bluuebunny/crossref_metadata_2025 huggingface dataset:
Access Pain During Transforaminal Endoscopic Lumbar Discectomy for Foraminal or Extraforaminal Disc Herniation
Authors: Ahn Yong , Choi Ji-Eun , Lee Sol
Published: October 2024

Background/Objectives: Transforaminal endoscopic lumbar discectomy (TELD) under local anesthesia is a promising minimally invasive surgical option for intractable lumbar disc herniation (LDH). However, our understanding of access pain prediction during foraminal pathological procedures is limited. To our knowledge, no predictive rules for access pain have been established during TELD for foraminal or extraforaminal LDH. This study, with its potential for predicting access pain during TELD and discussing strategies for pain prevention and management, could significantly benefit the field of endoscopic spine surgery. Methods: This observational study included 73 consecutive patients who underwent TELD for foraminal or extraforaminal LDH between January 2017 and December 2022. Preoperative clinical and radiographic factors affecting significant access pain and the impact of access pain on clinical outcomes were evaluated. Results: The rate of significant access pain was 13.70% (10 of 73 patients). Extraforaminal LDH tended to cause more severe pain than did foraminal LDH during TELD under local anesthesia (p < 0.05). Although the degree of access pain was not related to global clinical outcomes, increased pain was strongly associated with prolonged operative time and length of hospital stay (p < 0.05). Conclusions: TELD could be an effective surgical option for foraminal or extraforaminal LDH under local anesthesia. More access pain might develop during TELD for extraforaminal LDH. The extraforaminal component of LDH could narrow the safe working zone. Significant access pain might prolong the duration of surgery and hospitalization. Thus, a specialized technique is required for the clinical success of TELD.
Paving the Way for Sustainable UAVs Using Distributed Propulsion and Solar-Powered Systems
Authors: Valencia Esteban , Cruzatty Cristian , Amaguaña Edwin , Cando Edgar
Published: October 2024

Hybrid systems offer optimal solutions for unmanned aerial platforms, showcasing their technological development in parallel and series configurations and providing alternatives for future aircraft concepts. However, the limited energetic benefit of these configurations is primarily due to their weight, constituting one of the main constraints. Solar PV technology can provide an interesting enhancement to the autonomy of these systems. However, to create efficient propulsion architectures tailored for specific missions, a flexible framework is required. This work presents a methodology to assess hybrid solar-powered UAVs in distributed propulsion configurations through a two-level modeling scheme. The first stage consists of determining operational and design constraints through parametric models that estimate the baseline energetic requirements of flight. The second phase executes a nonlinear optimization algorithm tuned to find optimal propulsion configurations in terms of the degree of hybridization, number of propellers, different wing loadings, and the setup of electric distributed propulsion (eDP) considering fuel consumption as a key metric. The results of the study indicate that solar-hybrid configurations can theoretically achieve fuel savings of up to 80% compared to conventional configurations. This leads to a significant reduction in emissions during long-endurance flights where current battery technology is not yet capable of providing sustained flight.
Assessment of Nuclear Fusion Reaction Spontaneity via Engineering Thermodynamics
Authors: Tosti Silvano
Published: October 2024

This work recalls the basic thermodynamics of chemical processes for introducing the evaluation of the nuclear reactions’ spontaneity. The application and definition of the thermodynamic state functions of the nuclear processes have been described by focusing on their contribution to the chemical potential. The variation of the nuclear binding potentials involved in a nuclear reaction affects the chemical potential through a modification of the internal energy and of the other state functions. These energy changes are related to the mass defect between reactants and products of the nuclear reaction and are of the order of magnitude of 1 MeV per particle, about six orders of magnitude larger than those of the chemical reactions. In particular, this work assesses the Gibbs free energy change of the fusion reactions by assuming the Qvalue as the nuclear contribution to the chemical potential and by calculating the entropy through the Sackur–Tetrode expression. Then, the role of the entropy in fusion processes was re-examined by demonstrating the previous spontaneity analyses, which assume a perfect gas of DT atoms in the initial state of the fusion reactions, are conservative and lead to assessing more negative ΔG than in the real case (ionized gas). As a final point, this paper examines the thermodynamic spontaneity of exothermic processes with a negative change of entropy and discusses the different thermodynamic spontaneity exhibited by the DT fusion processes when conducted in a controlled or uncontrolled way.
Quantum-Like Approaches Unveil the Intrinsic Limits of Predictability in Compartmental Models
Authors: Rojas-Venegas José Alejandro , Gallarta-Sáenz Pablo , Hurtado Rafael G. , Gómez-Gardeñes Jesús , Soriano-Paños David
Published: October 2024

