scholarly journals Increasing the Energy-Efficiency in Vacuum-Based Package Handling Using Deep Q-Learning

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3185
Author(s):  
Felix Gabriel ◽  
Johannes Bergers ◽  
Franziska Aschersleben ◽  
Klaus Dröder
Keyword(s):  
Energy Efficiency ◽  
Prior Knowledge ◽  
Process Parameters ◽  
Energy Efficient ◽  
Generalization Capability ◽  
Q Learning ◽  
Robot Trajectory ◽  
Core Method ◽  
Pick And Place ◽  
Input Pressure

Billions of packages are automatically handled in warehouses every year. The gripping systems are, however, most often oversized in order to cover a large range of different carton types, package masses, and robot motions. In addition, a targeted optimization of the process parameters with the aim of reducing the oversizing requires prior knowledge, personnel resources, and experience. This paper investigates whether the energy-efficiency in vacuum-based package handling can be increased without the need for prior knowledge of optimal process parameters. The core method comprises the variation of the input pressure for the vacuum ejector, compliant to the robot trajectory and the resulting inertial forces at the gripper-object-interface. The control mechanism is trained by applying reinforcement learning with a deep Q-agent. In the proposed use case, the energy-efficiency can be increased by up to 70% within a few hours of learning. It is also demonstrated that the generalization capability with regard to multiple different robot trajectories is achievable. In the future, the industrial applicability can be enhanced by deployment of the deep Q-agent in a decentral system, to collect data from different pick and place processes and enable a generalizable and scalable solution for energy-efficient vacuum-based handling in warehouse automation.

scholarly journals Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 44 ◽  
Author(s):  
Yi-Han Xu ◽  
Jing-Wei Xie ◽  
Yang-Gang Zhang ◽  
Min Hua ◽  
Wen Zhou
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Energy Harvesting ◽  
Energy Efficient ◽  
Area Network ◽  
Allocation Strategy ◽  
Body Area ◽  
Q Learning ◽  
Efficient Resource ◽  
Energy Efficient Resource Allocation

Wireless body area networks (WBANs) have attracted great attention from both industry and academia as a promising technology for continuous monitoring of physiological signals of the human body. As the sensors in WBANs are typically battery-driven and inconvenient to recharge, an energy efficient resource allocation scheme is essential to prolong the lifetime of the networks, while guaranteeing the rigid requirements of quality of service (QoS) of the WBANs in nature. As a possible alternative solution to address the energy efficiency problem, energy harvesting (EH) technology with the capability of harvesting energy from ambient sources can potentially reduce the dependence on the battery supply. Consequently, in this paper, we investigate the resource allocation problem for EH-powered WBANs (EH-WBANs). Our goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor. In view of the characteristic of the EH-WBANs, we formulate the energy efficiency problem as a discrete-time and finite-state Markov decision process (DFMDP), in which allocation strategy decisions are made by a hub that does not have complete and global network information. Owing to the complexity of the problem, we propose a modified Q-learning (QL) algorithm to obtain the optimal allocation strategy. The numerical results validate the effectiveness of the proposed scheme as well as the low computation complexity of the proposed modified Q-learning (QL) algorithm.

scholarly journals Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1025 ◽  
Author(s):  
Yongjie Lu ◽  
Rongxi He ◽  
Xiaojing Chen ◽  
Bin Lin ◽  
Cunqian Yu
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Opportunistic Routing ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Q Value ◽  
Underwater Wireless Sensor Networks ◽  
Q Learning ◽  
Packet Delivery ◽  
Network Overhead

Underwater Wireless Sensor Networks (UWSNs) have aroused increasing interest of many researchers in industry, military, commerce and academe recently. Due to the harsh underwater environment, energy efficiency is a significant theme should be considered for routing in UWSNs. Underwater positioning is also a particularly tricky task since the high attenuation of radio-frequency signals in UWSNs. In this paper, we propose an energy-efficient depth-based opportunistic routing algorithm with Q-learning (EDORQ) for UWSNs to guarantee the energy-saving and reliable data transmission. It combines the respective advantages of Q-learning technique and opportunistic routing (OR) algorithm without the full-dimensional location information to improve the network performance in terms of energy consumption, average network overhead and packet delivery ratio. In EDORQ, the void detection factor, residual energy and depth information of candidate nodes are jointly considered when defining the Q-value function, which contributes to proactively detecting void nodes in advance, meanwhile, reducing energy consumption. In addition, a simple and scalable void node recovery mode is proposed for the selection of candidate set so as to rescue packets that are stuck in void nodes unfortunately. Furthermore, we design a novel method to set the holding time for the schedule of packet forwarding base on Q-value so as to alleviate the packet collision and redundant transmission. We conduct extensive simulations to evaluate the performance of our proposed algorithm and compare it with other three routing algorithms on Aqua-sim platform (NS2). The results show that the proposed algorithm significantly improve the performance in terms of energy efficiency, packet delivery ratio and average network overhead without sacrificing too much average packet delay.

