Cyber-Physical Human System Research Articles

Analisis Kesiapan Tenaga Pendidik dalam Menghadapi Era Society 5.0 di SMP Al-Muslim Sidoarjo

Amidst the rapid advancements in science and technology, particularly in the era of Society 5.0 that emphasizes the role of technology, the role of the education sector becomes pivotal in advancing the quality of human resources. Educators in the Society 5.0 era need to be equipped with digital skills and creative thinking abilities. Therefore, the objective of this research is to assess the readiness of educators to face the challenges of the Society 5.0 era. This study employs a qualitative method with a phenomenological approach, aiming to uncover and comprehend the unique phenomena experienced by individuals. Primary data is obtained through direct interviews with educators at SMP AL-MUSLIM Sidoarjo, while secondary data involves relevant books and journals. Data collection techniques include interviews, observations, and documentation. The research findings indicate that educators are prepared and matured to face the Society 5.0 era. This is evident in their competencies within domains such as teamwork, interpersonal and communication skills, problem-solving, critical thinking, soft skills and hard skills, as well as digital literacy. Their readiness is reflected in the implementation of human-centric and technology-based approaches in schools, encompassing digitization, CPS (Cyber Physical Human System) paradigm, and the utilization of Artificial Intelligence (AI). Factors influencing educators' readiness involve classroom management skills, mastery of teaching methods and tools, as well as the ability to plan and prepare for teaching.

An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions

An adaptive machine learning (ML) based smart manufacturing interactive cyber physical human system (ICPHS) is conceptualized, designed, and implemented. One of its significant properties is an ML model that during deployment self-evolves with the streaming data in a self-labeling manner. This automated adaptive ML system is realized by leveraging the underlying causality during human machine interactions, and initialized by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> domain knowledge and preprocessed public datasets. The system defines a causal and temporal mapping of worker and machine states where one side can label the other automatically. A case study in machines with different automation levels is conducted in a multiuser facility by using finite state machine representations for machine energy states, worker activity states, and interaction transition functions. The fully automated adaptive ML system improves its accuracy for human machine interaction detection by up to 12.5% and shows potential to recognize more fine-grained actions.

Workplace Flexibility Analysis in Cyberphysical Human Systems Using Cloud-Enabled Deep Autoencoder Networks

To examine the correlation between worker safety, workplace interpersonal problems, and individual flexibility within a cyberphysical human system (CPHS), we employed a stacked autoencoder (SAE) approach and a cloud-based computing environment. The study’s statistical population includes construction companies in Mashhad, Iran. To collect data, descriptive surveys and applied research approaches are employed. Thus, data is collected using a cloud-based platform, data processing tools, and information analysis methods. It is our main objective to figure out how to reduce construction accidents and make people safer. Our study used a sample of 200 people to study the entire study population because it is difficult to study the entire study population. There were 151 valid questionnaires collected after the questionnaire distribution. We developed a 28-item questionnaire as part of the study in addition to the Questionnaire on Experience and Evaluation of Work (QEEW). Implementing an optimized SAE network can reduce dangerous situations, physical injuries, supervisor conflict, workplace stress, interpersonal conflict, and colleagues’ involvement. As a consequence of the large amount of data needed for quick analysis and mechanism construction, cloud computing performed admirably. The study of interpersonal conflicts and individual flexibility among construction workers was necessary because only limited research had been conducted on these topics.

Control Engineering and Robotics since Primary School: an Infrastructure for creating the Digital Twin model of the Learning Class.

Control engineering has a cross-boundary nature because its applications span over a wide range of fields, among which science, technology, engineering, and mathematics (STEM). Creating an automation literacy from the Primary School's age is beneficial for pupils because it supports the development of valuable skills that allow the comprehension and analysis of real-world phenomena. Even if many researchers and policymakers have advocated for engineering education since early education, it is usually kept for undergraduate and graduate-level education. What prevents systems theory and control education from being integrated into K12 education is the lack of available educational resources and the lack of indicators to represent the learning gain of students. To help teachers in the latter aspect, a solution can be studying the entire process as a cyber-physical human system (CPHS). The paper consists of a brief report about the work carried out by authors to represent the entire classroom as a CPHS where the physical robots designed by students, humans (teachers and learners), and cybertechnologies are interconnected to accomplish a goal which is learning. The entire infrastructure could be seamlessly deployed into the classroom, supporting learning assessment and the feedback process starting from the deployment of a (quasi) real-time intelligent collection system.

A new safety-guided design methodology to complement model-based safety analysis for safety assurance

With the rapid advancement of Formal Methods, Model-based Safety Analysis (MBSA) has been gaining tremendous attention for its ability to rigorously verify whether the safety-critical scenarios are adequately addressed by the design solution of a cyber-physical human system. However, there is a gap. If specific safety-critical scenarios are not included in the given design solution (i.e., the model) in the first place, the results of MBSA cannot be trusted for safety assurance. To tackle this problem, we propose a new safety-guided design methodology (called STPA+) to complement MBSA. Inspired by STPA, STPA+ treats a system as a control structure, which is particularly fit for systems with complex interactions between human, machine, and automation. Three methods are developed in STPA+ to tackle the possible omissions of safety-critical scenarios caused by incorrectly defined safety constraints, improperly constrained process model, and inadequately designed controller. In this way, STPA+ directly derives an adequately defined design solution as the input to an MBSA verification program and bridges the gap between current MBSA approaches and safety assurance.

Shared Control Between Pilots and Autopilots: An Illustration of a Cyberphysical Human System

The 21st century is witnessing large transformations in several sectors related to autonomy, including energy, transportation, robotics, and health care. Decision making using real-time information over a large range of operations (as well as the ability to adapt online in the presence of various uncertainties and anomalies) is the hallmark of an autonomous system. To design such a system, a variety of challenges must be addressed. Uncertainties may occur in several forms, both structured and unstructured. Anomalies may often be severe and require rapid detection and swift action to minimize damage and restore normalcy. This article addresses the difficult task of making autonomous decisions in the presence of severe anomalies. While the specific application of focus is flight control, the overall solutions proposed are applicable for general complex dynamic systems.

Dynamic pricing for integrated energy-traffic systems from a cyber-physical-human perspective

This paper solves the vehicle assignment problem of an operating entity with dispatchable vehicles (e.g., an urban Integrated Energy-Traffic System (IETS) coordinator or an e-hailing company) in the context of energy-traffic coordinated modeling and analysis. Current research on electric vehicle (EV) scheduling and optimization has broadly assumed that human beings are rational decision-makers and obey the scheduling plan. In contrast, we proposed a user-centric dynamic pricing scheme that improves the system performance by incentivizing human participants to follow the expected behavioral patterns from a Cyber-Physical-Human System (CPHS) perspective. The interdisciplinary fields of power systems, traffic systems, and human decision-making were used to study the interactions in the CPHS. The proposed incentive scheme was applied to the urban IETS scenarios based on real-world Global Positioning System (GPS) data of EV taxis, and the results showed that it effectively optimized the integrated system.

Modeling &amp; Control of Human Actuated Systems

Abstract This paper investigates a Cyber-Physical & Human System (CPHS) comprised of a deterministic dynamical system plant model and a human actuator model. Namely, human decisions are stochastic inputs to the plant model. We examine a framework where human decisions cannot be directly controlled, but can be influenced via incentive control signals. Specifically, we use the framework of discrete choice models (DCMs) to capture human decision making, and then design optimal controllers for these human actuated dynamical systems. Existing literature on CPHS often treats human inputs as stochastic and exogenous inputs, and then formulates a disturbance rejection problem. Instead of treating human decision-making as an uncontrollable exogenous input, we directly incorporate human decision making into the modeling framework with DCMs. This paper thus adds two original contributions. (i) We develop a generalized human-actuated system framework based on DCM that predicts the probability of human decisions, conditioned on controllable incentives. (ii) We show that existing optimization schemes, such as Sequential Quadratic Programming (SQP) and Dynamic Programming (DP), can be applied to control the proposed human-actuated system. We conclude this paper by demonstrating the framework on a reference tracking problem and an inventory control problem.

Architectural Framework for Exploring Adaptive Human-Machine Teaming Options in Simulated Dynamic Environments

With the growing complexity of environments in which systems are expected to operate, adaptive human-machine teaming (HMT) has emerged as a key area of research. While human teams have been extensively studied in the psychological and training literature, and agent teams have been investigated in the artificial intelligence research community, the commitment to research in HMT is relatively new and fueled by several technological advances such as electrophysiological sensors, cognitive modeling, machine learning, and adaptive/adaptable human-machine systems. This paper presents an architectural framework for investigating HMT options in various simulated operational contexts including responding to systemic failures and external disruptions. The paper specifically discusses new and novel roles for machines made possible by new technology and offers key insights into adaptive human-machine teams. Landed aircraft perimeter security is used as an illustrative example of an adaptive cyber-physical-human system (CPHS). This example is used to illuminate the use of the HMT framework in identifying the different human and machine roles involved in this scenario. The framework is domain-independent and can be applied to both defense and civilian adaptive HMT. The paper concludes with recommendations for advancing the state-of-the-art in HMT.