Passive magnetic coil design for electromagnetic interference evaluation of axle counters

Measurement of magnetic fields near the railway tracks is crucial to ensure compatibility with the operation of axle counters. According to EN 50592 standard, the magnetic field is detected with a passive magnetic coil and an oscilloscope. From previous studies, in general, there has been no in-depth analysis of how the choice of coil winding parameters could affect the coil output voltage, which then affect the measurement sensitivity, in particular the coil design based on the standard and it is applicability for electromagnetic interference (EMI) evaluation of axle counters. Therefore, this paper will explore the design of a passive magnetic coil to obtain the optimum coil output voltage within the frequency range. Simulations showed that for 10-100 kHz and 100 kHz–1.3 MHz range, the optimum number of turns happened at 60-100 and 15-60 turns, respectively. Based on that, two example coils had been built. Simulations and measurements of their frequency response were in good agreement, with a deviation less than 1.0 dB

Predictive model for acute myocardial infarction in working-age population: a machine learning approach

Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction (AMI), which is the primary cause of atherosclerotic cardiovascular morbidity. This study aims to develop a predictive model for the probability of AMI occurrence in the working-age population, based on atherogenic indices, paraclinical variables, and anthropometric measures. The research conducted a cross-sectional study involving 427 workers aged 40 years or older in Popayán, Colombia. Out of this population, 202 individuals were screened with a 95% confidence interval and a 5% error margin. Epidemiological, anthropometric, and paraclinical data were collected. A binary logistic regression model was employed to identify variables directly associated with the probability of AMI. Predictive classification models were generated using statistical software JASP and the programming language Python. During the training stage, JASP produced a model with an accuracy of 87.5%, while Python generated a model with an accuracy of 90.2%. In the validation stage, JASP achieved an accuracy of 93%, and Python reached 95%. These results establish an effective model for predicting the probability of AMI in the working population

Investigation of auto-oscilational regimes of the system by dynamic nonlinearities

The paper proposes a method for the analysis and synthesis of self-oscillations in the form of a finite, predetermined number of terms of the Fourier series in systems reduced to single-loop, with one element having a nonlinear static characteristic of an arbitrary shape and a dynamic part, which is the sum of the products of coordinates and their derivatives. In this case, the nonlinearity is divided into two parts: static and dynamic nonlinearity. The solution to the problem under consideration consists of two parts. First, the parameters of self-oscillations are determined, and then the parameters of the nonlinear dynamic part of the system are synthesized. When implementing this procedure, the calculation time depends on the number of harmonics considered in the first approximation, so it is recommended to choose the minimum number of them in calculations. An algorithm for determining the self-oscillating mode of a control system with elements that have dynamic nonlinearity is proposed. The developed method for calculating self-oscillations is suitable for solving various synthesis problems. The generated system of equations can be used to synthesize the parameters of both linear and nonlinear parts. The advantage is its versatility

Efficient network management and security in 5G enabled internet of things using deep learning algorithms

The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model

Facial emotion recognition using enhanced multi-verse optimizer method

In recent years, facial emotion recognition has gained significant improvement and attention. This technology utilizes advanced algorithms to analyze facial expressions, enabling computers to detect and interpret human emotions accurately. Its applications span over a wide range of fields, from improving customer service through sentiment analysis, to enhancing mental health support by monitoring emotional states. However, there are several challenges in facial emotion recognition, including variability in individual expressions, cultural differences in emotion display, and privacy concerns related to data collection and usage. Lighting conditions, occlusions, and the need for diverse datasets also impacts accuracy. To solve these issues, an enhanced multi-verse optimizer (EMVO) technique is proposed to improve the efficiency of recognizing emotions. Moreover, EMVO is used to improve the convergence speed, exploration-exploitation balance, solution quality, and the applicability in different types of optimization problems. Two datasets were used to collect the data, namely YouTube and surrey audio-visual expressed emotion (SAVEE) datasets. Then, the classification is done using the convolutional neural networks (CNN) to improve the performance of emotion recognition. When compared to the existing methods shuffled frog leaping algorithm-incremental wrapper-based subset selection (SFLA-IWSS), hierarchical deep neural network (H-DNN) and unique preference learning (UPL), the proposed method achieved better accuracies, measured at 98.65% and 98.76% on the YouTube and SAVEE datasets, respectively

Maximum power point tracking and space vector modulation control of quasi-z-source inverter for grid-connected photovoltaic systems

The quasi-Z-source inverter (qZSI) become one of the most promising power electronic converters for photovoltaic (PV) applications, due to its capability to perform a buck-boost conversion of the input voltage in a single stage. The control strategy based maximum power point tracking (MPPT) and proportional integral (PI) controller are well known in grid-connected with traditional configuration but not in qZSI. This paper presents a control strategy for qZSI grid-connected based on the MPPT algorithm and the linear control by PI controllers. This is complemented by the capability to efficiently transfer the harvested power to the grid, ensuring a unity power factor. The proposed control strategy effectively separates the control mechanisms for the direct current (DC) and alternating current (AC) sides by utilizing the two control variables, the shoot-through duty ratio and the modulation index. An adapted space vector modulation technique is then utilized to generate the switching pulse width modulation (PWM) signals, using these two control variables as inputs. The proposed approach was tested and validated under MATLAB/Simulink and PLECS software

Testing nanometer memories: a review of architectures, applications, and challenges

Newer defects in memories arising from shrinking manufacturing technologies demand improved memory testing methodologies. The percentage of memories on chips continues to rise. With shrinking technologies (10 nm up to 1.8 nm), the structure of memories is becoming denser. Due to the dense structure and significant portion of a chip, the nanometer memories are highly susceptible to defects. High-frequency specifications, the complexity of internal connections, and the process variation due to newer manufacturing technology further increased the probability of the physical failure of memories to a great extent. Memories need to be defect-free for the chip to operate successfully. Therefore, testing embedded memories has become crucial and is taking significant test costs. Researchers have proposed multiple approaches considering these factors to test the nanometer memories. They include using new fault models, march algorithms, memory built-in self-test (MBIST) architectures, and validation strategies. This paper surveys the methodologies presented in recent times. It discusses the core principles used in them, along with benefits. Finally, it discusses key opens in each and offers the scope for future research

Research on the impact of sliding window and differencing procedures on the support vector regression model for load forecasting

Load forecasting is a critical aspect of energy management and grid operations. Machine learning techniques as support vector regression (SVR), have been widely used for load forecasting. However, the effectiveness of SVR is highly dependent on its hyperparameters, including the error sensitivity parameter, penalty factor, and kernel function. Furthermore, as the load data follows a time series pattern, the accuracy of the SVR model is influenced by the data's characteristics. In this regard, the present study aims to investigate the impact of combining the sliding window procedure and differencing the input data on the prediction accuracy of the SVR model. The study utilizes daily maximum load data from the Queensland and Victoria states in Australia. The analyses revealed that while the sliding window procedure had a minimal impact on the prediction results, the differencing of the input data significantly improved the accuracy of the predictions

Experimental and simulation analysis for insulation deterioration and partial discharge currents in nanocomposites of power cables

Partial discharge (PD) has a well-established relationship with the lifespan of power cables. This paper has been treated the polyvinyl chloride (PVC) with specified nanoparticles for enhancing dielectric degradation and reducing partial discharge current to extending lifespan of power cables. It has been succeeded to creation new polyvinyl chloride nanocomposites that have been synthesized experimentally via using solution-gel (SOL-GEL) technique and have high featured electric and dielectric properties. The validation of nanoparticles penetration inside polyvinyl chloride during synthesis process have been constructed and tested via scanning electron microscope (SEM) images. The partial discharge current mechanisms in polyvinyl chloride nanocomposites have also been simulated in this work by using MATLAB® software. This paper has explored the characterization of partial discharge current for variant void patterns (air, water, rubber impurity) in polyvinyl chloride nanocomposites insulations of power cables to clarify the benefit of filling different nanoparticles (Clay, MgO, ZnO, and BaTiO3) with varied patterns inside power cables dielectrics. A comparative study has been done for different partial discharges patterns to propose characterization of partial discharges using nanoparticles of appropriate types and concentrations

A simplified classification computational model of opinion mining using deep learning

Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation