bluuebunny/crossref_metadata_2025 huggingface dataset:
Radar False Alarm Suppression Based on Target Spatial Temporal Stationarity for UAV Detecting
Authors: Sun Chunlin , Mao Xingpeng , Tang Zhibo , Lou Peng
Published: November 2024

Due to its ease of implementation without an additional transmitter, radar communication integrated systems using conventional communication signals have become an effective means for monitoring unmanned aerial vehicles and other aircraft. However, their non-ideal radar signal form causes strong signals to mask weak signals, and clutter suppression is required to detect a target. As the energy of a target signal is extremely low and conventional clutter suppression methods have limited performance, the residual clutter persists after the time-varying clutter is suppressed, resulting in many false alarm points on the processed range–Doppler (RD) map. The two-dimensional distribution of these false alarms on the range–Doppler is very similar to that of the target and difficult to discriminate, which seriously affects target detection and tracking. To reduce false alarm points and improve the performance of target detection, the difference in the spatial–temporal stationarity between a target signal and clutter in a short time is discussed in this paper; a radar false alarm suppression method based on a target’s spatial–temporal stationarity is proposed by using the difference in the stationarity between the target signal and false alarm points in the range, Doppler, energy and azimuth. In this algorithm, to ensure the short-time stationarity of the target, the RD map of the short-time interval sub-frames is obtained by using the sliding matching filtering method and the peak points are extracted. Then, the Mahalanobis distance between the peak points of each sub-frame is used to eliminate the false alarm points. Finally, the false alarm points are further eliminated by target tracking and the real target information to improve the radar target detection performance. The simulation experiments show that this method can eliminate more than 90% of the false alarms points while maintaining the target detection performance. The analysis of actual data obtained from the field experiment indicate that the implementation of this algorithm, which effectively suppresses false alarms, leads to improved target detection outcomes. These enhancements can facilitate the tracking of UAVs and other aircraft.
Research on Particle Swarm Optimization-Based UAV Path Planning Technology in Urban Airspace
Authors: Cheng Qing , Zhang Zhengyuan , Du Yunfei , Li Yandong
Published: November 2024

Urban airspace, characterized by densely packed high-rise buildings, presents complex and dynamically changing environmental conditions. It brings potential risks to UAV flights, such as the risk of collision and accidental entry into no-fly zones. Currently, mainstream path planning algorithms, including the PSO algorithm, have issues such as a tendency to converge to local optimal solutions and poor stability. In this study, an improved particle swarm optimization algorithm (LGPSO) is proposed to address these problems. This algorithm redefines path planning as an optimization problem, constructing a cost function that incorporates safety requirements and operational constraints for UAVs. Stochastic inertia weights are added to balance the global and local search capabilities. In addition, asymmetric learning factors are introduced to direct the particles more precisely towards the optimal position. An enhanced Lévy flight strategy is used to improve the exploration ability, and a greedy algorithm evaluation strategy is designed to evaluate the path more quickly. The configuration space is efficiently searched using the corresponding particle positions and UAV parameters. The experiments, which involved mapping complex urban environments with 3D modeling tools, were carried out by simulations in MATLAB R2023b to assess their algorithmic performance. The results show that the LGPSO algorithm improves by 23% over the classical PSO algorithm and 18% over the GAPSO algorithm in the optimal path distance under guaranteed security. The LGPSO algorithm shows significant improvements in stability and route planning, providing an effective solution for UAV path planning in complex environments.
Correction: Telesca et al. Visibility Graph Analysis of Reservoir-Triggered Seismicity: The Case of Song Tranh 2 Hydropower, Vietnam. Entropy 2022, 24, 1620
Authors: Telesca Luciano , Thai Anh Tuan , Lovallo Michele , Cao Dinh Trong
Published: November 2024

There was an error in the original publication [...]
User Real Comments Incentive Mechanism Based on Blockchain in E-Commerce Transactions—A Tripartite Evolutionary Game Analysis
Authors: Le Chengyi , Zheng Ran , Lu Ting , Chen Yu
Published: November 2024

