An Overview of Digital Twin Concept for Key Components of Renewable Energy Systems

Renewable energy (RE) is green and low-carbon energy, which can not only protect the environment, promote the technological diversification of the energy supply system, accelerate the adjustment of energy structure, but also has important significance for the sustainable development of economy. With the increasing complexity of the problems of renewable energy system asset management and ensuring the operational reliability of electric power equipment, it's necessary to establish remote, online, reliable monitoring and inspection techniques for the state evaluation of electrical equipment during the full life cycle. In order to meet these demands, the digital twin is a very suitable technology. In recent years, there are numerous scientific papers demonstrating DT's capabilities in virtual simulation, condition monitoring (CM), power optimization and fault diagnosis for RE generation systems, transmission and transformation equipment and storage systems. The majority of the research focusing on product design, maintenance of operation, condition monitoring and fault decision-making has provided many valuable contributions to academia and industrial fields. Nevertheless, all this valuable information is scattered over many literatures and it is lack of systematic generalization. In this article, different applications of DT technology in RE system are analyzed, advanced methods and theories are summarized comprehensively, and the development trend of DT technology in renewable energy system in the future is introduced.

Robotization as One of the Prospects for Electromobility in Belarus

In order to create a competitive and resource-efficient transport system, the transport policy of the European Union provides for the achievement by 2030 of almost zero carbon dioxide content in the exhaust gases of vehicles used in large urban centers, and by 2050 the phasing out the use of cars, working on traditional fuels. The Republic of Belarus has a high scientific and sufficient industrial potential to participate in the process of promoting electric mobility, taking into account the use of robotics. JSC "Instrument-Making Plant Optron" developed the working documentation and produced prototypes of typical representatives of the line of personal electric vehicles. However, the ongoing research focused on the creation of a preventive diagnostic system for the electric motorcycle, developed by Belarussian researches and its intelligent onboard system, focused primarily on real-time simulation processes, related specifically to the level of artificial intelligence, and on the implementation of executive level algorithms.

Modeling and Control of Underactuated Three-Dimensional Overhead Crane Systems

Overhead crane systems play a vital role in different factories to transport heavy loads. This paper provides an overview of recent developments in the modeling and control of three-dimensional overhead crane systems. It provides a categorized survey of the published work. Different control methodologies when applied to overhead crane are examined, outlined and assessed to aid for future work.

Automatic Reconstruction of Building-Scale Indoor 3D Environment with a Deep-Reinforcement-Learning-Based Mobile Robot

The aim of this paper is to digitize the environments in which humans live, at low cost, and reconstruct highly accurate three-dimensional environments that are based on those in the real world. This three-dimensional content can be used such as for virtual reality environments and three-dimensional maps for automatic driving systems. In general, however, a three-dimensional environment must be carefully reconstructed by manually moving the sensors used to first scan the real environment on which the three-dimensional one is based. This is done so that every corner of an entire area can be measured, but time and costs increase as the area expands. Therefore, a system that creates three-dimensional content that is based on real-world large-scale buildings at low cost is proposed. This involves automatically scanning the indoors with a mobile robot that uses low-cost sensors and generating 3D point clouds. When the robot reaches an appropriate measurement position, it collects the three-dimensional data of shapes observable from that position by using a 3D sensor and 360-degree panoramic camera. The problem of determining an appropriate measurement position is called the “next best view problem,” and it is difficult to solve in a complicated indoor environment. To deal with this problem, a deep reinforcement learning method is employed. It combines reinforcement learning, with which an autonomous agent learns strategies for selecting behavior, and deep learning done using a neural network. As a result, 3D point cloud data can be generated with better quality than the conventional rule-based approach.

A Versatile Personal Assistant Dedicated to B/VIP

Today, there are different systems on the market that aim to help Blind or Visually Impaired People (B/VIP) to gain more independency and self-trust. From the classic white cane to various intelligent devices to guide people with vision problems, most of the systems are expensive, difficult to be used or with low efficiency. The subject is a very important one because, according to the World Health Organization, there are approximately 2.2 billion people in the world that have a vision impairment or blindness. Thus, the purpose of this work is to make a short survey on solutions available on the market, in order to develop a low-cost, efficient and easy-to-use system for both indoor and outdoor use. The versatile personal assistant dedicated to B/VIP aims to enhance their independence and also improved outdoor navigation. The smart warning system addressed here is an innovative device aiming to detect obstacles with the help of ultrasonic sensors and warning of their presence by means of a buzzer or vibration. The system, when used outdoor, also gives the possibility to send short message to the B/VIP’s own assistant, in the form of an SMS with the exact location of the B/VIP. Moreover, the SMS’ content can easily create the optimal route to the B/VIP’s location with the Google Maps support for a fast-time assistance.

