Large Number Of Inputs Research Articles

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

Storage properties of a quantum perceptron.

Driven by growing computational power and algorithmic developments, machine learning methods have become valuable tools for analyzing vast amounts of data. Simultaneously, the fast technological progress of quantum information processing suggests employing quantum hardware for machine learning purposes. Recent works discuss different architectures of quantum perceptrons, but the abilities of such quantum devices remain debated. Here, we investigate the storage capacity of a particular quantum perceptron architecture by using statistical mechanics techniques and connect our analysis to the theory of classical spin glasses. Specifically, we focus on one concrete quantum perceptron model and explore its storage properties in the limit of a large number of inputs.

Implementation of binarized neural networks immune to device variation and voltage drop employing resistive random access memory bridges and capacitive neurons

Resistive Random Access Memories (ReRAM) arrays provides a promising basement to deploy neural network accelerators based on near or in memory computing. However most popular accelerators rely on Ohm’s and Kirchhoff’s laws to achieve multiply and accumulate, and thus are prone to ReRAM variability and voltage drop in the memory array, and thus need sophisticated readout circuits. Here we propose a robust binary neural network, based on fully differential capacitive neurons and ReRAM synapses, used in a resistive bridge fashion. We fabricated a network layer with up to 23 inputs that we extrapolated to large numbers of inputs through simulation. Defining proper programming and reading conditions, we demonstrate the high resilience of this solution with a minimal accuracy drop, compared to a software baseline, on image classification tasks. Moreover, our solution can achieve a peak energy efficiency, comparable with the state of the art, when projected to a 22 nanometer technology.

ЗАСТОСУВАННЯ BBC MICRO:BIT ДЛЯ НАВЧАННЯ ПРОГРАМУВАННЮ

Considers the use of the BBC Microbit educational microcomputer to introduce the basics of programming to pupils and students in non-technical majors. The paper presents a comparison of the use of single-board computers. The available visual programming environment, as well as a large number of input and output devices on a single-board computer board, makes it much easier to interact with the environment. It is noted that the board can be programmed using visual block programming language, javascript or using Python. If you wish, you can load other systems into Micro:bit, for example, mbed OS and write in C++, but these are already opportunities for students of initial courses of technical specialties. It was determined that the board has an ARM Cortex-M0 processor, 256KB Flash ROM, 16KB RAM, 16MHz clock frequency. There is also BTLE support, a 2.4GHz peer-to-peer transmitter (101 channels), an accelerometer, a compass, a thermometer, and a line of GPIO pins. Even if there is no fee, there is a full-fledged simulator right in the browser, in which you can test all the functions of the program. Ready-made "blocks" are available to the student with many different functions: from simple input-output to sound reproduction or machine control. The board can be programmed using visual block programming language, javascript or Python. The Python programming language has a very simple, but simple editor, and the student can write the code independently. The work shows that the BBC Microbit microcomputer allows to simplify the process of learning program algorithms through the use of a visual programming environment. And the use of functional extensions with the use of third-party modules provides additional opportunities for creating powerful software applications.

At the Basis of Brain Malformations: Brain Plasticity, Developmental Neurobiology, and Considerations for Rehabilitation

AbstractFrom early age in the human brain occurs plasticity process that influences its development. The functioning of the brain is governed by its neuronal connectivity and the synaptic dynamics of these connections. A neuron, over thousands of synapses, can receive a large number of inputs and produce different outputs leading to the consolidation and integration of memory. Synaptic plasticity is the set of experience-dependent changes in neuronal pathways that support acquired habits. It is the ability of the nervous system to reshape connectivity between neurons, changing the functional and structural organization of neuronal circuits that allows us to adapt to the multiple and continuous changes in the environment and leading to processes such as cognitive development and the ability to learn. Synaptic plasticity is mainly due to short- and long-term mechanisms. Short-term synaptic plasticity refers to changes in synaptic strength that occurs very quickly (from one-thousandth of a second to 5 minutes) and are temporary and decay over minutes (maximum 30 minutes). Long-term synaptic plasticity is defined by a long-lasting, activity-dependent change in synaptic efficacy, last from hours up to a lifetime (from 30 minutes to weeks, months, and years) and is thought to constitute the basis of learning and memory. A significant difference occurs in the nature of the change; short-term plasticity adds only a functional change, whereas long-term plasticity causes not only functional but also structural changes. Aside from genetic factors and metabolic processes, brain development is mediated also by environmental factors. Interaction with the environment plays a key role in the development and growth of neural networks and neuroplasticity. Environmental interactions that can modify and increase the development of neural networks and intelligence in children are several and are herein discussed.

Zero energy building optimization for a residential complex with a new optimized cogeneration system for electricity, cooling, heating and freshwater production

In this research, a multi-generation system was proposed to meet the energy consumption of a 120-unit zero-energy residential complex with 100 m2 and 2 bedrooms in Sydney, Australia. To meet the heating and cooling demand of the residential complex, a compression chiller was used, and electrical energy was provided using the system's production power. The main goal of this research is to define a zero-energy building by providing the energy consumption of a residential complex with a solar system. Building Energy Optimization (BEopt) Software has been used for the process of simulating the residential complex and extracting the required load of the residential complex. The design of the load supply system of the residential complex was done using EES thermodynamic software. In this research, a large number of inputs have been used in different scenarios to optimize the residential complex, and the tool used in this research by all the modes introduced as inputs turns the problem into a multi-objective optimization problem. The results of the investigation of the residential complex showed that the amount of electricity consumption, heating, and cooling of the residential complex during the year is 36728, 133123, and 17,902 kWh, respectively. Also, by optimizing the energy consumption of the residential complex, it is possible to reduce CO2 emissions and prevent the increase in environmental pollution. The optimal results showed that the system in its most optimal state can reach an exergy efficiency of 25.18 % and a system cost rate of 46.6 $/h. The performance of the system in the city of Sydney showed that the system can produce 166,164 kWh of electricity, 467,945 kWh of heating, and 183,370 kWh of cooling in one year. Comparing the results of residential complex consumption load and system production showed that the proposed system can easily provide the residential complex consumption load throughout the year.

Making Likelihood Calculations Fast: Automatic Differentiation Applied to RooFit

With the growing datasets of current and next-generation HighEnergy and Nuclear Physics (HEP/NP) experiments, statistical analysis has become more computationally demanding. These increasing demands elicit improvements and modernizations in existing statistical analysis software. One way to address these issues is to improve parameter estimation performance and numeric stability using Automatic Differentiation (AD). AD’s computational efficiency and accuracy are superior to the preexisting numerical differentiation techniques, and it offers significant performance gains when calculating the derivatives of functions with a large number of inputs, making it particularly appealing for statistical models with many parameters. For such models, many HEP/NP experiments use RooFit, a toolkit for statistical modeling and fitting that is part of ROOT. In this paper, we report on the effort to support the AD of RooFit likelihood functions. Our approach is to extend RooFit with a tool that generates overheadfree C++ code for a full likelihood function built from RooFit functional models. Gradients are then generated using Clad, a compiler-based source-codetransformation AD tool, using this C++ code. We present our results from applying AD to the entire minimization pipeline and profile likelihood calculations of several RooFit and HistFactory models at the LHC-experiment scale. We show significant reductions in calculation time and memory usage for the minimization of such likelihood functions. We also elaborate on this approach’s current limitations and explain our plans for the future.

On the capacity of a quantum perceptron for storing biased patterns

Although different architectures of quantum perceptrons have been recently put forward, the capabilities of such quantum devices versus their classical counterparts remain debated. Here, we consider random patterns and targets independently distributed with biased probabilities and investigate the storage capacity of a continuous quantum perceptron model that admits a classical limit, thus facilitating the comparison of performances. Such a more general context extends a previous study of the quantum storage capacity where using statistical mechanics techniques in the limit of a large number of inputs, it was proved that no quantum advantages are to be expected concerning the storage properties. This outcome is due to the fuzziness inevitably introduced by the intrinsic stochasticity of quantum devices. We strengthen such an indication by showing that the possibility of indefinitely enhancing the storage capacity for highly correlated patterns, as it occurs in a classical setting, is instead prevented at the quantum level.

Decision-Making Based on a Conditional Fuzzy Measure

The application of a conditional logical formula of three-valued calculus in the decision-making system is considered. A conditional logical formula makes it possible to determine a conditional fuzzy measure on its basis, which is associated with the following positive aspects. First, there is no need for expert evaluation of the fuzzy measure of the truth of the conclusion for fuzzy premises, which reduces the degree of subjectivity and eliminates the need to ensure the completeness of statistical data, as well as the justification of completeness. Secondly, the proposed version of calculating conditional conclusions relatively simply allows for a multi-premise case and the ability to evaluate the importance of premises based on their priorities (in classical approaches like Mamdani, premises do not differ in their degree of importance for conclusions). Thirdly, there is no needto eva luate the degree of truth of the rules themselves for fuzzy conclusions. These advantages simplify practical use and ultimately improve the quality of decisions made, especially in the case of a large number of inputs (for exam ple, numbered in tens). An example of the practical use of the approach developed on the basis of a fuzzy conditional measure for making decisions about the correction of the learning process based on the testing results is given.

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.

An application for the earthquake spectral and source parameters and prediction using adaptive neuro fuzzy inference system and machine learning

Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune’s model for source is fitted to the measured seismic wave’s displacement spectrum in order to estimate the source’s size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation’s FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune’s source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω0), the corner frequency at which spectrum decays with a rate of 2(fc), the cut-off frequency at which spectrum again decays (fmax), and the rate of decay above fmax on its own (N). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN.

Realization and Optimization of Combinational Circuits Using Simulated Annealing and Partitioning Approach

Combinational logic circuits (CLCs) are basic building blocks of a system and optimization of these circuits in terms of reduced gates, transistors, or levels will lead to reduced area on chip, reduced power, and improved speed. Simulated annealing (SA) is a thermo-inspired metaheuristic used for solving various engineering and non-engineering problems. SA is also used for the realization and optimization of CLCs. Circuits with a large number of inputs and outputs require more generations for realization. Realization of the optimal circuit with fewer generations is desired as realization time increases with increase in the number of generations. In this paper, an attempt is made to realize circuits using population-based SA with fewer generations. SA with partitioning approach is proposed in this paper for circuits that could not be realized with fewer preset generations. To evaluate the performance of the proposed work, benchmark circuits from LGSynth'91 are considered, and it is observed that the success rate improved and realization time reduced with the proposed partitioning approach. During the evaluation, it is also observed that the gate count was reduced by 2.5–77.39% and the transistor count was reduced by 7.69–95.53% on using proposed work with fewer generations over circuits reported in the literature.

An intelligence expert system for quality health monitoring of power transformers using testing attributes

Power transformers are very vital components in electrical power network. Deterioration of transformer’s insulation has significant impacts on its health condition. Hence, detailed analysis of insulation testing attributes is very important in diagnosing the transformer. It is possible with an effective decision-making system. In the present paper, a new grey relational-based fuzzy logic expert system is proposed for continuous health monitoring of power transformers. In this approach, ranking-based qualitative relational analysis is initially carried out in order to reduce large number of input attributes into three input health grades. These input grades are further integrated with a technically designed expert fuzzy rule base. Outputs of the proposed model are validated with the outputs of multi-criterion based fuzzy logic model presented in [Mharakurwa E T and Goboza R 2019 Multiparameter-based fuzzy logic health index assessment for oil-immersed power transformers Adv. Fuzzy Syst. 2019]. Testing data of 200 in-service transformers are utilised in the validation of both the models. It is observed from the comparison that similarity index of the proposed grey-fuzzy system is 92 percent. Several issues exist in regular transformer health assessment models such as the large number of rules, uncertainties, requirement of large training data, complexity issues are overcome by integrating the grey relational analysis with the fuzzy logic system. The proposed model can be easily implemented by all utilities and diagnostic experts of transformers.

