Big Noises Research Articles

How Islamic is Islamic Banking? Developing the Falah Scoring

Queries are often raised regarding the extent to which Islamic banks are Islamic. As the biggest Muslim country in the world, it is interesting to investigate that many employees of Indonesian Islamic banks have resigned because they no longer view their banks as Islamic. Many of them join 90-thousand-strong anti-riba movement known as the xBank community. Accordingly, this study aims to investigate the Islamicity of Islamic banks from an internal perspective using mission drift and happiness theory. The study uses a mixed-method approach comprising of the Artificial Neural Networks (ANNs) technique to analyse questionnaire data, and the qualitative data originated from focused group discussions to confirm the findings from the primary data. The study concludes that Islamic banks have become un-Islamic due to the demands of regulatory policies that force them to compete with conventional banks. Consequently, the research identified a minor mission drift of only around 7%. However, the Muslim society responded very sensitively, making big noises. Thus, the findings become evidence for a need of a falah scoring development necessary to maintain the Islamicity of Islamic banking.

Joint state and actuator fault estimation for networked systems under improved accumulation-based event-triggered mechanism

The joint state and actuator fault estimation problem is investigated in this paper for a type of networked systems subject to loss of the actuator effectiveness (LAE). A so-called improved accumulation-based event-triggered mechanism (ETM) is used to regulate the transmission of signals between the sensors and the estimator for the purpose of communication resource saving. Compared with the traditional ETM schemes, such accumulation-based ETM is robust against the “undesired” abrupt changes of signals (which would occur due to certain big noises). Different from the integral-based ETM for continuous-time systems, the improved accumulation-based ETM proposed in this paper is a “weighted” ETM, where a given weight coefficient is employed to “balance” the weights of output measurements in different time instants. The multiplicative LAE is described by an unknown diagonal matrix. The object of this paper is to design a remote estimator such that both the fault signals and system states can be simultaneously estimated in the sense of minimizing an upper bound of the corresponding estimation error covariance at each sampling instant. First, the upper bound of the estimation error covariance is given by means of the induction method. Then, the desired estimator gain is calculated recursively by solving two sets of coupled matrix equations. Finally, two simulation examples are given to verify the usefulness of the strategy we proposed subject to the LAE under the improved accumulation-based ETM.

Complex Valued Deep Neural Networks for Nonlinear System Modeling.

Deep learning models, such as convolutional neural networks (CNN), have been successfully applied in pattern recognition and system identification recent years. But for the cases of missing data and big noises, CNN does not work well for dynamic system modeling. In this paper, complex valued convolution neural network (CVCNN) is presented for modeling nonlinear systems with large uncertainties. Novel training methods are proposed for CVCNN. Comparisons with other classical neural networks are made to show the advantages of the proposed methods.

Subsea Electrification: Is the Tide About To Turn?

The concept of a standalone production system on the seabed with automated wellbore construction and production processes has been an industry goal for a long time. Electrification of subsea facilities and of wellbore and reservoir equipment offers many opportunities to improve operational efficiency, reduce life-of-field capital and operating expenses, and reduce carbon footprint, among other benefits. Talk of a subsea electrification revolution being “just around the corner” has been ongoing for more than 20 years. And, millions of dollars in investments and numerous joint industry projects (JIPs) over the past decade have moved the vision closer to fruition (Fig. 1). But the upstream industry continues to lag others in replacing hydraulics with electrics. The reasons echo those for slow uptake of other new technologies and methodologies—fear of change, the unknown, and failure. Now, recent events are stirring up interest and expectations. “Four to five years ago, only a very small percentage of the buying community were making big noises about the future state of the electrified subsea or subsurface,” said John Kerr, subsea production systems and technology director for Baker Hughes, in a recent interview. “During the past 18 months the narrative has increased rapidly with many more operators looking at electrification as the base case for subsea solutions. We’ve seen a groundswell of interest to the point that we now see 3-, 5-, and 7-year lookaheads with electric solutions as the base case design concept,” Kerr said. What has changed? “Electrification of subsea devices has always been a solution to solve specific technical needs,” said Kerr. “The predominant one was extreme long-distance stepouts, where once you get to 250 miles or so, the ability to pump hydraulic fluids through small umbilicals presented so much pressure loss that it became impractical to implement a hydraulic solution, so all-electric became the solution of choice. Now we are seeing much more understanding of what electrification can deliver in the commercial and operational sense. “During the last 2 years, there has also been rapid adoption of dialogue around the aspect of increased carbon credentials and carbon reduction as an advantage,” Kerr continued. “The interest is much more comprehensive, driving different behavior in concept selection for operators.” Has the pandemic played a role? The consensus of participants in a subsea electrification panel at the virtual 2020 SPE Annual Technical Conference and Exhibition (ATCE) was that unless you’re surrounded by a crisis, you’re not encouraged to change. “The moment you put someone in a crisis situation, they understand that they have to change,” said Rory Mackenzie, leader for subsea electrical technologies at Total. “2020—the pandemic, oil price collapse, and environmental issues—this created a crisis. People are now much more open to considering change.” The panelists included Alvaro Arrazola, completions engineer, Chevron, North America Upstream; Glenn-Roar Halvorsen, project manager subsea all-electric, Equinor; Christina Johansen, managing director, Norway, TechnipFMC; Samantha McClean, intelligent wells technical advisor, BP; Rory Mackenzie, head of subsea electrical technologies, Total R&D; and Thomas Scott, global product line director, intelligent production systems and reservoir information, Baker Hughes. Edward O’Malley, director of strategy and portfolio, oilfield services, Baker Hughes, moderated the session.

