bluuebunny/crossref_metadata_2025 huggingface dataset:
Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis
Authors: Pei Di , Yue Jianhai , Jiao Jing
Published: March 2024

Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn–kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method.
Entanglement-Based CV-QKD with Information Reconciliation over Entanglement-Assisted Link
Authors: Djordjevic Ivan B. , Nafria Vijay
Published: March 2024

An entanglement-based continuous variable (CV) QKD scheme is proposed, performing information reconciliation over an entanglement-assisted link. The same entanglement generation source is used in both raw key transmission and information reconciliation. The entanglement generation source employs only low-cost devices operated in the C-band. The proposed CV-QKD scheme with information reconciliation over an entanglement-assisted link significantly outperforms the corresponding CV-QKD scheme with information reconciliation over an authenticated public channel. It also outperforms the CV-QKD scheme in which a classical free-space optical communication link is used to perform information reconciliation. An experimental demonstration over the free-space optical testbed established at the University of Arizona campus indicates that the proposed CV-QKD can operate in strong turbulence regimes. To improve the secret key rate performance further, adaptive optics is used.
Structural Properties of the Wyner–Ziv Rate Distortion Function: Applications for Multivariate Gaussian Sources
Authors: Gkagkos Michail , Charalambous Charalambos D.
Published: March 2024

The main focus of this paper is the derivation of the structural properties of the test channels of Wyner’s operational information rate distortion function (RDF), R¯(ΔX), for arbitrary abstract sources and, subsequently, the derivation of additional properties for a tuple of multivariate correlated, jointly independent, and identically distributed Gaussian random variables, {Xt,Yt}t=1∞, Xt:Ω→Rnx, Yt:Ω→Rny, with average mean-square error at the decoder and the side information, {Yt}t=1∞, available only at the decoder. For the tuple of multivariate correlated Gaussian sources, we construct optimal test channel realizations which achieve the informational RDF, R¯(ΔX)=▵infM(ΔX)I(X;Z|Y), where M(ΔX) is the set of auxiliary RVs Z such that PZ|X,Y=PZ|X, X^=f(Y,Z), and E{||X−X^||2}≤ΔX. We show the following fundamental structural properties: (1) Optimal test channel realizations that achieve the RDF and satisfy conditional independence, PX|X^,Y,Z=PX|X^,Y=PX|X^,EX|X^,Y,Z=EX|X^=X^. (2) Similarly, for the conditional RDF, RX|Y(ΔX), when the side information is available to both the encoder and the decoder, we show the equality R¯(ΔX)=RX|Y(ΔX). (3) We derive the water-filling solution for RX|Y(ΔX).
Conformity and Mass Media Influence in the Sznajd Model on Regular Lattices
Authors: Wołoszyn Maciej
Published: March 2024

The polarization of opinions and difficulties in reaching a consensus are central problems of many modern societies. Understanding the dynamics governing those processes is, therefore, one of the main aims of sociophysics. In this work, the Sznajd model of opinion dynamics is investigated with Monte Carlo simulations performed on four different regular lattices: triangular, honeycomb, and square with von Neumann or Moore neighborhood. The main objective is to discuss the interplay of the probability of convincing (conformity) and mass media (external) influence and to provide the details of the possible phase transitions. The results indicate that, while stronger bonds and openness to discussion and argumentation may help in reaching a consensus, external influence becomes destructive at different levels depending on the lattice.
Robust Free-Space Optical Communication Utilizing Polarization for the Advancement of Quantum Communication
Authors: Savino Nicholas , Leamer Jacob , Saripalli Ravi , Zhang Wenlei , Bondar Denys , Glasser Ryan
Published: March 2024

Free-space optical (FSO) communication can be subject to various types of distortion and loss as the signal propagates through non-uniform media. In experiment and simulation, we demonstrate that the state of polarization and degree of polarization of light passed though underwater bubbles, causing turbulence, is preserved. Our experimental setup serves as an efficient, low cost alternative approach to long distance atmospheric or underwater testing. We compare our experimental results with those of simulations, in which we model underwater bubbles, and separately, atmospheric turbulence. Our findings suggest potential improvements in polarization based FSO communication schemes.
Non-Thermal Solar Wind Electron Velocity Distribution Function
Authors: Yoon Peter H. , López Rodrigo A. , Salem Chadi S. , Bonnell John W. , Kim Sunjung
Published: March 2024