scholarly journals Enhancing Energy Efficiency of a 4-DOF Parallel Robot Through Task-Related Analysis

Machines ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Lorenzo Scalera ◽  
Paolo Boscariol ◽  
Giovanni Carabin ◽  
Renato Vidoni ◽  
Alessandro Gasparetto
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Parallel Robot ◽  
Optimal Position ◽  
Pick And Place ◽  
Mechanical Models ◽  
Energetic Performance ◽  
Pick And Place Operation ◽  
Robot Workspace

Enhancing energy efficiency is one of the main challenges of today’s industrial robotics and manufacturing technology. In this paper a task-related analysis of the energetic performance of a 4-DOF industrial parallel robot is presented, and the optimal location of a predefined task with respect to the robot workspace is investigated. An optimal position of the task relative to the robot can indeed reduce the actuators’ effort and the energy consumption required to complete the considered operation. The dynamic and electro-mechanical models of the manipulators are developed and implemented to estimate the energy consumption of a parametrized motion with trapezoidal speed profile, i.e., a pick-and-place operation. Numerical results provide energy consumption maps that can be adopted to place the starting and ending points of the task in the more energy-efficient location within the robot workspace.

scholarly journals An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2829 ◽  
Author(s):  
Yihang Du ◽  
Ying Xu ◽  
Lei Xue ◽  
Lijia Wang ◽  
Fan Zhang
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Energy Efficient ◽  
Radio Networks ◽  
Cross Layer ◽  
Q Learning ◽  
Learning Scheme ◽  
Routing Design

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ).

Analysis Criteria for Choosing Energy Efficient High-Quality Light Sources and Luminaires

2018 ◽  
pp. 113-119
Author(s):  
Gennady Ya. Vagin ◽  
Eugene B. Solntsev ◽  
Oleg Yu. Malafeev
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
National Standards ◽  
Light Sources ◽  
The European Union ◽  
High Quality ◽  
Eu Directive ◽  
The World ◽  
A New Technique ◽  
Energy Labelling

The article analyses critera applying to the choice of energy efficient high quality light sources and luminaires, which are used in Russian domestic and international practice. It is found that national standards GOST P 54993–2012 and GOST P 54992– 2012 contain outdated criteria for determining indices and classes of energy efficiency of light sources and luminaires. They are taken from the 1998 EU Directive #98/11/EU “Electric lamps”, in which LED light sources and discharge lamps of high intensity were not included. A new Regulation of the European Union #874/2012/EU on energy labelling of electric lamps and luminaires, in which these light sources are taken into consideration, contains a new technique of determining classes of energy efficiency and new, higher classes are added. The article has carried out a comparison of calculations of the energy efficiency classes in accordance with GOST P 54993 and with Regulation #874/2012/EU, and it is found out that a calculation using GOST P 54993 gives underrated energy efficiency classes. This can lead to interdiction of export for certain light sources and luminaires, can discredit Russian domestic manufacturer light sources and does not correspond to the rules of the World Trade Organization (WTO).

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

2017 ◽  
Vol 7 (8) ◽  
pp. 169
Author(s):  
A. Radhika ◽  
D. Haritha
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Congestion Aware

Wireless Sensor Networks, have witnessed significant amount of improvement in research across various areas like Routing, Security, Localization, Deployment and above all Energy Efficiency. Congestion is a problem of  importance in resource constrained Wireless Sensor Networks, especially for large networks, where the traffic loads exceed the available capacity of the resources . Sensor nodes are prone to failure and the misbehaviour of these faulty nodes creates further congestion. The resulting effect is a degradation in network performance, additional computation and increased energy consumption, which in turn decreases network lifetime. Hence, the data packet routing algorithm should consider congestion as one of the parameters, in addition to the role of the faulty nodes and not merely energy efficient protocols .Nowadays, the main central point of attraction is the concept of Swarm Intelligence based techniques integration in WSN.  Swarm Intelligence based Computational Swarm Intelligence Techniques have improvised WSN in terms of efficiency, Performance, robustness and scalability. The main objective of this research paper is to propose congestion aware , energy efficient, routing approach that utilizes Ant Colony Optimization, in which faulty nodes are isolated by means of the concept of trust further we compare the performance of various existing routing protocols like AODV, DSDV and DSR routing protocols, ACO Based Routing Protocol  with Trust Based Congestion aware ACO Based Routing in terms of End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. Simulation based results and data analysis shows that overall TBC-ACO is 150% more efficient in terms of overall performance as compared to other existing routing protocols for Wireless Sensor Networks.