In response to the widespread issue of fake comments on e-commerce platforms, this study aims to analyze and propose a blockchain-based solution to incentivize authentic user feedback and reduce the prevalence of fraudulent reviews. Specifically, this paper constructs a tripartite evolutionary game model between sellers, buyers, and e-commerce platforms to study the real comment mechanism of blockchain. The strategy evolution under different incentive factors is simulated using replication dynamic equation analysis and Matlab software simulation. The study found that introducing smart contracts and “tokens” for incentives not only increased incentives for real comments but also reduced the negative experiences caused by “speculative” sellers, thereby influencing buyers to opt for authentic reviews. By structuring interactions through blockchain, the mechanism helped lower informational entropy thus reducing disorder and unpredictability in buyer and seller behavior and contributing to system stability. Further, by increasing penalties for dishonest behavior under the “credit on the chain” system, the platform lowered entropy in the system by promoting trust and reducing fraudulent activities. The real comment mechanism based on blockchain proposed in this paper can effectively enhance the order and transparency within the comment ecosystem. These findings contribute to theory and practice by providing strategic insights for e-commerce platforms to encourage genuine feedback, reduce informational entropy, and mitigate fake comments, ultimately fostering a more reliable online marketplace.
Advancing Continuous Distribution Generation: An Exponentiated Odds Ratio Generator Approach
Authors: Chen Xinyu , Shi Zhenyu , Xie Yuanqi , Zhang Zichen , Cohen Achraf , Pu Shusen
Published: November 2024

This paper presents a new methodology for generating continuous statistical distributions, integrating the exponentiated odds ratio within the framework of survival analysis. This new method enhances the flexibility and adaptability of distribution models to effectively address the complexities inherent in contemporary datasets. The core of this advancement is illustrated by introducing a particular subfamily, the “Type 2 Gumbel Weibull-G family of distributions”. We provide a comprehensive analysis of the mathematical properties of these distributions, including statistical properties such as density functions, moments, hazard rate and quantile functions, Rényi entropy, order statistics, and the concept of stochastic ordering. To test the robustness of our new model, we apply five distinct methods for parameter estimation. The practical applicability of the Type 2 Gumbel Weibull-G distributions is further supported through the analysis of three real-world datasets. These real-life applications illustrate the exceptional statistical precision of our distributions compared to existing models, thereby reinforcing their significant value in both theoretical and practical statistical applications.
A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning
Authors: Chen Danmin , Zhang Zhiqiang , Zhou Funa , Wang Chaoge
Published: November 2024

A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data, construct a two-level transfer mechanism to fuse multi-source heterogeneous information, avoid convolutional operations, and achieve real-time fault diagnosis. Its main work is to build a feature extraction network model of screenshots, design a mechanism for transfer from the feature extraction model using screenshots to the deep learning model using one-dimensional sequence signals, and complete the transfer from a convolutional neural network to a deep neural network. After two-level transfer, the fault diagnosis model not only integrates the characteristics of one-dimensional sequence signals and screenshots but also avoids convolution operations and has a low time complexity. The effectiveness of the proposed method is verified using a gearbox dataset and a bearing dataset.
Reduced Gaussian Kernel Filtered-x LMS Algorithm with Historical Error Correction for Nonlinear Active Noise Control
Authors: Ku Jinhua , Han Hongyu , Zhou Weixi , Wang Hong , Zhang Sheng
Published: November 2024

This paper introduces a reduced Gaussian kernel filtered-x least mean square (RGKxLMS) algorithm for a nonlinear active noise control (NANC) system. This algorithm addresses the computational and storage challenges posed by the traditional kernel (i.e., KFxLMS) algorithm. Then, we analyze the mean weight behavior and computational complexity of the RGKxLMS, demonstrating its reduced complexity compared to existing kernel filtering methods and its mean stable performance. To further enhance noise reduction, we also develop the historical error correction RGKxLMS (HECRGKxLMS) algorithm, incorporating historical error information. Finally, the effectiveness of the proposed algorithms is validated, using Lorenz chaotic noise, non-stationary noise environments, and factory noise.
Beyond Traditional Lesson Study: How Mathematics Studio Supports Generative Teacher Learning
Authors: Lesseig Kristin , Hoppe Jessica
Published: November 2024

In this paper, we describe the interworking of a group of middle school mathematics teachers who engaged in Mathematics Studio, an adaptation of lesson study, across three years. We use this case to illuminate specific structures, protocols, and norms of interaction within Studio that create conditions for teacher learning and incremental changes in teachers’ instruction. Our analysis revealed several discourse practices, including the adoption of a research lens, a shared language of affordances and constraints, and an orientation toward student learning that supported a culture of inquiry as teachers investigated genuine questions they had about instruction. In this paper, we elaborate on these practices and share examples of pedagogically productive talk. We claim Mathematics Studio has the potential to circumvent previously identified challenges to support generative learning and the ongoing growth in teachers’ instructional practice.
A Shift from an Audio- to a Video-Based Exam Format to Reflect Real-Life Clinical Interactions in the Language-Learning Classroom
Authors: Hild Gabriella , Dávidovics Anna , Warta Vilmos , Németh Timea
Published: November 2024

Effective patient communication is vital in medical training. At a Hungarian Medical university, international students in the English-medium program are required to study Hungarian for two years to prepare for clinical rotations in Hungarian hospitals. The final language assessment traditionally included an audio-based listening exam, but both students and teachers raised concerns about its difficulty and its lack of relevance to real-life clinical interactions and the students’ actual language needs. A needs analysis was conducted with 52 second-year international medical students through focus-group interviews after they took the exam to address these issues. Based on the feedback, a video-based exam format was developed and piloted. The new format incorporated visual cues such as gestures and facial expressions, better reflecting face-to-face patient communication. A total of 38 third-year students who had previously taken the audio-based version of the exam participated in the pilot, with focus-group interviews conducted to directly compare the two formats. The majority of the students found the video-based exam more engaging and relevant to their clinical experience. The findings suggest that the video-based exam better prepares students for real-life medical communication and provides a more meaningful assessment experience, bridging the gap between language learning and clinical practice.
The Adaption of Sustainable Blended Global Discussion (SBGD) in English as a Foreign Language Teaching and Learning
Authors: Gayatri Putri , Sit Helena
Published: November 2024

The growing significance of English in global communication has heightened interest in sustainable English as a Foreign Language (EFL) instruction. Regrettably, sustainable EFL education has received insufficient attention, especially in the Indonesian context. To address this issue, recent conceptual research by Gayatri et al. has put forth a practical recommendation, namely the Sustainable Blended Global Discussion (SBGD) method, which combines classroom and online learning to engage students in global discussions with foreigners, promoting critical thinking. Despite being constructed on a solid foundation of theory and Indonesian context, the success of SBGD remains to be demonstrated. Hence, a multiple case study was conducted to examine the adaptation of SBGD in EFL classes at different universities. The EFL teachers were interviewed to explore the method’s advantages and challenges. Questionnaires were employed to study the perception and the critical thinking skills of 57 students, with some of them also being interviewed. Results showed that students indicated positive perception of the implementation of SBGD (M = 4.02 and M = 4.05). Additionally, students demonstrated a higher level of critical thinking skills through the teacher’s SBGD implementation in teaching and learning. Furthermore, greater student engagement, improved English language skills, and improved critical thinking were all observed; however, improvements like smaller group discussions, more facilitator involvement, and institutional supports were needed. This study is significant in addressing challenges and recommending the SBGD method as a solution for implementing online technologies in under-resourced contexts, specifically Indonesian higher education. The findings contribute to the literature on blended teaching and digital tools for second language education, with broader implications for similar educational settings.
Do High School Students Learn More or Shift Their Beliefs and Attitudes Toward Learning Physics with the Social Constructivism of Problem-Based Learning?
Authors: Sagatbek Amangul , Oni Temitayo Kehinde , Adah Miller Emily , Gabdullina Gulmira , Balta Nuri
Published: November 2024

Rooted in social constructivist learning theory, problem-based learning (PBL) is a tool that deepens students’ learning of complex subjects and improves students’ attitudes and beliefs towards learning. Physics is a subject that students themselves view as challenging. When taking physics, students develop negative beliefs about their own learning of the subject. There is a call for more innovation in the subject area of physics. This study addresses the following: (1) What is the effect of PBL on the achievement of 10th-grade students in mechanics when compared to traditional instruction? (2) How do students’ beliefs and attitudes towards physics change before and after the intervention, and how might these beliefs and attitudes relate to their competency outcomes? The sample of this study comprised 63 students in the 10th grade in a public (non-elite) school distributed across four classes, where the teacher used PBL with the experimental group and traditional teaching with the control group. The physics teacher who implemented PBL has 13 years of teaching experience. The two instruments used were the Force Concept Inventory and the Colorado Learning Attitudes About Science Survey. The result of this study revealed that, although students’ knowledge of physics increased when their teachers adopted the PBL approach, there were no significant changes in their attitudes and beliefs towards learning physics. The implications suggest that there is potential for PBL to be taken up by high school science teachers to improve their students’ physics knowledge, but may this not impact their attitudes and beliefs, which presents questions to investigate further.
Exploring Tutors’ Roles in Supporting Student Mental Health: Expectations and Perceptions in Higher Education
Authors: Douwes Rynke , Metselaar Janneke , van der Meulen Erik , Boonstra Nynke , Pijnenborg Gerdina Hendrika Maria
Published: November 2024

Student mental health is a challenging topic in higher education, with institutions aiming to develop policies and practices to support students’ personal and professional development. This study examined students’ expectations of their tutors regarding mental health support. An adaptation of the Mentor-Q questionnaire, focusing on tutor role perceptions, was used to measure student expectations. Confirmatory factor analysis showed a good fit of the adapted instrument. The questionnaire was completed by 869 students at a Dutch university of applied sciences, and the results were discussed with eight students. The findings revealed four role expectations of tutors: awareness raisers, connectors, referrers, and guardians. Latent class analysis revealed six clusters, with connector and referrer roles as well as awareness raiser and guardian roles being almost equally important within the clusters. The main difference between the clusters was the height of expectations. Satisfaction with the tutor was significantly related to cluster membership, such that higher expectations correlated with lower satisfaction. Additionally, students who discussed their mental health with their tutors were more represented in clusters with relatively low expectations. Discussions with the students highlighted the nuanced landscape of expectations and the importance of contextual factors and metacommunications. Implications for policy and practical development are also discussed.
Optimized High-Voltage Analog Switch and Its Control Circuit Based on Silicon-on-Insulator Technology
Authors: Li Zhuze , Lai Xinquan , Ding Chentao , Jin Dinghai , Wang Jiabao , Liu Chen
Published: November 2024

In recent years, high-voltage analog switches have been widely used in various fields. To handle complex use scenarios, high-voltage analog switches need to achieve the goals of low on-resistance, high isolation performance, high response speed, and high voltage withstand range. Traditional high-voltage analog switches have issues such as low integration, large area, and slow response speed. This paper uses a super junction MOSFET (SJ-MOS) with a 0.18 μm SOI process and a solid-state relay (SSR) structure to implement a high-voltage analog switch. A gate drive circuit suitable for low gate-source breakdown voltage is proposed to maintain the gate-source voltage, achieving a low on-resistance of 24 Ω and high isolation. Compared with traditional high-voltage analog switches, it achieves higher performance with a smaller area.
Band Alignment of AlN/InGaZnO Heterojunction for Thin-Film Transistor Application
Authors: Zhang Hongpeng , Huang Tianli , Cao Rongjun , Wang Chen , Peng Bo , Wu Jibao , Wang Shaochong , Zheng Kunwei , Jia Renxu , Zhang Yuming , Zhang Hongyi
Published: November 2024

Uncrystallized indium-gallium-zinc-oxide (InGaZnO) thin-film transistors (TFTs) combined with an aluminum nitride (AlN) dielectric have been used to promote performance and steadiness. However, the high deposition temperature of AlN films limits their application in InGaZnO flexible TFTs. In this work, AlN layers were deposited via low-temperature plasma-enhanced atomic layer deposition (PEALD), and InGaZnO films were fabricated via high-power impulse magnetron sputtering (HIPIMS). The band alignment of the AlN/InGaZnO heterojunction was studied using the X-ray photoemission spectrum and ultraviolet visible transmittance spectrum. It was found that the AlN/InGaZnO system exhibited a staggered band alignment with a valence band offset ΔEv of −1.25 ± 0.05 eV and a conduction band offset ΔEc of 4.01 ± 0.05 eV. The results imply that PEALD AlN could be more useful for surface passivation than a gate dielectric to promote InGaZnO device reliability under atmospheric exposure.
Estimation of the Immunity of an AC/DC Converter of an LED Lamp to a Standardized Electromagnetic Surge
Authors: Sabat Wiesław , Klepacki Dariusz , Kamuda Kazimierz , Kuryło Kazimierz , Jankowski-Mihułowicz Piotr
Published: November 2024

The method for estimating the immunity of an AC/DC converter built in a commercial LED lamp to a 1.2/50 µs (8/20 µs) surge has been presented in this paper. A lamp with a direct drive LED inverter was selected to present the methodology for determining the coefficient of immunity of the test object to a standardized type of surge. The choice of this configuration was important for the testing process and presentation of the methodology to estimate the immunity coefficient of the tested system. In this work, the methodology for determining the deterministic immunity factor of the model inverter to a normalized type of disturbance was presented. Considerations were carried out for a 1.2/50 µs (8/20 µs) surge in accordance with the recommendations of the EN 61000-4-5:2014 standard. This conventional surge is used in laboratory practice to test the immunity of electronic and electrical systems and devices to disturbances that can be generated in the power grid during switching processes, short circuits, and direct and indirect lightning. In the first stage of testing on test benches, the intensity of damage to the integral components of a model inverter was examined with increasing levels of disturbance. Statistical measures characterizing their impact resistance were determined for each of the elements tested. Knowing their values, the value of this coefficient was finally determined for the lamp selected for testing, and the mechanism of its damage was analyzed.
Leveraging AI for Network Threat Detection—A Conceptual Overview
Authors: Paracha Muhammad Ali , Jamil Syed Usman , Shahzad Khurram , Khan Muhammad Arif , Rasheed Abdul
Published: November 2024

Network forensics is commonly used to identify and analyse evidence of any illegal or unauthorised activity in a given network. The collected information can be used for preventive measures against potential cyber attacks and serve as evidence acceptable in legal proceedings. Several conventional tools and techniques are available to identify and collect such pieces of evidence; however, most of them require expensive commercial resources, longer investigation times, and costly human expertise. Due to modern networks’ diverse and heterogeneous nature, forensic operations through conventional means become a cumbersome and challenging process. This calls for a new look at how network forensics is approached, considering contemporary approaches to network analysis. In this work, we explore artificial intelligence (AI) techniques based on contemporary machine learning (ML) algorithms such as deep learning (DL) to conduct network forensics. We also propose an investigation model based on AI/ML techniques that can analyse network traffic and behavioural patterns to identify any prior or potential cyber attacks. The proposed AI-based network forensics model speeds up the investigation process, boosting network monitoring without human intervention. This also aims to provide timely and accurate information to network administrators for quick and effective decisions, enabling them to avoid and circumvent future cyber attacks.
Adaptive Dynamic Shuffle Convolutional Parallel Network for Image Super-Resolution
Authors: Long Yiting , Ruan Haoyu , Zhao Hui , Liu Yi , Zhu Lei , Zhang Chengyuan , Zhu Xinghui
Published: November 2024

Image super-resolution has experienced significant advancements with the emergence of deep learning technology. However, deploying highly complex super-resolution networks on resource-constrained devices poses a challenge due to their substantial computational requirements. This paper presents the Adaptive Dynamic Shuffle Convolutional Parallel Network (ADSCPN), a novel lightweight super-resolution model designed to achieve an optimal balance between computational efficiency and image reconstruction quality. The ADSCPN framework employs large-kernel parallel depthwise separable convolutions, dynamic convolutions, and an enhanced attention mechanism to optimize feature extraction and improve detail preservation. Extensive evaluations on standard benchmark datasets demonstrate that ADSCPN achieves state-of-the-art performance while significantly reducing computational complexity, making it well-suited for practical applications on devices with limited computational resources.
A Multi-Attack Adaptive Fake Face Detection Network Based on Global Feature Normalization
Authors: Xie Honggang , Liu Jia , Chen Zhiwei , Hou Kaiyuan , Yao Yuan
Published: November 2024

The advancement of deepfake technology has resulted in increasingly realistic forged faces, posing a challenge for existing fake face detection models, which often exhibit poor adaptability to complex and varied forgery techniques. To address this challenge, this paper proposes a fake face detection network that demonstrates high accuracy and generalization capabilities. A global normalization calibration module is designed to address the issue of ineffective feature extraction within the network, enhancing the model’s focus on key feature regions by establishing a mutual inhibition competition mechanism between different channels. This network maintains high accuracy even when facing various attack methods in both the objective and digital worlds. Experimental results indicate that the proposed method excels in both binary and multi-class classification tests on the CASIA-FASD dataset, the Celeb-DF dataset, and a self-constructed dataset. Specifically, for binary classification tasks, the accuracy reaches 98.19% on the CASIA-FASD dataset, 99.17% on the Celeb-DF dataset, and 98.03% on the self-constructed dataset. In comparison, other methods exhibit slightly inferior performance. For multi-class classification tasks, the proposed model achieves the best performance across all tested forgery types, demonstrating exceptional overall performance and adaptability.
Parameter-Efficient Tuning for Object Tracking by Migrating Pre-Trained Decoders
Authors: Zhang Ruijuan , Wang Li , Yang Song
Published: November 2024