The quiet-time solar wind electrons feature non-thermal characteristics when viewed from the perspective of their velocity distribution functions. They typically have an appearance of being composed of a denser thermal “core” population plus a tenuous energetic “halo” population. At first, such a feature was empirically fitted with the kappa velocity space distribution function, but ever since the ground-breaking work by Tsallis, the space physics community has embraced the potential implication of the kappa distribution as reflecting the non-extensive nature of the space plasma. From the viewpoint of microscopic plasma theory, the formation of the non-thermal electron velocity distribution function can be interpreted in terms of the plasma being in a state of turbulent quasi-equilibrium. Such a finding brings forth the possible existence of a profound inter-relationship between the non-extensive statistical state and the turbulent quasi-equilibrium state. The present paper further develops the idea of solar wind electrons being in the turbulent equilibrium, but, unlike the previous model, which involves the electrostatic turbulence near the plasma oscillation frequency (i.e., Langmuir turbulence), the present paper considers the impact of transverse electromagnetic turbulence, particularly, the turbulence in the whistler-mode frequency range. It is found that the coupling of spontaneously emitted thermal fluctuations and the background turbulence leads to the formation of a non-thermal electron velocity distribution function of the type observed in the solar wind during quiet times. This demonstrates that the whistler-range turbulence represents an alternative mechanism for producing the kappa-like non-thermal distribution, especially close to the Sun and in the near-Earth space environment.
PyDTS: A Python Toolkit for Deep Learning Time Series Modelling
Authors: Schirmer Pascal A. , Mporas Iosif
Published: March 2024

In this article, the topic of time series modelling is discussed. It highlights the criticality of analysing and forecasting time series data across various sectors, identifying five primary application areas: denoising, forecasting, nonlinear transient modelling, anomaly detection, and degradation modelling. It further outlines the mathematical frameworks employed in a time series modelling task, categorizing them into statistical, linear algebra, and machine- or deep-learning-based approaches, with each category serving distinct dimensions and complexities of time series problems. Additionally, the article reviews the extensive literature on time series modelling, covering statistical processes, state space representations, and machine and deep learning applications in various fields. The unique contribution of this work lies in its presentation of a Python-based toolkit for time series modelling (PyDTS) that integrates popular methodologies and offers practical examples and benchmarking across diverse datasets.
Towards Internationalization: Exploring Economic Diplomacy in the Middle East (GCC)
Authors: Pontes Daniel , Santos Vasco , Samões Orlando , Wang Shuangao , Figueiredo Ronnie
Published: March 2024

Internationalization is a crucial process for companies seeking growth and expansion in foreign markets, especially in the Middle East, where economies have been developing and diversifying business opportunities, seeing it as an attractive destination to expand their operations. This study presents a comprehensive analysis of the internationalization process of economic diplomacy by exploring the experiences of renowned Portuguese companies within the Gulf Cooperation Council. The Gulf Cooperation Council countries are integral players in the Middle East market, characterized as rentier states that are heavily reliant on oil and gas revenues and possess varying levels of economic and military strength, with Saudi Arabia and the UAE being the most prominent. The focus group method was applied in the qualitative research. It contributes to reinforcing the literature on internationalization processes, economic diplomacy, and the Middle East market. The findings provide valuable guidance to Portuguese companies, policymakers, and economic diplomats involved in promoting and facilitating international trade and investment.
Game and Simulation Stimulate Conceptual Change about Molecular Emergence in Different Ways, with Potential Cultural Implications
Authors: Gauthier Andrea
Published: March 2024

Many undergraduate students hold robust misconceptions about the emergent nature of molecular processes, believing them to be directed rather than random. Interactive simulations might help transform such misconceptions by visualizing stochastic processes in a time-independent medium and enabling students to manipulate the environment to test their naïve hypotheses. Furthermore, game-based learning (GBL) might enhance the effectiveness of such simulations by promoting productive negativity (PN), i.e., learning from failure. In a randomized controlled trial with pre-test, post-test and delayed one-year follow-up, undergraduates (n = 84) engaged with either a GBL or interactive simulation (SIM) environment for 20–45 min and were compared to a baseline group (n = 138). GBL (p = 0.035) and SIM (p = 0.069) resolved more misconceptions than baseline but did not differ from each other (p = 0.992). GBL group also trended toward more positive long-term conceptual change. In-game interactions generated in response to PN were predictive of conceptual change in the GBL group alone, suggesting that PN may only be effective when supported by game design. Participants’ native English-speaking status had a moderating effect, with native-speakers performing well in GBL and poorly in SIM environment, while the opposite was true for non-native-speakers, which, as discussed herein, may be aligned with cultural differences in acceptability of GBL. The GBL intervention generated longer voluntary use (p = 0.005), especially amongst frequent game-players. The results inform how GBL/SIM approaches can implement PN as a mechanism for conceptual change about molecular emergence.
Broadband Beam-Scanning Phased Array Based on Microwave Photonics
Authors: Luan Yuchen , Yang Tian , Ren Jian , Li Ruoming , Zhang Zhuo
Published: March 2024

