Exact Techniques for Hardware Modelling of Machine Learning Algorithms with Building of Neuron Network

System getting to know algorithms are complicated to version on hardware. that is due to the truth that those algorithms require quite a few complicated design systems, which are not effort lessly synthesizable. Therefore, through the years, multiple researchers have developed diverse kingdom-of-the artwork techniques, every of them has sure distinct advantages over the others. In this newsletter, we compare the specific strategies for hardware modelling of the various device gaining knowledge of machine learning algorithms, and their hardware-stage overall performance. this newsletter could be useful for any researcher or gadget dressmaker that needs to first evaluate the superior techniques for ML layout, and then inspired with the aid of this, they are able to similarly enlarge it and optimize the device’s performance. Our assessment is based on the 3 number one parameters of hardware layout; that is; place, power and postpone. Any layout approach that can find a stability among those three parameters may be termed as greatest. This work additionally recommends sure enhancements for some of the techniques, which can be taken up for similarly studies. Machine Learning is a concept to find out from examples and skill, while not being expressly programmed. Rather than writing code, you feed knowledge to the generic formula, and it builds logic supported the info given. for instance, one reasonably formula could be a classification formula. It will place knowledge into totally different teams. The classification formula accustomed notice written alphabets may even be accustomed classifies emails into spam and not-spam. Machine learning has resolve many errors ranging from simple arithmetic problems like TSP (Travelling Salesman Problem) to complex issues like predicting the variations in stock market price, Machine learning algorithms like genetic algorithm, particles swarm optimization, deep nets and Q-learning are currently being developed on software platforms due to the ease of implementation. But the full utilization of core algorithms can only be possible. If they are designed & integrated inside the silicon chip. Companies like Apple, Google and Snapdragon etc. are continuously updating their ICs to incorporate these algorithms. But there is no standard architecture defined to implement these algorithms at chip level, due to these inefficiencies of every alternative multiply when these devices connected together. In this research work, we plan to develop a standard architecture for implementation of machine learning algorithms on integrated circuits so that these circuits. connected together work seamlessly with each other & improve the overall system performance. Finally, we planned to implement at least two algorithms on the proposed architecture & verify its optimization capability for practical systems. Our assessment is based on the 3 number one parameters of hardware layout; i.e.; place, power and postpone. Any layout approach that can find a stability among those three parameters may be termed as greatest. This work additionally recommends sure enhancements for some of the techniques, which can be taken up for similarly studies. Machine learning has solved many problems ranging from simple arithmetic problems like TSP (Travelling Salesman Problem) to complex issues like predicting the variations in stock market price, Machine learning algorithms like genetic algorithm.

Prediction of Compressive Strength of Fly-Ash-Based Concrete Using Ensemble and Non-Ensemble Supervised Machine-Learning Approaches

The utilization of waste material, such as fly ash, in the concrete industry will provide a valuable alternative solution for creating an eco-friendly environment. However, experimental work is time-consuming; employing soft machine learning techniques can accelerate the process of forecasting the strength properties of concrete. Ensemble machine learning modeling using Python Jupyter Notebook was employed in the forecasting of compressive strength (CS) of high-performance concrete. Multilayer perceptron neuron network (MLPNN) and decision tree (DT) were used as individual learning which then ensembled with bagging and boosting to provide strong correlations. Random forest (RF) and gradient boosting regression (GBR) were also used for prediction. A total of 471 data points with input parameters (e.g., cement, fine aggregate, coarse aggregate, superplasticizer, water, days, and fly ash), and an output parameter of compressive strength (CS), were retrieved to train and test the individual learners. Cross-validation with K-fold and statistical error (i.e., MAE, MSE, RMSE, and RMSLE) analysis was applied to check the accuracy of all models. All models showed the best correlation with an ensemble model rather than an individual one. DT with AdaBoost and random forest gave a strong correlation of R2 = 0.89 with fewer errors. Cross-validation results revealed a good response with an error of less than 10 MPa. Thus, ensemble modeling not only trains the data by employing several weak learners but also produces a robust correlation that can then be used to model and predict the mechanical performance of concrete.

