Augmented Humanity: A Systematic Mapping Review

Augmented humanity (AH) is a term that has been mentioned in several research papers. However, these papers differ in their definitions of AH. The number of publications dealing with the topic of AH is represented by a growing number of publications that increase over time, being high impact factor scientific contributions. However, this terminology is used without being formally defined. The aim of this paper is to carry out a systematic mapping review of the different existing definitions of AH and its possible application areas. Publications from 2009 to 2020 were searched in Scopus, IEEE and ACM databases, using search terms “augmented human”, ”human augmentation” and “human 2.0”. Of the 16,914 initially obtained publications, a final number of 133 was finally selected. The mapping results show a growing focus on works based on AH, with computer vision being the index term with the highest number of published articles. Other index terms are wearable computing, augmented reality, human–robot interaction, smart devices and mixed reality. In the different domains where AH is present, there are works in computer science, engineering, robotics, automation and control systems and telecommunications. This review demonstrates that it is necessary to formalize the definition of AH and also the areas of work with greater openness to the use of such concept. This is why the following definition is proposed: “Augmented humanity is a human–computer integration technology that proposes to improve capacity and productivity by changing or increasing the normal ranges of human function through the restoration or extension of human physical, intellectual and social capabilities.”

Securing SCADA Energy Management System under DDos Attacks Using Token Verification Approach

The advanced connection requirements of industrial automation and control systems have sparked a new revolution in the Industrial Internet of Things (IIoT), and the Supervisory Control and Data Acquisition (SCADA) network has evolved into an open and highly interconnected network. In addition, the equipment of industrial electronic devices has experienced complete systemic integration by connecting with the SCADA network, and due to the control and monitoring advantages of SCADA, the interconnectivity and working efficiency among systems have been tremendously improved. However, it is inevitable that the SCADA system cannot be separated from the public network, which indicates that there are concerns over cyber-attacks and cyber-threats, as well as information security breaches, in the SCADA network system. According to this context, this paper proposes a module based on the token authentication service to deter attackers from performing distributed denial-of-service (DDoS) attacks. Moreover, a simulated experiment has been conducted in an energy management system in the actual field, and the experimental results have suggested that the security defense architecture proposed by this paper can effectively improve security and is compatible with real field systems.

Assessment of Building Automation and Control Systems in Danish Healthcare Facilities in the COVID-19 Era

A well-designed and properly operated building automation and control system (BACS) is key to attaining energy-efficient operation and optimal indoor conditions. In this study, three healthcare facilities of a different type, age, and use are considered as case studies to investigate the functionalities of BACS in providing optimal air quality and thermal comfort. IBACSA, the first-of-its-kind instrument for BACS assessment and smartness evaluation, is used to evaluate the current systems and their control functionalities. The BACS assessment is reported and analyzed. Then, three packages of improvements were implemented in the three cases, focusing on (1) technical systems enhancement, (2) indoor air quality and comfort, and (3) energy efficiency. It was found that the ventilation system domain is the best performer in the three considered cases with an overall score of 52%, 89% and 91% in Case A, B, and C,, respectively. On the other hand, domestic hot water domain scores are relatively low, indicating that this is an area where Danish healthcare facilities need to provide more concentration on. A key finding indicated by the assessment performed is that the three buildings score relatively very low when it comes to the impact criteria of energy flexibility and storage.

An Enabling Open-Source Technology for Development and Prototyping of Production Systems by Applying Digital Twinning

This article presents the most valuable and applicable open-source tools and communication technologies that may be employed to create models of production processes by applying the concept of Digital Twins. In recent years, many open-source technologies, including tools and protocols, have been developed to create virtual models of production systems. The authors present the evolution and role of the Digital Twin concept as one of the key technologies for implementing the Industry 4.0 paradigm in automation and control. Based on the presented structured review of valuable open-source software dedicated to various phases and tasks that should be realised while creating the whole Digital Twin system, it was demonstrated that the available solutions cover all aspects. However, the dispersion, specialisation, and lack of integration cause this software to usually not be the first choice to implement DT. Therefore, to successfully create full-fledged models of Digital Twins by proceeding with proposed open-source solutions, it is necessary to make additional efforts due to integration requirements.

Mechatronic Anti-Collision System for Electric Wheelchairs Based on 2D LiDAR Laser Scan

Electric wheelchairs make it easier for disabled and elderly people to live, move, interact, and participate in society. Moving a wheelchair in open spaces is relatively easy, but in closed and small spaces, maneuvering is difficult. Solutions to such problems for people with disabilities are applicable to a relatively small group of recipients and are mostly custom-made solutions, whose considerable cost is a significant barrier to accessibility. New technologies can provide an opportunity to improve the quality of life of people with disabilities in this aspect. Using selected elements of complex automation and control systems, cost-effective solutions can be created that facilitate the functioning of people with disabilities. This paper presents an analysis of hazards and problems when maneuvering a wheelchair in narrow passageways, as well as the authors’ solution to this problem, and the concept and assumptions of a mechatronic anti-collision system based on 2D LiDAR laser scanners. This solution is composed of a proprietary 2D rotating scanner mechanism that ensures the acquisition of 3D images of the environment around the wheelchair. Preliminary tests of this solution yielded promising results. Further research will include miniaturization of the device.

A Deep Learning-Based Dirt Detection Computer Vision System for Floor-Cleaning Robots with Improved Data Collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.