Distributed Analytics Framework for Integrating Brownfield Systems to Establish Intelligent Manufacturing Architecture

Intelligent manufacturing otherwise called as smart manufacturing concentrates upon optimising production and processes by making full use of data available. It is regarded as a new manufacturing model where the entire product life cycle can be simplified using various smart sensors, data-driven decision-making models, visualisation, intelligent devices, and data analytics. In the Industry 4.0 era, Industrial Internet of Things (IIoT) architecture platform is required to streamline and secure data transfer between machines, factories, etc. When certain manufacturing industry is equipped with this platform, an intelligent manufacturing model can be achieved. In today’s factories, most machines are brownfield systems and are not connected to any IoT platforms. Thus they cannot provide data or visibility into their performance, health, and optimal maintenance schedules, which would have improved their operational value. This paper attempts to bridge this gap by demonstrating how brownfield equipment can be IIoT enabled and how data analytics can be performed at the edge as well as cloud using two simple use cases involving industrial robot on the abrasive finishing process. The focus of this paper is on how a scalable data analytics architecture can be built for brownfield machines at the edge as well as the cloud

Mental Tasks EEG Signal Classification Using Support Vector Machine

This paper presents a result of electroencephalography (EEG) signal classification for mental tasks such as thinking forward, backward, left, and right. The EEG data in this study were recorded from Emotive device with 14 channels and 2 references. The aim of this study is to identify the most sensitive channels to the mental task classification. Prior to feature extraction, the EEG signal were decomposed using wavelet with three level decomposition. Eighteen features were extracted from the processed data. Principal component analysis (PCA) is then used to reduce 18 features into 3 principal components. The principal component were classified using support vector machine (SVM). The results show that the SVM classification accuracy of 75%

Classification Method of Hand Gestures Based on Support Vector Machine

This paper presents the EMG signal classification based on PCA and SVM method. The data is acquired from the 5 subjects and each subject perform 7 hand gestures includes the tripod, power, precision closed, finger point, mouse, hand open, and hand close. Each gesture is repeated 10 times (5 data as training data and the 5 remaining data as testing data). Each of training and testing data are processed using 16 features extraction in time–domain and reduced using principal component analysis (PCA) to obtain new set of features. Features classification using support vector machine classify new set of features from each subject result 85% - 89% percentage of training classification. Training data classification is tested using testing data of EMG signals and giving accuracy reach 80% - 86%

Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand

EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition

Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings

This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features\u27 trends and the estimation of the remaining useful life (RUL) of the slew bearing. In this step, kernel regression is used with time-domain kurtosis, WD kurtosis and the LLE feature. The application of the method is demonstrated with laboratory slew bearing acceleration data

REDUCING OVERHEAD OF SELF-STABILIZING BYZANTINE AGREEMENT PROTOCOLS FOR BLOCKCHAIN USING HTTP/3 PROTOCOL: A PERSPECTIVE VIEW

Today, there is a tendency to reduce the dependence on local computation in favor of cloud computing. However, this inadvertently increases the reliance upon distributed fault-tolerant systems. In a condition that forced to work together, these systems often need to reach an agreement on some state or task, and possibly even in the presence of some misbehaving Byzantine nodes. Although non-trivial, Byzantine Agreement (BA) protocols now exist that are resilient to these types of faults. However, there is still a risk for inconsistencies in the application state in practice, even if a BA protocol is used. A single transient fault may put a node into an illegal state, creating a need for new self-stabilizing BA protocols to recover from illegal states. As self-stabilization often comes with a cost, primarily in the form of communication overhead, a potential lowering of latency - the cost of each message - could significantly impact how fast the protocol behaves overall. Thereby, there is a need for new network protocols such as QUIC, which, among other things, aims to reduce latency. In this paper, we survey current state-of-the-art agreement protocols. Based on previous work, some researchers try to implement pseudocode like QUIC protocol for Ethereum blockchain to have a secure network, resulting in slightly slower performance than the IP-based blockchain. We focus on consensus in the context of blockchain as it has prompted the development and usage of new open-source BA solutions that are related to proof of stake. We also discuss extensions to some of these protocols, specifically the possibility of achieving self-stabilization and the potential integration of the QUIC protocol, such as PoS and PBFT. Finally, further challenges faced in the field and how they might be overcome are discussed

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0

Fault Diagnosis and Prognosis of Critical Components

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