Design and implementation of energy audit system employing embedded device

2014 ◽  
Author(s):  
A. Pournima ◽  
M. Krishna Paramathma
Keyword(s):  
Energy Audit ◽  
Embedded Device ◽  
Audit System ◽  
Design And Implementation

The Design and Implementation of Multi-Protocol Database Audit System

2014 ◽  
Vol 610 ◽  
pp. 658-666
Author(s):  
Hao Li ◽  
Jun Feng Qiao ◽  
Cheng Ying Liu ◽  
Zhao Wang
Keyword(s):  
Data Storage ◽  
Database System ◽  
Security Risk ◽  
Core Component ◽  
Audit System ◽  
Design And Implementation ◽  
The Core ◽  
Zero Copy ◽  
Flow Table ◽  
Key Technologies

the security of database system as the core component of data storage has been increasingly important. Effective audit of the database system can reduce the potential security risk. Practical database audit system should not affect the operation of the existing system which uses audited database. This paper presents and implements a multi-protocol database audit system based on bypass mode, and has a detailed description of the function of each module and the interface between them. This paper designs a detailed database audit policy to achieve effective audit. In the second half of this paper, we focus on the implementation of the key technologies which contain Zero-copy and flow table. Keywords-database;zero-copy;flow-table;policy;audit

scholarly journals Design and Implementation of Respiration Rate Measurement System Using Information Filter on Embedded Device

2018 ◽  
Author(s):  
Radius Bhayu Prasetiyo ◽  
Kyu-Sang Choi ◽  
Gi-Hun Yang
Keyword(s):  
Blood Flow ◽  
Respiration Rate ◽  
Intensity Variation ◽  
Major Peak ◽  
Rate Measurement ◽  
Embedded Device ◽  
Design And Implementation ◽  
Information Filter ◽  
Respiration Rate Measurement ◽  
Fft Analysis

In this work, an algorithm was developed to measure the respiration rate for an embedded device that can be used by a field robot for relief operation. With this algorithm, the rate measurement was calculated based on direct influences of respiratory-induced intensity variation (RIIV) on blood flow in cardiovascular pathways. For that, a photoplethysmogram (PPG) sensor was used to determine changes in heartbeat frequencies. The PPG sensor readings were filtered using an Information Filter and a Fast Fourier transform (FFT) to determine the state of RIIV. With a relatively light initialization, the information filter can estimate unknown variables based on a series of measurements containing noise and other inaccuraties. Therefore, this filter is suitable for application on an embedded device. For faster calculation time in the implementation, the FFT analysis was calculated only for a major peak in the frequency domain. Test and measurement of respiration rate was conducted based on the device algorithm and spirometer. Heartbeat measurement was also evaluated by comparing the heartbeat data of the PPG sensor and the medical tool kit. Based on the test, the implemented algorithm can measure respiration rate with about 80% accuracy compared with the spirometer.

scholarly journals Energy Audit System for Households using Machine Learning

2021 ◽  
Vol 10 (7) ◽  
pp. 33-36
Author(s):  
Nagesh* A.
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Random Forest ◽  
Energy Demand ◽  
Predictive Accuracy ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Energy Audit ◽  
Household Level ◽  
Audit System

the growth in population and economics the global demand for energy is increased considerably. The large amount of energy demand comes from houses. Because of this the energy efficiency in houses in considered most important aspect towards the global sustainability. The machine learning algorithms contributed heavily in predicting the amount of energy consumed in household level. In this paper, a energy audit system using machine learning are developed to estimate the amount of energy consumed at household level in order to identify probable areas to plug wastage of energy in household. Each energy audit system is trained using one machine leaning algorithm with previous power consumption history of training data. By converting this data into knowledge, gratification of analysis of energy consumption is attained. The performance of energy audit Linear Regression system is 82%, Decision Tree system is 86% and Random Forest 91% are predicted energy consumption and the performance of learning methods were evaluated based on the heir predictive accuracy, ease of learning and user friendly characteristics. The Random Forest energy audit system is superior when compare to other energy audit system.

scholarly journals Design and Implementation of Respiration Rate Measurement System Using an Information Filter on an Embedded Device

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4208 ◽  
Author(s):  
Radius Prasetiyo ◽  
Kyu-Sang Choi ◽  
Gi-Hun Yang
Keyword(s):  
Respiration Rate ◽  
Intensity Variation ◽  
Major Peak ◽  
Rate Measurement ◽  
Embedded Device ◽  
Design And Implementation ◽  
Information Filter ◽  
Relief Operations ◽  
Respiration Rate Measurement ◽  
Fft Analysis

In this work, an algorithm was developed to measure respiration rate for an embedded device that can be used by a field robot for relief operations. With this algorithm, the rate measurement was calculated based on direct influences of respiratory-induced intensity variation (RIIV) on blood flow in cardiovascular pathways. For this, a photoplethysmogram (PPG) sensor was used to determine changes in heartbeat frequencies. The PPG sensor readings were filtered using an Information Filter and a fast Fourier transform (FFT) to determine the state of RIIV. With a relatively light initialization, the information filter can estimate unknown variables based on a series of measurements containing noise and other inaccuracies. Therefore, this filter is suitable for application in an embedded device. For faster calculation time in the implementation, the FFT analysis was calculated only for a major peak in frequency domain. Test and measurement of respiration rate was conducted based on the device algorithm and spirometer. Heartbeat measurements were also evaluated by comparing the heartbeat data of the PPG sensor and pulse-oximeter. Based on the test, the implemented algorithm can measure the respiration rate with approximately 80% accuracy compared with the spirometer.

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