Video object tracking has taken advantage of pre-trained weights on large-scale datasets. However, most trackers fully fine-tune all the backbone’s parameters for adjusting to tracking-specific representations, where the utilization rate of parameter adjustment is inefficient. In this paper, we aim to explore whether a better balance can be achieved between parameter efficiency and tracking performance, and fully utilize the weight advantage of training on large-scale datasets. There are two main differences from a normal tracking paradigm: (i) We freeze the pre-trained weights of the backbone and add a dynamic adapter structure for every transformer block for tuning. (ii) We migrate the pre-trained decoder blocks to the tracking head for better generalization and localization. Extensive experiments are conducted on both mainstream challenging datasets and datasets for special scenarios or targets such as night-time and transparent objects. With the full utilization of pre-training knowledge, we found through experiments that a few tuned parameters can compensate for the gap between the pre-trained representation and the tracking-specific representation, especially for large backbones. Even better performance and generalization can be achieved. For instance, our AdaDe-B256 tracker achieves 49.5 AUC on the LaSOT⁢ext which contains 150 sequences.
Consequences of Non-Compliance with Technological Procedures in the Realisation of Construction Objects from the Point of View of Heat Consumption for Heating—A Case Study on Selected Construction Sites in the Slovak Republic
Authors: Hlina Ján , Makýš Peter , Šťastný Patrik , Paulovičová Lucia
Published: November 2024

This article focuses on the energy performance of buildings with an emphasis on the consequences of non-compliance with technological practices during the building process. We analyse the impact of construction deficiencies on the consumption of heat for heating, focusing on specific case studies of selected building constructions in the Slovak Republic. The results show that non-compliance with prescribed technological standards and procedures leads to significant deterioration in the building’s energy efficiency, which is manifested in increased heat consumption and higher operating costs. The findings of this study have key importance for future construction projects as they offer valuable recommendations for improving energy standards and construction quality, thus contributing to a more sustainable and efficient building process. When designing buildings with near-zero energy demand, it is necessary to eliminate all risks in the project that arise during the preparation and design itself, as well as during implementation.
Factors Influencing Differences Between Computed and Measured Ground Resistance Values for Horizontal Tape Electrodes
Authors: Muhammad Usman , Ahmad Nurul Nadia , Mohamad Nor Normiza , Aman Fazlul
Published: November 2024

This paper investigates the steady-state resistance (RDC) of copper tape electrodes across eight configurations. The study evaluates both field measurements and simulations using CDEGS at two distinct sites with varying soil characteristics. It emphasizes the impact of electrode size, installation sequence, and soil disturbance caused by sequential installations. Specifically, the results reveal that the first configuration, which maintained a 100% tape-to-trench ratio with no disturbance, yielded computed values within the measured range at both sites. Subsequent configurations demonstrated varying degrees of soil disturbance, affecting RDC values, particularly in high-resistivity conditions. At the low-resistivity Site 1, as the tape-to-trench ratio increased, discrepancies between measured and computed RDC values decreased, highlighting a strong dependency on electrode size and soil cohesion after backfilling. In contrast, at the high-resistivity Site 2, RDC values remained relatively stable with increasing tape-to-trench ratio, likely due to lower soil cohesion and higher air void presence. These findings underscore the importance of considering soil disturbance effects in computational models to enhance the accuracy of RDC predictions and optimize grounding performance.
Review of Voltage Balancing Techniques for Series-Connected SiC Metal–Oxide–Semiconductor Field-Effect Transistors
Authors: Sun Lucheng , Qiao Mingzhong , Xia Yihui , Wu Bo , Chen Fulin
Published: November 2024

Power devices in series are low-voltage power devices used in medium- and high-voltage applications in a more direct program. However, when power devices in series are used, because of their electrical performance parameters or external circuit conditions, there are unique short-circuit voltage imbalances, a serious threat to the safety of the device. The article first summarizes the research status and characteristics of the four models of SiC MOSFETs based on the domestic and international research on the models of SiC MOSFETs in recent years; second, the voltage balancing technology of series-connected SiC MOSFETs is sorted out and summarized, and then the driving circuits of SiC MOSFETs are sorted out and summarized. Again, several voltage balancing techniques reviewed are compared in six different aspects: cost, modularity, complexity, speed of voltage balancing, losses, and effectiveness of voltage balancing. Finally, an outlook of voltage balancing techniques for series SiC MOSFETs is provided.
Thin-Film Photovoltaic Modules Characterisation Based on I-V Measurements Under Outdoor Conditions
Authors: Gulkowski Slawomir , Krawczak Ewelina
Published: November 2024

The characterisation of photovoltaic modules requires a specialised laboratory that guarantees precise control of irradiance and its spectrum and control of the module temperature during testing. As an alternative, characteristic parameters can be extracted from the measurements of the current-voltage characteristics (I-V curves) carried out under outdoor conditions. This paper presents the results of the two commercial thin-film photovoltaic modules’ characterisation. The first analysed device was a cadmium telluride (CdTe) photovoltaic module fabricated on glass, while the second was the flexible copper indium gallium diselenide (CIGS) PV module. The main parameters of the PV modules were extracted based on the series of I-V curve measurements under real operating conditions in Poland with the use of the capacitor-based I-V tracer. Solar radiation together with the modules’ temperature were registered simultaneously with the I-V characterisation. Two approaches were proposed to estimate the main PV parameters at standard test conditions as output power, short circuit current or open circuit voltage. The difference in results of power for both approaches was below 1.5%. Energy, computed using the Osterwald model, was compared with the experimental measurements. The best results of absolute relative error (ARE) were found around 0.5% for both technologies. The lowest value of root mean squared error (RMSE) was 1.3% in terms of CdTe technology and 3.1% for CIGS.
Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach
Authors: Ali Waqar , El-Thalji Idriss , Giljarhus Knut Erik Teigen , Delimitis Andreas
Published: November 2024

Wind turbine blades are critical components of wind energy systems, and their structural health is essential for reliable operation and maintenance. Several studies have used time-domain and frequency-domain features alongside machine learning techniques to predict faults in wind turbine blades, such as erosion and cracks. However, a key gap remains in integrating these methods into a unified framework for fault prediction, which could offer a more comprehensive solution for diagnosing faults. This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. Open-source vibration data collected from an experimental setup (where a small wind turbine with an artificially eroded and cracked blade was tested) were utilized. The time- and frequency-domain features were extracted and analyzed using various machine learning algorithms. It was found that erosion and crack faults have unique time- and frequency-domain features. The crack fault introduces an amplitude modulation in the vibration time wave, which produces sidebands around the fundamental frequency in the frequency domain. However, erosion fault introduces asymmetricity and flatness to the vibration time wave, which produces harmonics in the frequency-domain plot. The results also highlighted that utilizing both time- and frequency-fault features enhances the performance of the machine learning algorithms. This study further illustrates that even though some machine learning algorithms provide similar high classification accuracy, they might differ in quantifying error Types I, II, and, III, which is extremely important for maintenance engineers, as it might lead to undetected fault events and false alarm events.
Cooperative Construction of Renewable Energy and Energy Storage System: Research on Evolutionary Game Model Based on Continuous Strategy and Random Disturbance
Authors: He Wei , Liu Rujie , Han Tao , Zhang Jicheng , Lei Yixun , Xu Shan , Yu Hongwei , Li Zhu
Published: November 2024

As the global push toward carbon neutrality accelerates, cooperation between power generation enterprises and energy storage companies plays a crucial role in the low-carbon transition of energy systems. However, there remains a lack of research on the stochastic dynamic mechanisms of cooperation evolution. This paper develops a stochastic evolutionary game model to analyze the cooperation evolution pathways between power generation enterprises and energy storage companies under different market parameter conditions. Sensitivity analysis is conducted to reveal the impact of factors such as market prices and power capacity on cooperation willingness. The results indicate that the dispatch optimization capability of storage technology and policy incentives significantly influence the willingness to cooperate. The study suggests that governments should enhance policy support and technological innovation to promote the sustainable development of energy systems.
The Estimation of Power Losses in Composite Cores Excited by Harmonic Flux Density Waveforms
Authors: Najgebauer Mariusz , Gzieł Damian , Kalinowski Jarosław , Koprivica Branko
Published: November 2024

This paper presents a study of the empirical approach to the estimation of power losses in composite cores excited by harmonic flux density waveforms. Nowadays, magnetic cores operating in power electronic devices are excited by distorted (e.g., harmonic) flux density waveforms. Magnetic material properties are mostly determined for sinusoidal waveforms of the magnetic flux density. However, these properties can vary significantly in the case of harmonic excitations, which affects the devices’ efficiency. The effect of harmonic flux density waveform parameters (amplitude ratio and phase angle) on the level of power losses in soft magnetic composites is analyzed. A simple, empirical model of harmonic losses based on standardized measurements, i.e., carried out for sinusoidal flux density waveforms, is modified and validated. It is revealed that the empirical approach to estimating harmonic losses can be applied to magnetic composites with satisfactory modelling results.
Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption
Authors: Powroźnik Piotr , Szcześniak Paweł
Published: November 2024

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented.
Pathways to Carbon Neutrality: Integrating Energy Strategies, Policy, and Public Perception in the Face of Climate Change—A Global Perspective
Authors: Pawłowski Artur , Rydzewski Paweł
Published: November 2024

The significance of the research presented in this article lies in its comprehensive view of global efforts to achieve carbon neutrality, integrating technological, policy, and social aspects. The urgent need to address climate change requires the development of renewable energy sources, technological innovations, and public engagement in shaping climate policies. This article presents an interdisciplinary approach, combining scientific data on carbon dioxide emissions with public opinion research. It highlights the need for global cooperation, emphasizing that the responsibility for achieving carbon neutrality extends beyond governments to entire societies. The analysis includes empirical findings from the International Social Survey Programme (ISSP), providing insights into public perceptions of climate change and their influence on climate policies. The key findings of the research indicate that accelerating the development of renewable energy sources, such as solar and wind energy, and implementing carbon capture and storage (CCS) technologies are crucial. Public opinion plays a vital role in the success of these initiatives. The article demonstrates that international cooperation, particularly in supporting developing countries, is essential to overcoming barriers to the adoption of renewable energy. Ultimately, achieving carbon neutrality by 2050 is possible, but it requires not only technological innovations but also public support and coherent climate policies at both global and regional levels.
How Can We Achieve a Long-Term Effect of Serious Energy Games on the Change in Residential Electricity Demand?
Authors: Belinskiy Anton , Lampropoulos Ioannis , Nasrollahi Hossein , Fijnheer Jan Dirk , Veltkamp Remco C. , van Sark Wilfried
Published: November 2024

As global energy concerns escalate, there is a growing need for effective strategies to promote sustainable energy practices among individuals and communities. Gamification, the integration of game-design elements in non-game contexts, emerges as a promising tool to enhance user engagement and foster sustainable behaviour in energy management. In this review, we examine the theoretical aspects of gamification and its application in energy management in users’ households, highlighting its potential to transform repetitive or even monotonous tasks into engaging activities, focusing on studies that measure a long-term effect. We delve into various gamified elements adopted in long-term studies, such as feedback, social interactions, point systems, leader boards, narrative-driven challenges, etc., to understand their effect on user motivation and behavioural changes. From our set of studies, we found out that strong social game elements contribute the most to the long-term behaviour change of energy usage. One more condition of behaviour change is strong positive user satisfaction: the game should be engaging. We highlight the possible limitations of gamification in an energy management situation, a strong need for better practices of design and evaluation, and innovative approaches (such as DSM; Demand Side Management) in gamification for long-term engagement in household energy management.
Integration of Water Transfers in Hydropower Operation Planning
Authors: Asano Roberto , Ferreira Fabiana de Oliveira , Gramulia Jacyro , Asano Patrícia Teixeira Leite
Published: November 2024

The rising demand for clean energy production due to climate change emphasizes the importance of optimizing water resources, particularly in countries with significant hydropower potential. Existing models for the Operational Planning of Hydropower Systems (HPSOP) typically focus on the natural flows of rivers, often overlooking the potential of water transfers between rivers and basins. To address this gap, this article employs an improved mathematical model of hydropower production, considering the adjustment of the water transfer in the operation schedule as an additional optimization variable. A customized meta-heuristic, named the Evolutionary Socio-Bio Inspired Technique (ESBIT), has been tailored to integrate water transfer mechanisms into the operational planning model. The proposed model was validated through a case study at the Henry Borden Complex in São Paulo, Brazil, using real power plant parameters and inflow data from the Brazilian system. The results obtained from the test case, both with and without water transfer, demonstrate that the proposed methodology effectively captures the operational characteristics of a system that allows water transfers between rivers or basins to optimize the available water resources and system costs.
Mapping Solar Global Radiation and Beam Radiation in Taiwan
Authors: Hsieh Tsung-En , Chang Keh-Chin
Published: November 2024

Data for solar radiation resources play a pivotal role in assessing the energy yield capability of solar applications. A nationwide database for the typical meteorological year from the 30 weather stations of the Central Weather Bureau (CWB) in Taiwan is used to determine the spatial distribution of global radiation over the terrain of Taiwan. There is no available beam radiation information in daily reports from all CWB stations. Information on the diffuse fraction for all CWB stations is estimated using three available correlation models that account for topographical and geographical effects in Taiwan. The databases for beam radiation are generated using these estimated diffuse fractions. The mappings of global and beam radiation on the Taiwanese mainland are performed with databases from 24 CWB stations using the residual kriging method. There are no mappings of the remote islands, where six CWB stations are located. The databases for global and beam radiation for these six CWB stations are applied to nearby remote islands. The effects of topography and geography on the distributions of global and beam radiation are discussed. The spatial distributions of solar radiation presented are good scientific references for assessing the performances of solar energy systems in Taiwan.