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi2S3 nanosheets (CuS QDs/Bi2S3 NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi2S3 NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO2 can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO2 sensitivity due to the rich sensitive sites and quantum size effect. The Bi2S3 NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO2. Furthermore, the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.

Backstepping Control of Switched Reluctance Motor with Artificial Neural Network based Flux Estimator

The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.

Meaningful Translation and Transliteration for Marathi Language

Meaningful translation and transliteration is NP problem in case of languages like Marathi language as there are so many word disambiguation and multiple use and meaning of single word in different context is available. That is why identifying correct informational need and translating text into meaningful information is a tedious and error prone task. Google translate works on machine neuron network and WorldNet is an online reference system works on psycholinguistic theory of human memory. Both approaches are promising tools for language translation. Complete translation of Marathi text to English or English to Marathi also having problem of more complicated meaningless or tedious translation. Proposed algorithm is taking into consideration meaningful translation or transliteration as per user’s informational need. This novel approach consider machine neuron network for meaningful formation of translated sentence and morphological structure for correct translation of word based on ontological analysis of word.

Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea

In today’s power systems, the widespread adoption of smart grid applications requires sophisticated control of load variability for effective demand-side management (DSM). Conventional Energy Storage System (ESS)-based DSM methods in South Korea are limited to real-time variability control owing to difficulties with model development using customers’ load profiles from sampling with higher temporal resolution. Herein, this study thus proposes a method of controlling the variability of customers’ load profiles for real-time DSM using customer-installed ESSs. To optimize the reserved capacity for the proposed maximum demand control within ESSs, this study also proposes a hybrid method of load generation, which synthesizes approaches based on Markov Transition Matrix (MTM) and Artificial Neuron Network (ANN) to estimate load variations every 15 min and, in turn reserve capacity in ESSs. The proposed ESS-based DSM strategy primarily reserves capacity in ESSs based on estimated variation in load, and performs real-time maximum demand control with the reserved capacity during scheduled peak shaving operations. To validate the proposed methods, this study used load profiles accumulated from industrial and general (i.e., commercial) customers under the time-of-use (TOU) rate. Simulation verified the improved performance of the proposed ESS-based DSM method for all customers, and results of Kolmogorov-Smirnov (K–S) testing indicate advances in the proposed hybrid estimation beyond the stand-alone estimation using the MTM- or ANN-based approach.

Neuron Network Modeling of Intensification of Isogumulone Extraction in a Rotary Pulse Generator

Introduction. Artificial neural networks are a popular tool of contemporary research and technology, including food science, where they can be used to model various technological processes. The present research objective was to develop an artificial neural network capable of predicting the content of isogumulone in a hop extract at given technological parameters of the rotary pulse generator. Study objects and methods. The mathematical modeling was based on experimental data. The isogumulone content in the hop extract I (mg/dm3) served as an output parameter. The input variables included: processing temperature t (°C), rotor speed n (rpm), processing time  (min), and the gap between the rotor teeth and stator s (mm). Results and discussion. The resulting model had the following parameters: two hidden layers, 30 neurons each; neuron activation function – GELU; loss function – MSELoss; learning step – 0.001; optimizer – Adam; L2 regularization at 0.00001; training set of four batches, 16 records each; 9,801 epochs. The accuracy of the artificial neural network (1.67%) was defined as the mean relative error. The error of the regression model was also low (2.85%). The neural network proved to be more accurate than the regression model and had a better ability to predict the value of the output variable. The accuracy of the artificial neural network was higher because it used test data not included in the training. The regression model when tested on test data showed much worse results. Conclusion. Artificial neural networks proved extremely useful as a means of technological modeling and require further research and application.