A Study on Optimum Weight Value and Cost MUSIC Algorithm of Array Steering Vector

2015 ◽  
Author(s):  
Kwan Hyeong Lee
Keyword(s):  
Music Algorithm ◽  
Steering Vector ◽  
Weight Value ◽  
Optimum Weight

scholarly journals Segmentation of Enhalus acoroides seagrass from underwater images using the Mask R-CNN method

2021 ◽  
Vol 944 (1) ◽  
pp. 012010
Author(s):  
S A Pamungkas ◽  
I Jaya ◽  
M Iqbal
Keyword(s):  
Confusion Matrix ◽  
Marine Ecosystem ◽  
Batch Size ◽  
Training Process ◽  
Seagrass Meadows ◽  
Enhalus Acoroides ◽  
Weight Value ◽  
Optimum Weight ◽  
Primary Producer ◽  
Labelling Process

Abstract Seagrass is a Spermatophyta plant that has many roles, including as a primary producer in the food chain in the waters. Monitoring of seagrass meadows and conditions needs to be done in order to achieve a healthy marine ecosystem. The steps taken in monitoring seagrass are by detecting and segmenting it. The purpose of the study is to implement and get information about the performance of the Mask R-CNN algorithm in detecting and segmenting the Enhalus acoroides. The dataset consists of 500 Enhalus acoroides images that had gone through a color correction and labelling process. The training process was performed with the configuration of 0.001 learning rate, batch size of 4 and some image augmentation was used to avoid overfitting. The optimum weight value was obtained after conducting the learning process with 100 epochs. A confusion matrix was used to evaluate detection performance, and linear regression was used to evaluate the segmentation produced by the model. The model evaluation results showed an accuracy value of 0.9246, a precision value of 0.9507, a recall value of 0.9712 and a correlation coefficient value of 0.8771. The value indicates that the model can detect and segment the seagrass Enhalus acoroides well and accurately.

Rancang Bangun Smart Case Sistem Monitoring Tabung Gas Elpiji Berbasis Mikrokontroler

CHIPSET ◽  
2020 ◽  
Vol 1 (02) ◽  
pp. 61-68
Author(s):  
Anisha Fadia Haya ◽  
Werman kasoep ◽  
Nefy Puteri Novani
Keyword(s):  
Error Rate ◽  
Load Cell ◽  
Sensor System ◽  
System Testing ◽  
A Value ◽  
Gas Leak ◽  
Weight Value ◽  
Gas Cylinders ◽  
Sms Notification ◽  
Module System

This study aims to create a system that can monitor gas cylinders where this device consists of two systems, the first is a system to measure the weight of 3kg LPG gas cylinders to find the remaining gas which will then be displayed on the LCD, and the second the system gives a notification (alarm) if there is a gas leak via SMS. This system consists of Arduino UNO Microcontroller components, Load cell Sensor, MQ-6 Sensor, and SIM800L GSM Module. For overall system testing, the load cell sensor system can display a percentage of the weight value obtained an error rate of 0%, this indicates that the formula used in the program runs according to what is desired. In the MQ-6 sensor system can make the buzzer on at a value >= 700 ppm, the results of the buzzer can live when the detected gas value >= 700 ppm, this is as desired. In the sim800L gsm module system can send leak notifications, the results obtained that the module can send SMS notifications. And the system turns on the buzzer when the LPG gas has reached the minimum limit, the results obtained by the buzzer will sound when the remaining gas value <= 16%. Based on tests conducted on this system the system can measure the desired weight of the cylinder to look for the remaining gas in the form of a percentage and detect a gas leak and then send an SMS notification.

MUSIC Algorithm Based on Sparse Coprime Electromagnetic Vector Arrays

2013 ◽  
Vol 34 (9) ◽  
pp. 2033-2038
Author(s):  
Hua Shao ◽  
Wei-min Su ◽  
Hong Gu ◽  
Can Wang
Keyword(s):  
Music Algorithm

Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

2019 ◽  
Vol 14 ◽  
Author(s):  
Iyappan Murugesan ◽  
Karpagam Sathish
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Power System ◽  
Electrical Power ◽  
Daubechies Wavelet ◽  
Electrical Power System ◽  
Gradient Ascent ◽  
Neural Learning ◽  
Weight Value ◽  
Transmission And Distribution

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

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