A review of wave-net identical learning &; filling-in in a decomposition space of (JPG-JPEG) sampled images

Continuous flow to send images via encrypted wireless channels may reduce the efficiency of transmission. This is due to the damage or loss of the numerous macro-blocks from these images. Therefore, it is difficult to rebuild these patches from the point of reception. Many algorithms have been proposed in the past decade, particularly error concealment (EC) algorithms. In this paper, we focus on the algorithms that have high efficiency to fill-in the corrupted patches. On the other hand, we also present a new way of detecting the horizontal and vertical gradients especially, in the un-smooth patches. This improves the edge detector filter. Ultimately, a novel scheme for vertical and horizontal interpolation between the corrupted pixels and the non-corrupted adjacent pixels is achieved by improving the efficiency of filling-in. We used a new technique known as the wave-net model. This model combines the wavelet with the neural network classifier (NNC). The neural network was trained in advance to reduce the error extent for the pixels that may occur in the error. The experimental results were convincing and close to the desired. The proposed method is able to enhance image quality in term of both visual perception and the blurriness effects (BE).     

Self-developing fuzzy expert system: a novel learning approach,fitting for manufacturing domain

The foremost challenge faced by expert systems, for their applicability to real world problems, is their inherent deficiency of dynamism. For an expert system to be more pragmatic and applicable, the whole structure of an expert system—including rule-base, fuzzy sets, and even user-interface—needs to be upgraded continuously. This continuous up gradation demands full-time, repetitive, and cumbersome involvement of knowledge engineers. Machine learning is an answer to this problem, but unfortunately, the solutions that have been provided are limited in scope. For example, most of the researchers put forward techniques of either generating just rules from data, or self-expanding and self-correcting knowledge-base only. The innovative approach presented in this paper is broader in scope. It enhances the efficacy and viability of expert systems to be more capable of coping with dynamic and ever-changing industrial environments. The objective is facilitated by rendering, concurrently, the self-learning, self-correcting, and self-expanding abilities to the expert system, without requiring knowledge engineering skills of the developers. This means that the user needs just to feed data in form of the values of input/output variables and the complete development of expert system is done automatically. The superiority of the proposed expert system, regarding its continuous self-development, has been explained with the help of three examples related to prediction and optimization of milling and welding processes.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Reliability analysis of nuclear containment without metallic liners against jet aircraft crash

The present study presents a methodology for detailed reliability analysis of nuclear containment without metallic liners against aircraft crash. For this purpose, a nonlinear limit state function has been derived using violation of tolerable crack width as failure criterion. This criterion has been considered as failure criterion because radioactive radiations may come out if size of crack becomes more than the tolerable crack width. The derived limit state uses the response of containment that has been obtained from a detailed dynamic analysis of nuclear containment under an impact of a large size Boeing jet aircraft. Using this response in conjunction with limit state function, the reliabilities and probabilities of failures are obtained at a number of vulnerable locations employing an efficient first-order reliability method (FORM). These values of reliability and probability of failure at various vulnerable locations are then used for the estimation of conditional and annual reliabilities of nuclear containment as a function of its location from the airport. To study the influence of the various random variables on containment reliability the sensitivity analysis has been performed. Some parametric studies have also been included to obtain the results of field and academic interest.     

Alzheimer paired helical filaments (PHFs) studied by high-resolution TEM: what can vertical Pt-C replication tell us about the organization of the pronase-digested PHF core?

Untreated paired helical filaments (PHFs) and pronase-digested PHF-core filaments were stereoscopically imaged with a freeze-drying vertical platinum-carbon replication preparation method for TEM. The untreated PHF have an average wide region (W) = 22.8 +/- 2.4 nm, a narrow region (T) = 10.6 +/- 1.7 nm, and a helical turn period (L) = 78.6 +/- 13.4. The surfaces of the untreated PHF's fuzzy coat appears disorganized. The widths of the pronase-treated PHF-core filaments were significantly reduced (W(d) = 14.8 +/- 1.2 nm, T(d) = 5.7 +/- 1.0 nm, and L(d) = 75.4 +/- 17 nm). The surfaces of the untreated PHF contained approximately 1.1 nm strands, the same size as tau monomer ( approximately 1.0 nm). The pronase-digested PHF cores mostly contained approximately 1.6 +/- 0.3 nm strands although strand diameters ranged from 0.6-2.5 nm. The strands sometimes appear to be wrapped around the filament axis; less often, they appear to be roughly parallel to the PHF axis, and otherwise appear to be randomly oriented. Images of pronase-digested PHF core images are discussed in relation to the core's biochemical composition, its proposed beta structure, and structural subunit models. Images of the untreated and the pronase-digested PHF support a helical ribbon morphology.     

Forecasting hydrogen production potential in islamabad from solar energy using water electrolysis

The focus of this study is the use of Machine Learning methods to forecast Solar Hydrogen production potential for the Islamabad region of Pakistan. For this purpose, we chose a Photovoltaic-Electrolytic (PV-E) system to forecast electricity and, hence, hydrogen production. The weather data used for forecasting and simulation were recorded with precise meteorological instruments stationed in Islamabad, over the course of 13 and a half months. Out of the three tested algorithms, Prophet performs the best with Mean Absolute Percentage Error of 3.7%, forecasting a daily average Hydrogen production of 93.3 × 10³ kg/Km². Although, the forecast in this study is made for the month of August and September, during which the local season moves towards winter, this study demonstrates solar hydrogen production, as a green energy source, has a tremendous potential in this region.     

Assessment of organic acid-rich bio-sap to generate electricity

The study has focused on electricity generation from organic acid-rich bio-substrate like star fruit (Averrhoa carambola). The sap of star fruit was selected as an electrolyte due to the presence of significant amounts of organic acids such as citric acid and ascorbic acid. To preserve the sap, 2% phenol by volume was used to reduce the growth of microorganisms, and the addition of phenol did not affect the initial pH. It was observed that due to an increase in the electrode surface area, reaction rate and current generation had been amplified. Internal resistance also decreased rapidly because of the large electrode surface area. Furthermore, internal resistance was the significant barrier in electricity generation, which was also successfully controlled by the baffle flow agitation system. Moreover, the baffle flow agitation system reduces the formation of dead zones and increases the total dissolved solids inside the electrochemical cell compartments during operation.     

Banana peel: an effective biosorbent for aflatoxins

This work reports the application of banana peel as a novel bioadsorbent for in vitro removal of five mycotoxins (aflatoxins (AFB1, AFB2, AFG1, AFG2) and ochratoxin A). The effect of operational parameters including initial pH, adsorbent dose, contact time and temperature were studied in batch adsorption experiments. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and point of zero charge (pH_pzc) analysis were used to characterise the adsorbent material. Aflatoxins’ adsorption equilibrium was achieved in 15 min, with highest adsorption at alkaline pH (6–8), while ochratoxin has not shown any significant adsorption due to surface charge repulsion. The experimental equilibrium data were tested by Langmuir, Freundlich and Hill isotherms. The Langmuir isotherm was found to be the best fitted model for aflatoxins, and the maximum monolayer coverage (Q₀) was determined to be 8.4, 9.5, 0.4 and 1.1 ng mg^?1 for AFB1, AFB2, AFG1 and AFG2 respectively. Thermodynamic parameters including changes in free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) were determined for the four aflatoxins. Free energy change and enthalpy change demonstrated that the adsorption process was exothermic and spontaneous. Adsorption and desorption study at different pH further demonstrated that the sorption of toxins was strong enough to sustain pH changes that would be experienced in the gastrointestinal tract. This study suggests that biosorption of aflatoxins by dried banana peel may be an effective low-cost decontamination method for incorporation in animal feed diets.