bluuebunny/crossref_metadata_2025 huggingface dataset:
RTINet: A Lightweight and High-Performance Railway Turnout Identification Network Based on Semantic Segmentation
Authors: Wei Dehua , Zhang Wenjun , Li Haijun , Jiang Yuxing , Xian Yong , Deng Jiangli
Published: October 2024

To lighten the workload of train drivers and enhance railway transportation safety, a novel and intelligent method for railway turnout identification is investigated based on semantic segmentation. More specifically, a railway turnout scene perception (RTSP) dataset is constructed and annotated manually in this paper, wherein the innovative concept of side rails is introduced as part of the labeling process. After that, based on the work of Deeplabv3+, combined with a lightweight design and an attention mechanism, a railway turnout identification network (RTINet) is proposed. Firstly, in consideration of the need for rapid response in the deployment of the identification model on high-speed trains, this paper selects the MobileNetV2 network, renowned for its suitability for lightweight deployment, as the backbone of the RTINet model. Secondly, to reduce the computational load of the model while ensuring accuracy, depth-separable convolutions are employed to replace the standard convolutions within the network architecture. Thirdly, the bottleneck attention module (BAM) is integrated into the model to enhance position and feature information perception, bolster the robustness and quality of the segmentation masks generated, and ensure that the outcomes are characterized by precision and reliability. Finally, to address the issue of foreground and background imbalance in turnout recognition, the Dice loss function is incorporated into the network training procedure. Both the quantitative and qualitative experimental results demonstrate that the proposed method is feasible for railway turnout identification, and it outperformed the compared baseline models. In particular, the RTINet was able to achieve a remarkable mIoU of 85.94%, coupled with an inference speed of 78 fps on the customized dataset. Furthermore, the effectiveness of each optimized component of the proposed RTINet is verified by an additional ablation study.
Research on Target Image Classification in Low-Light Night Vision
Authors: Li Yanfeng , Luo Yongbiao , Zheng Yingjian , Liu Guiqian , Gong Jiekai
Published: October 2024

In extremely dark conditions, low-light imaging may offer spectators a rich visual experience, which is important for both military and civic applications. However, the images taken in ultra-micro light environments usually have inherent defects such as extremely low brightness and contrast, a high noise level, and serious loss of scene details and colors, which leads to great challenges in the research of low-light image and object detection and classification. The low-light night vision image used as the study object in this work has an excessively dim overall picture and very little information about the screen’s features. Three algorithms, HE, AHE, and CLAHE, were used to enhance and highlight the image. The effectiveness of these image enhancement methods is evaluated using metrics such as the peak signal-to-noise ratio and mean square error, and CLAHE was selected after comparison. The target image includes vehicles, people, license plates, and objects. The gray-level co-occurrence matrix (GLCM) was used to extract the texture features of the enhanced images, and the extracted image texture features were used as input to construct a backpropagation (BP) neural network classification model. Then, low-light image classification models were developed based on VGG16 and ResNet50 convolutional neural networks combined with low-light image enhancement algorithms. The experimental results show that the overall classification accuracy of the VGG16 convolutional neural network model is 92.1%. Compared with the BP and ResNet50 neural network models, the classification accuracy was increased by 4.5% and 2.3%, respectively, demonstrating its effectiveness in classifying low-light night vision targets.
A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption
Authors: İnce Cemile , İnce Kenan , Hanbay Davut
Published: October 2024

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.
First Level Leadership in Schools: Evidence from Secondary Schools Across Australia
Authors: De Nobile John
Published: October 2024

This article presents the results of an investigation into the phenomenon of ‘first level leadership’ in schools. The current theorisation of school leadership largely includes senior and middle leaders. Yet there is evidence in schools, as well as recent research, about the existence of ‘first level leaders’. Some scholars might argue that this equates to teacher leadership, but that term is often confounded with middle leadership. First level leadership includes the efforts of teachers who hold positions of responsibility, but experience smaller spans of influence than middle leaders. The study reported here represents the initial phase of a multi-phase research program aimed at clarifying who first level leaders are and what they do. The aim of the present study was to identify evidence of first level leadership from role descriptions, explanations of governance and leadership structures. The official websites of 675 secondary schools from all sectors and states of Australia were investigated to identify potential first level leadership positions, resulting in evidence from 87 schools. Confirmation of these positions was achieved through descriptions of responsibilities and jurisdictional documents such as role descriptions. The positions included coaches, coordinators and assistant heads. The article concludes with implications for educational leadership theory as well as policy and practices relating to developing school leaders.
Identifying Gifted Potential Through Positive Psychology Content
Authors: Mo Fangfang , Desmet Ophélie Allyssa , Olenchak F. Richard
Published: October 2024

Traditional identification approaches have often excluded many students from underrepresented backgrounds from gifted and talented service programs. This study introduces an innovative identification method based on the Bull’s Eye Model for Affective Development—Expansion (BEM-e), which focuses on identifying giftedness and talents through targeted positive psychological traits. This method is integrated within an affective curriculum designed to create authentic learning environments that align with students’ interests and strengths, fostering deeper engagement, motivation, and self-efficacy. Grounded in the positive psychology theory, the curriculum includes engaging, activity-oriented modules with comprehensive dynamic assessments. These assessments allow teachers to identify a broad range of talents and abilities, promoting equity and a holistic identification process, which contributes to a more equitable and comprehensive education system.
Memorizing Swin-Transformer Denoising Network for Diffusion Model
Authors: Chen Jindou , Shen Yiqing
Published: October 2024

Diffusion models have garnered significant attention in the field of image generation. However, existing denoising architectures, such as U-Net, face limitations in capturing the global context, while Vision Transformers (ViTs) may struggle with local receptive fields. To address these challenges, we propose a novel Swin-Transformer-based denoising network architecture that leverages the strengths of both U-Net and ViT. Moreover, our approach integrates the k-Nearest Neighbor (kNN) based memorizing attention module into the Swin-Transformer, enabling it to effectively harness crucial contextual information from feature maps and enhance its representational capacity. Finally, we introduce an innovative hierarchical time stream embedding scheme that optimizes the incorporation of temporal cues during the denoising process. This method surpasses basic approaches like simple addition or concatenation of fixed time embeddings, facilitating a more effective fusion of temporal information. Extensive experiments conducted on four benchmark datasets demonstrate the superior performance of our proposed model compared to U-Net and ViT as denoising networks. Our model outperforms baselines on the CRC-VAL-HE-7K and CelebA datasets, achieving improved FID scores of 14.39 and 4.96, respectively, and even surpassing DiT and UViT under our experiment setting. The Memorizing Swin-Transformer architecture, coupled with the hierarchical time stream embedding, sets a new state-of-the-art in denoising diffusion models for image generation.
Real-Time Simulator for Dynamic Systems on FPGA
Authors: Silva Sérgio N. , Goldbarg Mateus A. S. de S. , Silva Lucileide M. D. da , Fernandes Marcelo A. C.
Published: October 2024

This work presents the development of an embedded platform using Field Programmable Gate Arrays (FPGAs) for real-time simulation of dynamic systems in industrial plants. The platform, Real-Time Simulator for Dynamic Systems in FPGA (RTSDS-FPGA), is designed for industrial and academic applications. In industrial contexts, the RTSDS-FPGA facilitates the optimization and tuning of embedded control algorithms, while in academia, it supports research on new embedded solutions in automation and control. It is also an educational tool for undergraduate and postgraduate students developing embedded control system projects. Additionally, the simulator accelerates the simulation of slow dynamic systems, significantly reducing overall simulation time. Experimental results demonstrate the platform’s capability to perform real-time simulations effectively, validating its accuracy and performance through comparative analyses with established software tools such as Matlab/Simulink.
Toward the Adoption of Explainable Pre-Trained Large Language Models for Classifying Human-Written and AI-Generated Sentences
Authors: Petrillo Luca , Martinelli Fabio , Santone Antonella , Mercaldo Francesco
Published: October 2024

Pre-trained large language models have demonstrated impressive text generation capabilities, including understanding, writing, and performing many tasks in natural language. Moreover, with time and improvements in training and text generation techniques, these models are proving efficient at generating increasingly human-like content. However, they can also be modified to generate persuasive, contextual content weaponized for malicious purposes, including disinformation and novel social engineering attacks. In this paper, we present a study on identifying human- and AI-generated content using different models. Precisely, we fine-tune different models belonging to the BERT family, an open-source version of the GPT model, ELECTRA, and XLNet, and then perform a text classification task using two different labeled datasets—the first one consisting of 25,000 sentences generated by both AI and humans and the second comprising 22,929 abstracts that are ChatGPT-generated and written by humans. Furthermore, we perform an additional phase where we submit 20 sentences generated by ChatGPT and 20 sentences randomly extracted from Wikipedia to our fine-tuned models to verify the efficiency and robustness of the latter. In order to understand the prediction of the models, we performed an explainability phase using two sentences: one generated by the AI and one written by a human. We leveraged the integrated gradients and token importance techniques, analyzing the words and subwords of the two sentences. As a result of the first experiment, we achieved an average accuracy of 99%, precision of 98%, recall of 99%, and F1-score of 99%. For the second experiment, we reached an average accuracy of 51%, precision of 50%, recall of 52%, and F1-score of 51%.
Traditional and Hybrid Topologies for Single-/Three-Phase Transformerless Multilevel Inverters
Authors: Aslam Ayesha Wajiha , Hassan Jamil , Minambres-Marcos Víctor , Al-salloomee Ali Gaeed Seger , Roncero-Clemente Carlos
Published: October 2024

With increasing interest in integrating solar power into the utility grid, multilevel inverters are gaining much more attention for medium- and high-power applications due to their high-quality waveform, low voltage stress across active components, and low total harmonic distortion in output voltage. However, to achieve these benefits, a large number of active and passive components are required. A transformer is also required to provide galvanic isolation, which increases its size and weight and reduces its power density and efficiency. In order to overcome the disadvantages posed by transformer-based inverters, research is being conducted on the transformerless topology of multilevel inverters. The first aim of this review article is to summarize traditional transformerless multilevel inverters (TMLIs) considering both single- and three-phase topologies. Secondly, the main aim of this article is to provide a detailed overview of the hybrid topologies of TMLIs that employ fewer components for photovoltaic applications. In addition, this study compares traditional and hybrid single-/three-phase topologies in terms of component count and performance factors, which will be useful to researchers.
Extremum Seeking-Based Radio Signal Strength Optimization Algorithm for Hoverable UAV Path Planning
Authors: Jung Sunghun , Kim Young-Joon
Published: October 2024

For the safe autonomous operations of unmanned aerial vehicles (UAVs) and ground control stations (GCS), including autonomous battery replacement, wireless power transfer, and more, the precise landing of UAVs on GCS is essential. Accurate landing is only possible when the link capacity strength exceeds a certain threshold, but this is often disturbed due to complex terrain. To address this, we developed an extremum seeking (ES)-based radio signal strength optimization (RSSO) algorithm, ES-RSSO, designed to find the optimal positions of the UAV using radio communication signals. This ensures energy-efficient path planning while guaranteeing the minimum received signal strength indication (RSSI) capacity. This algorithm is particularly useful in obstacle-rich environments, where UAVs are limited in power resources. Simulation results demonstrate a 2.37% decrease in the mean, a 62.08% improvement in variance, and a 3.72% decrease in the integration strength of the link capacity when ES-RSSO is applied. These results confirm that the RADIO.rssi maintenance ability remains above a critical boundary level, supporting robust communication links and energy-efficient path planning. Throughout the study, we showed how, in many cases, simply moving the UAV a few meters can significantly improve the communication link.
A Distributed Algorithm for Reaching Average Consensus in Unbalanced Tree Networks
Authors: Parlangeli Gianfranco
Published: October 2024

In this paper, a distributed algorithm for reaching average consensus is proposed for multi-agent systems with tree communication graph, when the edge weight distribution is unbalanced. First, the problem is introduced as a key topic of core algorithms for several modern scenarios. Then, the relative solution is proposed as a finite-time algorithm, which can be included in any application as a preliminary setup routine, and it is well-suited to be integrated with other adaptive setup routines, thus making the proposed solution useful in several practical applications. A special focus is devoted to the integration of the proposed method with a recent Laplacian eigenvalue allocation algorithm, and the implementation of the overall approach in a wireless sensor network framework. Finally, a worked example is provided, showing the significance of this approach for reaching a more precise average consensus in uncertain scenarios.
Interpretable Mixture of Experts for Decomposition Network on Server Performance Metrics Forecasting
Authors: Peng Fang , Ji Xin , Zhang Le , Wang Junle , Zhang Kui , Wu Wenjun
Published: October 2024

The accurate forecasting of server performance metrics, such as CPU utilization, memory usage, and network bandwidth, is critical for optimizing resource allocation and ensuring system reliability in large-scale computing environments. In this paper, we introduce the Mixture of Experts for Decomposition Kolmogorov–Arnold Network (MOE-KAN), a novel approach designed to improve both the accuracy and interpretability of server performance prediction. The MOE-KAN framework employs a decomposition strategy that breaks down complex, nonlinear server performance patterns into simpler, more interpretable components, facilitating a clearer understanding of how predictions are made. By leveraging a Mixture of Experts (MOE) model, trend and residual components are learned by specialized experts, whose outputs are transparently combined to form the final prediction. The Kolmogorov–Arnold Network further enhances the model’s ability to capture intricate input–output relationships while maintaining transparency in its decision-making process. Experimental results on real-world server performance datasets demonstrate that MOE-KAN not only outperforms traditional models in terms of accuracy but also provides a more trustworthy and interpretable forecasting framework. This makes it particularly suitable for real-time server management and capacity planning, offering both reliability and interpretability in predictive models.
Threshold Voltage Recovery Time Measurement Technique Post VTH Instability in Normally-Off p-Gate GaN High Electron Mobility Transistors
Authors: Murukesan Karthick , Udrea Florin
Published: October 2024

In this study, we propose a simple measurement technique to quantitatively measure the time taken by threshold voltage of normally-off p-GaN AlGaN/GaN HEMTs to recover from a nominal operational gate stress-induced instability. The proposed technique eliminates the requirement to perform a full transfer characteristic sweep post-stress, thereby eliminating the measurement-induced instability effect, often colluding precise recovery time measurement. The rate of recovery and extracted recovery times hold significance in empirically correlating the location of traps in the p-GaN or AlGaN barrier region causing VTH instability. The gate of the HEMT is stressed at nominal operational drive voltages 1.5 V, 2 V, and 4 V for various time intervals from 500 μs to 100 s, and the time taken for the drain current to recover to prestress levels measured at near-threshold voltage (~1.1 VTH) is measured as the threshold voltage recovery time. With increasing gate stress voltages, 2DEG gets trapped at relatively deeper trap energy levels at the AlGaN/GaN interface requiring more emission time during the process of recovery, mandating larger recovery times. At higher stress voltage of 4 V, the Schottky gate leakage current is high enough enabling injected holes to cross the AlGaN barrier and counter-compensate for the deeply trapped 2DEG, requiring relatively the same recovery times as lower stress voltages where the gate leakage is negligibly small. With increasing stress time, the amount of 2DEG trapped increases, requiring more recovery time to de-trap and beyond a certain time, saturation of the trap density occurs causing the recovery time to plateau.
Detection of Faulty Energizations in High Voltage Direct Current Power Cables by Analyzing Leakage Currents
Authors: Mahtani Kumar , Granizo Ricardo , Guerrero José M. , Platero Carlos A.
Published: October 2024

The use of multi-terminal high voltage direct current (HVDC) power transmission systems is being adopted in many new links between different generation and consumption areas due to their high efficiency. In these systems, cable energization must be performed at the rated voltage. Healthy energizations at the rated voltage result in large inrush currents, especially in long cables, primarily due to ground capacitance. State-of-the-art protection functions struggle to distinguish between transients caused by switching and those associated with ground faults, leading to potential unwanted tripping of the protection systems. To prevent this, tripping is usually blocked during the energization transient, which delays fault detection and clearing. This paper presents a novel method for prompt discrimination between healthy and faulty energizations. The proposed method outperforms conventional protection functions as this discrimination allows for earlier and more reliable tripping, thus avoiding extensive damage to the cable and the converter due to trip blocking. The method is based on the transient analysis of the current in the cable shields, therefore, another technical advantage is that high voltage-insulated measuring devices are not required. Two distinct tripping criteria are proposed: one attending to the change in current polarity, and the other to the change in current derivative sign. Extensive computer simulations and laboratory tests confirmed the correct operation in both cases.
Multifunctional Control Strategy for a Hybrid Solid-State Transformer Applied to Modern Distribution Electric Grids
Authors: Cozac Fádua , Amorim Thiago Silva , Yahyaoui Imene , Mendo David Benitez , Encarnação Lucas Frizera
Published: October 2024

This paper presents the control and hardware design for a Hybrid Solid-State Transformer (HSST) applied to modern distribution systems. The HSST combines the advantages of conventional transformers, such as high efficiency and low cost, with those of Solid-State Transformers (SST), such as multifunctionality and fast dynamic response. Real-time simulation using a Typhoon HIL404 device is performed to validate the proposed multifunctional control strategy. The corresponding results validate the HSST’s capability to provide, while only processing partial system power, a regulated output voltage with grid voltage harmonic suppression and, at the same time, current load reactive and harmonic compensation to ensure a high-power factor and improved power quality to the grid.
VonEdgeSim: A Framework for Simulating IoT Application in Volunteer Edge Computing
Authors: Alsenani Yousef
Published: October 2024

Recently, various emerging technologies have been introduced to host IoT applications. Edge computing, utilizing volunteer devices, could be a feasible solution due to the significant and underutilized resources at the edge. However, cloud providers are still reluctant to offer it as an edge infrastructure service because of the unpredictable nature of volunteer resources. Volunteer edge computing introduces challenges such as reliability, trust, and availability. Testing this infrastructure is prohibitively expensive and not feasible in real-world scenarios. This emerging technology will not be fully realized until dedicated research and development efforts have substantiated its potential for running reliable services. Therefore, this paper proposes VonEdgeSim, a simulation of volunteer edge computing. To the best of our knowledge, it is the first and only simulation capable of mimicking volunteer behavior at the edge. Researchers and developers can utilize this simulation to test and develop resource management models. We conduct experiments with various IoT applications, including Augmented Reality, Infotainment, and Health Monitoring. Our results show that incorporating volunteer devices at the edge can significantly enhance system performance by reducing total task delay, and improving task execution time. This emphasizes the potential of volunteers to provide reliable services in an edge computing environment. The simulation code is publicly available for further development and testing.
Combining Genetic Algorithm with Local Search Method in Solving Optimization Problems
Authors: Kralev Velin , Kraleva Radoslava
Published: October 2024

This research is focused on evolutionary algorithms, with genetic and memetic algorithms discussed in more detail. A graph theory problem related to finding a minimal Hamiltonian cycle in a complete undirected graph (Travelling Salesman Problem—TSP) is considered. The implementations of two approximate algorithms for solving this problem, genetic and memetic, are presented. The main objective of this study is to determine the influence of the local search method versus the influence of the genetic crossover operator on the quality of the solutions generated by the memetic algorithm for the same input data. The results show that when the number of possible Hamiltonian cycles in a graph is increased, the memetic algorithm finds better solutions. The execution time of both algorithms is comparable. Also, the number of solutions that mutated during the execution of the genetic algorithm exceeds 50% of the total number of all solutions generated by the crossover operator. In the memetic algorithm, the number of solutions that mutate does not exceed 10% of the total number of all solutions generated by the crossover operator, summed with those of the local search method.
Demodulating Optical Wireless Communication of FBG Sensing with Turbulence-Caused Noise by Stacked Denoising Autoencoders and the Deep Belief Network
Authors: Bogale Shegaw Demessie , Yao Cheng-Kai , Manie Yibeltal Chanie , Dehnaw Amare Mulatie , Tefera Minyechil Alehegn , Li Wei-Long , Zhong Zi-Gui , Peng Peng-Chun
Published: October 2024

Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO path is a potential problem. This work aims to deep denoise sensed signals from fiber Bragg grating (FBG) sensors based on FSO link transmission using advanced denoising deep learning techniques, such as stacked denoising autoencoders (SDAE). Furthermore, it will demodulate the sensed wavelength of FBGs by applying the deep belief network (DBN) technique. This is the first time the real FBG sensing experiment has utilized the actual noise interference caused by the environmental turbulence from an FSO link rather than adding noise through numerical processing. Consequently, the spectrum of the FBG sensors is clearly modulated by the noise and the issue with peak power variation. This complicates the determination of the center wavelengths of multiple stacked FBG spectra, requiring the use of machine learning techniques to predict these wavelengths. The results indicate that SDAE is efficient in denoising from the FBG spectrum, and DBN is effective in demodulating the central wavelength of the overlapped FBG spectrum. Thus, it is beneficial to implement an FSO link-based FBG sensing system in adverse weather conditions or atmospheric turbulence.
Feasibility of Transformer Model for User Authentication Using Electromyogram Signals
Authors: Choi Hyun-Sik
Published: October 2024

Transformer models are widely used in natural language processing (NLP) and time-series data analysis. Applications of these models include prediction systems and hand gesture recognition using electromyogram (EMG) signals. However, in the case of time-series analysis, the models perform similarly to traditional networks, contrary to expectations. This study aimed to compare the performance of the transformer model and its various modified versions in terms of accuracy through a user authentication system using EMG signals, which exhibit significant variability and pose challenges in feature extraction. A Siamese network was employed to distinguish subtle differences in the EMG signals between users, using Euclidean distance. Data from 100 individuals were used to create a challenging scenario while ensuring accuracy. Three scenarios were considered: data preprocessing, integration with existing models, and the modification of the internal structure of the transformer model. The method that achieved the highest accuracy was the bidirectional long short-term memory (BiLSTM)–transformer approach. Based on this, a network was further constructed and optimized, resulting in a user authentication accuracy of 99.7% using EMG data from 100 individuals.
A New Magnetic Coupler with High Misalignment Tolerance and Inherent Constant Current–Constant Voltage for Underground Wireless Charging
Authors: Yan Kai , Dang Ruirong , Feng Xudong , Wang Wenzhen
Published: October 2024

In an underground inductive power transfer (IPT), it is inevitable to produce the phenomenon of misalignment between the transmitter and the receiver, which will reduce the output current, voltage and output efficiency of the whole IPT system. Aiming to solve this problem, a universal hybrid coupler is proposed, which can still stabilize the output in the expected range and has the ability of anti-misalignment when the X and Z directions are misaligned. The coupler is composed of a BP coupler and Γ type network. The secondary edge of the coupler introduces a Γ network, which decouples the two main coils on the same side of the receiver from the auxiliary coil and reduces the complexity of the system. The coupler can effectively reduce the coupling fluctuation caused by physical movement between the downhole transmitting end and the receiving end, thereby ensuring the stable output of the coupler. As a widely used IPT system, it can access the rest of the circuit topology whose output is independent of the load and achieve misalignment-tolerant output. Finally, based on the proposed hybrid IPT coupler theory, a 500 W misalignment-tolerant coupler prototype was built, and the compensation topologies were configured as series–series (SS) and series/inductance/capacitance/capacitor (S/LCC) structures. When the X and Z direction is misaligned, the constant current and voltage independent of the load can be output by switching the compensation topology. The experimental results are the same as the theoretical analysis.
Multi-Stage Rolling Grid Expansion Planning for Distribution Networks Considering Conditional Value at Risk
Authors: Zhang Junxiao , Wang Chengmin , Zuo Jing , Gao Chong , Zheng Shurong , Cheng Ran , Duan Yao , Wang Yawu
Published: October 2024

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users’ electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users’ electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account.
Profitability of Energy Sector Companies in Poland: Do Internal Factors Matter?
Authors: Chudy-Laskowska Katarzyna , Rokita Sabina
Published: October 2024

The aim of this article is to examine which selected internal factors influence the profitability (ROA) of companies in the energy sector in Poland and how they do so, over the period 2018–2021, taking into account two groups: all types of activities (984 companies) and electricity production (508 companies). This study uses Pearson correlation analysis, Wilcoxon pairwise rank test, descriptive statistics and multiple linear regression to build eight ROA econometric models, four for each group. The research shows that in the energy sector, in particular, variables relating to the capital structure (total equity/total assets, long-term liabilities/total assets, short-term liabilities/total assets and long-term liabilities/short-term liabilities ratios) have a statistically significant impact (positive or negative) on the profitability (ROA). The aforementioned ratios appear in various combinations in all eight ROA models. The use of equity to finance the activities of companies in this sector seems to be particularly beneficial, as the total equity/total assets ratio occurs in as many as seven out of eight models and, moreover, it always has a positive impact on the ROA. The remaining analyzed variables relating to the structure of assets (fixed assets/total assets ratio), financial liquidity (current ratio) and the age of the company appear in the models as statistically significant quite rarely, having a different impact on the ROA (positively or negatively). However, variables such as the fixed assets/current assets and total liabilities/total equity ratios do not have a statistically significant impact on the ROA at all in any of the studied groups of enterprises. The research results suggest that managers, in order to shape profitability (measured by ROA), should pay special attention to the capital structure, i.e., the proportions of the use of equity, long-term liabilities and short-term liabilities to finance the operations of energy companies as these independent variables appear most often in ROA models. Other analyzed factors, such as the assets structure (the share of fixed assets in total assets) or financial liquidity, also have an impact on the return on assets; therefore, their use in energy companies should also be considered. Moreover, the research shows a large diversity of factors shaping ROA in econometric models, the way they affect the dependent variable (positive or negative) and the degree of model fit (R2), both in individual years and in the two groups of companies studied. This proves that it is not possible to clearly and finally determine which factors and how (positive or negative) they affect the profitability. This influence can change over time depending on the circumstances, which indicates the need for the continuous involvement of decision makers in the management process and making decisions based on reliable and appropriate-to-the-situation analyses.
Key Issues of Salt Cavern Flow Battery
Authors: Huang Si , Li Yinping , Shi Xilin , Liu Yahua , Ma Hongling , Li Peng , Liu Yuanxi , Liu Xin , Xu Mingnan , Yang Chunhe
Published: October 2024

Salt cavern flow batteries (SCFBs) are an energy storage technology that utilize salt caverns to store electrolytes of flow batteries with a saturated NaCl solution as the supporting electrolyte. However, the geological characteristics of salt caverns differ significantly from above-ground storage tanks, leading to complex issues in storing electrolytes within salt caverns. Therefore, investigating and summarizing these issues is crucial for the advancement of SCFB technology. This paper’s innovation lies in its comprehensive review of the current state and development trends in SCFBs both domestically and internationally. First, the current development status of SCFB energy storage technology both domestically and internationally is summarized. Then, eight main issues are proposed from the perspectives of salt cavern geological characteristics (tightness, conductivity, ions, and temperature) and electrolyte properties (selection, permeability, corrosion, and concentration). Finally, a novel SCFB system is proposed to address the most critical issue, which is the low concentration and uneven distribution of active materials in the current SCFB system. The review in this paper not only comprehensively summarizes the development status of SCFBs both domestically and internationally, but also points out the direction for the future research focussing on SCFBs.
The Green Economy in the Energy Transformation Process—Comparative Analysis of the European Union Member States
Authors: Wyrwa Joanna , Jaźwiński Ireneusz
Published: October 2024

The article mainly examines spatial diversification of the green economy in EU countries in 2014 and 2021 in the context of the energy transformation process. In the theoretical part of the work, the green economy concept, with reference to the conditions of the green energy, was analyzed. The research procedure used in the article is based on multidimensional comparative analysis. The empirical verification was conducted using green economy indicators that are published periodically by the OECD and Eurostat. Based on 21 indicators, a synthetic green economy index was designed for 27 EU member states. In the selected set of detailed indicators, those related to green energy economy played an important role. This approach allowed for the creation of rankings and comparisons between EU countries in 2014 and 2021, i.e., the implementation period of the Europe 2020 Strategy. In this period, the priority areas of EU development were: the low-carbon economy, including the use of renewable energy sources and improvement of energy efficiency, as well as the introduction of eco-innovation. Green energy should be the basis for the functioning of highly developed countries and socio-economic progress in the case of developing countries. Based on the analysis, a large discrepancy in terms of green economy was observed in the examined countries. Particular attention was paid to disproportions in the area of green energy. The average value of the synthetic measure of the green economy in the EU countries increased in the studied years from 0.4488 to 0.4529, which can be interpreted as a slight acceleration in the greening processes. The added value of the research presented in the paper and its novelty is the analysis of the current patterns of green transformation in EU member states, with particular emphasis on energy factors.
Multi-Criteria Decision-Making Approach for Optimal Energy Storage System Selection and Applications in Oman
Authors: Al-Abri Zayid M. , Alawasa Khaled M. , Al-Abri Rashid S. , Al-Hinai Amer S. , Awad Ahmed S. A.
Published: October 2024

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges.
Machine Learning-Aided Dual-Function Microfluidic SIW Sensor Antenna for Frost and Wildfire Detection Applications
Authors: Altakhaineh Amjaad T. , Alrawashdeh Rula , Zhou Jiafeng
Published: October 2024

In this paper, which represents a fundamental step in ongoing research, a new smart low-energy dual-function half-mode substrate integrated waveguide cavity-interdigital capacitor (HMSIWC-DIC) antenna-based sensor is developed and investigated for remote frost and wildfire detection applications at 5.7 GHz. The proposed methodology exploits the HMSIW antenna-based sensor, a microfluidic channel (microliter water channel (50 μL)), interdigital capacitor technologies, and the resonance frequency parameters combined with machine learning algorithms. This allows for superior interaction between the water channel and the TE101 mode, resulting in high sensitivity (∆f/∆ε = 5.5 MHz/ε (F/m) and ∆f/∆°C = 1.83 MHz/°C) within the sensing range. Additionally, it exhibits high decision-making ability and immunity to interference, demonstrating a best-in-class sensory response to weather temperature across two ranges: positive (≥0 °C, including frost and wildfire) and negative (<0 °C, including ice accumulation). To address the challenges posed by the non-linear, unpredictable behavior of resonance frequency results, even when dealing with weak sensor antenna responses, an innovative sensory intelligent system was proposed. This system utilizes resonance frequency results as features to classify and predict weather temperature ranges into three environmental states: Early Frost, Normal, and Early Wildfire, achieving an accuracy of 96.4%. Several machine learning techniques are employed, including artificial neural networks (ANNs), random forests (RF), decision trees (DT), support vector machines (SVMs), and Gaussian processes (GPs). This sensor serves as an ideal solution for energy management through its utilization in RF-based weather temperature sensing applications. It boasts stable performance, minimal energy consumption, and real-time sensitivity, eliminating the necessity for manual data recording.
A Coordinated Emergency Frequency Control Strategy Based on Output Regulation Approach for an Isolated Industrial Microgrid
Authors: Ding Xin , Zhang Sujie
Published: October 2024

Constructing isolated industrial microgrids with wind power is beneficial for improving the economic benefits of high-energy-consuming production, such as the electrolytic aluminum industry. Due to the specialized structure of industrial microgrids and the unique characteristics of the electrolytic aluminum load (EAL), the common emergency frequency control methods do not apply to the specific operational requirements of isolated industrial microgrids. Since EALs have huge regulating capacities and fast responses, this paper proposes a coordinated emergency frequency control scheme to deal with power disturbances in isolated industrial microgrids. The coordinated frequency control model of an industrial microgrid considering demand-side participation is derived. With the help of output regulation theory, a practical, feasible coordinated frequency controller is designed by introducing frequency deviation and power disturbance as feedback control signals. The proposed control scheme achieves reserve power distribution between the generation and demand sides. The microgrid frequency can be maintained within a permitted range in the presence of large power imbalances. The simulation results conducted in an actual isolated industrial microgrid validate the effectiveness and dynamic performance of the proposed control scheme.
Research on a Photovoltaic Panel Dust Detection Algorithm Based on 3D Data Generation
Authors: Xie Chengzhi , Li Qifen , Yang Yongwen , Zhang Liting , Liu Xiaojing
Published: October 2024

With the rapid advancements in AI technology, UAV-based inspection has become a mainstream method for intelligent maintenance of PV power stations. To address limitations in accuracy and data acquisition, this paper presents a defect detection algorithm for PV panels based on an enhanced YOLOv8 model. The PV panel dust dataset is manually extended using 3D modeling technology, which significantly improves the model’s ability to generalize and detect fine dust particles in complex environments. SENetV2 is introduced to improve the model’s perception of dust features in cluttered backgrounds. AKConv replaces traditional convolution in the neck network, allowing for more flexible and accurate feature extraction through arbitrary kernel parameters and sampling shapes. Additionally, a DySample dynamic upsampler accelerates processing by 8.73%, improving the frame rate from 87.58 FPS to 95.23 FPS while maintaining efficiency. Experimental results show that the 3D image expansion method contributes to a 4.6% increase in detection accuracy, an 8.4% improvement in recall, a 5.7% increase in mAP@50, and a 15.1% improvement in mAP@50-95 compared to the original YOLOv8. The expanded dataset and enhanced model demonstrate the effectiveness and practicality of the proposed approach.