With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.

A review and categorization of techniques on device-free human activity recognition

Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processing and analysis speed and reduce network costs. In this paper, an edge device capable of collecting, processing, storing and analyzing data is constructed by using a single-board computer and a sensor. And, a fault detection model for machine is developed based on the long short-term memory (LSTM) recurrent neural networks. The proposed system called LiReD was implemented for an industrial robot manipulator and the LSTM-based fault detection model showed the best performance among six fault detection models.

LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

Design of Smart Home Security System using Object Recognition and PIR Sensor

Internet of Things (IoT) is a revolutionary technology. It is revolutionizing our world with trillions of sensors and actuators by creating a smart environment around us. In scientific research, sensors are considered as a prospective field. Ubiquitous sensing abilities offer shared information to develop a common operating picture. IoT sensors are efficiently used in various IoT applications for creating a smart environment. This paper presents several IoT sensors and also explains various sensor based IoT applications. Furthermore, after analyzing different sensor applications, this article enlightens which IoT application requires which type of sensor. In the future, this work will serve as the basis for further research work in the related area.

Smart Sensors: Analysis of Different Types of IoT Sensors

Internet of things is the communication of anything with any other thing, the communication mainly transferring of use able data, for example a sensor in a room to monitor and control the temperature. It is estimated that by 2020 there will be about 50 billion internet-enabled devices. This paper aims to describe a security alarm system using low processing power chips using Internet of things which helps to monitor and get alarms when motion is detected and sends photos and videos to a cloud server. Moreover, Internet of things based application can be used remotely to view the activity and get notifications when motion is detected. The photos and videos are sent directly to a cloud server, when the cloud is not available then the data is stored locally on the Raspberry Pi and sent when the connection resumes. Therefore, advantages like these make this application ideal for monitoring homes in absence.

An Internet of things approach for motion detection using Raspberry Pi

Investigations on partially exfoliated graphite as electrode material for electric double layer capacitors (EDLCs)

Structural and Electrochemical Properties of KOH-Activated Carbon Soot Derived from Sinapis alba (Yellow Mustard Oil) for EDLC Application

In this work, carbonaceous material (carbon soot) was produced by burning of mustard oil (Kachi Ghani) with cotton wick using flame synthesis technique. The yield was chemically activated by impregnation in ZnCl2 followed by heat treatment at temperature of 800 °C for 60 min. The structural investigations of the activated material were completed using RAMAN spectroscopy and X-ray diffractometer (XRD). The carbon material has a well-ordered graphitic structure as investigated by XRD and RAMAN. The electrode fabrication was done using activated carbon soot for an electric double-layer capacitor (EDLC). Electrochemical investigation was done with a liquid electrolyte of NaPF6 salt. The EDLC delivers a maximum specific capacitance of ~ 16.7 F g−1 at a current density of 0.5 mA g−1 with power density of 506 W kg−1 and specific energy density around 2.1 Wh kg−1.

Structural and electrochemical investigations on impregnated carbon soot derived by flame synthesis method

In this work, carbonaceous material (carbon soot) was produced by burning of mustard oil (Kachi Ghani) with cotton wick using flame synthesis technique. The yield was chemically activated by impregnation in ZnCl_2 followed by heat treatment at temperature of 800 °C for 60 min. The structural investigations of the activated material were completed using RAMAN spectroscopy and X-ray diffractometer (XRD). The carbon material has a well-ordered graphitic structure as investigated by XRD and RAMAN. The electrode fabrication was done using activated carbon soot for an electric double-layer capacitor (EDLC). Electrochemical investigation was done with a liquid electrolyte of NaPF_6 salt. The EDLC delivers a maximum specific capacitance of ~ 16.7 F g^−1 at a current density of 0.5 mA g^−1 with power density of 506 W kg^−1 and specific energy density around 2.1 Wh kg^−1. Graphical abstract

Anurag Tyagi

Papers

An Internet of things approach for motion detection using Raspberry Pi

Investigations on partially exfoliated graphite as electrode material for electric double layer capacitors (EDLCs)

Structural and Electrochemical Properties of KOH-Activated Carbon Soot Derived from Sinapis alba (Yellow Mustard Oil) for EDLC Application

Structural and electrochemical investigations on impregnated carbon soot derived by flame synthesis method

Structural and electrochemical investigations on impregnated carbon soot derived by flame synthesis method