K-Nearest Neighbors and Grid Search CV Based Real Time Fault Monitoring System for Industries

2019 ◽  
Author(s):  
G S K Ranjan ◽  
Amar Kumar Verma ◽  
Sudha Radhika
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Nearest Neighbors ◽  
Grid Search ◽  
K Nearest Neighbors ◽  
Fault Monitoring

Klasifikasi Gerakan Jatuh Berbasis Accelerometer dan Gyroscope Menggunakan K-Nearest Neighbors

2020 ◽  
Vol 4 (2) ◽  
pp. 24-29
Author(s):  
Adlian Jefiza ◽  
Indra Daulay ◽  
Jhon Hericson Purba
Keyword(s):  
Real Time ◽  
Root Mean Square ◽  
Nearest Neighbors ◽  
Training Data ◽  
Mean Square ◽  
K Nearest Neighbors ◽  
Testing Data ◽  
Total Data

Permasalahan utama pada penelitian ini merujuk kepada semakin menurunnya daya tahan tubuh lanjut usia (lansia). Hal ini membutuhkan sistem monitoring aktivitas lansia secara real time. Untuk mendeteksi kegiatan para lansia, dirancang sebuah perangkat monitoring dengan accelerometer 3-sumbu dan gyroscope 3-sumbu. Data sensor diperoleh dari lima partisipan. Setiap partisipan melakukan lima gerakan yaitu terjatuh, duduk, tidur, rukuk dan sujud. Gerakan yang dipilih adalah gerakan yang menyerupai gerakan jatuh. Total data yang diperoleh dari partisipan adalah 75 data yang terbagi menjadi training data dan testing data. Penelitian ini menggunakan metode transformasi Wavelet untuk mengenali fitur dari gerakan. Untuk pengklasifikasian setiap gerakan, digunakan metode K-nearest neighbors (KNN). Hasil klasifikasi gerakan menggunakan lima kelas menghasilkan nilai root mean square sebesar 0.0074 dengan akurasi 100%.

scholarly journals Pengenalan Bentuk Tangan secara Real Time Menggunakan Leap Motion dan K-Nearest Neighbors sebagai Sistem Kendali Robot Beroda

2020 ◽  
Vol 5 (2) ◽  
pp. 178
Author(s):  
Supria Supria ◽  
Depandi Enda ◽  
Muhamad Nasir
Keyword(s):  
Real Time ◽  
Euclidean Distance ◽  
Nearest Neighbors ◽  
Leap Motion ◽  
K Nearest Neighbors ◽  
Accelerometer Sensor ◽  
Finger Tip

Sistem kendali robot saat ini telah banyak dibuat dengan menggunakan berbagai metode seperti sensor accelerometer, sensor suara, leap motion. Pada penelitian ini diusulkan pengenalan bentuk tangan secara real time menggunakan leap motion dan K-Nearest Neighbors (KNN) sebagai sistem kendali robot beroda. Leap motion digunakan untuk mendeteksi titik koordinat posisi tangan pada pandangan leap motion. pembentukan fitur dilakukan dengan mengukur jarak euclidean distance antara palm position dengan finger tip. KNN digunakan untuk menentukan kelas pada data testing. Uji coba dilakukan menggunakan tangan penulis dengan 5 jenis instruksi yaitu maju, mundur, stop, belok kanan, belok kiri dan setiap instruksi di ujicoba sebanyak 20 kali percobaan. Dari hasil ujicoba yang dilakukan menunjukkan bahwa sistem yang diusulkan memiliki tingkat akurasi pengenalan 94%.

scholarly journals Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2314 ◽  
Author(s):  
Giordana Florimbi ◽  
Himar Fabelo ◽  
Emanuele Torti ◽  
Raquel Lazcano ◽  
Daniel Madroñal ◽  
...  
Keyword(s):  
Real Time ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Human Brain Tumor ◽  
Real Time Processing ◽  
Surgical Guidance ◽  
Filtering Algorithm ◽  
Sequential Implementation ◽  
Very High

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

Towards Real-Time Illegal Logging Monitoring: Gas-Powered Chainsaw Logging Detection System using K-Nearest Neighbors

2020 ◽  
Author(s):  
John Daniel C. Arevalo ◽  
Pauline C. Calica ◽  
Bernadette Andree D. R. Celestino ◽  
Katami A. Dimapunong ◽  
Dylan Josh D. Lopez ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Nearest Neighbors ◽  
Illegal Logging ◽  
K Nearest Neighbors

scholarly journals Real Time Efficient Accident Predictor System using Machine Learning Techniques (kNN, RF, LR, DT)

2020 ◽  
Vol 10 (2) ◽  
pp. 108-111
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Learning Techniques

Real time crash predictor system is determining frequency of crashes and also severity of crashes. Nowadays machine learning based methods are used to predict the total number of crashes. In this project, prediction accuracy of machine learning algorithms like Decision tree (DT), K-nearest neighbors (KNN), Random forest (RF), Logistic Regression (LR) are evaluated. Performance analysis of these classification methods are evaluated in terms of accuracy. Dataset included for this project is obtained from 49 states of US and 27 states of India which contains 2.25 million US accident crash records and 1.16 million crash records respectively. Results prove that classification accuracy obtained from Random Forest (RF) is96% compared to other classification methods.

A Two Stage Real Time Fault Monitoring System

1991 ◽  
pp. 653-660
Author(s):  
A. Pouliezos ◽  
G. Stavrakakis ◽  
G. Tselentis
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Two Stage ◽  
Fault Monitoring

scholarly journals Design of Wireless Networked Electricity Pole Line Multi-Fault Monitoring System

2019 ◽  
Vol 8 (4S2) ◽  
pp. 321-324
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Electric Shock ◽  
Current Flow ◽  
Pole Position ◽  
Electrical Shock ◽  
Voltage Sensors ◽  
Fault Monitoring ◽  
Communication Module ◽  
Pole Point

Over the year’s Electrical shock on EB pole has become Defenseless to a transmission system. The main revelation of this project is to Defense from the electric shock. This plan is aimed at measuring the current flow in the transmi ssion line at the pole point and tracking parameters such as volt age sensors, current sensors in street lamps with pole position s ensors. The current and voltage sensors are continuously read the real-time values and send the analog values to the microcontroller present in the kit, if any one of the parameters levels goes beyond its normal value like wire disconnection, lamp failure or pole slanting and also the supply at wire disconnected point will be terminated by relays. Values are uploaded to the IoT cloud by means of the communication module. From the IoT cloud, the values can be monitored in the substation. From this, we can save lives and protect them from electricity.

Sign in / Sign up

Export Citation Format

Share Document