A one-dimensional active broadband phased array based on microwave photonics that works in the Ku band is proposed to achieve a large instantaneous bandwidth. The phased array uses a feeding network based on microwave photonics to provide the true time delay and a wide operating bandwidth. The array is mainly composed of a broadband horn antenna, an RF transmitting/receiving module, an optical network module, and a temperature control module. The form of a horn was selected for the antenna unit, and it was fed through a waveguide to obtain a wide operating bandwidth. An optical fiber delay line that could realize the true time delay at different frequencies was adopted for the time-delay module of the optical network. To obtain a large time delay and small quantization error, a hybrid time-delay diagram utilizing electrical and optical time delays was used in the design. In addition, a temperature control module was added to the antenna system to enhance the stability of the photonic time-delay module. For verification, a prototype of the presented antenna system was designed, fabricated, and measured. The experimental results showed that the optical phased array antenna was able to scan ±20° from 12 GHz to 17 GHz, and the beam pointing did not appear to be offset over the wide operating bandwidth.
Acoustic Target Strength of Thornfish (Terapon jarbua) Based on the Kirchhoff-Ray Mode Model
Authors: Li Bin , Liu Jiahao , Gao Xiujing , Huang Hongwu , Wang Fang , Huang Zhuoya
Published: March 2024

Thornfish (Terapon jarbua) is a significantly commercial species inhabiting the shallow coastal waters of the Indo-Pacific Ocean. To achieve effective underwater acoustic (UWA) monitoring on the abundance and population dynamics of this species, the comprehensive target strength (TS) characteristics should be investigated and understood. In this study, the Kirchhoff-ray mode (KRM) model was adopted to evaluate and analyze the acoustic TS of T. jarbua and its variations with the sound wave frequency, pitch angle distributions as well as morphological characteristics in the South China Sea. A total of 19 samples were captured and evaluated at four types of frequencies of 38 kHz, 70 kHz, 120 kHz, and 200 kHz. The results demonstrated that the TS of T. jarbua varied with the pitch angle shifts, and the number of secondary TS peaks increased as the increasing frequency accordingly. Two classic pitch angle distributions that included N[−5°, 15°] and N[0°, 10°] were adopted to calculate the average TS of T. jarbua. The fitted TS-L regression formulations and the standard b20 form equations were determined at different pitch angle distributions as well as frequencies. These results could support the accurate and reliable UWA abundance estimation in the South China Sea to facilitate a better understanding of the abundance and population dynamics of T. jarbua.
Research on the Short-Term Economic Dispatch Method of Power System Involving a Hydropower-Photovoltaic-Pumped Storage Plant
Authors: Guo Liang , Liu Shudi , Xi Litang , Zhang Guofang , Liu Ziqi , Zeng Qi , Lü Feipeng , Wang Yuhong
Published: March 2024

