bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Reliability Analysis of Unmanned Aerial Vehicle Swarm Based on Mathematical Models of Binary-State and Multi-State Systems
Authors: Zaitseva Elena , Mukhamediev Ravil , Levashenko Vitaly , Kovalenko Andriy , Kvassay Miroslav , Kuchin Yan , Symagulov Adilkhan , Oksenenko Alexey , Sultanova Zamzagul , Zhaxybayev Darkhan
Published: November 2024

A key aspect in evaluating the performance of a UAV or its swarm is reliability. The reliability is calculated based on various mathematical models. Traditionally, Binary-State System (BSS) models, which assess two states—operational and faulty—are employed. However, some studies suggest using a Multi-State System (MSS) model, which allows for a detailed analysis by considering multiple states beyond just operational and faulty. Both mathematical models allow for the evaluation of Unmanned Aerial Vehicle (UAV) swarms based on availability, which is considered as a probability of swarm mission implementation. There is one more similar assessment computed based on MSS, which is named the probabilities of the performance level. There are not any recommendations for applications of these mathematical models and assessments for reliability analyses of UAV swarms. This paper introduces a comparative study on the availability of UAV swarms using both BSS and MSS models and the probability of performance levels of UAV swarms. This study provides quantitative and qualitative recommendations to exploit these mathematical models and assessments for UAV swarms according to computational complexity and informativeness. The comparative analysis shows that the evaluation of UAV swarm failure should be based on BSS, and the analysis of operation states should be implemented based on probabilities’ performance levels instead of swarm availability. These results are confirmed by quantitative and statistical examinations of UAV swarms of different types based on both BSS and MSS. The number of UAVs is changed from 2 to 20 in these examinations.
Virtual Generator to Replace Backup Diesel GenSets Using Backstepping Controlled NPC Multilevel Converter in Islanded Microgrids with Renewable Energy Sources
Authors: Barros J. Dionísio , Silva J. Fernando A. , Rocha Luis
Published: November 2024

This work presents an islanded microgrid energy system that uses backstepping control applied to neutral point clamped (NPC) multilevel converters coupled with batteries to behave as virtual generators, able to absorb surplus renewable energy, therefore increasing the penetration of renewable energy sources. Additionally, on a charged battery the virtual generator allows turning-off the backup diesel generator set (GenSet). Aside from improving energy efficiency, the battery-connected multilevel converter aims to regulate frequency, improves power quality, and keeps the microgrid operational in the event of a GenSet failure. The backstepping controlled NPC multilevel converter emulates a virtual generator injecting power to perform as the primary and secondary microgrid frequency controller. Additionally, AC voltage control is implemented, which enables running the islanded microgrid only with multilevel converters, supplied by the battery while integrating solar and wind energy sources. Energy demand and renewable energy forecasts are used to manage the battery state-of-charge. Simulation results, obtained from switched and phasor models show that energy storage and the backstepping frequency control enables the compensation of power fluctuations from renewable energy sources. Furthermore, in the event of the main GenSet failure, the controlled virtual generator keeps the microgrid running for a few minutes, until another GenSet is ready to supply the microgrid. Therefore, the microgrid integration of the battery-connected multilevel converter results in a significant boost in energy efficiency by allowing the disconnection of the backup GenSet.
Dynamic Gust Detection and Conditional Sequence Modeling for Ultra-Short-Term Wind Speed Prediction
Authors: Zhou Liwan , Zhang Di , Zhang Le , Zhu Jizhong
Published: November 2024

As the foundation for optimizing wind turbine operations and ensuring energy stability, wind speed forecasting directly impacts the safe operation of the power grid, the rationality of grid planning, and the balance of supply and demand. Furthermore, gust events, characterized by sudden and rapid wind speed fluctuations, pose significant challenges for ultra-short-term wind speed forecasting, making the data more complex and thus harder to predict accurately. To address this issue, this paper proposes a novel hybrid model that combines dynamic gust detection with Conditional Long Short-Term Memory (Conditional LSTM) and incorporates dynamic window adjustment and wind speed difference threshold screening methods. The model dynamically adjusts the window size to accurately detect gust events and uses a conditional LSTM model to adjust predictions based on gust and non-gust conditions. Experimental results show that the proposed model exhibits higher prediction accuracy across various wind speed scenarios, particularly during gust events. Through detailed experiments using data from a single actual wind farm, the effectiveness and practicality of the proposed hybrid model are demonstrated. The experimental results indicate that the proposed model outperforms contrast models, especially in handling gust events, significantly enhancing the robustness of ultra-short-term wind speed predictions.
Temperature-Dependent Ferroelectric Behaviors of AlScN-Based Ferroelectric Capacitors with a Thin HfO2 Interlayer for Improved Endurance and Leakage Current
Authors: Joo Hyeong Jun , Yoon Si Sung , Oh Seung Yoon , Lim Yoojin , Lee Gyu Hyung , Yoo Geonwook
Published: November 2024