Obtaining accurate forecasts for the evolution of epidemic outbreaks from deterministic compartmental models represents a major theoretical challenge. Recently, it has been shown that these models typically exhibit trajectory degeneracy, as different sets of epidemiological parameters yield comparable predictions at early stages of the outbreak but disparate future epidemic scenarios. In this study, we use the Doi–Peliti approach and extend the classical deterministic compartmental models to a quantum-like formalism to explore whether the uncertainty of epidemic forecasts is also shaped by the stochastic nature of epidemic processes. This approach allows us to obtain a probabilistic ensemble of trajectories, revealing that epidemic uncertainty is not uniform across time, being maximal around the epidemic peak and vanishing at both early and very late stages of the outbreak. Therefore, our results show that, independently of the models’ complexity, the stochasticity of contagion and recovery processes poses a natural constraint for the uncertainty of epidemic forecasts.
Well-Being and Tourism: A Systematic Literature Review
Authors: Konstantopoulou Chryssoula , Varelas Sotirios , Liargovas Panagiotis
Published: October 2024

In recent decades, the concept of well-being has often been the subject of psychological studies. The current study examines how tourism relates to well-being from the perspective of tourists/visitors, through the study of the older and more recent literature. Especially when it comes to the complex and multifaceted issue of well-being, in relation to the also complex and sensitive sectors of tourism, the specificity of this relationship is reflected in the evidence and the growing number of articles published in mainstream and reliable journals. By using the PRISMA framework, this article compares and evaluates published papers on the study of well-being and tourist behavior to guide and direct future research. It has been found that the relationship between well-being and tourism, especially after the emergence of the COVID-19 pandemic, has been significantly strengthened. This study highlights the need to adopt a holistic approach that considers all the connections between well-being and tourism.
A Critical Analysis and Comparison of the Effect of Source Inductance on 3- and 4-Lead SuperJunction MOSFETs Turn-Off
Authors: Rizzo Santi Agatino , Salerno Nunzio , Ventura Cristina , Scuto Alfio , Sorrentino Giuseppe
Published: October 2024

This paper critically compares the turn-off performance of two package solutions, 3-lead (3L) vs. 4-lead (4L), in SuperJunction MOSFETs. It is commonly assumed that the better performance (lower switching losses) of the 4L MOSFET is obtained thanks to the decoupling of the power and driving loops. On the contrary, in this work, the experimental results, circuit models and Kirchhoff laws show that the turn-off improvement (lower turn-off losses) obtained by adopting the Kelvin source is due to the lower inductance of the driver loop of the 4L MOSFET, instead of the decoupling between the driver and power loops. In detail, an inductor is added to the gate path of the 4L MOSFET to obtain a total inductance in the driver loop equal to the 3L counterpart. The experimental results show that the 4L MOSFET presents the same drain current slew rate under this condition, although the driver and power loops are still decoupled.
A3GT: An Adaptive Asynchronous Generalized Adversarial Training Method
Authors: He Zeyi , Liu Wanyi , Huang Zheng , Chen Yitian , Zhang Shigeng
Published: October 2024

Adversarial attack methods can significantly improve the classification accuracy of deep learning models, but research has found that although most deep learning models with defense methods still show good classification accuracy in the face of various adversarial attack attacks, the improved robust models have a significantly lower classification accuracy when facing clean samples compared to themselves without using defense methods. This means that while improving the model’s adversarial robustness, it is necessary to find a defense method to balance the accuracy of clean samples (clean accuracy) and the accuracy of adversarial samples (robust accuracy). Therefore, in this work, we propose an Adaptive Asynchronous Generalized Adversarial Training (A3GT) method, which is an improvement over the existing Generalist method. It employs an adaptive update strategy without the need for extensive experiments to determine the optimal starting iteration for global updates. The experimental results show that compared with other advanced methods, A3GT can achieve a balance between clean sample classification accuracy and robust classification accuracy while improving the model’s adversarial robustness.
Truncated Differential-Neural Key Recovery Attacks on Round-Reduced HIGHT
Authors: Seok Byoungjin
Published: October 2024

Recently, differential-neural cryptanalysis, which integrates deep learning with differential cryptanalysis, has emerged as a powerful and practical cryptanalysis method. It has been particularly applied to lightweight block ciphers, which are characterized by simple structures and operations, and relatively small block and key sizes. In resource-constrained environments, such as Internet of Things (IoT), it is essential to verify the resistance of existing lightweight block ciphers against differential-neural cryptanalysis to ensure security. In differential-neural cryptanalysis, a deep learning model, known as a neural distinguisher, is trained to differentiate a target cipher from others, facilitating key recovery through statistical analysis. For successful differential-neural cryptanalysis, it is crucial to develop a highly accurate neural distinguisher and to optimize the key recovery attack algorithm. In this paper, we introduce a novel neural distinguisher and key recovery attack against the 15-round reduced HIGHT cipher. Our proposed neural distinguisher is capable of distinguishing HIGHT ciphertext by analyzing only a portion of the ciphertext, which we refer to as a truncated neural distinguisher. Notably, our experiments demonstrate that the truncated neural distinguisher achieves performance comparable to existing distinguishers trained on entire ciphertext blocks, while enabling a more efficient key recovery attack through a divide-and-conquer strategy. Furthermore, we observe a significant improvement in key recovery efficiency compared to traditional cryptanalysis methods.
Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network
Authors: Guo Xiaojuan , Wang Jianping , Gao Guohong , Li Li , Zhou Junming , Li Yancui
Published: October 2024

With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability to handle complex text classifications. The diversity and evolving nature of agricultural texts make deep semantic understanding and integration of contextual knowledge especially challenging. To tackle these challenges, this paper introduces a Bidirectional Encoder Recurrent Convolutional Neural Network (AES-BERCNN) tailored for short-text classification in agricultural expert systems. We designed an Agricultural Text Encoder (ATE) with a six-layer transformer architecture to capture both preceding and following word information. A recursive convolutional neural network based on Gated Recurrent Units (GRUs) was also developed to merge contextual information and learn complex semantic features, which are then combined with the ATE output and refined through max-pooling to form the final feature representation. The AES-BERCNN model was tested on a self-constructed agricultural dataset, achieving an accuracy of 99.63% in text classification. Its generalization ability was further verified on the Tsinghua News dataset. Compared to other models such as TextCNN, DPCNN, BiLSTM, and BERT-based models, the AES-BERCNN shows clear advantages in agricultural text classification. This work provides precise and timely technical support for intelligent agricultural expert systems.
Total Power Factor Smart Contract with Cyber Grid Guard Using Distributed Ledger Technology for Electrical Utility Grid with Customer-Owned Wind Farm
Authors: Piesciorovsky Emilio C. , Hahn Gary , Borges Hink Raymond , Werth Aaron
Published: October 2024

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs.
Dimensionality Reduction for the Real-Time Light-Field View Synthesis of Kernel-Based Models
Authors: Courteaux Martijn , Mareen Hannes , Ramlot Bert , Lambert Peter , Van Wallendael Glenn
Published: October 2024

