The effects of silicon on the properties of tantalum

The microalloying of tantalum with silicon has been effectively used on a commercial scale. However, data on effects of microalloying on processibility, microstructure and mechanical properties are not readily available. To develop some data, tantalum powder with various levels of silicon (0–50 ppm) was processed under identical conditions. Sheet samples were then annealed at different temperatures and evaluated via microstructural, chemical and mechanical tests. The recrystallization temperature was found to increase with silicon content, most probably as a result of the presence of an intermetallic (Ta₃Si) phase. The mechanical properties of unrecrystallized tantalum are, as expected, significantly different from those of recrystallized tantalum.     

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.     

Collaborative jamming and collaborative defense in cognitive radio networks

In cognitive radio networks, cognitive nodes operate on a common pool of spectrum where they opportunistically access and use parts of the spectrum not being used by others. Though cooperation among nodes is desirable for efficient network operations and performance, there might be some malicious nodes whose objective could be to hinder communications and disrupt network operations. The absence of a central authority or any policy enforcement mechanism makes these kinds of open-access network more vulnerable and susceptible to attacks.In this paper, we analyze a common form of denial-of-service attack, i.e., collaborative jamming. We consider a network in which a group of jammers tries to jam the channels being used by legitimate users who in turn try to evade the jammed channels. First, we compute the distribution of the jamming signal that a node experiences by considering a random deployment of jammers. Then, we propose different jamming and defending schemes that are employed by the jammers and legitimate users, respectively. In particular, we model and analyze the channel availability when the legitimate users randomly choose available channels and the jammers jam different channels randomly. We propose a multi-tier proxy-based cooperative defense strategy to exploit the temporal and spatial diversity for the legitimate secondary users in an infrastructure-based centralized cognitive radio network. Illustrative results on spectrum availability rates show how to improve resiliency in cognitive radio networks in the presence of jammers.     

Reduced complexity two stage vector quantization

We address the issue of complexity for vector quantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate–distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity, but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods.     

Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic

This paper presents a fuzzy relational approach to human emotion recognition from facial expressions and its control. The proposed scheme uses external stimulus to excite specific emotions in human subjects whose facial expressions are analyzed by segmenting and localizing the individual frames into regions of interest. Selected facial features such as eye opening, mouth opening, and the length of eyebrow constriction are extracted from the localized regions, fuzzified, and mapped onto an emotion space by employing Mamdani-type relational models. A scheme for the validation of the system parameters is also presented. This paper also provides a fuzzy scheme for controlling the transition of emotion dynamics toward a desired state. Experimental results and computer simulations indicate that the proposed scheme for emotion recognition and control is simple and robust, with good accuracy.     

An application of PSO technique for harmonic elimination in a PWM inverter

Harmonic elimination problem in PWM inverter is treated as an optimization problem and solved using particle swarm optimization (PSO) technique. The derived equation for computation of total harmonic distortion (THD) of the output voltage of PWM inverter is used as the objective function in the PSO algorithm. The objective function is minimized to contribute the minimum THD in the voltage waveform and the corresponding switching angles are computed. The method is applied to investigate the switching patterns of both unipolar and bipolar case. While minimizing the objective function, the individual selected harmonics like 5th, 7th, 11th and 13th can be controlled within the allowable limits by incorporating the constraints in the PSO algorithm. The results of the unipolar case using five switching angles are compared with that of a recently reported work and it is observed that the proposed method is effective in reducing the voltage THD in a wide range of modulation index. The simulated results are also validated through suitable experiments.     

A class of new transforms tailored for the hypergeometric series

A new class of transforms, tailored for the hypergeometric series, is proposed and tested on a number of series. From a knowledge of a given number of terms of a sequence, these transforms reproduce the functions with a better accuracy than the Levin-like transforms. Though there exists a correlation between the approximative power of a rational approximant and its ability to predict the leading terms of a series, there are exceptions to this, especially in the case of divergent series. The new transforms can, in most cases, predict a number of extra terms not used in the construction of the approximants.     

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

Selection of industrial robots using compromise ranking and outranking methods

Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is ‘VIsekriterijumsko KOmpromisno Rangiranje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.     

A new social and momentum component adaptive PSO algorithm for image segmentation

In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu’s method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting ‘social’ and ‘momentum’ components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935–952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653–666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337–344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85–95) and the basic PSO variant employing linearly decreasing inertia weight factor.