Simple Mathematical Operations Research Articles

Enhancing the performance of solar-powered EV charging stations using the TOSSI-based CTF technique

In this paper, the design and analysis of a novel solar-powered EV-charging system employing a third-order sinusoidal signal integrator (TOSSI) based-CTF (character of triangular function) is proposed. The TOSSI-based CTF is used to extract fundamental active components by eliminating harmonic distortions from the load currents. This control structure has the unique capability of active current separation, employing simple mathematical operations. Moreover, the response is further refined with the help of optimized gain parameters. The designed system is capable of handling various power quality issues, including harmonic mitigation, current balancing, and power factor improvement. The EV battery is primarily charged by solar-PV power employing a bidirectional DC-DC converter. Alternatively, the EV battery may be charged by the grid supply during the unavailability of sunlight by taking the power quality issues into due consideration. The suggested control topology is used to enhance the dynamic operation of solar-powered EV charging stations experiencing solar power intermittency and variation of load. Using MATLAB, the efficacy of the proposed control topology is tested for different operating scenarios. The suggested control topology is also verified and validated using prototype hardware developed in the laboratory, where the suggested controller has proven its utility over and above existing state-of-the-art controllers in the domain.

Terahertz Frequency-Domain Spectroscopy with a Method for Suppressing Water Vapor Absorption Peaks for Analysis of Pharmaceutical Hydrate Samples

Measurements of terahertz absorption spectra of pharmaceutical hydrate samples are achieved under a normal humidity condition by combining terahertz frequency-domain spectroscopy with a newly proposed method for suppressing absorption peaks caused by water vapor. In this method, only simple mathematical operations such as subtraction, thresholding, interpolation, and smoothing are applied to extinction (or absorbance) data obtained by locating samples under a normal humidity condition. By considering the difference in spectral line width between narrow absorption peaks caused by water vapor and the relatively wide absorption peaks caused by active pharmaceutical ingredients (APIs) in solid forms, the absorption peaks caused by water vapor can be effectively suppressed without affecting the absorption peaks of the APIs in the samples. In the present study, levofloxacin hydrates were used as samples to investigate the performance of the proposed method. Spectra were obtained under both dry and normal humidity conditions. The temperature of the samples was raised from 300 to 363 K to dehydrate them and brought back to 313 K to observe hydration under the normal humidity condition. Spectra obtained under the normal humidity condition were processed with the proposed method. The spectra of the hydrates obtained under the dry condition were slightly different from those obtained under the normal humidity condition and processed by our method. Dehydration during the measurements under the dry condition was suggested. Stable and reliable results are expected by measuring spectra under normal humidity conditions and applying the proposed method to suppress absorption peaks by water vapor.

Simple Calculator Application with Client-Server Connection Using Java Socket

This research aims to develop a simple calculator application using a client-server connection based on Java Socket. The research method involves the software development process that integrates a client-server architecture to enable users to perform basic mathematical calculations over a network. The main findings of this research are the successful implementation that enables effective communication between the client and server, facilitating the exchange of data for simple mathematical operations. The conclusion of this research indicates that the use of Java Socket in a calculator application can provide reliable and efficient data transmission performance. The contribution of this research lies in the development of a practical application that demonstrates the benefits of implementing a client-server connection using Java Socket in the context of mathematical calculations.

Vectorial characterization of surface wave via one-dimensional photonic-atomic structure

Quantitative assessment of polarization properties of waves opens up the way for effective exploitation of them in many amazing applications. Tamm surface waves (TSW) that propagate on the interface of periodic dielectric media are proposed for many applications in numerous reports. The polarization state of TSW is not simply intuitive and would not be extracted from reflection spectra. Here considering orientation sensitive nature of the interaction between polarized electromagnetic wave and atom, we try to quantitatively characterize the polarization state of TSWs, excited on the surface of the 1D photonic crystal. To do this we performed direct contact between TSW and rubidium atomic gas by fabrication of a one-dimensional photonic crystal-atomic vapor cell and applied a moderate external magnetic field to create geometrical meaning and a sense of directionality to dark lines in reflection intensity. Our experimental results indicate that transition lines in the reflection spectrum of our hybrid system modify dependent on the orientation of the applied magnetic field and the transverse spin of TSW. We have used these changes to redefine the geometry of Voigt and Faraday for evanescent waves, especially Tamm surface waves. In the end, we performed simple mathematical operations on absorption spectra and extract the ratio of longitudinal and transverse electric field components of the polarization vector of TSW equal to 25\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{2}{5}$$\\end{document}.

Sustainable eco-friendly ratio-based spectrophotometric and HPTLC-densitometric methods for simultaneous analysis of co-formulated anti-migraine drugs with overlapped spectra

Considering the green chemistry perspective and improving the environmental impact of quality control labs; two direct techniques with less hazardous solvents, less waste production and less energy consumption were developed for simultaneous analysis of Aspirin and Metoclopramide in bulk powder and pharmaceutical formulation. The ratio between the two drugs in their co-formulated preparation is very challenging; (90: 1, Aspirin: Metoclopramide). The first technique is spectrophotometry using simple mathematical operations; ratio difference and derivative ratio–zero crossing. The second technique is high-performance thin-layer chromatography (HPTLC) -densitometry which used a mobile phase consisting of cyclo-hexane: methanol: methylene chloride in a ratio of (1:4:1, v/v/v). The greenest solvents which give acceptable resolution were chosen. Following the International Conference on Harmonization (ICH) guidelines, the methods were found to be accurate, precise, and selective. Those methods were statistically compared to the reported spectrophotometric method and the results proved that there is no significant difference in accuracy and precision. Furthermore, the developed methods were assessed using the Analytical Eco-scale, Green Analytical Procedure Index (GAPI) and the Analytical Greenness calculator (AGREE), which gave a full image about their greenness profile. The spectrophotometry was found to be an excellent green technique compared to HPTLC with was considered an acceptable green one. The developed HPTLC-densitometric method was used for the first time for the analysis of this binary mixture. The two proposed spectrophotometric methos have advantages over the published methods as they used easy manipulation steps and are applied on the market pharmaceutical formulation. Owing to the advantages of the developed techniques; being green, do not require expensive sophisticated equipment or large volume of solvents; they could be used for routine analysis in quality control aspects.

Attack Detection using DL based Feature Selection with Improved Convolutional Neural Network

Decentralized wireless networks that may connect without a central hub are named Mobile Ad-hoc Networks (MANET). Attacks and threats of the most common kind can easily penetrate MANETs. Malware, APTs, and Distributed Denial of Service (DDoS) assaults all work together to make Internet services less reliable and less secure. Existing methods have been created to counter these assaults, but they either need more hardware, result in significant delivery delays, or fall short in other key areas like as energy consumption. This research therefore provides an intelligent agent system that can automatically choose and classify features to identify DDoS assaults. In this study, we provide an automated attack detector for MANETs based on a multilayer, (1D) convolutional neural network (CNN). Grey relational analysis classifiers are employed to screen attack levels in the classification layer because of their simple mathematical operation. The sunflower optimization technique is also used to fine-tune the classifier's weight. The research suggested a supervised feature classifier and fed the compressed data from an unsupervised auto encoder to it. In our experiment, conducted on the custom-generated dataset CICDDoS2018, the system outperformed state-of-the-art deep learning-based DDoS attack finding methods by a factor of 98%. Our suggested technique utilizes the freshest CICDDoS2018 dataset in combination with automated feature selection and classification to achieve state-of-the-art detection accuracy at a fraction of the processing time.

Fatigue Monitoring Detection System

The main idea behind this project is to develop a unobtrusive system which can detect fatigue of any human and can issue a timely warning. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough. According to the expert’s studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drnk-driving. This system will monitor the driver eyes using a camera and by developing an algorithm we can detect symptoms of driver fatigue early enough to avoid the person from sleeping. So, this project will be helpful in detecting driver fatigue in advance and will give warning output in form of alarm and pop-ups. Moreover, the warning will be deactivated manually rather than automatically. For this purpose, a de-activation dialog will be generated which will contain some simple mathematical operation which when answered correctly will dismiss the warning. Moresssover, if driver feels drowsy there is possibility of incorrect response to the dialog. We can judge this by plotting a graph in time domain. If all the three input variables show a possibility off fatigue at one moment, then a Warning signal is given in form of sound. This will directly give an indication of drowsiness/fatigue which can be further used as record of driver performance.

A new gene tree algorithm employing DNA sequences of bovine genome using discrete Fourier transformation.

Within the realms of human thoughts on nature, Fourier analysis is considered as one of the greatest ideas currently put forwarded. The Fourier transform shows that any periodic function can be rewritten as the sum of sinusoidal functions. Having a Fourier transform view on real-world problems like the DNA sequence of genes, would make things intuitively simple to understand in comparison with their initial formal domain view. In this study we used discrete Fourier transform (DFT) on DNA sequences of a set of genes in the bovine genome known to govern milk production, in order to develop a new gene clustering algorithm. The implementation of this algorithm is very user-friendly and requires only simple routine mathematical operations. By transforming the configuration of gene sequences into frequency domain, we sought to elucidate important features and reveal hidden gene properties. This is biologically appealing since no information is lost via this transformation and we are therefore not reducing the number of degrees of freedom. The results from different clustering methods were integrated using evidence accumulation algorithms to provide in insilico validation of our results. We propose using candidate gene sequences accompanied by other genes of biologically unknown function. These will then be assigned some degree of relevant annotation by using our proposed algorithm. Current knowledge in biological gene clustering investigation is also lacking, and so DFT-based methods will help shine a light on use of these algorithms for biological insight.

Brain-Inspired Hyperdimensional Computing: How Thermal-Friendly for Edge Computing?

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) methods. It is based on large vectors of binary or bipolar symbols and a few simple mathematical operations. The promise of HDC is a highly efficient implementation for embedded systems like wearables. While fast implementations have been presented, other constraints have not been considered for edge computing. In this work, we aim at answering how thermal-friendly HDC for edge computing is. Devices like smartwatches, smart glasses, or even mobile systems have a restrictive cooling budget due to their limited volume. Although HDC operations are simple, the vectors are large, resulting in a high number of CPU operations and thus a heavy load on the entire system potentially causing temperature violations. In this work, the impact of HDC on the chip's temperature is investigated for the first time. We measure the temperature and power consumption of a commercial embedded system and compare HDC with conventional CNN. We reveal that HDC causes up to 6.8{\deg}C higher temperatures and leads to up to 47% more CPU throttling. Even when both HDC and CNN aim for the same throughput (i.e., perform a similar number of classifications per second), HDC still causes higher on-chip temperatures due to the larger power consumption.

A Theoretical Study on Energy of a Gaseous System Vis-a-Vis Mass and Temperature

To study various properties of a gas has been a subject of rational curiosity in pneumatic sciences. A gaseous system, in general, is studied by using four measurable parameters namely, the pressure, volume, number of moles and temperature. In the present work, an attempt is made to study the variation of energy of an ideal gas with the two measurable parameters, the mass and temperature of the gas. Using the well known ideal gas equation, PV = nRT where symbols have their usual meanings and some simple mathematical operations widely used in physics, chemistry and mathematics in a transparent manner, an equation of state relating the three variables, the energy, mass and temperature of an ideal gas is obtained. It is found that energy of an ideal gas is equal to the product of mass and temperature of the gas. This gives a direct relationship between the energy, mass and temperature of the gas. Out of the three variables, the energy, mass and temperature of an ideal gas, if one of the parameters is held constant, the other two variables can be measured. At a constant temperature, when the power or energy is stabilized, the increase in the mass of the gas may affect the new works and an engine can therefore be prevented from overheating.

Adaptive Rectified Linear Unit (Arelu) for Classification Problems to Solve Dying Problem in Deep Learning

A convolutional neural network (CNN) is a subset of machine learning as well as one of the different types of artificial neural networks that are used for different applications and data types. Activation functions (AFs) are used in this type of network to determine whether or not its neurons are activated. One non-linear AF named as Rectified Linear Units (ReLU) which involves a simple mathematical operations and it gives better performance. It avoids rectifying vanishing gradient problem that inherents older AFs like tanh and sigmoid. Additionally, it has less computational cost. Despite these advantages, it suffers from a problem called Dying problem. Several modifications have been appeared to address this problem, for example; Leaky ReLU (LReLU). The main concept of our algorithm is to improve the current LReLU activation functions in mitigating the dying problem on deep learning by using the readjustment of values (changing and decreasing value) of the loss function or cost function while number of epochs are increased. The model was trained on the MNIST dataset with 20 epochs and achieved lowest misclassification rate by 1.2%. While optimizing our proposed methods, we received comparatively better results in terms of simplicity, low computational cost, and with no hyperparameters.

A new protection method for the power transformers using Teager energy operator and a fluctuation identifier index

• Using a high-frequency based method, a very fast protection algorithm is provided. • Teager energy operator is a very fast and simple method for the extraction of high-frequency components of the signal. • A very simple index is proposed here which makes the internal fault identification really fast. • A very simple and fast method is provided for the fault type classification. • The algorithm is successfully tested by the experimental signals. Transformers are one of the most important tools of a power system. Differential protection relays with the second harmonic restraint criterion are traditionally used for the transformer protection. This conventional criterion may fail to perform properly in some cases which results in the incorrect relay operation. A novel high-frequency-based algorithm is proposed in this paper to discriminate between the magnetizing inrush currents and the internal faults. Teager energy operator (TEO) is used here to extract high-frequency information from the differential current signals. Differential currents resulted from an internal fault have more high-frequency transients than those resulted from a magnetizing inrush and this is the basic principle used in the proposed algorithm. A very simple and effective operator is proposed in this paper to quantify the overall high-frequency transients of a signal. The proposed algorithm uses a quarter of a cycle of the differential currents along with the very simple mathematical operators to do its task. Therefore, this is a very fast and simple algorithm. The algorithm is tested by the simulated and experimental test signals. Results show the robustness and reliability of the proposed algorithm.

EEG-based Emotion Recognition Using Multiple Kernel Learning

Emotion recognition based on electroencephalography (EEG) has a wide range of applications and has great potential value, so it has received increasing attention from academia and industry in recent years. Meanwhile, multiple kernel learning (MKL) has also been favored by researchers for its data-driven convenience and high accuracy. However, there is little research on MKL in EEG-based emotion recognition. Therefore, this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition. Thus, we proposed a support vector machine (SVM) classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems. We designed two data partition methods, random division to verify the validity of the MKL method and sequential division to simulate practical applications. Then, tri-categorization experiments were performed for neutral, negative and positive emotions based on a commonly used dataset, the Shanghai Jiao Tong University emotional EEG dataset (SEED). The average classification accuracies for random division and sequential division were 92.25% and 74.37%, respectively, which shows better classification performance than the traditional single kernel SVM. The final results show that the MKL method is obviously effective, and the application of MKL in EEG emotion recognition is worthy of further study. Through the analysis of the experimental results, we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance. It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition. In summary, this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.

Extremely simple structure hydrogen gas sensor based on single metallic thin-wire under sweep heating

We here propose a sweep heating of a single metallic thin-wire for thermal conductivity detector (TCD)-based gas sensor. Owing to the sweep heating, much information can be acquired compared with the typical heating. Accordingly, even though the sensing element is only the single metallic thin-wire, it can detect hydrogen without the compensator. After the simple mathematical operation, the relation between input power (P) and resistance (R), namely P-R diagram, can be calculated and it depicts a change in thermal conductivity of surrounding gas clearly. Indeed, hydrogen gas over a wide range of concentrations from 1 to 100%H2 can be detected via a slope and hysteresis area of the diagram. The sensor response was stable and related almost to an inverse of thermal conductivity of gas. The observed temperature increase during the sweep heating shows a good agreement with a temperature profile calculated from finite element method (FEM). Therefore, the sensor temperature represents the thermal conductivity of gas, although the sweep heating works under non-equilibrium state. From a comparison between platinum, tungsten and cobalt thin-wires, it was confirmed that any metal can be employed for sensing element of this sensor. The proposed sweep heating thin-wire gas sensor is a cost-effective and expected to be suitable for practical use in harsh environment such as automotive applications because of its simple structure and simple working principle. The sweep heating expands diversity on TCD-based gas sensor and contributes to realizing the sustainable development goals (SDGs).

A new signal processing approach/method for classification of power quality disturbances

The disadvantage of conventional signal processing methods used for Power Quality (PQ) classification is that their mathematical calculations are complex. Therefore, it is necessary to develop simple, effective, and fast signal processing tools to analyze the PQ signal. In this study, a new signal processing method with simple mathematical operations based on the Integer Factor (IF) down sampling/Approximation Derivatives (AD) was developed to analyze PQ signals. IF was employed to analyze the signals at various sampling frequencies, while the AD method was employed to obtain different degrees of detail and approximation coefficients for the signals. In order to prove that the IF/AD signal processing method can perform a fast and detailed signal processing analysis, the IF/AD method and the Discrete Wavelet Transforms (DWT)/Multiresolution method, which is one of the basic signal processing methods, were compared. It was determined that the developed signal processing method has analyzed PQ disturbances in a shorter time and in more detail than DWT/Multiresolution method. After it was determined that the IF/AD method was effective in the analysis of PQ signals, a new classification method based on the IF-AD signal processing approach, the Slime Mould feature selection algorithm, and Support Vector Machine was developed. The classification method was applied to synthetic signals with noise, real-time data generated in the laboratory and PQ online data. The results proved that the developed classification method was successful. Therefore, this study will provide a different approach to PQ classification methods and non-stationary signal analysis studies.

The impact of anxiety and life quality on the mathematical performance of dyscalculic middle school children

Dyscalculia is a specific difficulty in learning mathematics that strongly influences activities of daily living that require skills such as counting and simple mathematical operations. The aim of this study is to investigate the effect of negative emotions on mathematical performance in children with and without developmental dyscalculia using psychosocial tests, a quality of life test, an anxiety test and the Zareki-R mathematical performance test. This pilot study was realized on a sample of 20 children in the first year of secondary school (a group of 10 dyscalculic children and another group of 10 control children with an average age of 12.65 years). Descriptive statistics showed that dyscalculic children had low scores on all Zareki-R subtests. The Mann Whitney analysis revealed a significant difference between dyscalculic children and typically developing children on the Zareki-R subtests and the quality of life test, but no significance was found for the anxiety test. Analysis of the ANOVA by gender revealed no significant differences for the three tests, and the opposite for the ANOVA by age (F = 3.86, dll = 2, p ˂ 0.05). Using multiple linear regression, the subtests of physical quality of life, emotional quality of life and academic quality of life were significantly different for the two groups. In conclusion, the psychosocial quality of life and the high level of anxiety in dyscalculic children strongly influence their performance in mathematics.

Information Bottleneck Signal Processing and Learning to Maximize Relevant Information for Communication Receivers

Digital communication receivers extract information about the transmitted data from the received signal in subsequent processing steps, such as synchronization, demodulation and channel decoding. Technically, the receiver-side signal processing for conducting these tasks is complex and hence causes bottleneck situations in terms of power, delay and chip area. Typically, many bits per sample are required to represent and process the received signal in the digital receiver hardware accurately. In addition, demanding arithmetical operations are required in the signal processing algorithms. A popular recent trend is designing entire receiver chains or some of their crucial building blocks from an information theoretical perspective. Signal processing blocks with very simple mathematical operations can be designed to directly maximize the relevant information that flows through them. At the same time, a strong quantization reduces the number of bits processed in the receiver to further lower the complexity. The described system design approach follows the principle of the information bottleneck method. Different authors proposed various ideas to design and implement mutual information-maximizing signal processing units. The first important aim of this article is to explain the fundamental similarities between the information bottleneck method and the functionalities of communication receivers. Based on that, we present and investigate new results on an entire receiver chain that is designed following the information bottleneck design principle. Afterwards, we give an overview of different techniques following the information bottleneck design paradigm from the literature, mainly dealing with channel decoding applications. We analyze the similarities of the different approaches for information bottleneck signal processing. This comparison leads to a general view on information bottleneck signal processing which goes back to the learning of parameters of trainable functions that maximize the relevant mutual information under compression.

Enhancement of power system stability by real-time prediction of instability and early activation of steam turbine fast valving

Turbine FV is a stability enhancing method used to protect power systems against the loss of synchronism after extreme contingency events. To avoid unnecessary FV action, smart control schemes can be used based on real-time instability prediction. In the last decade, several WAMS-based instability prediction methods have been developed. However, relatively long cumulative latencies in WAMS structures significantly reduce the efficiency of preventive control and can lead to system instability. To speed up FV action initiated by smart control scheme, this paper proposes a fast stability prediction method which is completely different from other methods described in literature. It uses only local measurements and requires performing simple mathematical operations. The basis of this method is the prediction of the magnitude of the power-angle characteristic. Just after the fault clearance, the method allows to predict transient instability and initiate MFV action. Based on the magnitude of the power-angle characteristic, the steady-state stability margin can be also computed. When this margin is too small, SFV action can be initiated as well. The validity of the proposed method has been verified by simulations performed for a large-scale real power system and detailed models of power system elements.

An Evolutionary Computation Approach for Twitter Bot Detection

Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and genetic programming to discover interpretable classification models for Twitter bot detection with competitive qualitative performance, high scalability, and good generalization capabilities. Specifically, we use a genetic programming method with a set of primitives that involves simple mathematical operators. This enables us to discover a human-readable detection algorithm that exhibits a detection accuracy close to the top state-of-the-art methods on the TwiBot-20 dataset while providing predictions that can be interpreted, and whose uncertainty can be easily measured. To the best of our knowledge, this work is the first attempt at adopting evolutionary computation techniques for detecting bot profiles on social media platforms.

MULTIPLICATIVE APPROXIMATION METHOD OF FUNCTIONAL DEPENDENCIES BY LINE SEGMENTS

The article is devoted to the approximation problems of functional dependencies during conversions performed in intelligent measurement devices. Non-linearities are essential parts of most control processes and systems. When using a nonlinear transmitter with a conversion function in measurement information systems used in various fields, it is necessary to perform nonlinear functional conversion operations on numbers in microprocessors/microcontrollers during direct and indirect measurements. For this purpose, various approximation methods are used. The purpose of the approximation is to describe nonlinear functions in a simpler, more convenient way for utilization and calculations, with an insignificantly small loss of accuracy. Existent methods for linearization, although some of them are effective, can be burdensome for implementation in microprocessor-based systems. Here, one of the proposed methods for the approximation of nonlinear functional dependencies by line segments is proposed. In this method, the range of the argument changes in the function is divided into line segments, and the parts of the coordinate system bisector, remaining within the line segments of the function, is swapped to perform approximation. Having involved few simple mathematical operations, the proposed method can be implemented efficiently in microprocessors/microcontrollers to perform approximations in measurement systems.