Dynamic rod worth measurement method based on eqilibrium -kinetics status

KHNP had licensed Dynamic Control rod Reactivity Measurement (DCRM) method using detector current signals of PWRs in 2006. The method has been applied to all PWRs in Korea for about 15 years successfully. However, the original method was inapplicable to PWRs using low-sensitivity integral fission chamber as ex-core detectors because of their pulse pile-up and the nonlinearity of the mean-square voltage at low power region. Therefore, to overcome this disadvantage, a modified method, DCRM-EK, was developed using kinetics behavior after equilibrium condition where the pulse counts maintain the maximum value before pulse pile-up. Overall measurement, analysis procedure, and related computer codes were changed slightly to reflect the site test condition. The new method was applied to a total of 15 control rods of 1000 MWe and 1400 MWe PWRs in Korea with worths in the range of 200 pcm–1200 pcm. The results show the average difference of -0.4% and the maximum difference of 7.1% compared to the design values. Therefore, the new DCRM-EK will be applied to PWRs using low sensitivity integral fission chambers, and also can replace the original DCRM when the evaluation fails by big noises present in current or voltage signals of uncompensated/compensated ion chambers.

Wind turbines and adverse health effects: Applying Bradford Hill's criteria for causation by Anne Dumbrille, Robert McMurtry, and Carmen Krogh – 'Big noises: Tobacco and Wind'

Wind turbines and adverse health effects: Applying Bradford Hill's criteria for causation by Anne Dumbrille, Robert McMurtry, and Carmen Krogh – 'Big noises: Tobacco and Wind'

Neural Modeling With Guaranteed Input–Output Probability Distributions

Neural networks (NNs) are effective models for data-driven system identification. However, these data-based models do not give the probability properties of the system. Is it possible to use NNs to learn both the dynamics and input–output probability distributions to improve the modeling? In this article, we propose a special neural model, which combines the restricted Boltzmann machine (RBM) and the feedforward NNs. The stochastic RBM learns the input–output probability distributions. The feedforward NN learns the dynamics of nonlinear systems. For the input–output probability distributions, we give the calculation methods of their conditional and joint probabilities. The approximation abilities of this neural model are analyzed. We use two nonlinear systems to compare our guaranteed probability distribution method with the other black-box identification methods. The results show that this novel model is much better, when there are big noises and the dynamics of the system are complex.

Precise Markov random field model-based phase unwrapping method for airborne interferometric synthetic aperture radar imaging

Markovian model approach with graph cuts is effectively applied for phase unwrapping (PU) in interferometric synthetic aperture radar (InSAR) domain particularly for data with big noises and discontinuities. The first-order prior model in Markov random field (MRF) is widely used in Markovian model approach, which is able to solve PU problem in homogeneous scenarios. However, the first-order MRF method cannot interpret local details well in abundant patterns of interferograms under the requirement of high accuracy due to the limitation of first-order prior information. Hence, the conventional first-order model is no longer applicable in this situation. In terms of this problem, the shortcoming of the first-order model is analyzed, and a precise second-order MRF approach is proposed in this paper. Furthermore, a graph cuts-based optimization PU algorithm is designed under a high-order MRF mode, which improves the PU accuracy in detail areas. Without apparent increase of computational complexity, the proposed method is more robust compared with the conventional first-order MRF-based methods. A set of experimental results on both simulation and real measured millimeter-wave InSAR data illustrate the effectiveness of the proposal.

Pure endmember extraction using robust kernel archetypoid analysis for hyperspectral imagery

A robust kernel archetypoid analysis (RKADA) method is proposed to extract pure endmembers from hyperspectral imagery (HSI). The RKADA assumes that each pixel is a sparse linear mixture of all endmembers and each endmember corresponds to a real pixel in the image scene. First, it improves the re8gular archetypal analysis with a new binary sparse constraint, and the adoption of the kernel function constructs the principal convex hull in an infinite Hilbert space and enlarges the divergences between pairwise pixels. Second, the RKADA transfers the pure endmember extraction problem into an optimization problem by minimizing residual errors with the Huber loss function. The Huber loss function reduces the effects from big noises and outliers in the convergence procedure of RKADA and enhances the robustness of the optimization function. Third, the random kernel sinks for fast kernel matrix approximation and the two-stage algorithm for optimizing initial pure endmembers are utilized to improve its computational efficiency in realistic implementations of RKADA, respectively. The optimization equation of RKADA is solved by using the block coordinate descend scheme and the desired pure endmembers are finally obtained. Six state-of-the-art pure endmember extraction methods are employed to make comparisons with the RKADA on both synthetic and real Cuprite HSI datasets, including three geometrical algorithms vertex component analysis (VCA), alternative volume maximization (AVMAX) and orthogonal subspace projection (OSP), and three matrix factorization algorithms the preconditioning for successive projection algorithm (PreSPA), hierarchical clustering based on rank-two nonnegative matrix factorization (H2NMF) and self-dictionary multiple measurement vector (SDMMV). Experimental results show that the RKADA outperforms all the six methods in terms of spectral angle distance (SAD) and root-mean-square-error (RMSE). Moreover, the RKADA has short computational times in offline operations and shows significant improvement in identifying pure endmembers for ground objects with smaller spectrum differences. Therefore, the RKADA could be an alternative for pure endmember extraction from hyperspectral images.

An Approach to Identifying Litchi Fruits under Nature Scenes

In order to detect litchi fruit under nature scenes, an identification method was studied. A simple process was proposed to segment the image and remove noises. Its key steps include image converting; segmenting by Two-Cluster K-means clustering; labeling by white and black; small noises removing by open-close combined morphology; and big noises removing by blob area thresholding. A new contribution has been made to weighing by ultrasonic sensor and machine vision which allows for identifying litchi weight under nature scenes. A max error of 27% was recorded in tests of such weighing method.

Application of Modified Self-Adaptive Kalman Filter in Integrated Navigation System of Autonomous Underwater Vehicle

Owing to the complex operating environment of underwater vehicles, many uncertainties of sensors data, big noises of sensors , low precision and high rate of wild points of underwater acoustic sensors, data processing of motion sensors data for underwater vehicle navigation system becomes extremely important. The integrated navigation system of autonomous underwater vehicle based on dead-reckoning is introduced. An modified adaptive Kalman filter is adopted for underwater vehicle sensors information data processing. Experimental results show that the modified self-adaptive Kalman filter(SAKF) is effective, and can meet the underwater robots perform a variety of tasks in the navigation and positioning accuracy..

Uncalibrated Visual Tracking Control Without Visual Velocity

This paper presents a new adaptive controller for image-based tracking of a robot manipulator without using visual velocity when the intrinsic and extrinsic parameters of the camera are not calibrated. Most of existing controllers require the measurement of the visual velocity, which is subject to big noises in general due to low sampling rates of the vision loop. To avoid performance decaying caused by measurement errors of the visual velocity we propose a new image-based tracking controller based on the proposal of an estimator of visual velocity. With a full consideration of dynamic responses of the robot manipulator, we proved the convergence of the image errors of the trajectory to zero and the convergence of the estimated parameters to the real values up to a scale by the Lyapunov method. Experiments have been conducted to demonstrate good convergence of the trajectory errors under the control of the proposed method.

All together now: Little voices making big noises

Mike Reinstein shares his experience of using songs to improve the self-esteem and positive image of children with special needs.

GMDH algorithms for complex systems modelling

At present, GMDH algorithms give us a way to identify and forecast economic processes in the case of noised and short input sampling. In contrast to neural networks, the results are explicit mathematical models, obtained in a relatively short time. For ill-defined objects with very big noises, better results are obtained by analog complexing methods. Nets with active neurons should be applied to increase accuracy. Active neurons are able, during the self-organizing process, to estimate which inputs are necessary to minimize the given objective function of the neuron. In the neuronet with such neurons, we have a twofold multilayered structure: neurons themselves are multilayered, and they will be united into a multilayered network. SelfOrganize! is an easy-to-use modelling tool which realizes twice-multilayered neu-ronets and enables the creation of time series, single input/single output, multi-input/single output and multi-input/multi-output systems (system of equations). Successful applications are shown in the field of analysis and prediction of characteristics of stock markets in financial risk control modelling.