Several frameworks have been proposed for delivering interactive, panoramic, camera-captured, six-degrees-of-freedom video content. However, it remains unclear which framework will meet all requirements the best. In this work, we focus on a Steered Mixture of Experts (SMoE) for 4D planar light fields, which is a kernel-based representation. For SMoE to be viable in interactive light-field experiences, real-time view synthesis is crucial yet unsolved. This paper presents two key contributions: a mathematical derivation of a view-specific, intrinsically 2D model from the original 4D light field model and a GPU graphics pipeline that synthesizes these viewpoints in real time. Configuring the proposed GPU implementation for high accuracy, a frequency of 180 to 290 Hz at a resolution of 2048×2048 pixels on an NVIDIA RTX 2080Ti is achieved. Compared to NVIDIA’s instant-ngp Neural Radiance Fields (NeRFs) with the default configuration, our light field rendering technique is 42 to 597 times faster. Additionally, allowing near-imperceptible artifacts in the reconstruction process can further increase speed by 40%. A first-order Taylor approximation causes imperfect views with peak signal-to-noise ratio (PSNR) scores between 45 dB and 63 dB compared to the reference implementation. In conclusion, we present an efficient algorithm for synthesizing 2D views at arbitrary viewpoints from 4D planar light-field SMoE models, enabling real-time, interactive, and high-quality light-field rendering within the SMoE framework.
Maximum Doppler Shift Identification Using Decision Feedback Channel Estimation
Authors: Handa Yudai , Hayakawa Hiroya , Tanaka Riku , Tamura Kosuke , Cha Jaesang , Ahn Chang-Jun
Published: October 2024

This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler shifts. This leads to inter-carrier interference (ICI), significantly degrading communication quality. To mitigate this effect, systems that estimate the maximum Doppler shift and adaptively adjust communication parameters have been extensively studied. One of the most promising methods for maximum Doppler shift estimation involves inserting pilot signals at both the beginning and end of the packet. Although this method achieves high estimation accuracy, it introduces significant latency due to the insertion of the pilot signal at the packet’s end. To address this issue, this paper proposes a new method for rapid estimation using DFCE. The proposed approach compensates for faded signals using channel state information obtained from decision feedback. By treating the compensated signal as a reference, the Doppler shift can be accurately estimated without the need for pilot signals at the end of the packet. This method not only maintains high estimation accuracy but also significantly reduces the latency associated with conventional techniques, making it well-suited for the requirements of next-generation communication systems.
AMW-YOLOv8n: Road Scene Object Detection Based on an Improved YOLOv8
Authors: Wu Donghao , Fang Chao , Zheng Xiaogang , Liu Jue , Wang Shengchun , Huang Xinyu
Published: October 2024

This study introduces an improved YOLOv8 model tailored for detecting objects in road scenes. To overcome the limitations of standard convolution operations in adapting to varying targets, we introduce Adaptive Kernel Convolution (AKconv). AKconv dynamically adjusts the convolution kernel’s shape and size, enhancing the backbone network’s feature extraction capabilities and improving feature representation across different scales. Additionally, we employ a Multi-Scale Dilated Attention (MSDA) mechanism to focus on key target features, further enhancing feature representation. To address the challenge posed by YOLOv8’s large down sampling factor, which limits the learning of small target features in deeper feature maps, we add a small target detection layer. Finally, to improve model training efficiency, we introduce a regression loss function with a Wise-IoU dynamic non-monotonic focusing mechanism. With these enhancements, our improved YOLOv8 model excels in road scene object detection tasks, achieving a 5.6 percentage point improvement in average precision over the original YOLOv8n on real road datasets.
Unsupervised Anomaly Detection via Normal Feature-Enhanced Reverse Teacher–Student Distillation
Authors: Wang Xiaodong , Fan Jiangtao , Yan Fei , Hu Hongmin , Zeng Zhiqiang , Wu Pengtao , Huang Haiyan , Zhang Hangqi
Published: October 2024

In modern industrial production, unsupervised anomaly detection methods have gained significant attention due to their ability to address the challenge posed by the scarcity of labeled anomaly samples. Among them, unsupervised anomaly detection methods based on reverse distillation (RD) have become a mainstream choice, which has attracted extensive research due to their excellent anomaly detection performance. However, there is a problem of “feature leakage” in the RD model, which may lead to non-anomalous regions being incorrectly identified as defects. To solve this problem, we propose a Normal Feature-Enhanced Reverse teacher–student Distillation (NFERD) method. Specifically, we designed and incorporated a normal feature bank (NFB) module into the basic RD network. This module stores normal features extracted by the teacher model, assisting the student model in learning normal features more efficiently, thereby addressing the problem of “feature leakage”. In addition, to effectively fuse the feature maps extracted by the student model with the feature maps in NFBs, we designed a Hybrid Attention Fusion Module (HAFM), which ensures the preservation of key information during the feature fusion process by the parallel processing of spatial and channel attention mechanisms. Through experimental verification on two publicly available datasets, i.e., MVTec and KSDD, our method outperformed the existing mainstream methods in both image-level and pixel-level anomaly detection. Specifically, we achieved an average I-AUROC score of 99.32% on MVTec and a 98.75% P-AUROC on the KSDD, showing clearer segmentation results, especially in complex scenarios. Furthermore, our method surpassed the second-best method by over 1.4% PRO on MVTec, demonstrating its effectiveness.
Distributed Multi-Robot SLAM Algorithm with Lightweight Communication and Optimization
Authors: Han Jin , Ma Chongyang , Zou Dan , Jiao Song , Chen Chao , Wang Jun
Published: October 2024

Multi-robot SLAM (simultaneous localization and mapping) is crucial for the implementation of robots in practical scenarios. Bandwidth constraints significantly influence multi-robot SLAM systems, prompting a reliance on lightweight feature descriptors for robot cooperation in positioning tasks. Real-time map sharing among robots is also frequently ignored in such systems. Consequently, such algorithms are not feasible for autonomous multi-robot navigation tasks in the real world. Furthermore, the computation cost of the global optimization of multi-robot SLAM increases significantly in large-scale scenes. In this study, we introduce a novel distributed multi-robot SLAM framework incorporating sliding window-based optimization to mitigate computation loads and manage inter-robot loop closure constraints. In particular, we transmit a 2.5D grid map of the keyframe-based submap between robots to promote map consistency among robots and maintain bandwidth efficiency in data exchange. The proposed algorithm was evaluated in extensive experimental environments, and the results validate its effectiveness and superiority over other mainstream methods.
Evolution of the “4-D Approach” to Dynamic Vision for Vehicles
Authors: Dickmanns Ernst Dieter
Published: October 2024

Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on.
Heterogeneous Hierarchical Fusion Network for Multimodal Sentiment Analysis in Real-World Environments
Authors: Huang Ju , Chen Wenkang , Wang Fangyi , Zhang Haijun
Published: October 2024

Multimodal sentiment analysis models can determine users’ sentiments by utilizing rich information from various sources (e.g., textual, visual, and audio). However, there are two key challenges when deploying the model in real-world environments: (1) the limitations of relying on the performance of automatic speech recognition (ASR) models can lead to errors in recognizing sentiment words, which may mislead the sentiment analysis of the textual modality, and (2) variations in information density across modalities complicate the development of a high-quality fusion framework. To address these challenges, this paper proposes a novel Multimodal Sentiment Word Optimization Module and a heterogeneous hierarchical fusion (MSWOHHF) framework. Specifically, the proposed Multimodal Sentiment Word Optimization Module optimizes the sentiment words extracted from the textual modality by the ASR model, thereby reducing sentiment word recognition errors. In the multimodal fusion phase, a heterogeneous hierarchical fusion network architecture is introduced, which first utilizes a Transformer Aggregation Module to fuse the visual and audio modalities, enhancing the high-level semantic features of each modality. A Cross-Attention Fusion Module then integrates the textual modality with the audiovisual fusion. Next, a Feature-Based Attention Fusion Module is proposed that enables fusion by dynamically tuning the weights of both the combined and unimodal representations. It then predicts sentiment polarity using a nonlinear neural network. Finally, the experimental results on the MOSI-SpeechBrain, MOSI-IBM, and MOSI-iFlytek datasets show that the MSWOHHF outperforms several baselines, demonstrating better performance.
Unmanned Agricultural Machine Operation System in Farmland Based on Improved Fuzzy Adaptive Priority-Driven Control Algorithm
Authors: Che Yinchao , Zheng Guang , Li Yong , Hui Xianghui , Li Yang
Published: October 2024

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.
Time-Domain Fault Detection and Location Scheme for Flexible DC Distribution Networks
Authors: Li Yafei , Li Jie , Qian Kejun , Yu Xiuyong , Zhang Xinsong
Published: October 2024

Accurately detecting and locating the fault point of the DC line is significant for eliminating the fault and restoring the power supply of the flexible DC distribution network as soon as possible. Firstly, a direction pilot protection scheme for a complex DC distribution network is proposed based on the integral of the current superposition to identify the fault direction. Then, an online time-domain fault location method based on the least square (LS) method to solve the overdetermined equations is proposed by analyzing the fault loop circuit after the DC line fault. The proposed location scheme utilizes fault data at both ends of the line to eliminate the theoretical impact of fault resistance, and the calculation of the current difference method is discussed to reduce the location error of whether the fault current-limiting reactor (CLR) exists. Finally, various simulations by PSCAD/EMTDC V4.5 demonstrate that the proposed scheme has high protection reliability and location results after different fault positions and resistances. The proposed scheme has low requirements for the sampling rate, fault data length, and implementation costs, which can meet practical application requirements.
Correction: Venkatesan, S.; Cho, Y. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture. Energies 2024, 17, 4322
Authors: Venkatesan Saravanakumar , Cho Yongyun
Published: October 2024

In the original publication [...]
Optimized Fault-Tolerant Control of Dual Three-Phase PMSM Under Open-Switch Faults
Authors: Chen Lei , Chen Min , Li Bodong , Sun Xinnan , Jiang Feng
Published: October 2024

In this article, an optimized fault-tolerant control (FTC) method without current judgement is proposed for open-switch faults (OSFs) in dual three-phase permanent magnet synchronous motor (DTPMSM) drives. The reason for the torque ripple under OSFs has been investigated. The theoretical analysis reveals a significant increase in torque ripple under OSFs. Then, an optimized FTC method is proposed for a DTPMSM with two isolated neutral points. The proposed method maintains the original control scheme, enabling the smooth transitions of current and torque between faulty operation and FTC without introducing noticeable torque ripples. In addition, the universality and robustness are enhanced by eliminating the need for current judgement, thereby avoiding misjudgments due to sinusoidal current zero crossings, sudden load, or speed changes. The experimental results are presented to validate the effectiveness of the proposed FTC strategy under OSFs on a laboratory DTPMSM.
Jerusalem Artichoke: Nitrogen Fertilization Strategy and Energy Balance in the Production Technology of Aerial Biomass
Authors: Jankowski Krzysztof Józef , Dubis Bogdan
Published: October 2024

Jerusalem artichoke (Helianthus tuberosus L.) is a plant with considerable potential for energy generation due to its rapid growth, high biomass yield, and resistance to environmental stresses. The aim of this study was to determine the influence of the nitrogen fertilization strategy on the yield and energy balance in the production technology of Jerusalem artichoke (JA) in a perennial cropping system. The article presents the results of a three-year experiment which was conducted in Poland to determine the effect of different N rates (0, 50, 75, and 100 kg ha−1) supplied with mineral fertilizers and liquid digestate on the energy balance in the production of JA aerial biomass. The experiment had a randomized block design with three replications. The demand for energy in JA cultivation reached 16.2–26.3 (year 1) and 2.9–14.6 GJ ha−1 (years 2 and 3). Energy inputs in the cultivation technology were reduced by 17–19% (year 1) and 35–47% (years 2 and 3) when mineral fertilizers were replaced with digestate. Jerusalem artichoke yields were lowest in the technology without fertilization (12.5 Mg ha−1 DM). Dry matter yield increased significantly (by 43–55%) after the application of 75 kg N ha−1, regardless of fertilizer type. The energy output of biomass peaked (230.1 GJ ha−1) in response to a mineral fertilizer rate of 75 kg N ha−1. In turn, the highest energy gain (218.5 GJ ha−1) was noted after the application of digestate at a rate equivalent to 75 kg N ha–1. The energy efficiency ratio was highest in the technology without fertilization (20.1) and after the application of digestate at a rate equivalent to 75 kg N ha−1 (19.7). Regardless of the factors that limit agricultural production, the energy balance of JA biomass production was most favorable when JA was fertilized with digestate at a rate equivalent to 75 kg N ha−1. The results of this study may pave the way for future research on novel agronomic strategies for sustainable bioenergy production, including nutrient recycling.
Recovering Low-Grade Heat from Flue Gas in a Coal-Fired Thermal Power Unit
Authors: Huang Linbin , Chen Guoqing , Xu Xiang , Tan Rui , Gao Xinglong , Zhang Haifeng , Yu Jie
Published: October 2024

To achieve the goals of carbon peaking and carbon neutrality, the retrofitting of existing coal-fired power plants is crucial to achieving energy-saving and emission reduction goals. A conventional recovery system of waste heat typically occurs downstream of the air preheater, where the energy quality in flue gas is low, resulting in limited coal-saving benefits. This study proposes a scheme involving a flue gas exchanger bypassing the air preheater and low-temperature economizers, which is used to transfer the waste heat from flue gas to primary and secondary air (System I). Additionally, a heat pump can be introduced to provide supplementary energy for primary and secondary air, as well as the condensate from the steam turbine (System II). The coal consumption rate and exergy efficiency are used to evaluate the two schemes. The results show that both waste heat recovery systems can increase the power output of the coal-fired unit by recovering waste heat. System II can boost power output by approximately 13.98 MW. The power increase in both waste heat recovery systems show a declining trend as the unit load decreases. This increased power is primarily attributed to the medium- and low-pressure cylinders, while the contributions from ultra-high-pressure and high-pressure cylinders are negligible. The increased power output for the medium-pressure cylinder ranges from approximately 3.49 to 3.58 MW, while the low-pressure cylinder has an increased power output of around 10.10 to 10.19 MW. The coal consumption rate is decreased from 250.3 g/(kW·h) to 247.5 g/(kW·h) under a full load condition for both systems, which can be augmented at lower load conditions. System II outperforms System I at 30% load condition, achieving a reduced coal consumption rate of 3.36 g/(kW·h). System I has an exergy efficiency of 40%, while System II shows a higher efficiency of 44%.
Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model
Authors: Wang Yongtao , Feng Yinhui , Xi Chengfeng , Wang Bochao , Tang Bo , Geng Yanzhao
Published: October 2024

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.
Utility-Scale Grid-Connected Microgrid Planning Framework for Sustainable Renewable Energy Integration
Authors: Abantao Gerald A. , Ibañez Jessa Alesna , Bundoc Paul Eugene Delfin , Blas Lean Lorenzo F. , Penisa Xaviery N. , Esparcia Eugene A. , Castro Michael T. , Pilario Karl Ezra , Tio Adonis Emmanuel D. , Cruz Ivan Benedict Nilo C. , Ocon Joey D. , Odulio Carl Michael F.
Published: October 2024

Microgrids have emerged as a crucial focus in power engineering and sustainable energy research, with utility-scale microgrids playing a significant role in both developed and developing countries like the Philippines. This study presents a comprehensive framework for utility-scale microgrid planning, emphasizing the sustainable integration of renewable energy resources to the distribution grid. The framework addresses the operational modes of grid-connected and islanded microgrids, emphasizing the seamless transition between these modes to ensure a continuous power supply. By leveraging local distributed energy resources, the microgrid aims to reduce dependence on the main transmission grid while enhancing resilience and reliability. The proposed planning framework not only eases the economic burden of constructing renewable energy sources but also aids distribution utilities in maximizing local resources to achieve sustainable energy goals. Through a detailed network analysis and modeling, the framework provides a robust foundation for optimizing the energy mix and enhancing the overall system performance. This research contributes to advancing microgrid technology as a key driver towards achieving UN Sustainable Development Goals, particularly in promoting clean and affordable energy access.
Multi-Agent Reinforcement Learning for Smart Community Energy Management
Authors: Wilk Patrick , Wang Ning , Li Jie
Published: October 2024

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.
Assessing the Impact of Phase-Change Materials on Enhancing the Thermal Efficiency of Buildings in Tropical Climates
Authors: Luiz dos Santos Tássio , Silva Arthur Santos , dos Reis Diogo Duarte
Published: October 2024

Civil construction and buildings account for a significant 36% of worldwide energy consumption, contributing to 37% of global CO2 emissions. In Brazil, buildings consume a substantial 51.2% of the nation’s electricity production. Remarkably, approximately one-third of this energy is allocated specifically for maintaining thermal comfort within these structures. The thermal performance of a building has a significant impact on its energy efficiency; in this way, technologies developed to contribute to the energy efficiency of envelopes can directly contribute to the reduction in the building’s overall energy consumption. PCMs are technologies capable of absorbing heat without increasing temperature and can contribute to the better energy performance of envelopes. PCMs are used as a thermal performance solution in cold climate regions, and studies show that they are likely to work in buildings in tropical climates. The objective of this work is to analyze the performance of PCMs in tropical regions of the southern hemisphere, specifically in Brazil, and their behavior according to the constructive system used. Computer simulation contributes to an analysis closer to the reality of the implementation of this technology in these regions. This work is carried out with simulations in the software EnergyPlusTM version 24.1. The results demonstrate that PCMs can effectively contribute to a reduction in energy consumption for the thermal comfort of buildings in tropical climates, demonstrating the possible feasibility of the development of this technology for tropical climates.
Pressure Change in a Duct with a Flow of a Homogeneous Gaseous Substance in the Presence of a Point Mass and Momentum Sink of Gas
Authors: Ptaszyński Bogusław , Łuczak Rafał , Kuczera Zbigniew , Życzkowski Piotr , Zwolińska-Glądys Klaudia , Borowski Marek
Published: October 2024

The flow characteristics of homogeneous gases in complex systems are an important issue in many areas, including underground mines. The flow in mine excavations and ventilation systems is described by known mathematical relationships that could be applied to various cases. In this paper, a flow in a duct with a local sink of mass and momentum for multiple variants of cooperation of a mechanical fan was analyzed. The relationships for the total and static pressure of air in the duct were derived. In the next stage, a calculation example of how the mass flow rate of air, and the total and static pressure of the flowing air will change in the tested sections for the duct with and without a sink, is presented. The derived formulas and calculated values for the considered calculation case allow the verification of the obtained relationships at the measurement station. Analyzing the results of the examples presented in the article, it can be concluded that the total and static pressure at the sink point differ depending on the equation of motion used. In the case of the classic equation, the value of total pressure is lower than the value calculated from the new equation of motion, and the difference between them is about 20 Pa. In the case of static pressure, this difference is about 46 Pa. Qualitative differences in the static pressure distribution at the release location were also demonstrated. Depending on the applied approach, positive or negative changes in the static pressure are noticed. The presented form of the equation of motion made it possible to determine the flow characteristics in the duct with a point mass and momentum sink in the case of the operation with and without a fan.
Smart Grid Forecasting with MIMO Models: A Comparative Study of Machine Learning Techniques for Day-Ahead Residual Load Prediction
Authors: Nikolaidis Pavlos
Published: October 2024

With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs.