for diagnostics of the processes of intellectual activity

Advanced Biomedical Laboratory (ABL) Synergy with Communication, Robotics, and IoT

This paper proposes a framework for integrating IoT and automation in a biomedical laboratory to improve safety, optimize processes, and enhance students\u27 learning experience. The framework incorporates a centralized control unit and distributed subsystems to control equipment and machinery and includes autonomous robotics and intelligent monitoring systems. The paper presents the results of undergraduate students\u27 work on automating various biomedical processes, including developing an Intelligent Autonomous Monitoring (IAM) device. IAM utilizes machine learning algorithms to identify outliers in processes and safety hazards. Moreover, IAM autonomously detects and localizes biomedical tools and equipment. Results show the feasibility of delivering real-time results for localizing specific tools necessary within a biomedical laboratory. This framework offers a systemic approach toward process automation, aiding researchers in developing new equipment and automating existing processes. Furthermore, it assists students in gaining a fundamental understanding of the theory behind biomedical principles while providing a repeatable experimental environment through more accurate data and event collection

Decision-making and problem-solving methods in automation technology

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming

Scalable Systems for Large Scale Dynamic Connected Data Processing

As the proliferation of sensors rapidly make the Internet-of-Things (IoT) a reality, the devices and sensors in this ecosystem—such as smartphones, video cameras, home automation systems, and autonomous vehicles—constantly map out the real-world producing unprecedented amounts of dynamic, connected data that captures complex and diverse relations. Unfortunately, existing big data processing and machine learning frameworks are ill-suited for analyzing such dynamic connected data and face several challenges when employed for this purpose.This dissertation focuses on the design and implementation of scalable systems for dynamic connected data processing. We discuss simple abstractions that make it easy to operate on such data, efficient data structures for state management, and computation models that reduce redundant work. We also describe how bridging theory and practice with algorithms and techniques that leverage approximation and streaming theory can significantly speed up connected data computations. The systems described in this dissertation achieve more than an order of magnitude improvement over the state-of-the-art

Hands-On Learning Environment and Educational Curriculum on Collaborative Robotics

The objective of this paper is to describe teaching modules developed at Wayne State University integrate collaborative robots into existing industrial automation curricula. This is in alignment with Oakland Community College and WSU’s desire to create the first industry-relevant learning program for the use of emerging collaborative robotics technology in advanced manufacturing systems. The various learning program components will prepare a career-ready workforce, train industry professionals, and educate academicians on new technologies. Preparing future engineers to work in highly automated production, requires proper education and training in CoBot theory and applications. Engineering and Engineering Technology at Wayne State University offer different robotics and mechatronics courses, but currently there is not any course on CoBot theory and applications. To follow the industry needs, a CoBot learning environment program is developed, which involves theory and hands-on laboratory exercises in order to solve many important automaton problems. This material has been divided into 5-modules: (1) Introduce the concepts of collaborative robotics, (2) Collaborative robot mechanisms and controls, (3) Safety considerations for collaborative robotics, (4) Collaborative robot operations and programming, (5) Collaborative robot kinematics and validation. These modules cover fundamental knowledge of CoBots in advanced manufacturing systems technology. Module content has been developed based on input and materials provided by CoBot manufacturers. After completing all modules students must submit a comprehensive engineering report to document all requirements

LETHAL AUTONOMOUS WEAPON SYSTEMS (LAWS). ON THE ETHICS OF AUTOMATION IN THE MILITARY FROM THE PERSPECTIVE OF SOCIAL SYSTEMS THEORY

The debate about weapon systems that function “autonomously” on artifi- cial intelligence is giving a new impetus to the old question about the role of auto- mation in social systems.1 This is especially true for the debate on Lethal Autonomous Weapon Systems (LAWS), i.e., autonomous systems designed to kill in the context of warlike conflicts. This article provides an insight into how artificial intelligence auto- mation can be modelled in social theory, referring in particular to Niklas Luhmann’s systems and communication theory. From this modelling, conclusions arise with regard to ethical questions in the military context. As a first step, following Elena Esposito, I examine how artificial intelligence automation participates in commu- nication processes and where its limits lie. Following that, ethical questions are dis- cussed step by step. First, the problem of violating human dignity will be considered. In the context of organisations—and especially military organisations—the question of accountability always arises. Accountability refers to social roles and their cor- responding communication processes. Machine processes, however, cannot replace accountability. Furthermore, five aspects are discussed which arise from the per- spective of the codified moral programme of Innere Führung in relation to artificial intelligence automation. These are trust in technology, time frame, standardisation of assisting information, communicative attribution as action, and building media competence, including moral routines. With recourse to Luhmann’s concept of risk, the importance of implementation processes and learning is pointed out at the end. Overall, this paper is about raising questions, i.e., about problematising. It is not about formulating definitive answers

How Supervisors Influence Performance: A Multilevel Study of Coaching and Group Management in Technology-Mediated Services

This multilevel study examines the role of supervisors in improving employee performance through the use of coaching and group management practices. It examines the individual and synergistic effects of these management practices. The research subjects are call center agents in highly standardized jobs, and the organizational context is one in which calls, or task assignments, are randomly distributed via automated technology, providing a quasi-experimental approach in a real-world context. Results show that the amount of coaching that an employee received each month predicted objective performance improvements over time. Moreover, workers exhibited higher performance where their supervisor emphasized group assignments and group incentives and where technology was more automated. Finally, the positive relationship between coaching and performance was stronger where supervisors made greater use of group incentives, where technology was less automated, and where technological changes were less frequent. Implications and potential limitations of the present study are discussed

A proposed psychological model of driving automation

This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation