Series Arc Fault Breaker in Low Voltage Using Microcontroller Based on Fast Fourier Transform

Series Arc Fault is one of the disturbances of arcing jump is caused by gas ionization between two ends of damaged conductors or broken wire forming a gap in the insulator. Series arc fault is the primary driver of electrical fire. However, lack of knowledge of the disturbance of series arc fault causes the problem of electrical fire not be mitigated. Magnitude current is not capable to detect of series arc fault. Therefore, this paper proposes fast fourier transform (FFT) to detect series AC arc fault in low voltage using microcontroller ARM STM32F7NGH in real time. A cheap and high speed of microcontroller ARM STM32F7NGH can be used for FFT computation to transform signal in time domain to frequency domain. Moreover, in this paper, protection of series AC arc fault is proposed in the real time mode. In this experimental process, some various experiments are tested to evaluate the reliability of FFT and protection with various load starts from 1 A, 2 A, 3 A, 4 A in resistive load. The result of this experiment shows that series AC arc fault protection with STM32F7 microcontroller and FFT algorithm can be utilized to ensure series AC arc fault properly.

Plant disease prediction using convolutional neural network

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared. The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity

SDN-Based Network Intrusion Detection as DDoS defense system for Virtualization Environment

Nowadays, DDoS attacks are often aimed at cloud computing environments, as more people use virtualization servers. With so many Nodes and distributed services, it will be challenging to rely solely on conventional networks to control and monitor intrusions. We design and deploy DDoS attack defense systems in virtualization environments based on Software-defined Networking (SDN) by combining signature-based Network Intrusion Detection Systems (NIDS) and sampled flow (sFlow). These techniques are practically tested and evaluated on the Proxmox production Virtualization Environment testbed, adding High Availability capabilities to the Controller. The evaluation results show that it promptly detects several types of DDoS attacks and mitigates their negative impact on network performance. Moreover, it also shows good results on Quality of Service (QoS) parameters such as average packet loss about 0 %, average latency about 0.8 ms, and average bitrate about 860 Mbit/s.

The Development of A Reliability Evaluation Application for Power Plant Steam Turbine Vibrations to Predict Its Failure

A steam turbine is the most critical component in a thermal power plant. Due to its crucial function, it should be maintained to be able to operate without failure. This paper aims to develop an application that can be used to analyze the reliability and synchronization of vibrations in a single evaluation through the application. The application is helpful to decide the proper time the maintenance should be performed in order to provide a better maintenance strategy. In this paper, the application was used to make an ease in evaluating the reliability and vibration of a 670 MW power plant steam turbine. The reliability was analyzed by qualitative and quantitative methods. The vibration evaluation using Fast Fourier Transform (FFT) was done by diagnosing the failure symptoms from vibration spectrum. The analysis of synchronization of vibrations conducted by comparing the vibration frequency and the natural frequency of the system which can be calculated easily using the application. The algorithm program of both evaluations was built using GNU Octave software to make a friendly user interface. From the evaluation result, the most critical components of the steam turbine are coupling, labyrinth seals, bearing, diaphragm, turbine control valve, and turbine stop valve. The maintenance interval based on the expected reliability of 90% produces the highest reliability improvement. Based on the vibration analysis, there is no failure symptoms detected in the turbine bearings. Furthermore, the dominant frequencies of vibration are distant from the natural frequency. Therefore, the steam turbine condition is acceptable to operate.

Optimized Graph Search Algorithms for Exploration with Mobile Robot

Graph search algorithms and shortest path algorithms, designed to allow real mobile robots to search unknown environments, are typically run in a hybrid manner, which results in the fast exploration of an entire environment using the shortest path. In this study, a mobile robot explored an unknown environment using separate depth-first search (DFS) and breadth-first search (BFS) algorithms. Afterward, developed DFS + Dijkstra and BFS + Dijkstra algorithms were run for the same environment. It was observed that the newly developed hybrid algorithm performed the identification using less distance. In experimental studies with real robots, progression with DFS for the first-time discovery of an unknown environment is very efficient for detecting boundaries. After finding the last point with DFS, the shortest route was found with Dijkstra for the robot to reach the previous node. In defining a robot that works in a real environment using DFS algorithm for movement in unknown environments and Dijkstra algorithm in returning, time and path are shortened. The same situation was tested with BFS and the results were examined. However, DFS + Dijkstra was found to be the best algorithm in field scanning with real robots. With the hybrid algorithm developed, it is possible to scan the area with real autonomous robots in a shorter time. In this study, field scanning was optimized using hybrid algorithms known.

Analysis of control factors and surface integrity during wire-EDM of Inconel 718 alloy using T-GRA approach

In today’s competitive modern manufacturing sectors, there is a vital need of utter precision and rigorous processing using various manufacturing approaches that directly influences the cost and processing duration of mechanized materials in addition to the consistency of the finished products. Therefore, it’s essential to figure out the required output by adjusting the control factors of any machining techniques which resulted in optimal values of the desired outcome. In this study, machining evaluation and process optimization is carried out on volumetric extraction of material namely material removal rate (MRR), kerf obtained during the machining (KW) and surface roughness (SR) of Inconel 718 superalloy during CNC controlled wire- electrical discharge machining. Four controllable factors- pulse interval, wire speed, pulse duration and peak current are considered to investigate the influence on performance measures. Taguchi's L16 has been used to construct the set of experiments before physical experimental runs and most influencing factors have been evaluated using ANOVA. SEM images and EDXS analysis have been resorted to examine the morphology of Inconel 718. These findings assist in identifying the topography of the machined surface. Further, the optimum integration has been obtained for the best yield and recorded using grey relational analysis integrated with Taguchi’s technique (T-GRA). The unfamiliarity of the work is based on consideration of zinc coated thin wire electrode and Taguchi-Grey combined approach of modelling with four levels of experimental design.

HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition

Human Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automatically extract and learn features and recognize activities in a rapid, precise and consistent manner. To solve the problem of imbalanced positive and negative data, we have created two datasets, one is HARDataset1 dataset which is created by extracted image frames from KTH dataset, and another one is HARDataset2 dataset prepared from activity video frames performed by us. The comprehensive experiment shows that our model performs better with respect to the present state of the art models. The proposed model attains an accuracy of 99.5% on HARDatase1 and almost 100% on HARDataset2 dataset. The proposed model also performed well on real data.

Virtual Reality Technology and Speech Analysis for People Who Stutter

Virtual reality (VR) technology provides an interactive computer-generated experience that artificially simulates real-life situations by creating a virtual environment that looks real and stimulates the user’s feelings. During the past few years, the use of VR technology in clinical interventions for assessment, rehabilitation and treatment have received increased attention. Accordingly, many clinical studies and applications have been proposed in the field of mental health, including anxiety disorders. Stuttering is a speech disorder in which affected individuals have a problem with the flow of speech. This can manifest in the repetition and prolongation of words or phrases, as well as in involuntary silent pauses or blocks during which the individual is unable to produce sounds. Stuttering is often accompanied by a social anxiety disorder as a secondary symptom, which requires separate treatment. In this study, we evaluated the effectiveness of using a VR environment as a medium for presenting speech training tasks. In addition, we evaluated the accuracy of a speech analyzer module in detecting stuttering events.

A Review on Forwarding Strategies in NDN based Vehicular Networks

Named Data Networking (NDN) is a model that has been proposed by many researchers to alter the long-established IP based networking model. It derives the content centric approach rather than host-based approach. This is gaining even more traction in the wireless network and is able to replace the conventional IP-based networking. Up to now, NDN has proven to be fruitful when used with certain limitations in vehicular networks. Vehicular networks deal with exchanging information across fast moving complex vehicle network topology. The sending and receiving of information in such a scenario acts as a challenge and thus requires an effective forwarding strategy to address this problem. Different research work has provided with multiple forwarding strategy that solves the current problem up to some limit but further research work is still longed for to get an optimum solution. This paper provides a brief survey on current existing forwarding strategies related to vehicular networks using NDN as well as providing information on various resources and technologies used in it.

Hungarian Mechanism based Sectored FFR for Irregular Geometry Multicellular Networks

The growing demands for mobile broadband application services along with the scarcity of the spectrum have triggered the dense utilization of frequency resources in cellular networks. The capacity demands are coped accordingly, however at the detriment of added inter-cell interference (ICI). Fractional Frequency Reuse (FFR) is an effective ICI mitigation approach when adopted in realistic irregular geometry cellular networks. However, in the literature optimized spectrum resources for the individual users are not considered. In this paper Hungarian Mechanism based Sectored Fractional Frequency Reuse (HMS-FFR) scheme is proposed, where the sub-carriers present in the dynamically partitioned spectrum are optimally allocated to each user. Simulation results revealed that the proposed HMS-FFR scheme enhances the system performance in terms of achievable throughput, average sum rate, and achievable throughput with respect to load while considering full traffic.