The auxiliary regulation capacity of pumped-storage power stations can be utilized as an effective method to regulate the output of a hydro-photovoltaic complementary system, further mitigating the power fluctuations of the system and enhancing the photovoltaic absorption. This study aims to minimize power fluctuations and maximize the economic benefits of electricity generation in a hydropower-photovoltaic-pumped-storage complementary system (HPPCS), which are treated as the objective functions. It explores the participation of the HPPCS in grid active power balance auxiliary services. By modulating the participation ratio of the HPPCS in the grid’s active balance service, the system output is aligned to fluctuate proportionally with the daily load curve trend. Consequently, a short-term economic dispatch model for the integrated HPPCS is developed. The case study focuses on the considerable impact of weather conditions on photovoltaic (PV) power generation. In this model, the outputs of cascading hydro-power stations and pumped-storage power stations are considered as the decision variables. A decomposition-based multi-objective evolutionary algorithm is applied to derive an optimized intra-day dispatch Pareto solution set for the cascading HPPCS in each of these scenarios. Additionally, this study compares the Pareto solution sets for the HPPCS in various extents of its participation in grid auxiliary services. The results of the case study suggest that the system is capable of timely adjustments during the peak and trough periods of load demand. Considering the economic benefits, it enables the pumped-storage station to generate electricity for the grid during periods of high electricity prices and to store energy by pumping water when prices are low.
Advancing User Privacy in Virtual Power Plants: A Novel Zero-Knowledge Proof-Based Distributed Attribute Encryption Approach
Authors: Yang Ruxia , Gao Hongchao , Si Fangyuan , Wang Jun
Published: March 2024

In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants.
End-to-End Dataset Collection System for Sport Activities
Authors: Fresta Matteo , Bellotti Francesco , Capello Alessio , Dabbous Ali , Lazzaroni Luca , Ansovini Flavio , Berta Riccardo
Published: March 2024

Datasets are key to developing new machine learning-based applications but are very costly to prepare, which hinders research and development in the field. We propose an edge-to-cloud end-to-end system architecture optimized for sport activity recognition dataset collection and application deployment. Tests in authentic contexts of use in four different sports have revealed the system’s ability to effectively collect machine learning-usable data, with an energy consumption compatible with the timeframe of most of the sport types. The proposed architecture relies on a key feature of the Measurify internet of things framework for the management of measurement data (i.e., .csv dataset management) and supports a workflow designed for efficient data labeling of signal timeseries. The architecture is independent of any specific sport, and a new dataset generation application can be set up in a few days, even by novice developers. With a view to concretely supporting the R&D community, our work is released open-source.
Identifying System Non-Linearities by Fusing Signal Bispectral Signatures
Authors: Koukiou Georgia
Published: March 2024

Higher-order statistics investigate the phase relationships between frequency components, an aspect which cannot be treated using conventional spectral measures such as the power spectrum. Among the widely used higher-order statistics, the bispectrum ranks prominently. By delving into higher-order correlations, the bispectrum offers a means of extracting additional merits and insights from frequency coupling, enhancing our understanding of complex signal interactions. This analytical approach overcomes the limitations of traditional methods, providing a more comprehensive view of the complex relationships within the frequency domain. In this paper, the extensive use of the bispectrum in various scientific and technical areas is firstly emphasized by presenting very recent applications. The main scope of this work is to investigate the consequences of various non-linearities in the creation of phase couplings. Specifically, the quadratic, the cubic and the logarithmic non-linearities are examined. In addition, simple recommendations are given on how the underlying nonlinearity could be detected. The total approach is novel, considering the capability to distinguish from the bispectral content if two non-linearities are simultaneously present.
Improved Diagnostic Approach for BRB Detection and Classification in Inverter-Driven Induction Motors Employing Sparse Stacked Autoencoder (SSAE) and LightGBM
Authors: Khan Muhammad Amir , Asad Bilal , Vaimann Toomas , Kallaste Ants
Published: March 2024

This study introduces an innovative approach to diagnostics, employing a unique combination of techniques including a stratified group K-fold cross-validation method and a sparse stacked autoencoder (SSAE) alongside LightGBM. By examining signatures derived from motor current, voltage, speed, and torque, the framework aims to effectively detect and classify broken rotor bars (BRBs) within inverter-fed induction machines. In this kind of cross-validation method, class labels and grouping factors are spread out across folds by distributing motor operational data attributes equally over target label stratification and extra grouping information. By integrating SSAE and LightGBM, a gradient-boosting framework, we elevate the precision and efficacy of defect diagnosis. The SSAE feature extraction algorithm proves to be particularly effective in identifying small BRB signatures within motor operational data. Our approach relies on comprehensive datasets collected from motor systems operating under diverse loading conditions, ranging from 0% to 100%. Using a sparse stacked autoencoder, the model lowers the dimensionality and noise of the motor fault data. It then sends the cleaned data to the LightGBM network for fault diagnosis. LightGBM leverages the attributes of the sparse stacked autoencoder to showcase the distinctive qualities associated with BRBs. This integration offers the potential to improve defect identification by furnishing input representations that are both more precise and more concise. The proposed model (SSAE with LightGBM) was trained using 80% of the data, while the remaining 20% was used for testing. To validate the proposed architecture, we evaluate the accuracy, precision, recall, and F1-scores of the results using motor global signals, with the help of confusion matrices with receiver operating characteristic (ROC) curves. Following the training of a new LightGBM model with refined hyperparameters through Bayesian optimization, we proceed to conduct the final classification utilizing the optimal feature subset. Evaluation of the test dataset indicates that the BRBs diagnostic framework facilitates the detection and classification of issues with induction motor BRBs, achieving accuracy rates of up to 99% across all loading conditions.
A Ship Detection Method in Infrared Remote Sensing Images Based on Image Generation and Causal Inference
Authors: Zhang Yongmei , Li Ruiqi , Du Zhirong , Ye Qing
Published: March 2024

Aiming at the problems of scarce public infrared ship data and the difficulty of obtaining them, a ship image generation method based on improved StyleGAN2 is proposed. The mapping network in StyleGAN2 is replaced with a Variational Auto-Encoder, enabling the generated latent variables to retain original image information while reducing computational complexity. This benefits the construction of the image. Additionally, a self-attention mechanism is introduced to capture dependency information between distant features, generating more detailed object representation. By reducing the number of input noises in the generator, the quality of the generated images is effectively enhanced. Experimental results show that the images generated by the proposed method closely resemble the structure, content and data distribution of the original real images, achieving a higher level of detail. Regarding ship detection methods based on deep learning, they often suffer from complex detection networks, numerous parameters, poor interpretability, and limited real-time performance. To address these issues, a lightweight multi-class ship detection method for infrared remote sensing images is designed. This method aims to improve real-time performance while maintaining accurate ship detection. Based on ship detection, an interpretable ship detection approach based on causal reasoning is presented. By integrating singular value decomposition with the Transformer architecture, the model focuses on causal ship features associated with labels in the images. This enhances the model’s robustness against non-causal information, such as background details, and improves its interpretability.
Evaluation and Mitigation of Weight-Related Single Event Upsets in a Convolutional Neural Network
Authors: Cai Yulong , Cai Ming , Wu Yanlai , Lu Jian , Bian Zeyu , Liu Bingkai , Cui Shuai
Published: March 2024

Single Event Upsets (SEUs) are most likely to cause bit flips within the trained parameters of a convolutional neural network (CNN). Therefore, it is crucial to analyze and implement hardening techniques to enhance their reliability under radiation. In this paper, random fault injections into the weights of LeNet-5 were carried out in order to evaluate and propose strategies to improve the reliability of a CNN. According to the results of an SEU fault injection, the accuracy of the CNN can be classified into the following three categories: benign conditions, poor conditions, and critical conditions. Two efficient methods for mitigating weight-related SEUs are proposed, as follows: weight limiting and Triple Modular Redundancy (TMR) for the critical bit of the critical layer. The hardening results show that when the number of SEU faults is small, the weight limiting almost completely eliminates the critical and poor conditions of LeNet-5’s accuracy. Additionally, even when the number of SEU faults is large enough, combining the weight limiting and TMR methods for the critical bit of the critical layer can retain the occurrence rate of benign conditions at 98%, saving 99.3% of the hardware resources compared to the Full-TMR hardening method.
Real-Time Human Movement Recognition Using Ultra-Wideband Sensors
Authors: Noh Minseong , Ahn Heungju , Lee Sang C.
Published: March 2024

This study introduces a methodology for the real-time detection of human movement based on two legs using ultra-wideband (UWB) sensors. Movements were primarily categorized into four states: stopped, walking, lingering, and the transition between sitting and standing. To classify these movements, UWB sensors were used to measure the distance between the designated point and a specific point on the two legs in the human body. By analyzing the measured distance values, a movement state classification model was constructed. In comparison to conventional vision/laser/LiDAR-based research, this approach requires fewer computational resources and provides distinguished real-time human movement detection within a CPU environment. Consequently, this research presents a novel strategy to effectively recognize human movements during human–robot interactions. The proposed model effectively discerned four distinct movement states with classification accuracy of around 95%, demonstrating the novel strategy’s efficacy.
Task-Aligned Oriented Object Detection in Remote Sensing Images
Authors: Qian Xiaoliang , Zhao Jiakun , Wu Baokun , Chen Zhiwu , Wang Wei , Kong Han
Published: March 2024

Oriented object detection (OOD) can recognize and locate various objects more precisely than horizontal object detection; however, two problems have not been satisfactorily resolved so far. Firstly, the absence of interactions between the classification and regression branches leads to inconsistent performance in the two tasks of object detection. Secondly, the traditional convolution operation cannot precisely extract the features of objects in extremely aspect ratio in remote sensing images (RSIs). To address the first problem, the task-aligned detection module (TADM) and the task-aligned loss function (TL) are proposed in this paper. On the one hand, a spatial probability map and a spatial offset map are inferred from the shared features in the TADM and separately incorporated into the classification and regression branches to obtain consistency in the two tasks. On the other hand, the TL combines employing the generalized intersection over union (GIoU) metric with classification loss to further enhance the consistency in the two tasks. To address the second problem, a two-stage detection framework based on alignment convolution (TDA) is proposed. The features extracted from the backbone network are refined through alignment convolution in the first stage, and the final OOD results are inferred from refined features in the second stage. The ablation study verifies the effectiveness of the TADM, TL, and TDA. The comparisons with other advanced methods, on two RSI benchmarks, demonstrate the overall effectiveness of our method.
Analyzing Amazon Products Sentiment: A Comparative Study of Machine and Deep Learning, and Transformer-Based Techniques
Authors: Ali Hashir , Hashmi Ehtesham , Yayilgan Yildirim Sule , Shaikh Sarang
Published: March 2024

In recent years, online shopping has surged in popularity, with customer reviews becoming a crucial aspect of the decision-making process. Reviews not only help potential customers make informed choices, but also provide businesses with valuable feedback and build trust. In this study, we conducted a thorough analysis of the Amazon reviews dataset, which includes several product categories. Our primary objective was to accurately classify sentiments using natural language processing, machine learning, ensemble learning, and deep learning techniques. Our research workflow encompassed several crucial steps. We explore data collection procedures; preprocessing steps, including normalization and tokenization; and feature extraction, utilizing the Bag-of-Words and TF–IDF methods. We conducted experiments employing a variety of machine learning algorithms, including Multinomial Naive Bayes, Random Forest, Decision Tree, and Logistic Regression. Additionally, we harnessed Bagging as an ensemble learning technique. Furthermore, we explored deep learning-based algorithms, such as CNNs, Bidirectional LSTM, and transformer-based models, like XLNet and BERT. Our comprehensive evaluations, utilizing metrics such as accuracy, precision, recall, and F1 score, revealed that the BERT algorithm outperformed others, achieving an impressive accuracy rate of 89%. This research provides valuable insights into the sentiment analysis of Amazon reviews, aiding both consumers and businesses in making informed decisions and enhancing product and service quality.
Self-Supervised Hypergraph Learning for Knowledge-Aware Social Recommendation
Authors: Li Munan , Li Jialong , Yang Liping , Ding Qi
Published: March 2024

Social recommendations typically utilize social relationships and past behaviors to predict users’ preferences. In real-world scenarios, the connections between users and items often extend beyond simple pairwise relationships. Leveraging hypergraphs to capture high-order relationships provides a novel perspective to social recommendation. However, effectively modeling these high-order relationships is challenging due to limited external knowledge and noisy feedback. To tackle these challenges, we propose a novel framework called self-supervised hypergraph learning for knowledge-aware social recommendation (SHLKR). In SHLKR, we incorporated three main types of connections: behavior, social, and attribute context relationships. These dependencies serve as the basis for defining hyperedges in the hypergraphs. A dual-channel hypergraph structure is created based on these relationships. Then, the hypergraph convolution is applied to model the high-order interactions between users and items. Additionally, we adopted a self-supervised learning task to maximize the consistency between different views. It helps to mitigate the model’s sensitivity to noisy feedback. We evaluated the performance of SHLKR through extensive experiments on publicly available datasets. The results demonstrate that leveraging hypergraphs for modeling can better capture the complexity and diversity of user preferences and interactions.
Optimal Design of Voltage Equalization Ring for the 1100 kV DC Voltage Proportional Standard Device Based on the Nation Standard Device Neural Network and Grey Wolf Optimization Algorithm
Authors: Zhu Wanjun , Gao Yin , Qin Liang , Duan Yuqing , Bian Zhigang , He Min , Liu Kaipei
Published: March 2024

The DC voltage ratio standard device is an important tool for calibrating DC voltage transformers. At the 1100 kV voltage level, an increase in electric field intensity will increase the local heat generated inside the device, affecting the accuracy of its measurement. Using a suitable grading ring can even out the electric field intensity and reduce the maximum field strength to improve its measurement accuracy. This article mainly optimizes the design of the grading-ring structure of the 1100 kV DC voltage ratio standard device. First, a finite-element model of the 1100 kV DC voltage ratio standard device was built based on ANSYS; the electric field distribution around the voltage divider was calculated and analyzed, and a data set was constructed based on the calculation results. Secondly, for the optimization of electric field strength, this article presents the design of the nation standard device neural network, which learns the relationship between the structural parameters of the toroidal core and the field strength under the PyTorch 1.8 deep learning framework. Due to the strong convergence performance, few parameters, and ease of implementation of the grey wolf optimization algorithm, this study selected this algorithm to optimize the structural parameters of the grading ring. Finally, simulation examples are established in Python for validation. The experimental results indicate that the maximum field strength of the grading ring decreased from 12,161.1348 V/cm to 10,009.2881 V/cm, a reduction of 17.69%. The optimized structural parameters of the grading ring effectively reduced the electric field intensity around the 1100 kV DC voltage proportional standard device, providing a reliable and practical design approach for the selection of the DC voltage ratio standard device.
Novel Series-Parallel Phase-Shifted Full-Bridge Converters with Auxiliary LC Networks to Achieve Wide Lagging-Leg ZVS Range
Authors: Wang Yunzhi , Sun Fei , Chen Jun , Cai Huafeng , Gao Shen
Published: March 2024

Under light load conditions, the phase-shifted full-bridge (PSFB) converter often has difficulty in realizing the zero-voltage switching (ZVS) of the lagging-leg by relying on the energy of its resonant inductor; however, for the series-parallel PSFB converter applied in high-power applications, the lagging-leg still has the problem of difficult realization of ZVS. Based on this, the paper analyzes the reasons why the series-parallel PSFB converter has difficulty in achieving ZVS for the lagging-leg under light and heavy loads. Under interleaved control, the ZVS of the lagging-leg over the full load range is realized by adding an auxiliary LC branch at the midpoint of the lagging-leg of both submodules. Based on the double-bridge input-parallel-output-series (IPOS) PSFB converter, analyzing the working principle of the circuit after adding the auxiliary LC branch and extending it to the series-parallel PSFB converter. The design requirements of the LC auxiliary branch of the dual-bridge series-parallel PSFB converter are given and the effects of the LC auxiliary branch on the module operating state and device stress are analyzed. On this basis, an extension is carried out to give the working principle and design method of the auxiliary LC branch of the N-bridge series-parallel PSFB converter. Finally, a 100 kW Matlab/Simulink simulation model verifies the superior performance of the proposed LC auxiliary branch to realize the lagging-leg ZVS of the series-parallel PSFB converter under light and heavy loads and achieves a 1.09% peak efficiency improvement at rated load.
Frequency Stability Analysis and Charging Area Expanding Optimal Design for Matrix Coupling Mechanism in Wireless Power Transfer System
Authors: Jiang Jincheng , Meng Zhenya
Published: March 2024

The matrix coupling mechanism brings flexibility and reliability to energy supply in the form of multiple transmitting coils and is now favored in most wireless energy transmission applications, such as wireless charging boards for mobile phones, inspection robots, and so on. However, the process of considering the layout of the matrix coupling mechanism and the output performance based on the target in space is very complex, while also taking into account the need to avoid impedance changes caused by cross-interference between each unit, which can lead to frequency inconsistency. This paper proposes a design method based on space charging area expansion for matrix coupling mechanisms, and on this basis, it focuses on analyzing the impedance mismatch phenomenon caused by cross mutual inductance and inconsistent mutual inductance between coil units, in order to obtain the optimal topology to avoid frequency drift as much as possible. Finally, the effectiveness of the method is validated through simulation and experiments.
Anti-Interference Control Method of Buck–Boost Converter Based on High-Order Nonlinear Disturbance Observer
Authors: Chang Yufang , Yu Wenjin , Luo Mengyao , Zhou Feifei , Huang Wencong , Zhai Guisheng
Published: March 2024

A compound anti-interference control method based on a high-order nonlinear disturbance observer (HONDO) is proposed to address the impact of system disturbances on output voltage when applying the Buck–Boost converter in a microgrid to provide power to loads. Initially, the dynamic circuit model of the Buck–Boost converter is formulated, taking into account the system disturbance. Subsequently, HONDOs are designed to provide real-time estimates of uncertain factors in both the voltage loop and current loop of the circuit. These estimates are then utilized to design backstepping controllers aimed at effectively tracking the load output voltage set point. The simulation and experimental findings indicate that, when faced with load mutations, input voltage variations, and uncertainties in system parameters, the proposed compound anti-interference control method outperforms traditional control techniques by offering superior response speed, broader disturbance estimation capabilities, and enhanced dynamic performance of the Buck–Boost converter.
Charging Behavior Portrait of Electric Vehicle Users Based on Fuzzy C-Means Clustering Algorithm
Authors: Yang Aixin , Zhang Guiqing , Tian Chenlu , Peng Wei , Liu Yechun
Published: March 2024

The rapid increase in electric vehicles (EVs) has led to a continuous expansion of electric vehicle (EV) charging stations, imposing significant load pressures on the power grid. Implementing orderly charging scheduling for EVs can mitigate the impact of large-scale charging on the power grid. However, the charging behavior of EVs significantly impacts the efficiency of orderly charging plans. By integrating user portrait technology and conducting research on optimized scheduling for EV charging, EV users can be accurately classified to meet the diverse needs of various user groups. This study establishes a user portrait model suitable for park areas, providing user group classification based on the user response potential for scheduling optimization. First, the FCM and feature aggregation methods are utilized to classify the quantities of features of EV users, obtaining user portrait classes. Second, based on these classes, a user portrait inventory for each EV is derived. Third, based on the priority of user response potential, this study presents a method for calculating the feature data of different user groups. The individual data information and priorities from the user portrait model are inputted into the EV-optimized scheduling model. The optimization focuses on the user charging cost and load fluctuation, with the non-dominated sorting genetic algorithm II utilized to obtain the solutions. The results demonstrate that the proposed strategy effectively addresses the matching issue between the EV user response potential and optimal scheduling modes without compromising the normal use of EVs by users. This classification approach facilitates the easier acceptance of scheduling tasks by participating users, leading to optimized outcomes that better meet practical requirements.
Enhancing Biodiesel Production: A Review of Microchannel Reactor Technologies
Authors: Subramaniam Koguleshun , Wong Kang Yao , Wong Kok Hoe , Chong Cheng Tung , Ng Jo-Han
Published: March 2024

The depletion of fossil fuels, along with the environmental damages brought by their usage, calls for the development of a clean, sustainable and renewable source of energy. Biofuel, predominantly liquid biofuel such as biodiesel, is a promising alternative to fossil fuels, due to its compatible direct usage within the context of compression ignition engines. However, the industrial production of biodiesel is far from being energy and time efficient, which contributes to its high production cost. These inefficiencies are attributed to poor heat and mass transfer of the transesterification reaction. The utilisation of microchannel reactors is found to be excellent in escalating heat and mass transfer of the reactants, benefitting from their high surface area-to-volume ratio. The microchannel also intensifies the mixing of reactants via the reactor design, micromixers and the slug flow patterns within the reactor, thus enhancing the contact between reactants. Simulation studies have aided in the identification of mixing regimes within the microchannel reactors, induced by various reactor designs. In addition, microwave irradiation heating is found to enhance biodiesel production by localised superheating delivered directly to the reactants at a molecular level. This enables the reaction to begin much earlier, resulting in rapid biodiesel production. It is postulated that the synergy between microchannel reactors and microwave heating would catapult a pathway towards rapid and energy-efficient biodiesel production by enhancing heat and mass transfer between reactants.
Effective Modeling of CO2 Emissions for Light-Duty Vehicles: Linear and Non-Linear Models with Feature Selection
Authors: Vu Hang Thi Thanh , Ko Jeonghan
Published: March 2024

Predictive modeling is important for assessing and reducing energy consumption and CO2 emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO2 emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO2 emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption.
Multi-Wind Turbine Wind Speed Prediction Based on Weighted Diffusion Graph Convolution and Gated Attention Network
Authors: Qiao Yakai , Chen Hui , Fu Bo
Published: March 2024

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively.