bluuebunny/crossref_metadata_2025 huggingface dataset:
A Path-Planning Method Considering Environmental Disturbance Based on VPF-RRT*
Authors: Chen Zhihao , Yu Jiabin , Zhao Zhiyao , Wang Xiaoyi , Chen Yang
Published: February 2023

In the traditional rapidly exploring random tree (RRT) algorithm, the planned path is not smooth, the distance is long, and the fault tolerance rate of the planned path is low. Disturbances in an environment can cause unmanned surface vessels (USVs) to deviate from their planned path during navigation. Therefore, this paper proposed a path-planning method considering environmental disturbance based on virtual potential field RRT* (VPF-RRT*). First, on the basis of the RRT* algorithm, a VPF-RRT* algorithm is proposed for planning the planning path. Second, an anti-environmental disturbance method based on a deep recurrent neural networks PI (DRNN-PI) controller is proposed to allow the USV to eliminate environmental disturbance and maintain its track along the planning path. Comparative simulation experiments between the proposed algorithm and the other algorithms were conducted within two different experimental scenes. In the path-planning simulation experiment, the VPF-RRT* algorithm had a shorter planning path and a smaller total turning angle when compared to the RRT* algorithm. In the path-tracking simulation experiment, when using the proposed algorithm, the USV could effectively compensate for the impact of environmental disturbance and maintain its navigation along the planning path. In order to avoid the contingency of the experiment and verify the effectiveness and generality of the proposed algorithm, three experiments were conducted. The simulation results verify the effectiveness of the proposed algorithm.
An Energy-Based Complex Brain Network Model—Part 1: Local Electrophysiological Dynamics
Authors: Yang Chun-Lin , Shettigar Nandan , Suh C. Steve
Published: February 2023

The human brain is a complex network of connected neurons whose dynamics are difficult to describe. Brain dynamics are the global manifestation of individual neuron dynamics and the synaptic coupling between neurons. Membrane potential is a function of synaptic dynamics and electrophysiological coupling, with the parameters of postsynaptic potential, action potential, and ion pump dynamics. By modelling synaptic dynamics using physical laws and the time evolution of membrane potential using energy, neuron dynamics can be described. This local depiction can be scaled up to describe mesoscopic and macroscopic hierarchical complexity in the brain. Modelling results are favorably compared with physiological observation and physically acquired action potential profiles as reported in the literature.
Detecting Nonlinear Interactions in Complex Systems: Application in Financial Markets
Authors: Fotiadis Akylas , Vlachos Ioannis , Kugiumtzis Dimitris
Published: February 2023

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H0) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H0; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H0, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times.
Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations
Authors: Benaroya Laurent , Obin Nicolas , Roebel Axel
Published: February 2023

Voice conversion (VC) consists of digitally altering the voice of an individual to manipulate part of its content, primarily its identity, while maintaining the rest unchanged. Research in neural VC has accomplished considerable breakthroughs with the capacity to falsify a voice identity using a small amount of data with a highly realistic rendering. This paper goes beyond voice identity manipulation and presents an original neural architecture that allows the manipulation of voice attributes (e.g., gender and age). The proposed architecture is inspired by the fader network, transferring the same ideas to voice manipulation. The information conveyed by the speech signal is disentangled into interpretative voice attributes by means of minimizing adversarial loss to make the encoded information mutually independent while preserving the capacity to generate a speech signal from the disentangled codes. During inference for voice conversion, the disentangled voice attributes can be manipulated and the speech signal can be generated accordingly. For experimental evaluation, the proposed method is applied to the task of voice gender conversion using the freely available VCTK dataset. Quantitative measurements of mutual information between the variables of speaker identity and speaker gender show that the proposed architecture can learn gender-independent representation of speakers. Additional measurements of speaker recognition indicate that speaker identity can be recognized accurately from the gender-independent representation. Finally, a subjective experiment conducted on the task of voice gender manipulation shows that the proposed architecture can convert voice gender with very high efficiency and good naturalness.
Residence Time vs. Adjustment Time of Carbon Dioxide in the Atmosphere
Authors: Stallinga Peter
Published: February 2023

We study the concepts of residence time vs. adjustment time time for carbon dioxide in the atmosphere. The system is analyzed with a two-box first-order model. Using this model, we reach three important conclusions: (1) The adjustment time is never larger than the residence time and can, thus, not be longer than about 5 years. (2) The idea of the atmosphere being stable at 280 ppm in pre-industrial times is untenable. (3) Nearly 90% of all anthropogenic carbon dioxide has already been removed from the atmosphere.
Gynecological Speculums in the Context of the Circular Economy
Authors: Hospodková Petra , Rogalewicz Vladimír , Králíčková Michaela
Published: February 2023

This study discusses the issue of disposable and reusable medical devices in the context of the circular economy. Its objective is to analyze the opinions of physicians in gynecological practice on the use of disposable and reusable gynecological speculums (GS). A questionnaire survey was carried out in a sample of 206 healthcare facilities in the Czech Republic. In addition to this, the cost of both examination methods was calculated and compared using data provided by a gynecological department of a typical district hospital. The calculations and cost analysis were performed using the life-cycle costing (LCC) method. The economic comparison shows that the use of disposable gynecological speculums is less expensive, having, however, a greater negative impact on the environment. The cost of reusable speculums for 25 examinations per day for 15 years is EUR 51,579, while it is EUR 23,998 for disposable speculums for the same use and time horizon. The questionnaire survey shows that both physicians and patients are more likely to prefer disposable speculums for examination, although without a clear rationale.
A Literature Review Comparing Experts’ and Non-Experts’ Visual Processing of Graphs during Problem-Solving and Learning
Authors: Ruf Verena , Horrer Anna , Berndt Markus , Hofer Sarah Isabelle , Fischer Frank , Fischer Martin R. , Zottmann Jan M. , Kuhn Jochen , Küchemann Stefan
Published: February 2023

The interpretation of graphs plays a pivotal role in education because it is relevant for understanding and representing data and comprehending concepts in various domains. Accordingly, many studies examine students’ gaze behavior by comparing different levels of expertise when interpreting graphs. This literature review presents an overview of 32 articles comparing the gaze behavior of experts and non-experts during problem-solving and learning with graphs up to January 2022. Most studies analyzed students’ dwell time, fixation duration, and fixation count on macro- and meso-, as well as on micro-level areas of interest. Experts seemed to pay more attention to relevant parts of the graph and less to irrelevant parts of a graph, in line with the information-reduction hypothesis. Experts also made more integrative eye movements within a graph in terms of dynamic metrics. However, the determination of expertise is inconsistent. Therefore, we recommend four factors that will help to better determine expertise. This review gives an overview of evaluation strategies for different types of graphs and across various domains, which could facilitate instructing students in evaluating graphs.
Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes
Authors: Zhou Feng , Hu Shijing , Du Xin , Wan Xiaoli , Lu Zhihui , Wu Jie
Published: February 2023

With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly.
Exploring the Potential of BERT-BiLSTM-CRF and the Attention Mechanism in Building a Tourism Knowledge Graph
Authors: Xu Hongsheng , Fan Ganglong , Kuang Guofang , Wang Chuqiao
Published: February 2023

As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs is an effective solution to the problem of information clutter in the tourism field. This paper first analyzes the current state of domestic and international research on constructing tourism knowledge graphs and highlights the problems associated with constructing knowledge graphs, which are that they are time-consuming, laborious and have a single function. In order to make up for these shortcomings, this paper proposes a set of systematic methods to build a tourism knowledge graph. This method integrates the BiLSTM and BERT models and combines these with the attention mechanism. The steps of this methods are as follows: First, data preprocessing is carried out by word segmentation and removing stop words; second, after extracting the features and vectorization of the words, the cosine similarity method is used to classify the tourism text, with the text classification based on naive Bayes being compared through experiments; third, the popular tourism words are obtained through the popularity analysis model. This paper proposes two models to obtain popular words: One is a multi-dimensional tourism product popularity analysis model based on principal component analysis; the other is a popularity analysis model based on emotion analysis; fourth, this paper uses the BiLSTM-CRF model to identify entities and the cosine similarity method to predict the relationship between entities so as to extract high-quality tourism knowledge triplets. In order to improve the effect of entity recognition, this paper proposes entity recognition based on the BiLSTM-LPT and BiLSTM-Hanlp models. The experimental results show that the model can effectively improve the efficiency of entity recognition; finally, a high-quality tourism knowledge was imported into the Neo4j graphic database to build a tourism knowledge graph.
Prior-Driven NeRF: Prior Guided Rendering
Authors: Jin Tianxing , Zhuang Jiayan , Xiao Jiangjian , Ge Jianfei , Ye Sichao , Zhang Xiaolu , Wang Jie
Published: February 2023

Neural radiation field (NeRF)-based novel view synthesis methods are gaining popularity. NeRF can generate more detailed and realistic images than traditional methods. Conventional NeRF reconstruction of a room scene requires at least several hundred images as input data and generates several spatial sampling points, placing a tremendous burden on the training and prediction process with respect to memory and computational time. To address these problems, we propose a prior-driven NeRF model that only accepts sparse views as input data and reduces a significant number of non-functional sampling points to improve training and prediction efficiency and achieve fast high-quality rendering. First, this study uses depth priors to guide sampling, and only a few sampling points near the controllable range of the depth prior are used as input data, which reduces the memory occupation and improves the efficiency of training and prediction. Second, this study encodes depth priors as distance weights into the model and guides the model to quickly fit the object surface. Finally, a novel approach combining the traditional mesh rendering method (TMRM) and the NeRF volume rendering method was used to further improve the rendering efficiency. Experimental results demonstrated that our method had significant advantages in the case of sparse input views (11 per room) and few sampling points (8 points per ray).
A Direct Position Determination Method under Unknown Multi-Perturbation with Moving Distributed Arrays
Authors: Zhang Qiting , Li Jianfeng , Tang Yawei , Deng Weiming , Zhang Xiaofei
Published: February 2023

Distributed array manifold perturbation, which includes synchronization errors, amplitude-phase errors, and path attenuation, has seriously degraded the accuracy of existing direct position determination (DPD) methods. In this paper, a DPD method under unknown multi-perturbation with moving distributed coprime arrays is advocated for. Firstly, by means of array position interchange, the integrated signals received from distributed arrays can be fused, which contributes to multi-position fusion. Subsequently, by resorting to the orthogonality between the noise subspace and steering vector received via distributed arrays, a quadratic optimization problem is constructed. Finally, we realize multi-parameter decoupling and achieve localization regardless of unknown perturbations. The superiority of the advocated method is substantiated from simulation examples.
Distilling Monolingual Models from Large Multilingual Transformers
Authors: Singh Pranaydeep , De Clercq Orphée , Lefever Els
Published: February 2023

Although language modeling has been trending upwards steadily, models available for low-resourced languages are limited to large multilingual models such as mBERT and XLM-RoBERTa, which come with significant overheads for deployment vis-à-vis their model size, inference speeds, etc. We attempt to tackle this problem by proposing a novel methodology to apply knowledge distillation techniques to filter language-specific information from a large multilingual model into a small, fast monolingual model that can often outperform the teacher model. We demonstrate the viability of this methodology on two downstream tasks each for six languages. We further dive into the possible modifications to the basic setup for low-resourced languages by exploring ideas to tune the final vocabulary of the distilled models. Lastly, we perform a detailed ablation study to understand the different components of the setup better and find out what works best for the two under-resourced languages, Swahili and Slovene.
Predicting Alzheimer’s Disease Using Deep Neuro-Functional Networks with Resting-State fMRI
Authors: Sethuraman Sambath Kumar , Malaiyappan Nandhini , Ramalingam Rajakumar , Basheer Shakila , Rashid Mamoon , Ahmad Nazir
Published: February 2023

Resting-state functional connectivity has been widely used for the past few years to forecast Alzheimer’s disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies’ original functional connectivity relationships. Previous works focuses on low-order neurodynamics and precisely manipulates the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI). They specifically use the mono-band frequency span of rs-fMRI, leaving out the high-order neurodynamics. By creating a high-order neuro-dynamic functional network employing several levels of rs-fMRI time-series data, such as slow4, slow5, and full-band ranges of (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz), we suggest an automated AD diagnosis system to address these challenges. It combines multiple customized deep learning models to provide unbiased evaluation, and a tenfold cross-validation is observed We have determined that to differentiate AD disorders from NC, the entire band ranges and slow4 and slow5, referred to as higher and lower frequency band approaches, are applied. The first method uses the SVM and KNN to deal with AD diseases. The second method uses the customized Alexnet and Inception blocks with rs-fMRI datasets from the ADNI organizations. We also tested the other machine learning and deep learning approaches by modifying various parameters and attained good accuracy levels. Our proposed model achieves good performance using three bands without any external feature selection. The results show that our system performance of accuracy (96.61%)/AUC (0.9663) is achieved in differentiating the AD subjects from normal controls. Furthermore, the good accuracies in classifying multiple stages of AD show the potentiality of our method for the clinical value of AD prediction.
Incentive Mechanism for Improving Task Completion Quality in Mobile Crowdsensing
Authors: Wang Kun , Chen Zhigang , Zhang Lizhong , Liu Jiaqi , Li Bin
Published: February 2023

Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure the stability of the sensing data quality. Most of the existing incentive mechanisms for data quality in crowdsensing are based on traditional economics, which believe that the decision of participants to complete a task depends on whether the benefit of the task is greater than the cost of completing the task. However, behavioral economics shows that people will be affected by the cost of investment in the past, resulting in decision-making bias. Therefore, different from the existing incentive mechanism researches, this paper considers the impact of sunk cost on user decision-making. An incentive mechanism based on sunk cost called IMBSC is proposed to motivate participants to improve data quality. The IMBSC mechanism stimulates the sunk cost effect of participants by designing effort sensing reference factor and withhold factor to improve their own data quality. The effectiveness of the IMBSC mechanism is verified from three aspects of platform utility, participant utility and the number of tasks completed through simulation experiments. The simulation results show that compared with the system without IMBSC mechanism, the platform utility is increased by more than 100%, the average utility of participants is increased by about 6%, and the task completion is increased by more than 50%.
APSN: Adversarial Pseudo-Siamese Network for Fake News Stance Detection
Authors: Zhou Zhibo , Yang Yang , Li Zhoujun
Published: February 2023

Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic news quickly is an essential purpose for this research to enhance the trustworthiness of news in online social networks, which will be the task studied in this paper. The fake news stance detection can contribute to detect a startling amount of fake news, which aims at evaluating the relevance between the headline and text bodies. There exists a significant difference between news article headline and text body, since headlines with several key phrases are usually much shorter than the text bodies. Such an information imbalance challenge may cause serious problems for the stance detection task. Furthermore, news article data in online social networks is usually exposed to various types of noise and can be contaminated, which poses more challenges for the stance detection task. In this paper, we propose a novel fake news stance detection model, namely Adversarial Pseudo-Siamese Network model (APSN), to solve these challenges. With coupled input components with imbalanced parameters, APSN can learn and compute feature vectors and similarity score of news article headlines and text bodies simultaneously. In addition, by adopting adversarial setting, besides the regular training set, a set of noisy training instances will be generated and fed to APSN in the learning process, which can significantly enhance the robustness of the model. Extensive experiments have been conducted on a real-world fake news dataset, and the experimental results reveal that the presented model exceeds compared suspicious information detection models with significant advantages.
Multi-Stage Ensemble-Based System for Glaucomatous Optic Neuropathy Diagnosis in Fundus Images
Authors: Vásquez-Rochín Carlos A. , Martínez-Rosas Miguel E. , de Ávila Humberto Cervantes , Romo-Cárdenas Gerardo , Luque-Morales Priscy A. , Miranda-Velasco Manuel M.
Published: February 2023

Recent developments in Computer-aided Diagnosis (CAD) systems as a countermeasure to the increasing number of untreated cases of eye diseases related to visual impairment (such as diabetic retinopathy or age-related macular degeneration) have the potential to yield in low-to-mid income countries a comfortable and accessible alternative to obtaining a general ophthalmological study necessary for follow-up medical attention. In this work, a multi-stage ensemble-based system for the diagnosis of glaucomatous optic neuropathy (GON) is proposed. GON diagnosis is based on a binary classification procedure working in conjunction with a multi-stage block based on image preprocessing and feature extraction. Our preliminary data show similar results compared to current studies considering metrics such as Accuracy, Sensitivity, Specificity, AUC (AUROC), F1score, and the use of Matthews Correlation Coefficient (MCC) as an additional performance metric is proposed.
Correlation of Crystal Defects with Device Performance of AlGaN/GaN High-Electron-Mobility Transistors Fabricated on Silicon and Sapphire Substrates
Authors: Pharkphoumy Sakhone , Janardhanam Vallivedu , Jang Tae-Hoon , Shim Kyu-Hwan , Choi Chel-Jong
Published: February 2023

Herein, the performance of AlGaN/GaN high-electron-mobility transistor (HEMT) devices fabricated on Si and sapphire substrates is investigated. The drain current of the AlGaN/GaN HEMT fabricated on sapphire and Si substrates improved from 155 and 150 mA/mm to 290 and 232 mA/mm, respectively, at VGS = 0 V after SiO2 passivation. This could be owing to the improvement in the two-dimensional electron gas charge and reduction in electron injection into the surface traps. The SiO2 passivation resulted in the augmentation of breakdown voltage from 245 and 415 V to 400 and 425 V for the AlGaN/GaN HEMTs fabricated on Si and sapphire substrates, respectively, implying the effectiveness of SiO2 passivation. The lower transconductance of the AlGaN/GaN HEMT fabricated on the Si substrate can be ascribed to the higher self-heating effect in Si. The X-ray rocking curve measurements demonstrated that the AlGaN/GaN heterostructures grown on sapphire exhibited a full-width half maximum of 368 arcsec against 703 arcsec for the one grown on Si substrate, implying a better crystalline quality of the AlGaN/GaN heterostructure grown on sapphire. The AlGaN/GaN HEMT fabricated on the sapphire substrate exhibited better performance characteristics than that on the Si substrate, owing to the high crystalline quality and improved surface.
Depth-Based Dynamic Sampling of Neural Radiation Fields
Authors: Wang Jie , Xiao Jiangjian , Zhang Xiaolu , Xu Xiaolin , Jin Tianxing , Jin Zhijia
Published: February 2023

Although the NeRF approach can achieve outstanding view synthesis, it is limited in practical use because it requires many views (hundreds) for training. With only a few input views, the Depth-DYN NeRF that we propose can accurately match the shape. First, we adopted the ip_basic depth-completion method, which can recover the complete depth map from sparse radar depth data. Then, we further designed the Depth-DYN MLP network architecture, which uses a dense depth prior to constraining the NeRF optimization and combines the depthloss to supervise the Depth-DYN MLP network. When compared to the color-only supervised-based NeRF, the Depth-DYN MLP network can better recover the geometric structure of the model and reduce the appearance of shadows. To further ensure that the depth depicted along the rays intersecting these 3D points is close to the measured depth, we dynamically modified the sample space based on the depth of each pixel point. Depth-DYN NeRF considerably outperforms depth NeRF and other sparse view versions when there are a few input views. Using only 10–20 photos to render high-quality images on the new view, our strategy was tested and confirmed on a variety of benchmark datasets. Compared with NeRF, we obtained better image quality (NeRF average at 22.47 dB vs. our 27.296 dB).
Noise2Clean: Cross-Device Side-Channel Traces Denoising with Unsupervised Deep Learning
Authors: Yu Honggang , Wang Mei , Song Xiyu , Shan Haoqi , Qiu Hongbing , Wang Junyi , Yang Kaichen
Published: February 2023

Deep learning (DL)-based side-channel analysis (SCA) has posed a severe challenge to the security and privacy of embedded devices. During its execution, an attacker exploits physical SCA leakages collected from profiling devices to create a DL model for recovering secret information from victim devices. Despite this success, recent works have demonstrated that certain countermeasures, such as random delay interrupts or clock jitters, would make these attacks more complex and less practical in real-world scenarios. To address this challenge, we present a novel denoising scheme that exploits the U-Net model to pre-process SCA traces for “noises” (i.e., countermeasures) removal. Specifically, we first pre-train the U-Net model on the paired noisy-clean profiling traces to obtain suitable parameters. This model is then fine-tuned on the noisy-only traces collected from the attacking device. The well-trained model will be finally deployed on the attacking device to remove the noises (i.e., countermeasures) from the measured power traces. In particular, a new inductive transfer learning method is also utilized in our scheme to transfer knowledge learned from the source domain (i.e., profiling device) to the target domain (i.e., attacking device) to improve the model’s generalization ability. During our experimental evaluations, we conduct a detailed analysis of various countermeasures separately or combined and show that the proposed denoising model outperforms current state-of-the-art work by a large margin, e.g., a reduction of at least 30% in computation costs and 5× in guessing entropy.
Investigation of Recent Metaheuristics Based Selective Harmonic Elimination Problem for Different Levels of Multilevel Inverters
Authors: Ürgün Satılmış , Yiğit Halil , Mirjalili Seyedali
Published: February 2023

Multilevel inverters (MLI) are popular in high-power applications. MLIs are generally configured to have switches reduced by switching techniques that eliminate low-order harmonics. The selective harmonic elimination (SHE) method, which significantly reduces the number of switching, determines the optimal switching moments to obtain the desired output voltage and eliminates the desired harmonic components. To solve the SHE problem, classical methods are primarily employed. The disadvantages of such methods are the high probability of trapping in locally optimal solutions and their dependence on initial controlling parameters. One solution to overcome this problem is the use of metaheuristic algorithms. In this study, firstly, 22 metaheuristic algorithms with different sources of inspiration were used to solve the SHE problem at different levels of MLIs, and their performances were extensively analyzed. To reveal the method that offers the best solution, these algorithms were first applied to an 11-level MLI circuit, and six methods were determined as a result of the performance analysis. As a result of the evaluation, the outstanding methods were SPBO, BMO, GA, GWO, MFO, and SPSA. As a result of the application of superior methods to 7-, 11-, 15-, and 19-level MLIs according to the IEEE 519—2014 standard, it has been shown that BMO outperforms in 7-level MLI, GA in 11-level MLI, and SPBO in 15- and 19-level MLIs in terms of THD, while in terms of output voltage quality, GA in 7-level MLI, BMO in 11-level MLI, GA and SPSA in 15-level MLI, and SPSA in 19-level MLI come forward.
Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study
Authors: Lewis Donovin D. , Patrick Aron , Jones Evan S. , Alden Rosemary E. , Hadi Abdullah Al , McCulloch Malcolm D. , Ionel Dan M.
Published: February 2023

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems.
Piezoelectric Energy Harvesting Gyroscopes: Comparative Modeling and Effectiveness
Authors: Serrano Manuel , Larkin Kevin , Tretiak Sergei , Abdelkefi Abdessattar
Published: February 2023

Given its versatility in drawing power from many sources in the natural world, piezoelectric energy harvesting (PEH) has become increasingly popular. However, its energy harvesting capacities could be enhanced further. Here, a mathematical model that accurately simulates the dynamic behavior and energy harvested can facilitate further improvements in the performance of piezoelectric devices. One of the goals of this study is to create a dependable reduced-order model of a multi-purpose gyroscope. This model will make it possible to compute the harvested voltage and electrical power in a semi-analytical manner. The harvested voltage is often modeled as an average value across the whole electrode surface in piezoelectric devices. We propose a model which provides practical insights toward optimizing the performance of the system by considering a spatially varying electric field across the electrode surface length. Our framework allows investigation of the limits of applicability of the modeling assumptions across a range of load resistances. The differential quadrature method (DQM) provides the basis for the suggested numerical solution. The model is also employed to examine energy harvesting under various resistance loads. The newly developed spatially varying model is evaluated for open- and closed-circuit conditions and is proved to be accurate for various values of load resistance that have not previously been considered. The results show that using a spatially varying model is more versatile when modeling the performance of the piezoelectric multifunctional energy harvester. The performance may be accurately captured by the model for load resistances ranging between 103 Ω and 108 Ω. At optimum load resistance and near 65 KHz, the maximum power output predicted by the spatially varying (SV) model is 1.3 mV, 1.5 mV for the open-circuit (OC) model, and 2.1 mV for the closed circuit (CE) model. At a high-load resistance, the SV and OC models all predict the maximum power output to be 1.9 mV while the CE model predicted the maximum voltage to be 3 mV.
Development of the Biomethane Market in Europe
Authors: Sulewski Piotr , Ignaciuk Wiktor , Szymańska Magdalena , Wąs Adam
Published: February 2023

The challenges related to climate policy and the energy crisis caused the search for alternative ways of obtaining energy, one of the essential tasks faced by scientists and political decision-makers. Recently, much attention has been paid to biomethane, which is perceived as a substitute for natural gas. Compared to the traditional combustion of biogas in cogeneration units (CHP), upgrading it to the form of biomethane can bring both environmental benefits (reduction in GHG emissions) and economic benefits (higher efficiency of energy use contained in biomass). The purpose of this review was a comprehensive assessment of the conditions and opportunities for developing the biomethane sector in the EU in the face of challenges generated by the energy and climate crisis. The article reviews the condition of the biomethane market in the European Union, focusing on such issues as biomethane production technologies, current and future supply and demand for biomethane, and biomethane production costs with particular emphasis on upgrading processes and financial support systems used in the EU countries. The review showed that the market situation in biomethane production has recently begun to change rapidly. However, the share of biomethane in meeting the needs for natural gas remains small. Moreover, the available expert studies indicate a significant development potential, which is desirable because of the need to increase energy security and environmental and economic reasons. However, this will require organizing the legal environment and creating a transparent system of incentives.
Development and Validation of Control Algorithm for Variable Speed Fixed Pitch Small Wind Turbine
Authors: Jeong Donggeun , Jeon Taesu , Paek Insu , Lim Deokjin
Published: February 2023

In this study, a power control algorithm of a variable-speed fixed-pitch horizontal-axis lift-type 20 kW small wind turbine (SWT) was proposed and verified through dynamic simulations. The power control algorithm proposed in this study consists of algorithms for Region II to track the maximum power coefficient, for Region II-1/2 to maintain the rated rotor speed, and for Region III to maintain the rated power. To verify the proposed power control algorithm, simulations were performed at the rated wind speed and above the rated wind speed, to which turbulence intensity based on the IEC regulation’s normal turbulence model was applied. As a result, it was confirmed that the proposed controller operates properly in the whole three regions including Regions II, II-1/2, and III. The controller performance was then compared with the variable-speed variable-pitch power controller. Although the performance of the proposed controller was considered good for the target VSVP wind turbine, it was lower than that of the conventional controller applied to the same wind turbine. Compared to the VSVP wind turbine, the VSFP wind turbine with the proposed controller was found to have higher mean loads on the blade and the tower but the fatigue loads in terms of Damage Equivalent Load (DEL) were found to be reduced.
Research on the Method of Near-Field Measurement and Modeling of Powerful Electromagnetic Equipment Radiation Based on Field Distribution Characteristics
Authors: Chen Hao , Liu Qifeng , Li Yongming , Huang Chen , Zhang Huaiqing , Xu Yinxiang
Published: February 2023

With the wide application of power equipment consisting of high switching frequencies and large current switching devices in the integrated power system on ships, the low-frequency radiation interference caused by the powerful electromagnetic equipment becomes more and more serious. Establishing an equivalent model of radiation interference based on the near-field measurement data is the key to subsequent electromagnetic compatibility design. Due to the large size of the equipment, there will be many measurement points in the near-field measurement area, which leads to long testing times and low modeling efficiency. To solve the above problems, this article first proposes a method of near-field measurement and a modeling method of powerful electromagnetic equipment radiation based on field distribution characteristics. Firstly, the near-field measurement data are obtained by the sparse and uniform sampling of the near-field measurement plane at large sampling intervals. Then, the near-field measuring plane is separated into several regions by the magnetic field’s distribution characteristics. The near-field measurement data required for modeling are obtained by further sampling in the region with a large magnetic field amplitude. Finally, the equivalent radiation model is obtained by the equivalent dipole method. Simulations and experiments show that the method can significantly reduce the amount of measurement data and testing time while improving the efficiency of equivalent radiation modeling and maintaining the accuracy of the modeling.
A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles
Authors: Tao Siyi , Jiang Bo , Wei Xuezhe , Dai Haifeng
Published: February 2023

The precise estimation of the state of charge (SOC) is fundamental to the reliable operation of lithium-ion batteries. The development of deep learning techniques makes it possible to employ advanced methods to estimate a battery’s SOC. In order to better utilize a recurrent neural network (RNN) for battery SOC estimation, this paper conducts a comparative study of SOC estimation methods based on different RNN models. First, a general framework for deep-learning-based SOC estimation is undertaken, followed by the description of four kinds of RNNs employed in the estimation. Then, the estimation performances of these RNN models are compared under three scenarios, including the SOC estimation accuracy, the adaptability against different battery aging statuses, and the robustness against measurement uncertainties, in which the estimation performances of different RNN models are quantitively evaluated. Finally, a multiple-criteria decision-making method based on the analytic hierarchy process (AHP) is utilized to reflect the comprehensive performance of each RNN model, and the model with the highest score could be chosen for online SOC estimation during actual applications. This paper provides an in-depth analysis of RNN models in battery SOC estimation and could help battery management engineers develop the most appropriate estimation methods.
Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization
Authors: Sun Jikai , Chen Mingrui , Kong Linghe , Hu Zhijian , Veerasamy Veerapandiyan
Published: February 2023

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10−3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems.
Characteristics and Significance of Acid-Soluble Organic Matter in Marine Carbonate Source Rocks
Authors: Feng Danting , Wang Xiaofeng , Liu Wenhui , Zhang Dongdong , Wang Jie , Luo Houyong , Liu Peng
Published: February 2023

Since the 1950s, major breakthroughs have been made in the field of carbonate oil and gas exploration, and large reservoirs have been found in carbonate strata in many countries; however, the validity of highly evolved carbonate source rocks has been controversial. Because of the loss of organic carbon during acid-solution processing in conventional total organic carbon (TOC) determination, we decided to use a new method of reducing acid-solution losses in order to more effectively quantify acid-soluble organic matter in carbonate rock samples. Different levels of acid-soluble organic matter were present in different types of samples, and there was no positive correlation between the content of acid-soluble organic matter and conventionally measured TOC values. However, the thermal evolutionary maturity of samples with high acid-soluble organic matter content is relatively low, and the high hydrocarbon generation conversion rate of acid-soluble organic matter and conventional TOC in carbonate rocks is an important reason for the low residual TOC in highly evolved carbonate rocks. The new method is helpful in re-evaluating the abundance of organic matter in carbonate rocks and in providing evidence for studying hydrocarbon generation and the hydrocarbon capacity of carbonate source rocks.
Implications of 5G Technology in the Management of Power Microgrids: A Review of the Literature
Authors: Taveras Cruz Armando J. , Aybar-Mejía Miguel , Díaz Roque Yobany , Coste Ramírez Karla , Durán José Gabriel , Rosario Weeks Dinelson , Mariano-Hernández Deyslen , Hernández-Callejo Luis
Published: February 2023

Microgrids have a lot to offer, including helping smart grids operate on distribution grids or bringing electricity to some cities. The management system receives and transmits different states. This is because the elements adapt to the conditions of the network in the shortest possible time. The 5G communication technology has high transmission speed, owing to which it can improve equipment connectivity and reduce latency, allowing the real-time analysis and monitoring of electrical microgrids considerably better than earlier generations. In addition, it is estimated that, in the near future, many cities will be connected using communication systems that allow the interconnection of different systems safeguarding the connectivity, speed, and response time of these elements in an electrical system, smart grid, or microgrids with the growing development of the Internet of Things. For this reason, it is essential to analyze the integration of 5G technology to improve the management of microgrids. This literature review analyzes and presents the advantages of using 5G technologies in reducing communication latency and improving connectivity to enhance microgrids’ control and management. The active implementation of 5G in the management and control of microgrids increases the transmission and reception of data and states, reduces latency, and allows for a greater density of information, collaborating positively with resilience to the various changes that microgrids can suffer in continuous working conditions. The implementation of 5G allows electrical microgrids to be more resilient in their management and control, directly and indirectly impacting the sustainable development goals.