Predict and Measure Air Quality Monitoring System Using Machine Learning

This article looks at how artificial intelligence can help expect the hourly consolidation of air toxinSulphur ozone, element matter (PM2.5), and Sulphur dioxide. As one of the most excellently procedures, AI can efficiently prepare a model on a large amount of data by using large-scale streamlining computations. Even thoughseveral works use AI to predict air quality, most of the earlier studies are limited to long-term data and easilyinstruct regular relapse designs (direct or nonlinear) to expect the hourly air pollution focus. This paper suggestsadvanced analysis to simulate the hourly environmental change focus based on previous days' weather-related data by calculating the expectation for more than 24 hours as an execute multiple tasks learning (MTL) issue. This allows us to choose a suitable model with a variety of regularization strategies. We suggest a useful regularization that maintains the assumption patterns of concurrent hours to be nearby to each other, and we evaluate it to a few common MTL expect completion such as normal Frobenius standard regularization, normal atomicregularization, and '2,1-standard regularization. Our tests revealed that the suggested boundary declining concepts and constant hour-related regularizations outperform open product relapse models and regularizations in terms of execution.

Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks

We propose Absum, which is a regularization method for improving adversarial robustness of convolutional neural networks (CNNs). Although CNNs can accurately recognize images, recent studies have shown that the convolution operations in CNNs commonly have structural sensitivity to specific noise composed of Fourier basis functions. By exploiting this sensitivity, they proposed a simple black-box adversarial attack: Single Fourier attack. To reduce structural sensitivity, we can use regularization of convolution filter weights since the sensitivity of linear transform can be assessed by the norm of the weights. However, standard regularization methods can prevent minimization of the loss function because they impose a tight constraint for obtaining high robustness. To solve this problem, Absum imposes a loose constraint; it penalizes the absolute values of the summation of the parameters in the convolution layers. Absum can improve robustness against single Fourier attack while being as simple and efficient as standard regularization methods (e.g., weight decay and L1 regularization). Our experiments demonstrate that Absum improves robustness against single Fourier attack more than standard regularization methods. Furthermore, we reveal that robust CNNs with Absum are more robust against transferred attacks due to decreasing the common sensitivity and against high-frequency noise than standard regularization methods. We also reveal that Absum can improve robustness against gradient-based attacks (projected gradient descent) when used with adversarial training.

Cloud based IoT Applications for Industrial and Home Automation

IoT for Industrial and Home Automation is emerging with a big bang, has huge potential for every field to be used. When there is a need for efficient means to seek IoT interface, a cloud server is what strikes in every design and applications. There are numerous aspects in building a real-time IoT interface, but IoT through cloud can be a source of multiple gains in contrast to its peers such as edge computing [1]. Industrial and Home Automation involve an excellent delivery protocol for an error-free effective transmission in the internet. MQTT protocol is a better option to facilitate the requirements of IoT through its smooth implementation, Quality of Service and data delivery. In today’s world, IoT cloud providers compete to provide reasonable and precise IoT based utilities. Despite extensive engagement of these IoT clouds, we have not initiated standard regularization or few comparative analytical investigations across the research databases. An IoT application calls for diverse resources of a particular cloud and hence it calls for a survey on IoT cloud concerning Latency, interval for subsequent update, user-friendliness, IFTTT compatibility, data handling, processing data, storage limits, servers used and security. An analyses of five of the most eminent clouds (Adafruit IO, Amazon Web Service (AWS), Blynk, Thingspeak and Ubidots) based on the above-described specifications are the factors of motivation for this paper and hence matches the best cloud suited to serve specific purpose and applications.

Assimilation of Standard Regularizer Contextual Model and Composite Kernel with Fuzzy-based Noise Classifier

The paper assay the effect of assimilating smoothness prior contextual model and composite kernel function with fuzzy based noise classifier using remote sensing data. The concept of the composite kernel has been taken by fusing two kernels together to improve the classification accuracy. Gaussian and Sigmoid kernel functions have opted for kernel composition. As a contextual model, Markov Random Field (MRF) Standard regularization model (smoothness prior) has been studied with the composite kernel-based Noise Classifier. Comparative analysis of new classifier with the conventional construes increase in overall accuracy.

An efficient preconditioning method for state box-constrained optimal control problems

An efficient preconditioning technique used earlier for two-by-two block matrix systems with square matrix blocks is shown to be applicable also for a state variable box-constrained optimal control problem. The problem is penalized by a standard regularization term for the control variable and for the box-constraint, using a Moreau–Yosida penalization method. It is shown that there occur very few nonlinear iteration steps and also few iterations to solve the arising linearized equations on the fine mesh. This holds for a wide range of the penalization and discretization parameters. The arising nonlinearity can be handled with a hybrid nonlinear-linear procedure that raises the computational efficiency of the overall solution method.

Hidden Degeneracies in Piecewise Smooth Dynamical Systems

When a flow suffers a discontinuity in its vector field at some switching surface, the flow can cross through or slide along the surface. Sliding along the switching surface can be understood as the flow along an invariant manifold inside a switching layer. It turns out that the usual method for finding sliding modes — the Filippov convex combination or Utkin equivalent control — results in a degeneracy in the switching layer whenever the flow is tangent to the switching surface from both sides. We derive the general result and analyze the simplest case here, where the flow curves parabolically on either side of the switching surface (the so-called fold–fold or two-fold singularities). The result is a set of zeros of the fast switching flow inside the layer, which is structurally unstable to perturbation by terms nonlinear in the switching parameter, terms such as [Formula: see text] [where the superscript does mean “squared”]. We provide structurally stable forms, and show that in this form the layer system is equivalent to a generic singularity of a two timescale system. Finally we show that the same degeneracy arises when a discontinuity is smoothed using standard regularization methods.

Image Edge Respecting Denoising with Edge Denoising by a Designer Nonisotropic Structure Tensor Method

We consider image denoising as the problem of removing spurious oscillations due to noise while preserving edges in the images. We will suggest here how to directly make infinitesimal adjustment to standard variational methods of image denoising, to enhance desirable target assumption of the noiseless image. The standard regularization method is used to define a suitable energy functional to penalize the data fidelity and the smoothness of the solution. This energy functional is tailored so that the region with small gradient is isotropically smoothed whereas in a neighborhood of an edge presented by a large gradient smoothing is allowed only along the edge contour. The regularized solution that arises in this fashion is then the solution of a variational principle. To this end the associated Euler — Lagrange equation needs to be solved numerically and the half-quadratic minimization is generally used to linearize the equation and to derive an iterative scheme. We describe here a method to modify Euler — Largrange equation from commonly used energy functionals, in a way to enhance certain desirable preconceived assumptions of the image, such as edge preservation. From an algorithmic point of view, we may deem this algorithm as a smoothing by a local average with an adaptive gradient-based weight. However, this algorithm may result in noisy edges although the edge is preserved and noise is suppressed in the low-gradient regions of the image. The main focus here is to present an edge-preserving regularization in the aforementioned view point, and to provide an alternative and simple way to modify the existing algorithm to mitigate the phenomena of noisy edges without explicitly defining step where we specify an energy functional to be minimized.

REAL-TIME DECONVOLUTION IN ULTRASONIC IMAGING SYSTEMS

We address the problem of improving the lateral resolution of ultrasonic images by a regularization technique in the wavelet domain. With a very low additional computational cost, the proposed approach increases the efficiency of the standard regularization technique because it efficiently remove the additive noise. Under the assumption that the point spread function is known, we applied our restoration technique to both synthetic and real ultrasonic imaging data. Moreover, experimental results show that the proposed method reduces also speckle artifacts, which generally are enhanced by the deconvolution.