Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.     

Very low-noise ENG amplifier system using CMOS technology.

In this paper, we describe the design and testing of a system for recording electroneurographic signals (ENG) from a multielectrode nerve cuff (MEC). This device, which is an extension of the conventional nerve signal recording cuff, enables ENG to be classified by action potential velocity. In addition to electrical measurements, we provide preliminary in vitro data obtained from frogs that demonstrate the validity of the technique for the first time. Since typical ENG signals are extremely small, on the order of 1 1 microV, very low-noise, high-gain amplifiers are required. The ten-channel system we describe was realized in a 0.8 microm CMOS technology and detailed measured results are presented. The overall gain is 10 000 and the total input-referred root mean square (rms) noise in a bandwidth 1 Hz-5 kHZ is 291 nV. The active area is 12 mm(2) and the power consumption is 24 mW from +/-2.5 V power supplies.     

Cashing in on Caching: An Architecture for Time-Bounded Knowledge-Based Problem Solving

Knowledge-based computing, in general, suffers from an inherent open-endedness that precludes its application in time-bounded domains where an answer must be computed within a stipulated time limit. We examine a two-way improvement of the shortcomings: a knowledge representation scheme that provides easy access to relevant knowledge and thereby reduces search time, and a reasoning scheme that is algorithmic in nature and thus makes computational requirements meaningfully estimable.In this work, we offer a cache-based architecture that is capable of both storing knowledge in different formats (e.g. rules, cases), and invoking an appropriate reasoning scheme to fit the available computing time. The cache helps in retrieving the most relevant pieces of knowledge (not only exact matches) required for solving a given problem. This cache relies on a reasoning tactic, knowledge interpolation, that can generate a solution from two near-matches in an algorithmic way, to generate time-bounded solutions. We illustrate the design of such a cache for solving resource allocation problems in the domain of shortwave radio transmission and evaluate its performance in observing imposed temporal bounds.     

Influential rule search scheme (IRSS) - a new fuzzy pattern classifier

Automatic generation of fuzzy rule base and membership functions from an input-output data set, for reliable construction of an adaptive fuzzy inference system, has become an important area of research interest. We propose a new robust, fast acting adaptive fuzzy pattern classification scheme, named influential rule search scheme (IRSS). In IRSS, rules which are most influential in contributing to the error produced by the adaptive fuzzy system are identified at the end of each epoch and subsequently modified for satisfactory performance. This fuzzy rule base adjustment scheme is accompanied by an output membership function adaptation scheme for fine tuning the fuzzy system architecture. This iterative method has shown a relatively high speed of convergence. Performance of the proposed IRSS is compared with other existing pattern classification schemes by implementing it for Fisher's iris data problem and Wisconsin breast cancer data problems.     

Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization

The particle swarm optimization (PSO) is a relatively new generation of combinatorial metaheuristic algorithms which is based on a metaphor of social interaction, namely bird flocking or fish schooling. Although the algorithm has shown some important advances by providing high speed of convergence in specific problems it has also been reported that the algorithm has a tendency to get stuck in a near optimal solution and may find it difficult to improve solution accuracy by fine tuning. The present paper proposes a new variation of PSO model where we propose a new method of introducing nonlinear variation of inertia weight along with a particle's old velocity to improve the speed of convergence as well as fine tune the search in the multidimensional space. The paper also presents a new method of determining and setting a complete set of free parameters for any given problem, saving the user from a tedious trial and error based approach to determine them for each specific problem. The performance of the proposed PSO model, along with the fixed set of free parameters, is amply demonstrated by applying it for several benchmark problems and comparing it with several competing popular PSO and non-PSO combinatorial metaheuristic algorithms.     

Solid particle erosion behaviour of hardfacing deposits on cast iron—Influence of deposit microstructure and erodent particles

Solid particle erosion (SPE) behaviour of different hardfacing electrodes deposited on gray cast iron (ASTM 2500) was studied using quartz sand and iron ore as erodent particles. Erosion test was carried out as per ASTM G76 test method. Considerable differences in erosion rates were found among different hardfacing electrodes at normal impact. Both volume fraction of carbides and type of carbides played an important role in the erosion behaviour of the deposits when quartz sand was used as erodent particles. On the other hand, only volume fraction of carbides irrespective of carbide type mainly controlled the erosion rate of the same deposits when iron ore was used as erodent particles. Such difference is attributed due to difference in metal removal mechanisms by the two erodent particles used. Hard quartz sand particles were capable of causing damage to most of the carbides while relatively softer iron ore particles were unable to fracture any carbides present in the microstructures. Furthermore, relatively brittle matrix led to high erosion rate which is significant in case of quartz sand as erodent, but not in case of iron ore particles. Like abrasion resistance, hardness is not a true index of erosion resistance of hardfacing deposits.     

Combined removal of BTEX in air stream by using mixture of sugar cane bagasse, compost and GAC as biofilter media

Biofiltration of air stream containing mixture of benzene, toluene, ethyl benzene and o-xylene (BTEX) has been studied in a lab-scale biofilter packed with a mixture of compost, sugar cane bagasse and granulated activated carbon (GAC) in the ratio 55:30:15 by weight. Microbial acclimation was achieved in 30 days by exposing the system to average BTEX inlet concentration of 0.4194 gm(-3) at an empty bed residence time (EBRT) of 2.3 min. Biofilter achieved maximum removal efficiency more than 99% of all four compounds for throughout its operation at an EBRT of 2.3 min for an inlet concentration of 0.681 gm(-3), which is quite significance than the values reported in the literature. The results indicate that when the influent BTEX loadings were less than 68 gm(-3)h(-1) in the biofilter, nearly 100% removal could be achieved. A maximum elimination capacity (EC) of 83.65 gm(-3)h(-1) of the biofilter was obtained at inlet BTEX load of 126.5 gm(-3)h(-1) in phase IV. Elimination capacities of BTEX increased with the increase in influent VOC loading, but an opposite trend was observed for the removal efficiency. The production of CO(2) in each phase (gm(-3)h(-1)) was also observed at steady state (i.e. at maximum removal efficiency). Moreover, the high concentrations of nitrogen in the nutrient solution may adversely affect the microbial activity possibly due to the presence of high salt concentrations. Furthermore, an attempt was also made to isolate the most profusely grown BTEX-degrading strain. A Gram-positive strain had a high BTEX-degrading activity and was identified as Bacillus sphaericus by taxonomical analysis, biochemical tests and 16S rDNA gene analysis methods.     

Electrochemical Study of Diffusion Bonded Joints between Micro-duplex Stainless Steel and Ti6A14V Alloy

In the present study,corrosion behavior of diffusion bonded joints formed between micro-duplex stainless steel(MDSS) and Ti6AI4V alloy(TiA)(at 900 ℃ for 60 min under 4 MPa uniaxial pressure in vacuum) was investigated in 1 mol/L HCI and 1 mol/L NaOH solutions using various electrochemical measurements such as open circuit potential(OCP),electrochemical impedance spectroscopy(EIS) and potentiodynamic polarization(PD).For comparison,corrosion behavior of base metals(MDSS and TiA) was also evaluated.Bonded joint was characterized by light optical microscopy and scanning electron microscopy using backscattered electron mode.The layer wise σ phase and λ + FeTi phase mixture has been observed at the bond interface and the bond tensile strength and shear strength were ~556.4 MPa and ~420.2 MPa,respectively.     

Anomaly Detection Using Real-Valued Negative Selection

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.