Memory Machines Research Articles

An Improved Variable Flux Memory Machine Topology With Multi-Stage Magnetization Strategy

An Improved Variable Flux Memory Machine Topology With Multi-Stage Magnetization Strategy

A Series-Parallel Variable Reluctance Memory Machine With Zero-Sequence Current Adjustment Magnetization Capability

A Series-Parallel Variable Reluctance Memory Machine With Zero-Sequence Current Adjustment Magnetization Capability

Triangle Centrality

Triangle centrality is introduced for finding important vertices in a graph based on the concentration of triangles surrounding each vertex. It has the distinct feature of allowing a vertex to be central if it is in many triangles or none at all. Given a simple, undirected graph \(G=(V,E)\) with \(n=|V|\) vertices and \(m=|E|\) edges, let \(\triangle(v)\) and \(\triangle(G)\) denote the respective triangle counts of \(v\) and \(G\) . Let \(N(v)\) be the neighborhood set of \(v\) . Respectively, \(N_{\triangle}(v)\) and \(N_{\triangle}[v]=\{v\}\cup N_{\triangle}(v)\) denote the set of neighbors that are in triangles with \(v\) and the closed set including \(v\) . Then the triangle centrality for a vertex \(v\) is \(\begin{align*}TC(v)=\frac{\frac{1}{3}\sum_{u\in N_{\triangle}[v]}\triangle(u)+\sum_{w\in\{N(v)\setminus N_{\triangle}(v)\}}\triangle(w)}{\triangle(G)}.\end{align*}\) We show experimentally that triangle centrality is broadly applicable to many different types of networks. Our empirical results demonstrate that 30% of the time triangle centrality identified central vertices that differed with those found by five well-known centrality measures, which suggests novelty without being overly specialized. It is also asymptotically faster to compute on sparse graphs than all but the most trivial of these other measures. We introduce optimal algorithms that compute triangle centrality in \(O(m\overline{\delta})\) time and \(O(m+n)\) space, where \(\overline{\delta}\leq O(\sqrt{m})\) is the average degeneracy introduced by Burkhardt, Faber, and Harris (2020). In practical applications, \(\overline{\delta}\) is much smaller than \(\sqrt{m}\) so triangle centrality can be computed in nearly linear time. On a Concurrent Read Exclusive Write (CREW) Parallel Random Access Machine (PRAM), we give a near work-optimal parallel algorithm that takes \(O(\log n)\) time using \(O(m\sqrt{m})\) CREW PRAM processors. In MapReduce, we show it takes four rounds using \(O(m\sqrt{m})\) communication bits and is therefore optimal. We also derive a linear algebraic formulation of triangle centrality which can be computed in \(O(m\overline{\delta})\) time on sparse graphs.

Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model

A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain’s integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.

Real-time solar PV generation in a building using LSTM-based time series forecasting

The increasing demand for renewable energy has forced the researchers to lay importance on studying the behavior of these sources by monitoring the operational data and performing data analysis. The application of data analytics to renewable energy is one of the most powerful methods to maintain the system's reliability and stability. As the power system becomes more complex the necessity to perform data analytics also increases. When data analytic techniques are applied to solar energy generations through Photovoltaic (PV) dataset, the possible behavior of PV generation performance which is affected by changes in environmental conditions can be predicted and further analytical approaches allow us to detect possible PV panel and inverter failures. This paper is an attempt towards applying the intelligent data analytics approaches to solar PV generation of a real-time photovoltaic plant. The main purpose of the data analytics platform is to analyze the energy yield, assess the PV system performance, and forecast solar PV generation. In this article, Long Short-Term Memory (LSTM) machine learning model is developed to assess and interpret the available information from the gathered data of the PV plant. The proposed model is a sequence-to-sequence regression-based forecasting model which performs time series forecasting in a real-time 100 kW PV plant in the University building of SRM Institute of Science and Technology, Kattankulathur, Tamilnadu.

Enhancing machine failure prediction with a hybrid model approach

<span lang="EN-US">The industrial sector is undergoing a substantial transformation by embracing predictive maintenance approaches, aiming to minimize downtime and reduce operational expenses. This transformative shift involves the incorporation of machine learning techniques to refine the accuracy of predicting machinery failures. In this article, we delve into an in-depth exploration of machine failure prediction, employing a hybrid model amalgamating long short-term memory (LSTM) and support vector machine (SVM). Our comprehensive study meticulously assesses the hybrid model’s performance, comparing it with standalone LSTM and SVM models across three distinct datasets. The results showcase that the hybrid model outperformed, providing the modest dependable, and highest F1-score values in our evaluation.</span>

Sensorless Control of Variable Flux Memory Machines Based on Improved Extended EMF Model and Adaptive Extended State Observer

Sensorless Control of Variable Flux Memory Machines Based on Improved Extended EMF Model and Adaptive Extended State Observer

Comparative Investigation on Dual Three-Phase Variable Flux Memory Machines for Vehicle Tractions

Comparative Investigation on Dual Three-Phase Variable Flux Memory Machines for Vehicle Tractions

A Novel Dual Magnetizing Current Controller to Mitigate Speed Fluctuation During Magnetization State Manipulation for Variable Flux Memory Machines

A Novel Dual Magnetizing Current Controller to Mitigate Speed Fluctuation During Magnetization State Manipulation for Variable Flux Memory Machines

Finite Set Model Predictive Current Control for Variable Flux Memory Machine With a Novel Three-Layer Optimization Strategy

Finite Set Model Predictive Current Control for Variable Flux Memory Machine With a Novel Three-Layer Optimization Strategy

A Novel Counter-Rotating Axial-Flux Permanent Magnet Memory Machine With Dual-Rotor

A Novel Counter-Rotating Axial-Flux Permanent Magnet Memory Machine With Dual-Rotor

Novel Magnetization State Control Method Utilizing d-Axis Current to Eliminate Unintentional Demagnetization of Low-Coercive Force PM for Variable Flux Memory Machine

Novel Magnetization State Control Method Utilizing <i>d</i>-Axis Current to Eliminate Unintentional Demagnetization of Low-Coercive Force PM for Variable Flux Memory Machine

In the time loop: Data remanence in main memory of virtual machines

Data remanence in the physical memory of computers, i.e., the fact that data remains temporarily in memory even after power is cut, is a well-known issue which can be exploited for recovering cryptographic keys and other data in forensic investigations. Since virtual machines in many aspects mimic their physical counterparts, we investigate whether data remanence is also observable in virtual machines. Using KVM as an example of virtualization technology, we experimentally show that it is common for a substantial amount of volatile data to remain in the memory of virtual machines after a reboot. In digital forensic analysis scenarios such as malware analysis using virtual machines, our observations imply high risks of evidence contamination if no precautions are taken. So while the symptoms of data remanence in virtual machines are similar to physical machines, the implications for digital forensic analysis appear very different.

Multi-fidelity fusion for soil classification via LSTM and multi-head self-attention CNN model

An effective soil classification method is essential for soil stratigraphy interpretation. Traditional methods rely on classification systems in design codes or empirical formulae based on single source data, which may not be accurate or suitable for every site. Recent advancements in data fusion techniques and deep learning have shown promising results in a wide range of practical domains, which provides motivation to explore its potential application in soil classification. To that end, this paper introduces a novel machine learning architecture using boosting long-short term memory (LSTM) machine to map low fidelity cone penetration test (CPT) data to high fidelity laboratory test (LT) data which are then fused and fed into a multi-head self-attention convolutional neural network (MSCNN) for soil classification. A rigorous loss function is developed to train a LSTM-MSCNN model in an end-to-end workflow. The MSCNN classifier is benchmarked against traditional machine learning methods to demonstrate its superior outperformance, with ablation analyses exploring the related merits of the attention mechanism and the influence of attention heads on classification results. The results reveal a significant improvement in the prediction of true ground profiles using the proposed LSTM-MSCNN model over conventional methods.

A multi-factor combined traffic flow prediction model with secondary decomposition and improved entropy weight method

Traffic flow (TF) prediction is vital in traffic systems. Aiming at the effect of noise on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an improved CEEMDAN based on amplitude-aware permutation entropy (AAPE), named ICEEMDAN, is proposed. Aiming at the parameter selection problems on variational mode decomposition (VMD), VMD improved by pelican optimization algorithm (POA) is proposed, called PVMD. Aiming at the parameter selection problems of bidirectional long short-term memory (BiLSTM) and kernel extreme learning machine (KELM), BiLSTM improved by sea-horse optimizer (SHO), named SHOBiLSTM, and KELM improved by nutcracker optimizer algorithm (NOA), named NOAKELM are proposed, respectively. Since the chaotic characteristics of TF, multi-factor combined TF prediction model with ICEEMDAN, PVMD, SHOBiLSTM, NOAKELM, improved entropy weight method is proposed. First, use ICEEMDAN to decompose TF and get intrinsic mode functions (IMFs). Second, refined composite multiscale dispersion entropy (RCMDE) is used to divide IMFs into the fuzzy and detail components, fuzzy components are secondarily decomposed by PVMD, IMFs and factors are multi-factor predicted by SHOBiLSTM and NOAKELM. Finally, improved entropy weight method is proposed to weight SHOBiLSTM and NOAKELM results. TF of the UK highway proves the superiority. Experiments indicate that the proposed model is better than other nine models with RMSE, MAE, MAPE and R2 of 29.5868, 22.0431, 0.0701 and 0.9947 for TF I. The outcomes suggest this proposed model precedes nine models at 99% confidence level, providing data basis and theoretical for travel plans and traffic system.

Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management.

Novel Magnetization State Closed-Loop Control for Variable Flux Memory Machine

Novel Magnetization State Closed-Loop Control for Variable Flux Memory Machine

Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine

As climate change increases the risk of extreme rainfall events, concerns over flood management have also increased. To recover quickly from flood damage and prevent further consequential damage, flood waste prediction is of utmost importance. Therefore, developing a rapid and accurate prediction of flood waste generation is important in order to reduce disaster. Several approaches of flood waste classification have been proposed by various researchers, however only a few focus on prediction of flood waste. In this study, a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) approach is adapted to address these challenges. Two different raw datasets were obtained from the “Advancing Sustainable Materials Management: Facts and Figures 2015” source. The datasets were for 9 years (1960, 1970, 1980, 1990, 2000, 2005, 2010, 2014, 2015), and are labelled as the materials generated in the Municipal Waste Stream from 1960 to 2015 and the materials Recycled and Composted in Municipal Solid Waste from 1960 to 2015. The waste types were grouped as paper and paperboard (PP), glass (GI), metals (Mt), plastics (PI), rubber and leather (RL), textiles (Tt), wood (Wd), food (Fd), yard trimmings (YT) and miscellaneous inorganic wastes (IW).

A physics‐informed machine learning prediction for thermal analysis in a convective‐radiative concave fin with periodic boundary conditions

AbstractThe present research is focused on the inspection of unsteady heat dissipation through a radiative‐convective concave profiled fin along with the periodic boundary conditions. Additionally, the long‐short‐term memory machine learning (LSTM‐ML) approach is used in this study to examine the periodic fluctuation in the temperature of the fin. The current research is devoted to solving the highly non‐linear equation using a physics‐informed neural network (PINN) approach. Using the proper dimensionless terms, the associated fin problem is transformed into a non‐dimensional system, and the resulting partial differential equation (PDE) is then numerically solved using the finite difference method (FDM). Using the data‐driven LSTM‐ML technique, the time‐dependent periodic heat transmission in the concave fin is also examined. The impact of various factors on the temperature profile of the concave extended surface is explained, and the results are visually displayed. The temperature distribution in the concave fin diminishes as the convection‐conduction parameter and radiation‐conduction parameter rise. As the amplitude and thermal conductivity parameters improve, so does the temperature of the concave fin. Furthermore, it is demonstrated that although LSTM‐ML and PINN closely matched the FDM findings during the training domain, only PINN with designed characteristics has the potential to predict accurately beyond the trained region by capturing the physics of the problem.

Multi‐objective hierarchical optimisation design and experimental verification of an alterable‐magnetic‐circuit variable‐flux memory machine

AbstractRare‐earth permanent magnet synchronous machines face challenges in manipulating their magnetic fields, which hinders the ability to extend the operation speed range. Moreover, this inflexibility leads to reduced efficiency in high‐speed scenarios when the machine is under flux‐weakening control and increases the risk of the magnets becoming demagnetised. The authors propose an alterable‐magnetic‐circuit variable‐flux memory machine (AMC‐VFMM) and a multi‐objective hierarchical optimisation method is conducted to optimise the machine. Firstly, the topology and alterable‐magnetic‐circuit principle of the proposed AMC‐VFMM are introduced. Then, optimisation objectives including torque production capability, flux regulation capability, and resisting unintentional demagnetisation capability are defined, and the hierarchical optimisation approach is established by stratifying the optimisation objectives and variables through the sensitivity analysis. Finite element analysis indicates that electromagnetic performances of the optimised design scheme are significantly enhanced. The bench test of the prototype demonstrates the superiority of the proposed AMC‐VFMM and validates the effectiveness of the optimisation design method.