The Experts below are selected from a list of 54015 Experts worldwide ranked by ideXlab platform
Xiangyu Wang - One of the best experts on this subject based on the ideXlab platform.
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jointly dampening traffic oscillations and improving Energy Consumption with Electric connected and automated vehicles a reinforcement learning based approach
Applied Energy, 2020Co-Authors: Mofan Zhou, Chinteng Lin, Xiangyu WangAbstract:It has been well recognized that human driver’s limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for Electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve Electric Energy Consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average Electric Energy Consumption.
Makoto Takizawa - One of the best experts on this subject based on the ideXlab platform.
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Energy efficient quorum based locking protocol in virtual machine environments
Complex Intelligent and Software Intensive Systems, 2020Co-Authors: Tomoya Enokido, Makoto TakizawaAbstract:In object-based information systems, an application is composed of multiple objects and each object is replicated on multiple virtual machines installed in multiple physical servers to make an application service reliable and available. On the other hand, the total amount of Electric Energy consumed in physical servers is larger than non-replication systems since each method to manipulate replicas of each object is performed on multiple virtual machines. In this paper, an EEQS-VM (Energy-efficient quorum selection with virtual machines) algorithm is proposed to reduce the total Electric Energy Consumption of servers to manipulate multiple replicas of each object. In the evaluation, we show that the total Electric Energy Consumption of servers, the average execution time of each transaction, and the average number of aborted transactions can be reduced in the EEQS-VM algorithm compared with the random algorithm.
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A fault-tolerant tree-based fog computing model
International Journal of Web and Grid Services, 2019Co-Authors: Ryuji Oma, Shigenari Nakamura, Dilawaer Duolikun, Tomoya Enokido, Makoto TakizawaAbstract:In the fog computing model of the IoT, subprocesses of an application process to handle sensor data are performed on fog nodes. Since the IoT is scalable, we have to reduce the Electric Energy Consumption. In the tree-based fog computing (TBFC) model, fog nodes are hierarchically structured. In this paper, we propose a fault-tolerant TBFC (FTBFC) model. In addition, we newly propose a pair of fault-tolerant strategies. In one data transmission strategy, data processed by disconnected fog nodes is sent to a new parent fog node. Here, we propose a minimum Energy (ME) algorithm to select a new parent fog node whose Energy Consumption is minimum. In another subprocess transmission strategy, the subprocess of the faulty fog node is sent to another fog node. In the evaluation, the Energy Consumption and execution time of a new parent fog node can be reduced by the ME algorithm.
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a tree based model of Energy efficient fog computing systems in iot
Complex Intelligent and Software Intensive Systems, 2018Co-Authors: Ryuji Oma, Shigenari Nakamura, Tomoya Enokido, Makoto TakizawaAbstract:A huge number and various types of devices like sensors and actuators are interconnected with clouds of servers in the IoT (Internet of Things). Here, a large volume of data created by sensors have to be efficiently transmitted and processed and actions have to be efficiently delivered to actuators at an opportune time. In order to reduce the delay time and increase the performance, data and processing are distributed to not only servers but also fog nodes in fog computing systems. On the other hand, the total Electric Energy consumed by fog nodes increases since a huge number of fog nodes are interconnected. In this paper, we newly propose a tree-based fog computing model to deploy processes and data to fog nodes so that the total Electric Energy Consumption of nodes can be reduced in the IoT. In the evaluation, we show the total Electric Energy Consumption of nodes in the tree-based model is smaller than the cloud model where processes and data are centralized.
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An Energy-Efficient Model of Fog and Device Nodes in IoT
2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2018Co-Authors: Ryuji Oma, Shigenari Nakamura, Tomoya Enokido, Makoto TakizawaAbstract:Various types and huge number of devices like sensors and actuators are interconnected with clouds of servers in IoT (Internet of Things). Here, a large volume of data including multimedia data like image created by sensors are transmitted to servers in clouds through networks. Processes in servers decide on actions and send the actions to actuators under time constraints like realtime one. However, huge volume of data are transmitted in networks and servers are heavily loaded. An intermediate layer named fog layer is introduced between clouds and devices in IoT to overcome the difficulties. Processing to be done and data to be stored in servers are distributed to fog nodes while centralized on servers in clouds in traditional cloud computing systems. In this paper, we newly propose a linear IoT model to deploy processes and data to devices, fog nodes, and servers in IoT so that the total Electric Energy Consumption of nodes can be reduced. We show the total Electric Energy of the IoT model is smaller than the cloud model in the evaluation.
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unicast routing protocols to reduce Electric Energy Consumption in wireless ad hoc networks
Advanced Information Networking and Applications, 2018Co-Authors: Emi Ogawa, Shigenari Nakamura, Tomoya Enokido, Makoto TakizawaAbstract:In wireless ad-hoc networks, messages have to be Energy-efficiently exchanged among neighboring nodes. In our previous studies, the reactive type EAO (Energy-Aware One-to-one routing) and LEU (Low-Energy Unicast Ad-hoc routing) protocols are proposed to unicast messages to the destination node. In the EAO protocol, the total Electric Energy of nodes and delay time from a source node to a destination node can be reduced compared with the ESU and AODV protocols. However, a source-to-destination route may not be found if the communication range of each node is shorter. In this paper, we newly proposed an IEAO (Improved Energy-Aware One-to-one routing) protocol to overcome the difficulties of the EAO protocol. Here, after a shortest route is found to the destination node, a more Energy-efficient prior node is found in nearest neighbor of each node starting from the destination node. In this paper, a neighbor node which has an uncovered neighbor node is selected as a prior node for each node to make a route. In the evaluation, we show the number of nodes in a source-to-destination route can be reduced and a route can be found even in shorter communication range in the IEAO protocol compared with the LEU and EAO protocols.
Wei Sun - One of the best experts on this subject based on the ideXlab platform.
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Forecasting Monthly Electric Energy Consumption Using Feature Extraction
Energies, 2011Co-Authors: Ming Meng, Dongxiao Niu, Wei SunAbstract:Monthly forecasting of Electric Energy Consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and stochastic series. After the elimination of the stochastic series, the rising trend and periodic waves were modeled separately by a grey model and radio basis function neural networks. Adding the forecasting values of each model can yield the forecasting results for monthly Electricity Consumption. The grey model has a good capability for simulating any smoothing convex trend. In addition, this model can mitigate minor stochastic effects on the rising trend. The extracted periodic wave series, which contain relatively less information and comprise simple regular waves, can improve the generalization capability of neural networks. The case study on Electric Energy Consumption in China shows that the proposed method is better than those traditionally used in terms of both forecasting precision and expected risk.
M M Q Carvalho - One of the best experts on this subject based on the ideXlab platform.
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analysis of variables that influence Electric Energy Consumption in commercial buildings in brazil
Renewable & Sustainable Energy Reviews, 2010Co-Authors: M M Q Carvalho, E L La Rovere, A C M GoncalvesAbstract:Air conditioning systems in commercial buildings in Brazil are responsible for about 70% share of their Energy Consumption. According to BEN 2009 (The Brazilian Energy Balance), Energy Consumption in the residential, commercial and public sectors, where most buildings are found, represents 9.3% of the final Energy Consumption in Brazil. This paper aims to examine design factors that could contribute to greater reductions of Electric Energy Consumption in commercial buildings, with emphasis on air conditioning. Simulations were carried out using shades and different types of glass, walls, flooring and roofing. The VisualDOE 2.61 was used as a simulation tool for calculating Energy Consumption of the analyzed building. This paper shows that the Energy performance of the building is considerably influenced by the facade protection and shows, through tables, the impact that decisions related to the top-level and facades have on the Energy Consumption of the building. The authors concluded that the results confirm the importance of taking Energy use into account in the very first design stages of the project, since appropriate choices of types of glass, external shading and envelope materials have a significant impact on Energy Consumption.
Ming Meng - One of the best experts on this subject based on the ideXlab platform.
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Forecasting Monthly Electric Energy Consumption Using Feature Extraction
Energies, 2011Co-Authors: Ming Meng, Dongxiao Niu, Wei SunAbstract:Monthly forecasting of Electric Energy Consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and stochastic series. After the elimination of the stochastic series, the rising trend and periodic waves were modeled separately by a grey model and radio basis function neural networks. Adding the forecasting values of each model can yield the forecasting results for monthly Electricity Consumption. The grey model has a good capability for simulating any smoothing convex trend. In addition, this model can mitigate minor stochastic effects on the rising trend. The extracted periodic wave series, which contain relatively less information and comprise simple regular waves, can improve the generalization capability of neural networks. The case study on Electric Energy Consumption in China shows that the proposed method is better than those traditionally used in terms of both forecasting precision and expected risk.