The ferroelectric switching behavior of a metal–ferroelectric AlScN–HfO2 interlayer–metal (MFIM) capacitor was investigated at variable temperatures and compared with an MFM capacitor. Although the MFIM capacitor demonstrated an inferior remnant polarization (2Pr value of 74 µC/cm2), it exhibited a reduced leakage current (×1/100) and higher breakdown field. The MFIM showed a stable change in 2Pr from room temperature to 200 °C and an enhanced endurance of ~104 cycles at 200 °C; moreover, the leakage current was less degraded after the cycling tests. Thus, the ferroelectric AlScN with a thin HfO2 interlayer can enhance the reliability of ferroelectric switching.
PredXGBR: A Machine Learning Framework for Short-Term Electrical Load Prediction
Authors: Zabin Rifat , Haque Khandaker Foysal , Abdelgawad Ahmed
Published: November 2024

The growing demand for consumer-end electrical load is driving the need for smarter management of power sector utilities. In today’s technologically advanced society, efficient energy usage is critical, leaving no room for waste. To prevent both electricity shortage and wastage, electrical load forecasting becomes the most convenient way out. However, the conventional and probabilistic methods are less adaptive to the acute, micro, and unusual changes in the demand trend. With the recent development of artificial intelligence (AI), machine learning (ML) has become the most popular choice due to its higher accuracy based on time-, demand-, and trend-based feature extractions. Thus, we propose an Extreme Gradient Boosting (XGBoost) regression-based model—PredXGBR-1, which employs short-term lag features to predict hourly load demand. The novelty of PredXGBR-1 lies in its focus on short-term lag autocorrelations to enhance adaptability to micro-trends and demand fluctuations. Validation across five datasets, representing electrical load in the eastern and western USA over a 20-year period, shows that PredXGBR-1 outperforms a long-term feature-based XGBoost model, PredXGBR-2, and state-of-the-art recurrent neural network (RNN) and long short-term memory (LSTM) models. Specifically, PredXGBR-1 achieves an mean absolute percentage error (MAPE) between 0.98 and 1.2% and an R2 value of 0.99, significantly surpassing PredXGBR-2’s R2 of 0.61 and delivering up to 86.8% improvement in MAPE compared to LSTM models. These results confirm the superior performance of PredXGBR-1 in accurately forecasting short-term load demand.
Accelerated Life Tests for Time-Dependent Response Characterization of Functionalized Piezoelectric Microcantilever-Based Gas Sensors
Authors: Nsubuga Lawrence , de Oliveira Hansen Roana
Published: November 2024

This article explores the accelerated lifetime test approach to characterize the time-dependent response of a piezoelectrically driven microcantilever (PD-MC) based gas sensor. The novelty here relies on demonstrating how accelerated lifetime tests can be useful to differentiate sensing mechanisms for non-linear gas sensors. The results show the determination of the sensor’s optimum operation time while maintaining result validity. The approach is demonstrated for 1,5-diaminopentane (cadaverine), a volatile organic compound (VOC) whose concentration in meat and fish products has been proven viable for determining the shelf life. A PD-MC functionalized with a cadaverine-specific binder was therefore incorporated into a hand-held electronic nose, and the response was found to be highly reliable within a specific resonance frequency shift, enabling the accurate prediction of meat and fish expiration dates. To identify the limits of detection in terms of cadaverine concentration and sensor lifetime, this study applies the results of accelerated life tests into a Weibull distribution analysis to extract the expected time to failure. For the accelerated life tests, a functionalized PD-MC was exposed to high concentrations of cadaverine, i.e., 252.3 mg/kg, 335.82 mg/kg, and 421.08 mg/kg, compared to the nominal concentration of 33 mg/kg observed in meat and fish samples. Furthermore, we demonstrate the differentiation of the response mechanisms of the system accruing from the concentration-dependent interaction of cadaverine with the binder. This enables the determination of the upper limit of the analyte concentration for a stable response. The findings suggest that the functionalized PD-MC sensor exhibits a linear and predictable response when exposed to a standard cadaverine concentration of 33 mg/kg for up to 93.01 min.
Clean Collector Algorithm for Satellite Image Pre-Processing of SAR-to-EO Translation
Authors: Kim Min-Woo , Park Se-Kil , Ju Jin-Gi , Noh Hyeon-Cheol , Choi Dong-Geol
Published: November 2024

In applications such as environmental monitoring, algorithms and deep learning-based methods using synthetic aperture radar (SAR) and electro-optical (EO) data have been proposed with promising results. These results have been achieved using already cleaned datasets for training data. However, in real-world data collection, data are often collected regardless of environmental noises (clouds, night, missing data, etc.). Without cleaning the data with these noises, the trained model has a critical problem of poor performance. To address these issues, we propose the Clean Collector Algorithm (CCA). First, we use a pixel-based approach to clean the QA60 mask and outliers. Secondly, we remove missing data and night-time data that can act as noise in the training process. Finally, we use a feature-based refinement method to clean the cloud images using FID. We demonstrate its effectiveness by winning first place in the SAR-to-EO translation track of the MultiEarth 2023 challenge. We also highlight the performance and robustness of the CCA on other cloud datasets, SEN12MS-CR-TS and Scotland&India.
SG-LPR: Semantic-Guided LiDAR-Based Place Recognition
Authors: Jiang Weizhong , Xue Hanzhang , Si Shubin , Min Chen , Xiao Liang , Nie Yiming , Dai Bin
Published: November 2024

Place recognition plays a crucial role in tasks such as loop closure detection and re-localization in robotic navigation. As a high-level representation within scenes, semantics enables models to effectively distinguish geometrically similar places, therefore enhancing their robustness to environmental changes. Unlike most existing semantic-based LiDAR place recognition (LPR) methods that adopt a multi-stage and relatively segregated data-processing and storage pipeline, we propose a novel end-to-end LPR model guided by semantic information—SG-LPR. This model introduces a semantic segmentation auxiliary task to guide the model in autonomously capturing high-level semantic information from the scene, implicitly integrating these features into the main LPR task, thus providing a unified framework of “segmentation-while-describing” and avoiding additional intermediate data-processing and storage steps. Moreover, the semantic segmentation auxiliary task operates only during model training, therefore not adding any time overhead during the testing phase. The model also combines the advantages of Swin Transformer and U-Net to address the shortcomings of current semantic-based LPR methods in capturing global contextual information and extracting fine-grained features. Extensive experiments conducted on multiple sequences from the KITTI and NCLT datasets validate the effectiveness, robustness, and generalization ability of our proposed method. Our approach achieves notable performance improvements over state-of-the-art methods.
A 3D Coverage Method Involving Dynamic Underwater Wireless Sensor Networks for Marine Ranching Monitoring
Authors: Fu Lei , Wang Ji
Published: November 2024

In view of the poor adaptability and uneven coverage of static underwater wireless sensor networks (UWSNs) to environmental changes and the need for dynamic monitoring, a three-dimensional coverage method involving a dynamic UWSNs for marine ranching, based on an improved sparrow search algorithm (ISSA), is proposed. Firstly, the reverse learning strategy was introduced to generate the reverse sparrow individuals and fuse with the initial population, and the individual sparrows with high fitness were selected to improve the search range. Secondly, Levy flight was introduced to optimize the location update of the producer, which effectively expanded the local search capability of the algorithm. Finally, the Cauchy mutation perturbation mechanism was introduced into the scrounger location to update the optimal solution, which enhanced the ability of the algorithm to obtain the global optimal solution. When deploying UWSNs nodes, an autonomous underwater vehicle (AUV) was used as a mobile node to assist the deployment. In the case of underwater obstacles, the coverage hole in the UWSNs was covered by an AUV at specific times. The experimental results show that compared with other algorithms, the ISSA has a shorter mobile path and achieves a higher coverage rate, with lower node energy consumption.
Low-Complexity Convolutional Neural Network for Channel Estimation
Authors: Sibio Simona , Sestito Cristian , Smida Souheil Ben , Ding Yuan , Goussetis George
Published: November 2024

This paper presents a deep learning algorithm for channel estimation in 5G New Radio (NR). The classical approach that uses neural networks for channel estimation requires more than one stage to obtain the full channel matrix. First, the channel has to be constructed by the received reference signal, and then, the precision is improved. In contrast, to reduce the computational cost, the proposed neural network method generates the channel matrix from the information captured from a few subcarriers along the slot. This information is extrapolated by applying the Least Square technique only on the Demodulation Reference Signal (DMRS). The received DMRS placed in the grid can be seen as a 2D low-resolution image and it is processed to generate the full channel matrix. To reduce complexity in the hardware implementation, the convolutional neural network (CNN) structure is selected. This solution is analyzed comparing the Mean Square Error (MSE) and the computational cost with other deep learning-based channel estimation, as well as the traditional channel estimation methods. It is demonstrated that the proposed neural network delivers substantial complexity savings and favorable error performance. It reduces the computational cost by an order of magnitude, and it has a maximum error discrepancy of 0.018 at 5 dB compared to Minimum Mean Square Error (MMSE) channel estimation.
Integrated Anti-Aliasing and Fully Shared Convolution for Small-Ship Detection in Synthetic Aperture Radar (SAR) Images
Authors: He Manman , Liu Junya , Yang Zhen , Yin Zhijian
Published: November 2024