A Framework for Fully Automated Home using IoT Reliable Protocol Stack and Smart Gateway

A fully automated house must ensure all the appliances must be connection and provide a smart way of working for the human. The insight of Internet of Things (IoT) network provision the necessary platform to implement the automated home. The proposed system emphasizes, how all the appliances would be connected to IoT to ensure fully automated home. In this framework the smart home has been divided into various areas like smart kitchen, smart gardening, home safety and security system, and smart lightning system. The reliable protocol stack has been utilized to provide efficient communication along with proper security measures. The reliable protocol suite works on top of MQTT and TCP to ensure reliable communication. The smart gateway utilized for this framework and provides firewall security as with a two-phase filtering mechanism as well as scalability. among all the appliances in the home.

Adaptive Structures and Biomimetic Robots – A Perspective

This paper gives an overview of some recent full-scale demonstrations of morphing devices capable of providing innovative capabilities to general systems in changing shape and improving performance significantly during operations. In aeronautics, large progress has been observed over the last few years, meaning that this technology is rapidly transitioning from laboratory scale to high TRL demonstrators. The most advanced concepts already proved to withstand loads with minimal deformation while having the capability to change their geometry to attain additional benefits with respect to their original mission. In the same way, robotics has become one of the most prominent technological trends of the current century. The rapid increase in their use and development has significantly changed our society by gradually replacing a large share of human jobs. Such an evolution is also rapidly accelerating, as technological advances in automation, engineering, artificial intelligence, and machine learning converge. Since both domains involve the integration of actuators, sensors and controllers and face integrity challenges in harsh environments, they may be seen somehow related and probably share a common future. In this article, the authors propose an original view of a possible future scenario that is likely to consider a unique development path for research on adaptive structures and robotics.

Interventional Status Awareness Based Manipulating Strategy for Robotic Soft Endoscopy

Traditional soft endoscopy is operated with naked eyes and use of hands. Robotic soft endoscopy frees the hands of endoscopists, which reduces the labor-intensity and complexity of operation and improves the operational accuracy of endoscope, but it’s hardly to be reliably performed because the operator lacks of situational awareness of endoscopic interventional status when the hands are detached from the endoscope. This paper first presents a method to perceive the interventional status of endoscope based on image processing, the interventional status includes insertion length and velocity. A manipulating strategy was designed according to the perceived endoscope interventional status and construction parameters of dual robotic arms in order to achieve reliable interventional endoscopy. Human phantom experiments are carried out to verify the effectiveness and feasibility of the proposed interventional status awareness method and manipulating strategy. The results show that the robotic soft endoscopy can be well performed with the ability of interventional status awareness and coordinated manipulation of dual arms. The perceived insertion length indicates the position of the tip of endoscope in human body and the designed manipulating strategy is effective in endoscopic shape retention and torque transmission.

Automated Agricultural Robot and Sensor Data Collection and Analysis through a Biomass Feedstock Production Information System

The increasing environmental pollution resulting from the use of non-renewable fossil fuels as well as the development of economic dependencies among countries because of the lack of such types of fuels underline the intense need for the use of sustainable forms of energy. Biomass derived biofuels provide such an alternative. The main tasks of biomass feedstock production are planting and cultivation, harvest, storage, and transportation. A number of complex decisions characterize each of these tasks. These decisions are related to the monitoring of crop health, the improvement of crop productivity using innovative technologies, and the examination of limitations in existing processes and technologies associated with biomass feedstock production. Other critical issues are the development of sustainable methods for the delivery of the biomass while maintaining product quality. There is the need for the development of an automated integrated research tool based on resilience and sustainability which will allow the coordination of different research fields but also perform research on its own. The specific tool should aim in the optimization of different parameters which specify the research done and in the case of biomass feedstock production; such parameters are the transportation of biomass from the field to the biorefinery, the equipment used, and the biomass storage conditions. This optimization would enhance decision making in the field of bioenergy production. Based on the need for such an automated integrated research tool, this paper presents an information system that provides automated functionalities for better decision making in the bioenergy production field based on the collection and analysis of agricultural robot and sensor data.

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.