Addition machines, automatic functions and open problems of Floyd and Knuth

Floyd and Knuth investigated in 1990 register machines which can add, subtract and compare integers as primitive operations. They asked whether their current bound on the number of registers for multiplying and dividing fast (running in time linear in the size of the input) can be improved and whether one can output fast the powers of two summing up to a positive integer in subquadratic time. Both questions are answered positively. Furthermore, it is shown that every function computed by only one register is automatic and that the automatic functions with one input can be computed with four registers in linear time; automatic functions with a larger number of inputs can be computed with 5 registers in linear time. There is a nonautomatic function with one input which can be computed with two registers in linear time.

Automated Test Data Generation for Embedded System Models Using Combinatorial Testing

Abstract— Embedded systems have been playing very important roles in modern society. They have appeared in every aspect of life from automotive to avionics industries to home appliances, etc. These embedded systems therefore must satisfy high quality requirements. As a consequence, quality assurance for these kinds of systems has attracted much attention and investment from both academic research and industry communities. In developing embedded systems, testing often requires high coverage with different measures with respect to international standards like Decision Coverage (DC), Condition Coverage (CC), Modified Condition/Decision Coverage (MC/DC) respected to ISO 26262. However, it is difficult to generate test cases with high coverage due to the complexity of the embedded system, the larger number of inputs, and the complex continuous signals of inputs. In order to have good test cases with high DC, CC, MC/DC coverage measures, this paper proposes a method to automatically generate test-cases by applying Combinatorial testing technique. The requirements of the inputs of the embedded model will be encoded to the input of Combinatorial testing technique in order to generate test cases that cover all pair values of any two inputs. Experiments on case studies showed that proposed method has coverage result better than that of the random testing method.

Primena fleksibilne DEA strukture u upravljanju poslovnim procesima

This paper presents some statistical tests that can be used to test hypotheses of interest for implementing new technologies and improving the efficiency, functionality and business performance of business systems or companies. DEA is a non-parametric technique based on linear programming for measuring the efficiency of decision-making units (DMU) with heterogeneous inputs/outputs. Generally speaking, DEA has a possible large number of inputs and outputs, and the essential problem is that it is often not clear which inputs and which outputs should be chosen when applied to a real problem, that is, when evaluating the efficiency of the system. In tests, the deviation from the DEA borderline can be viewed as a stochastic variable. The DEA estimate is certainly biased in finite samples (debatable statistics), while the expected value of the DEA efficiency is almost certainly the true value of the parameter in large samples (complete statistics). The application of a flexible DEA structure is significant, if there are situations where insufficient information can prevent the use of parametric statistical tests in the processes and outcomes of management and production. The possibility of using DEA analysis in researching the efficiency of constituent units (branches; decision-making unit - DMU) of business systems should be taken into account in research concerning the functionality of business systems and their optimization in terms of productivity, efficiency and functionality. The research results in this paper are presented in tabular and graphical form.

Effective cyber security system to secure optical data based on deep learning approach for healthcare application

In the recent days, data’s plays a significant role and its enormous quantity are extracted based on various IoT devices. Various IoT devices generates huge data and it is processed to extract the knowledge data through data analytics. To process those data, which are extracted and needs Deep Learning (DL) model as it needs large number of input and its attributes. The deep learning models helps in classifying the data and process those data generated from IoT and those useful optical data will be useful to those consumers. As huge data are circulated and it contains some private information’s related to optics to be secure from the third parties along with effective data processing. The modified deep learning approach based on Cyber Physical Systems (CPS) is propose to effectively process those IoT based data with enhanced data security. Based on deep learning model, the data access algorithm helps to process the raw data generated from IoT time to time and it helps to convert the data to knowledge data. Then the data are secured based on policy access control against various attacks like DoS and DDoS. Based on the performance analysis, the approach helps to provide effective classification of data and reliable data’s to be maintained. Then security on IoT data to be verified against DoS and DDoS attacks on cyber physical system on real time applications.

