Minimum energy consumption process synthesis for energy saving

2008 ◽  
Vol 52 (7) ◽  
pp. 1000-1005 ◽  
Author(s):  
Jia Xiao-Ping ◽  
Wang Fang ◽  
Xiang Shu-Guang ◽  
Tan Xin-Sun ◽  
Han Fang-Yu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Minimum Energy ◽  
Process Synthesis ◽  
Minimum Energy Consumption

A Energy-Saving Path-Shared Protection Based on Diversity Network Coding for Multi-rate Multicast in WDM Mesh Networks

2017 ◽  
Vol 38 (3) ◽  
Author(s):  
Danling Zheng ◽  
Lei Lv ◽  
Huanlin Liu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Network Coding ◽  
Wavelength Division Multiplexing ◽  
Mesh Networks ◽  
Minimum Energy ◽  
Bandwidth Utilization ◽  
Shared Protection ◽  
Minimum Energy Consumption ◽  
Save Energy

AbstractFor improving the survivability and energy saving of multi-rate multicast, a novel energy-saving path-shared protection based on diversity network coding (EPP-DNC) for multi-rate multicast in wavelength division multiplexing (WDM) mesh networks is proposed in the paper. In the EPP-DNC algorithm, diversity network coding on the source node for multi-rate multicast is adopted to reduce the coding energy consumption by avoiding network coding on the network’s intermediate nodes. To decrease the transmission energy, shortest path shared based on heuristic is proposed to transmit the protection information for the request. To provision request’s working paths efficiency, the working paths are routed on the preselected P-cycles with minimum required links and minimum energy consumption. Simulation results show that the proposed EPP-DNC can save energy consumption and improve bandwidth utilization.

scholarly journals Motion Planning of Ground Simulator for Space Instable Target Based on Energy Saving

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 368
Author(s):  
Xinlin Bai ◽  
Xiwen Li ◽  
Zhen Zhao ◽  
Mingyi Yang ◽  
Zhang Zhang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Energy Saving ◽  
Planning Process ◽  
Minimum Energy ◽  
Robot Motion ◽  
Dijkstra Algorithm ◽  
Minimum Energy Consumption ◽  
Motion Simulator ◽  
Save Energy

In order to achieve the high-precision motion trajectory in ground experiment of space instable target (SIT) while reducing the energy consumption of the motion simulator, a robot motion planning method based on energy saving is proposed. Observable-based ground robot motion experiment system for SIT is designed and motion planning process is illustrated. Discrete optimization mathematical model of energy consumption of motion simulator is established. The general motion form of the robot joints in ground test is given. The optimal joint path of motion simulator based on energy consumption under discontinuous singularity configuration is solved by constructing the complete energy consumption directed path and Dijkstra algorithm. An improved method by adding the global optimization algorithm is used to decouple the coupled robot joints to obtain the minimum energy consumption path under the continuous singularity configuration of the motion simulator. Simulations are carried out to verify the proposed solution. The simulation data show that total energy saving of motion simulator joints adopting the proposed method under the condition of non-singularity configuration, joints coupled motion with continuous singularity configuration, and coexistence of non-singularity path and continuous singularity path are, respectively, 72.67%, 28.24%, and 62.23%, which proves that the proposed method can meet the requirements of ground motion simulation for SIT and effectively save energy.

scholarly journals Optimization of Energy-Efficient Speed Profile for Electrified Vehicles

2018 ◽  
Author(s):  
Hadi Abbas ◽  
Youngki Kim ◽  
Jason B. Siegel ◽  
Denise M. Rizzo
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Minimum Energy ◽  
Efficient Operation ◽  
Operating Modes ◽  
Minimum Energy Consumption ◽  
System A ◽  
Trip Time ◽  
Route Information

This paper presents a study of energy-efficient operation of vehicles with electrified powertrains leveraging route information, such as road grades, to adjust the speed trajectory. First, Pontryagin’s Maximum Principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only 5 modes are required to achieve minimum energy consumption; full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. The minimum energy consumption problem is reformulated and solved in the distance domain using Dynamic Programming to optimize speed profiles. A case study is shown for a light weight military robot including road grades. For this system, a tradeoff between energy consumption and trip time was found. The optimal cycle uses 20% less energy for the same trip duration, or could reduce the travel time by 14% with the same energy consumption compared to the baseline operation.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

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