Intelligent detection of unstable power swing for correct distance relay operation using S-transform and neural networks

The conventional power swing schemes used in distance relay operation are not fast enough to detect and distinguish a fault, stable swing and unstable swing and this may lead to unintended tripping of protection devices. Therefore, there is a need for fast detection of unstable swings so as to improve the reliability of distance relay operation. This paper presents an intelligent approach for detecting unstable swings during distance relay operation using the S-transform signal processing technique and artificial neural networks. To illustrate the effectiveness of the proposed approach, simulations were carried out on the IEEE 39 bus test system using the PSS/E software. Test results showed that the proposed approach using S-transform, multi layer perceptron network and probabilistic neural network can accurately detect and classify fault, stable swing, unstable swing, fault clearance and post fault events for correct distance relay operation.     

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.     

Texture features’ based classification of MR images of normal and herniated intervertebral discs

Multimedia Tools and Applications - Disc herniation is considered as a very common spine abnormality resulting in severe pain in back and legs. Besides it has great impact on economy of suffering...     

Neural network based controller for Cr–Fe batch reduction process

An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr⁶⁺–Fe²⁺ reduction process. Simulated data from the Cr⁶⁺–Fe²⁺ model derived are validated with online data and laboratory analysis using ICP-AES analysis method. The distinctive trend or patterns exhibited in the ORP profiles for the non-equilibrium model derived have been utilized to train neural network-based controllers for the process. The implementation of this process control is to ensure sufficient Fe²⁺ solution is dosed into the wastewater sample in order to reduce all Cr⁶⁺–Cr³⁺. The neural network controller has been utilized to compare the capability of set-point tracking with a PID controller in this process. For this process neural network-based controller dosed in less Fe²⁺ solution compared to the PID controller which hence reduces wastage of chemicals. Industrial Cr⁶⁺ wastewater samples obtained from an electro-plating factory has also been tested on the pilot plant using the neural network-based controller to determine its effectiveness to control the reduction process for a real plant. The results indicate the proposed controller is capable of fully reducing the Cr⁶⁺–Cr³⁺ in the batch treatment process with minimal dosage of Fe²⁺.     

An Analytical Approach to the Concept of Counter-UA Operations (CUAOPS)

Unmanned Aircraft (UA) have become an integral part of the present-day joint air operations. UA have potential to be employed across the full spectrum of Air Force functions. On the other hand, UA technology could be a subject of an asymmetric use by state actors in high or low density conflict, and/or by non-state actors in many ways including terrorism, drug smuggling, and limited attack with unconventional payloads i.e. biological or chemical agents. Therefore countering the threat associated with the hostile UA use could be necessary in the future. First part of the research revealed that UA can be a threat in the future. In the second part of the research, the SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis supplemented with the Tree analysis (SWOT+Tree) provided a broader look for investigating the factors related to hostile UA use analytically. In literature, there is little information about the concepts for CUAOPS. The final goal of the paper is to find possible solutions and means for a better understanding of the nature of CUAOPS.     

Smart home security: challenges,issues and solutions at different IoT layers

The Journal of Supercomputing - The Internet of Things is a rapidly evolving technology in which interconnected computing devices and sensors share data over the network to decipher different...     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

Erratum to: Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear

Multimedia Tools and Applications -     

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.

     

Numerical investigation on 2D viscoelastic fluid due to exponentially stretching surface with magnetic effects: an application of non-Fourier flux theory

Two-dimensional flow of Casson fluid toward an exponentially stretched surface in view of Cattaneo–Christove flux theory is discoursed in current communication. Flow pattern within boundary layer under the effectiveness of magnetic field is also contemplated in the communication. Non-dimensionalized governing expressions are attained through transformation procedure. To anticipate the fascinating features of present work, solution of resulted nonlinear differential system is computed with the collaborated help of shooting scheme and Runge–Kutta method. The influence of involved variables on velocity and temperature fields is scrutinized. Contribution of thermal relaxation is explicitly pointed out. Evaluation of convective heat transfer and friction factor in the fluid flow is visualized through graphs and tables. Additionally, the assurance of present work is affirmed by developing comparison with previous findings in the literature which sets a trade mark for the implementation of numerical approach. It is inferred from the thorough examination of the analysis that present formulation reduces to classical Fourier’s problem by considering \(\varLambda = 0\). Furthermore, decreasing pattern in temperature distribution is depicted in the presence of Cattaneo–Christove flux law as compared to heat transfer due to the Fourier’s law.