Postnatal development of hippocampal CA2 structure and function during the emergence of social recognition of peers.

It is now well-established that the hippocampal CA2 region plays an important role in social recognition memory in adult mice. The CA2 is also important for the earliest social memories, including those that mice have for their mothers and littermates, which manifest themselves as a social preference for familiarity over novelty. The role of the CA2 in the development of social memory for recently encountered same-age conspecifics, that is, peers, has not been previously reported. Here, we used a direct social interaction test to characterize the emergence of novelty preference for peers during development and found that at the end of the second postnatal week, pups begin to significantly prefer novel over familiar peers. Using chemogenetic inhibition at this time, we showed that CA2 activity is necessary for the emergence of novelty preference and for the ability to distinguish never encountered fromrecently encountered peers. In adulthood, the CA2 region is known to integrate a large number of inputs from various sources, many of which participate in social recognition memory, but previous studies have not determined whether these afferents are present at adult levels by the end of the second postnatal week. To explore the development of CA2 inputs, we used immunolabeling and retrograde adenovirus circuit tracing and found that, by the end of the second postnatal week, the CA2 is innervated by many regions, including the dentate gyrus, supramammillary nucleus of the hypothalamus, the lateral entorhinal cortex, and the median raphe nucleus. Using retroviral labeling of postnatally generated granule cells in the dentate gyrus, we found that mossy fiber projections to the CA2 mature faster during development than those generated in adulthood. Together, our findings indicate that the CA2 is partially mature in afferent connectivity by the end of the second postnatal week, connections that likely facilitate the emergence of social recognition memory and preference for novel peers.

Multifidelity Model Calibration in Structural Dynamics Using Stochastic Variational Inference on Manifolds.

Bayesian techniques for engineering problems, which rely on Gaussian process (GP) regression, are known for their ability to quantify epistemic and aleatory uncertainties and for being data efficient. The mathematical elegance of applying these methods usually comes at a high computational cost when compared to deterministic and empirical Bayesian methods. Furthermore, using these methods becomes practically infeasible in scenarios characterized by a large number of inputs and thousands of training data. The focus of this work is on enhancing Gaussian process based metamodeling and model calibration tasks, when the size of the training datasets is significantly large. To achieve this goal, we employ a stochastic variational inference algorithm that enables rapid statistical learning of the calibration parameters and hyperparameter tuning, while retaining the rigor of Bayesian inference. The numerical performance of the algorithm is demonstrated on multiple metamodeling and model calibration problems with thousands of training data.

Diffractive interconnects: all-optical permutation operation using diffractive networks

Abstract Permutation matrices form an important computational building block frequently used in various fields including, e.g., communications, information security, and data processing. Optical implementation of permutation operators with relatively large number of input–output interconnections based on power-efficient, fast, and compact platforms is highly desirable. Here, we present diffractive optical networks engineered through deep learning to all-optically perform permutation operations that can scale to hundreds of thousands of interconnections between an input and an output field-of-view using passive transmissive layers that are individually structured at the wavelength scale. Our findings indicate that the capacity of the diffractive optical network in approximating a given permutation operation increases proportional to the number of diffractive layers and trainable transmission elements in the system. Such deeper diffractive network designs can pose practical challenges in terms of physical alignment and output diffraction efficiency of the system. We addressed these challenges by designing misalignment tolerant diffractive designs that can all-optically perform arbitrarily selected permutation operations, and experimentally demonstrated, for the first time, a diffractive permutation network that operates at THz part of the spectrum. Diffractive permutation networks might find various applications in, e.g., security, image encryption, and data processing, along with telecommunications; especially with the carrier frequencies in wireless communications approaching THz-bands, the presented diffractive permutation networks can potentially serve as channel routing and interconnection panels in wireless networks.