Synthetic Aperture Radar (SAR) imaging plays a vital role in maritime surveillance, yet the detection of small vessels poses a significant challenge when employing conventional Constant False Alarm Rate (CFAR) techniques, primarily due to the limitations in resolution and the presence of clutter. Deep learning (DL) offers a promising alternative, yet it still struggles with identifying small targets in complex SAR backgrounds because of feature ambiguity and noise. To address these challenges, our team has developed the AFSC network, which combines anti-aliasing techniques with fully shared convolutional layers to improve the detection of small targets in SAR imagery. The network is composed of three key components: the Backbone Feature Extraction Module (BFEM) for initial feature extraction, the Neck Feature Fusion Module (NFFM) for consolidating features, and the Head Detection Module (HDM) for final object detection. The BFEM serves as the principal feature extraction technique, with a primary emphasis on extracting features of small targets, The NFFM integrates an anti-aliasing element and is designed to accentuate the feature details of diminutive objects throughout the fusion procedure, HDM is the detection head module and adopts a new fully shared convolution strategy to make the model more lightweight. Our approach has shown better performance in terms of speed and accuracy for detecting small targets in SAR imagery, surpassing other leading methods on the SSDD dataset. It attained a mean Average Precision (AP) of 69.3% and a specific AP for small targets (APS) of 66.5%. Furthermore, the network’s robustness was confirmed using the HRSID dataset.
Unleashing the Power of AI for Intraoperative Neuromonitoring During Carotid Endarterectomy
Authors: Hindi Roaa , Pappas George
Published: November 2024

This research investigates the use of a 1D Convolutional Neural Network (CNN) to classify electroencephalography (EEG) signals into four categories of ischemia severity: normal, mild, moderate, and severe. The model’s accuracy was lower in moderate instances (75%) and severe cases (65%) compared to normal cases (95%) and mild cases (85%). The preprocessing pipeline now incorporates Power Spectral Density (PSD) analysis, and segment lengths of 32, 64, and 128 s are thoroughly examined. The work highlights the potential of the model to identify ischemia in real time during carotid endarterectomy (CEA) to prevent perioperative stroke. The 1D-CNN effectively captures both temporal and spatial EEG signals, providing a combination of processing efficiency and accuracy when compared to existing approaches. In order to enhance the identification of moderate and severe instances of ischemia, future studies should prioritize the integration of more complex datasets, specifically for severe ischemia, as well as increasing the current dataset. Our contributions in this study are implementing a novel 1D-CNN model to achieve a classification accuracy of over 93%, improving feature extraction by utilizing Power Spectral Density (PSD), automating the ischemia detection procedure, and enhancing model performance using a well-balanced dataset.
Forensic Investigation Capabilities of Microsoft Azure: A Comprehensive Analysis and Its Significance in Advancing Cloud Cyber Forensics
Authors: Morić Zlatan , Dakić Vedran , Kapulica Ana , Regvart Damir
Published: November 2024

This article delves into Microsoft Azure’s cyber forensic capabilities, focusing on the unique challenges in cloud security incident investigation. Cloud services are growing in popularity, and Azure’s shared responsibility model, multi-tenant nature, and dynamically scalable resources offer unique advantages and complexities for digital forensics. These factors complicate forensic evidence collection, preservation, and analysis. Data collection, logging, and virtual machine analysis are covered, considering physical infrastructure restrictions and cloud data transience. It evaluates Azure-native and third-party forensic tools and recommends methods that ensure effective investigations while adhering to legal and regulatory standards. It also describes how AI and machine learning automate data analysis in forensic investigations, improving speed and accuracy. This integration advances cyber forensic methods and sets new standards for future innovations. Unified Audit Logs (UALs) in Azure are examined, focusing on how Azure Data Explorer and Kusto Query Language (KQL) can effectively parse and query large datasets and unstructured data to detect sophisticated cyber threats. The findings provide a framework for other organizations to improve forensic analysis, advancing cloud cyber forensics while bridging theoretical practices and practical applications, enhancing organizations’ ability to combat increasingly sophisticated cybercrime.
Open Tool for Automated Development of Renewable Energy Communities: Artificial Intelligence and Machine Learning Techniques for Methodological Approach
Authors: Piras Giuseppe , Muzi Francesco , Ziran Zahra
Published: November 2024

The architecture, engineering, construction, and operations (AECO) sector exerts a considerable influence on energy consumption and CO2 emissions released into the atmosphere, making a notable contribution to climate change. It is therefore imperative that energy efficiency in buildings is prioritized in order to reduce environmental impacts and meet the targets set out in the European 2030 Agenda. In this context, renewable energy communities (RECs) have the potential to play an important role, promoting the use of renewable energy at the local level, optimizing energy management, and reducing consumption by sharing resources and advanced technologies. This paper introduces an open tool (OT) designed for the configuration of energy systems dedicated to RECs. The OT considers several inputs, including thermal and electrical loads, energy consumption, the type of building, surface area, and population size. The OT employs artificial intelligence (AI) algorithms and machine learning (ML) techniques to generate forecast optimized scenarios for the sizing of photovoltaic systems, thermal, and electrical storage, and the estimation of CO2 emission reductions. The OT features a user-friendly interface, enabling even non-experts to obtain comprehensive configurations for RECs, aiming to accelerate the transition toward sustainable and efficient district energy systems, driving positive environmental impact and fostering a greener future for communities and cities.
Integrated Energy and Environmental Modeling to Design Cost-Effective Building Solutions at a Regional Level
Authors: Januário Mariana , Gomes Ricardo , Baptista Patrícia , Ferrão Paulo
Published: November 2024

This study introduces a computationally efficient urban building energy model (UBEM) to assess decarbonization strategies for the residential sector at the regional level. The model considers a range of inputs, including building characteristics, climate data, technology penetration, and occupant behavior. The model provides an economic analysis associating emission reduction potential with economic returns through an abatement cost curve, which is critical to designing cost-effective solutions. The model was validated at its full scale in Portugal, using actual consumption data from all municipalities. Key findings showed that lighting upgrades (100% LEDs) are the most cost-effective measure, offering the lowest abatement cost (−521 EUR/tonCO2eq) and a low discounted payback period of 2 years, while heat pumps for water heating provide the highest emission reduction potential, with an annual reduction of 863 tonnes of CO2eq annually, equivalent to a 20% reduction in national emissions. Additionally, behavioral measures achieved an annual reduction of 147 tonnes of CO2eq. The analysis further reveals that, while some measures might have a negative abatement cost at the national level, their economic viability varies locally, with certain municipalities incurring positive abatement costs, highlighting how local context affects the economic viability of decarbonization strategies.
Optimization Dispatch of Distribution Network–Prosumer Group–Prosumer Considering Flexible Reserve Resources of Prosumer
Authors: Zhong Hao , Li Lanfang , Wang Qiujie , Wang Xueting , Wang Xinghuo
Published: November 2024

The bidirectional uncertainty of source and load creates scarcity in the reserve resources of the distribution network. Therefore, it is highly significant for the safe and economic operation of the system to harness spare energy storage capacity from prosumers to provide reserves. This paper proposes a bi-layer optimal scheduling model of “distribution network–prosumer group–prosumer” that considers the flexible reserve resources of a prosumer. The upper layer is the “distribution network–prosumer group” optimization model, in which the distribution network sets the electricity price and reserve price according to its own economic benefit and sends it to the prosumer group and guides it to participate in the scheduling of the resources of the prosumer. The lower layer is the “prosumer group–prosumer” optimization model, where the prosumer group incentivizes the prosumer to adjust its energy storage charging and discharging plans through prices and mobilize its own resources to provide flexible reserve resources. The results show that the optimal method proposed in this paper can fully utilize flexible reserve resources from prosumers, improve the economy of distribution network operations, and reduce the pressure of providing reserves using the upper grid.
A Finite-Element-Based Investigation of the Influence of the Production Environment on Fuel Cell Membrane Electrode Assemblies During Manufacturing
Authors: Ma Ling , Yan Zhuoqi , Schabel Sebastian , Fleischer Jürgen
Published: November 2024

The manufacturing process for membrane electrode assemblies (MEAs), from coating to stack assembly, is typically performed under climate-controlled conditions due to the hygroscopic properties of catalyst-coated membranes (CCMs). Large climate-controlled areas in the assembly line not only increase the energy consumption but also limit the scalability of the production line. In this study, experiments were conducted to analyze the effects of ambient humidity on the mechanical properties of a CCM. The hygroscopic swelling behavior of a commercial CCM with an ePTFE-reinforced membrane was also characterized. Using the finite element method, a 3D numerical model covering the entire MEA assembly process was developed, allowing for a numerical investigation of different climate control strategies. The influence of ambient humidity on the dimensional changes in the CCM, which leads to significant stress on the CCM due to mechanical constraints and thus to deformation of the MEA product, was simulated and validated experimentally using optical measurements. Finally, the critical steps during MEA assembly were identified, and a recommendation for the optimal humidity range for climate control was derived.
Blockchain-Enabled Smart Grids for Optimized Electrical Billing and Peer-to-Peer Energy Trading
Authors: Din Jalalud , Su Hongsheng
Published: November 2024

This research investigates the integration of blockchain technology into smart grids, focusing on optimizing both electrical billing and peer-to-peer energy trading between producers and consumers. Using blockchain smart contracts, the system automates and secures energy consumption recording, bill calculation, payment processing, and energy transactions. In the electrical billing framework, a blockchain-based approach was developed to model these functionalities, utilizing an EnergyBilling smart contract to calculate bills and an EnergyPayment smart contract to ensure payment accuracy. Validation using actual consumption data from Sinoma Handan’s project site confirmed the system’s accuracy and reliability when cross-verified with mathematical models. Simultaneously, the study explores peer-to-peer energy trading, where producers (represented by Askari Cement Plant.Nizampur, Pakistan) and consumers (Sinoma Handan Ltd, Handan, China.) conduct automated, transparent transactions. Blockchain’s decentralized nature ensures transparency, data immutability, and a secure, tamper-proof record of transactions. The system eliminates intermediaries, enhancing operational efficiency and reducing costs. Key outcomes demonstrate successful transaction execution with detailed settlements, ensuring financial accountability. Our research highlights blockchain’s transformative potential in revolutionizing electrical billing and energy trading. It offers a secure, transparent, and efficient solution while acknowledging scalability, transaction costs, and regulatory hurdles. Future work could focus on real-world implementation, integration with IoT devices for real-time data collection, and scaling these technologies for broader industrial applications in global energy markets.
Novel Burst-Mode Control for Medium-to-Light Load Operation of Dual-Active-Bridge Converters, Achieving Minimum Backflow Power, Zero-Voltage-Switching, and DC Bias Suppression
Authors: Truong Chanh-Tin , Na Byeong-Ryeol , Park Jeong-Soo , Choi Sung-Jin
Published: November 2024

The dual-active-bridge (DAB) converter is widely used in many applications such as solid-state transformers, multi-port converters, and on-board chargers. Nevertheless, its efficiency degrades significantly under light-load conditions due to high switching and conduction losses. Since a detailed analysis for burst-mode design has not been presented in the literature, effective burst-mode control for the light-load condition is proposed in this paper. In the proposed burst-mode, the regular duty cycle and the burst duty cycle are optimally coordinated to achieve the zero-voltage-switching (ZVS) condition and the minimum backflow power at the same time. Moreover, DC bias current is effectively eliminated in the proposed burst-mode. The switching loss and conduction loss are simultaneously minimized in the proposed burst-mode control. Therefore, the light-load efficiency is significantly improved. The detailed analysis and design procedure are also presented for both buck- and boost-mode operations to deal with widely varying output voltage ranges. The control mode switching condition is determined for achieving the ZVS condition for the whole load condition; thus, the proposed burst-mode control flowchart is presented. A 4 kW DAB converter prototype is built to verify the proposed method and the experiment results show about a 2% increment in efficiency of the proposed method compared to the conventional burst-mode method.
Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks
Authors: Vladov Serhii , Bulakh Maryna , Czyżewski Jan , Lytvynov Oleksii , Vysotska Victoria , Vasylenko Victor
Published: November 2024

This research is devoted to the development of a method for helicopter turboshaft engine energy characteristics control by regulating the free turbine rotor speed and fuel consumption using neural network technologies. A mathematical model was created that links the main rotor and free turbine rotor speed parameters, based on which a relation with the engine output power was established. In this research, a differential equation was obtained that links fuel consumption, output power, and rotor speed, which makes it possible to monitor engine dynamics in various operating modes. A fuel consumption controller was developed based on a neuro-fuzzy network that processes input data, including the desired and current rotor speed, which allows real-time adjustments to improve the operational efficiency. In the research, based on the flight data analysis obtained during the Mi-8MTV helicopter with a TV3-117 turboshaft engine flight test, improved signal processing quality was obtained due to time sampling and adaptive quantisation methods (this is confirmed by assessing the homogeneity and representativeness of the training and test datasets). A comparative analysis of the developed and traditional controllers showed that the neuro-fuzzy network use reduces the transient fuel consumption process time by 8.92% while increasing the accuracy and F1 score by 18.28% and 21.32%, respectively.
Optimal Scheduling and Compensation Pricing Method for Load Aggregators Based on Limited Peak Shaving Budget and Time Segment Value
Authors: Yang Hanyu , Sun Zhihao , Dou Xun , Li Linxi , Yu Jiancheng , Huo Xianxu , Pang Chao
Published: November 2024

Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods based on peak shaving demand forecasts. However, due to the randomness of supply and demand, fluctuations in peak shaving demand occur, making it a significant technical challenge to meet peak shaving needs under limited budget allocations. To address this issue, this paper first conducts a clustering analysis of various adjustable load characteristics to derive typical electricity consumption curves, and then proposes a differentiated calculation method for the value of multi-time-segment peak shaving. Subsequently, an optimization model for LA scheduling and compensation pricing is established based on the limited peak shaving budget and time-segment peak shaving value. While ensuring the economic benefits of LAs, the model also analyzes the impact of different peak shaving budget allocations on the scale of peak shaving that can be achieved. Finally, case studies demonstrate that, compared to traditional fixed compensation pricing, the proposed pricing method reduces scheduling costs by an average of 16.5%, while significantly improving the overall satisfaction of adjustable users.
The Intelligent Sizing Method for Renewable Energy Integrated Distribution Networks
Authors: Yang Zhichun , Yang Fan , Liu Yu , Min Huaidong , Zhou Zhiqiang , Zhou Bin , Lei Yang , Hu Wei
Published: November 2024

The selection of the optimal 35 kV network structure is crucial for modern distribution networks. To address the problem of balancing investment costs and reliability benefits, as well as to establish the target network structure, firstly, the investment cost of the distribution network is calculated based on the determined number of network structure units. Secondly, reliability benefits are measured by combining the comprehensive function of user outage losses with the System Average Interruption Duration Index (SAIDI). Then, a multi-objective planning model of the network structure is established, and the weighted coefficient transformation method is used to convert reliability benefits and investment costs into the total cost of power supply per unit load. Finally, by using the influencing factors of the network structure as the initial population and setting the minimum total cost of the unit load as the fitness function, the DE algorithm is employed to obtain the optimal grid structure under continuous load density intervals. Case studies demonstrate that different load densities correspond to different optimal network structures. For load densities ranging from 0 to 30, the selected optimal network structures from low to high are as follows: overhead single radial, overhead three-section with two ties, cable single ring network, and cable dual ring network.
Numerical Analysis of Flow in U-Type Solid Oxide Fuel Cell Stacks
Authors: Yin Hao Yuan , Yi Kun Woo , Kim Young Jin , Kim Hyeon Jin , Yun Kyong Sik , Yu Ji Haeng
Published: November 2024

Numerical analysis of a U-type solid oxide fuel cell stack was performed using computational fluid dynamics to investigate the effects of stack capacities and fuel/air utilization rates on the internal flow uniformity. The results indicated that increasing the fuel/air utilization rate improved the gas flow uniformity within the stack for the same stack capacity. The uniformity in the anode fluid domain was better than that in the cathode fluid domain. Furthermore, the flow uniformity within the stack was associated with the percentage of pressure drop in the core region of the stack. The larger the percentage of pressure drop in the core region, the more uniform the flow inside the stack. Additionally, under a fuel utilization rate of 75%, the computational results exhibited excessively high fuel utilization rates in the top cell of a 3 kWe stack, indicating a potential risk of fuel depletion during actual stack operation.
Research on Oil and Gas-Bearing Zone Prediction and Identification Based on the SVD–K-Means Algorithm—A Case Study of the WZ6-1 Oil-Bearing Structure in the Beibu Gulf Basin, South China Sea
Authors: Chen Zhilong , Wang Renyi , Xu Biao , Zhu Jianghang
Published: November 2024

The WZ6-1 oil-bearing structure in the Beibu Gulf Basin of the South China Sea has well-developed faults with significant variations in fault sealing capacity, resulting in a complex and highly variable distribution of oil, gas, and water, and limited understanding of hydrocarbon accumulation patterns. Traditional methods, such as single seismic attributes and linear fusion of multiple seismic attributes, have proven ineffective in identifying and predicting oil and gas-bearing areas in this region, leading to five unsuccessful wells. Through comprehensive analysis of drilled wells and seismic data, six types of horizon seismic attributes were selected. A novel approach for predicting oil-bearing zones was proposed, employing SVD–K-means nonlinear clustering for multiple seismic attribute fusion. The application results indicate: ① Singular value decomposition (SVD) technology not only reduces the correlation redundancy among seismic attribute data variables, but enables data dimensionality reduction and noise suppression, decreasing ambiguity in prediction results and enhancing reliability. ② The K-means nonlinear clustering method facilitates the nonlinear fusion of multiple seismic attribute parameters, effectively uncovering the nonlinear features of the underlying relationship between seismic attributes and reservoir oil-bearing characteristics, thereby amplifying the hydrocarbon information within the seismic attribute variables. ③ Compared to K-means, SVD–K-means demonstrates superior performance across all metrics, with an 18.4% increase in the SC coefficient, a 57.8% increase in the CH index, and a 24.7% improvement in the DB index. ④ The results of oil-bearing zone prediction using the SVD–K-means algorithm align well with the drilling outcomes in the study area and correspond to the geological patterns of hydrocarbon enrichment in this region. This has been confirmed by the high-yield industrial oil flow obtained from the newly drilled WZ6-1-A3 well. The SVD–K-means algorithm for predicting oil and gas-bearing zones provides a new approach for predicting hydrocarbon-rich areas in complex fault block structures with limited drilling and poor-quality seismic data.
Design and Visual Implementation of a Regional Energy Risk Superposition Model for Oil Tank Farms
Authors: Yang Yufeng , Zhang Xixiang , Xie Shuyi , Qu Shanqi , Chen Haotian , Xu Qiming , Chen Guohua
Published: November 2024

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks.
Electrical Tortuosities of Porous Structures Based on Triply Periodic Minimal Surfaces and Honeycombs for Power-to-Heat Systems
Authors: Ott Thorsten , Dreißigacker Volker
Published: November 2024

Power-to-heat (P2H) systems offer an efficient solution for decarbonization by facilitating the integration of renewable energy into the industrial, heating, and transport sectors. Its key requirements include high thermal efficiency and an appropriate electrical resistivity to meet application-specific electrical needs. When designing P2H systems, materials and electrical boundary conditions are often limited by application-specific requirements, whereas geometric structures offer high degrees of freedom. While thermal design calculations are often straightforward due to a variety of available Nusselt and pressure loss correlations, simplified design pathways, particularly for porous structures, are lacking in electrical design. Given the wide range of geometric degrees of freedom for porous structures and the fact that detailed modeling involves substantial computational effort, this work employed electrical tortuosity to capture and correlate the geometry-dependent impacts on the effective electrical resistance in a compact way. Honeycomb and triply periodic minimal surface (TPMS)-based structures were selected for this purpose, as they are characterized by high specific surfaces, allowing for high total heat transfer coefficients. The results show that the effective electrical resistance of both TPMS and honeycomb structures can be adjusted by the geometric structure. It was found that the electrical tortuosities of the investigated TPMS structures are nearly identical, while honeycomb structures show slightly higher values. Furthermore, the electrical tortuosity is mainly a function of the void fraction and does not change with the specific surface when the void fraction is kept constant. Finally, correlations for electrical tortuosity depending on geometric parameters with a mean error below 5% are derived for the first time, thereby providing a basis for simplified and computationally efficient electrical design calculations for P2H systems.
Interspecific Relationship Between Monochamus alternatus Hope and Arhopalus rusticus (L.) in Pinus thunbergii Affected by Pine Wilt Disease
Authors: Ji Yingchao , Song Chenyu , Chen Long , Zheng Xue , Jia Chunyan , Liu Yanxue
Published: November 2024

Monochamus alternatus Hope and Arhopalus rusticus (L.) are important stem-boring pests that co-occur on weakened Pinus spp. Their larvae damage the xylem and phloem of the trunks and branches. At present, the consequences of the interspecific relationship between two longicorn beetles on the same host of Pinus trees are unclear. The population dynamics and spatial distribution of these two species on Pinus thunbergii trees were investigated to clarify the ecological niches and interspecific relationship of two longicorn beetles on the different degrees of decline in P. thunbergii trees. The results showed temporal niche overlap values from 0.02 ± 0.01 to 0.05 ± 0.02, suggesting a very high degree of temporal ecological niche segregation and no competition in temporal niche resources. There is significant interspecific competition between the two longicorn beetles in spatial distribution, and the spatial niche overlap values are 0.67 ± 0.11 and 0.61 ± 0.09 in the middle and late stages of the decline in P. thunbergii trees, respectively.
Remote Sensing Identification of Picea schrenkiana var. tianschanica in GF-1 Images Based on a Multiple Mixed Attention U-Net Model
Authors: Zheng Jian , Chen Donghua , Zhang Hanchi , Zhang Guohui , Zhen Qihang , Liu Saisai , Zhang Naiming , Zhao Haiping
Published: November 2024

Remote sensing technology plays an important role in woodland identification. However, in mountainous areas with complex terrain, accurate extraction of woodland boundary information still faces challenges. To address this problem, this paper proposes a multiple mixed attention U-Net (MMA-U-Net) semantic segmentation model using 2015 and 2022 GF-1 PMS images as data sources to improve the ability to extract the boundary features of Picea schrenkiana var. tianschanica forest. The U-Net architecture serves as its underlying network, and the feature extraction ability of the Picea schrenkiana var. tianschanica is improved by adding hybrid attention CBAM and replacing the original skip connection with the DCA module to improve the accuracy of the model segmentation. The results show that on the remote sensing dataset with GF-1 PMS images, compared with the original U-Net and other models, the accuracy of the multiple mixed attention U-Net model is increased by 5.42%–19.84%. By statistically analyzing the spatial distribution of Picea schrenkiana var. tianschanica as well as their changes, the area was 3471.38 km2 in 2015 and 3726.10 km2 in 2022. Combining the predicted results with the DEM data, it was found that the Picea schrenkiana var. tianschanica were most distributed at an altitude of 1700–2500 m. The method proposed in this study can accurately identify Picea schrenkiana var. tianschanica and provides a theoretical basis and research direction for forest monitoring.
Virtualization vs. Containerization, a Comparative Approach for Application Deployment in the Computing Continuum Focused on the Edge
Authors: Sturley Hamish , Fournier Augustin , Salcedo-Navarro Andoni , Garcia-Pineda Miguel , Segura-Garcia Jaume
Published: November 2024

With the emergence of containerization 10 years ago, we saw a compact, convenient and portable way of running apps directly concurrently with virtualization. The major difference is in the architecture. Containers share the same kernel as the guest and then do not virtualize low-layer components like the Central Processing Unit (CPU). On the one hand, they are lighter and more flexible than virtual machines (VMs). On the other hand, VMs can more precisely meet the low-layer needs and are completely autonomous systems. Nowadays, what is the best architecture to use to develop an application? In this paper, we will study the two main virtual methods of deploying this. We will compare both methods on several criteria: compatibility based on user experience and the ease of installation/deployment, scalability based on the automatic elasticity facing the workload and energy efficiency in terms of energy and computer resources. After the tests, we conclude that the containerization option is the most ecologically advantageous option in terms of energy consumption.
Evasion Differential Games in the Space of Square Summable Sequences
Authors: Aminov Bekhzod , Ruziboev Marks
Published: November 2024

In this article, we consider simple-motion pursuit–evasion differential games in the Hilbert space of square summable sequences. We show that when the players have the same dynamic capabilities, evasion is possible under some assumptions about the initial positions of the players.