Energy-Efficient Operation of a Utility Plant via Predictive Model-Based Optimization Technology

2013 ◽  
Vol 53 (13) ◽  
pp. 5274-5283
Author(s):  
Bijan Sayyar-Rodsari ◽  
Alexander B. Smith ◽  
Apurva Samudra
Keyword(s):  
Predictive Model ◽  
Energy Efficient ◽  
Efficient Operation ◽  
Model Based ◽  
Utility Plant

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.

Energy efficient fixed-cluster architecture for wireless sensor networks

2021 ◽  
Vol 40 (5) ◽  
pp. 8727-8740
Author(s):  
Rajvir Singh ◽  
C. Rama Krishna ◽  
Rajnish Sharma ◽  
Renu Vig
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Wireless Sensor ◽  
Efficient Operation ◽  
Cluster Architecture ◽  
Dynamic Cluster

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.

scholarly journals Energy Efficient Operation of Cellular Network Using On/Off Base Stations

2015 ◽  
Vol 11 (8) ◽  
pp. 108210 ◽  
Author(s):  
Yong-Hoon Choi ◽  
Jungerl Lee ◽  
Juhoon Back ◽  
Suwon Park ◽  
Young-uk Chung ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Cellular Network ◽  
Base Stations ◽  
Efficient Operation

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

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