MANUFACTURING OF ENERGY EFFICIENT SOLAR PANELS

2020 ◽  
Author(s):  
Андрей Дмитриевич Бухтеев ◽  
Виктория Буянтуевна Бальжиева ◽  
Анна Романовна Тарасова ◽  
Фидан Гасанова ◽  
Светлана Викторовна Агасиева
Keyword(s):  
Energy Efficiency ◽  
Solar Cells ◽  
Energy Efficient ◽  
Solar Panels

В данном обзоре приведены проблемы при использовании солнечных элементов и существующие решения этих проблем по повышению энергоэффективности фотоэлементов. Также сравнивается КПД этих солнечных элементов и рассматриваются их особенности. Одним из самых эффективных способов стало применение нанотехнологий. This review presents the problems of using solar cells and existing solutions to these problems to improve the energy efficiency of solar cells. The efficiency of these solar cells is also compared and their features are considered. One of the most effective methods was the use of nanotechnology.

Energy efficient biomorphic pulse information coding in electronic neurons for the entrance unit of the neuroprocessor

2019 ◽  
Vol 5 (3) ◽  
pp. 186-212 ◽  
Author(s):  
Alexander D. Pisarev
Keyword(s):  
Neural Networks ◽  
Energy Efficiency ◽  
Energy Efficient ◽  
Information Transfer ◽  
Biological Systems ◽  
Technological Parameters ◽  
Input Unit ◽  
Efficient Operation ◽  
Electric Circuits ◽  
Electronic Neuron

This article studies the implementation of some well-known principles of information work of biological systems in the input unit of the neuroprocessor, including spike coding of information used in models of neural networks of the latest generation.<br> The development of modern neural network IT gives rise to a number of urgent tasks at the junction of several scientific disciplines. One of them is to create a hardware platform&nbsp;— a neuroprocessor for energy-efficient operation of neural networks. Recently, the development of nanotechnology of the main units of the neuroprocessor relies on combined memristor super-large logical and storage matrices. The matrix topology is built on the principle of maximum integration of programmable links between nodes. This article describes a method for implementing biomorphic neural functionality based on programmable links of a highly integrated 3D logic matrix.<br> This paper focuses on the problem of achieving energy efficiency of the hardware used to model neural networks. The main part analyzes the known facts of the principles of information transfer and processing in biological systems from the point of view of their implementation in the input unit of the neuroprocessor. The author deals with the scheme of an electronic neuron implemented based on elements of a 3D logical matrix. A pulsed method of encoding input information is presented, which most realistically reflects the principle of operation of a sensory biological neural system. The model of an electronic neuron for selecting ranges of technological parameters in a real 3D logic matrix scheme is analyzed. The implementation of disjunctively normal forms is shown, using the logic function in the input unit of a neuroprocessor as an example. The results of modeling fragments of electric circuits with memristors of a 3D logical matrix in programming mode are presented.<br> The author concludes that biomorphic pulse coding of standard digital signals allows achieving a high degree of energy efficiency of the logic elements of the neuroprocessor by reducing the number of valve operations. Energy efficiency makes it possible to overcome the thermal limitation of the scalable technology of three-dimensional layout of elements in memristor crossbars.

scholarly journals Determinants of Consumers’ Intentions towards the Purchase of Energy Efficient Appliances in Pakistan: An Extended Model of the Theory of Planned Behavior

2021 ◽  
Vol 13 (2) ◽  
pp. 565
Author(s):  
Muhammad Rizwan Ali ◽  
Muhammad Shafiq ◽  
Murad Andejany
Keyword(s):  
Energy Efficiency ◽  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Energy Efficient ◽  
Behavioral Intentions ◽  
Partial Least Square ◽  
Least Square ◽  
Policy Implications ◽  
Extended Model ◽  
Awareness Campaigns

Amplified energy demand due to technologically advanced electrical and electronic appliances has accentuated the importance of energy efficiency to overcome energy shortage and environmental concerns. As adoption of energy efficient appliances depends on perception of the consumers, this study focuses on behavioral exploration of the consumers’ intentions towards the purchase of energy efficient appliances using an extended model of the theory of planned behavior (TPB). The study is based on a survey comprising 289 respondents. Partial least square (PLS) method is used to analyze the data. The results show that the attitude, perceived behavioral control, policy information campaigns, and past-purchase experiences significantly impact behavioral intentions of the consumers, whereas subjective and moral norms are insignificant in shaping behavioral intentions. Based on analyses, policy implications emphasizing (i) strong awareness campaigns, (ii) energy efficiency incentives, and (iii) replacement initiatives are proposed to help policy makers and administrators in achieving required goals of energy efficiency and conservation. The proposed research model and policy initiatives are a blueprint for synergies among policymakers, practitioners, and researchers in understanding and shaping consumers’ behaviors towards the purchase of energy efficient products, particularly, in developing countries.

scholarly journals Towards DC Energy Efficient Homes

2021 ◽  
Vol 11 (13) ◽  
pp. 6005
Author(s):  
Daniel Villanueva ◽  
Moisés Cordeiro-Costas ◽  
Andrés E. Feijóo-Lorenzo ◽  
Antonio Fernández-Otero ◽  
Edelmiro Miguez-García
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Energy Efficient ◽  
Electricity Consumption ◽  
Real Data ◽  
Mean Values ◽  
Led Lighting ◽  
Shed Light

The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values.

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