bluuebunny/crossref_metadata_2025 huggingface dataset:
The Particle of Haag’s Local Quantum Physics: A Critical Assessment
Authors: Jaeger Gregg
Published: September 2024

Rudolf Haag’s Local Quantum Physics (LQP) is an alternative framework to conventional relativistic quantum field theory for combining special relativity and quantum theory based on first principles, making it of great interest for the purposes of conceptual analysis despite currently being relatively limited as a tool for making experimental predictions. In LQP, the elementary particles are defined as species of causal link between interaction events, together with which they comprise its most fundamental entities. This notion of particle has yet to be independently assessed as such. Here, it is captured via a set of propositions specifying particle characteristics and then compared to previous particle notions. Haag’s particle differs decisively with respect to mechanical intuitions about particles by lacking, among other things, even an approximate independent space–time location. This notion is thus found to differ greatly even from those of relativistic quantum mechanics and quantum field theory, which have been applied to the known elementary particles.
IMF Conditionality and Government Education Spending: The Case of 10 MENA Countries
Authors: Sherry Hassan , Zeaiter Hussein
Published: September 2024

This study explores the impact of International Monetary Fund (IMF)-linked conditionality on government education expenditures in the Middle East and North Africa (MENA) region. Understanding the impact of conditional lending by international financial institutions on education spending is important due to the pivotal role education plays in fostering social and economic development. We use country-level panel data encompassing a representative set of 10 MENA countries from 1990 to 2020 and employ a cross-national fixed effects regression model. Our findings suggest that IMF conditionality demonstrates a positive relationship with government education expenditures in the MENA region. The proposed explanation is that the application of IMF policy advice can have a catalytic effect on donor financing, including for education. This indicates that the Fund’s financing arrangements in the region can free up fiscal space for social spending, which, in turn, signals a sort of departure of the IMF from the reputation that typically precedes it—its traditional bias for macroeconomic stability irrespective of social costs. We argue that our findings are instructive for policy, especially if one shares the idea that education is a necessary prerequisite for achieving Sustainable Development Goal (SDG) 4: guaranteeing inclusive and equitable quality education and promoting enduring learning opportunities for all.
A Self-Biased Triggered Dual-Direction Silicon-Controlled Rectifier Device for Low Supply Voltage Application-Specific Integrated Circuit Electrostatic Discharge Protection
Authors: Pan Jie , Li Fanyang , Wen Liguo , Jin Jiazhen , Huang Xiaolong , Han Jiaxun
Published: September 2024

A direct bidirectional current discharge path between the input/output (I/O) and ground (GND) is essential for the robust protection of charging device models (CDM) in the tightly constrained design parameters of advanced low-voltage (LV) processes. Dual-direction silicon controlled rectifiers (DDSCRs) serve as ESD protection devices with high efficiency unit area discharge, enabling bidirectional electrostatic protection. However, the high trigger voltage of conventional DDSCR makes it unsuitable for ASICs used for the preamplification of biomedical signals, which only operate at low supply voltage. To address this issue, a self-biased triggered DDSCR (STDDSCR) structure is proposed to further reduce the trigger voltage. When the ESD pulse comes, the external RC trigger circuit controls the PMOS turn-on by self-bias, and the current release path is opened in advance to reduce the trigger voltage. As the ESD pulse voltage increases, the SCR loop opens to establish positive feedback and drain the amplified current. Additionally, the junction capacitance is decreased through high-resistance epitaxy and low-concentration P-well injection to further lower the trigger voltage. The simulation results of LTspice and TCAD respectively demonstrate that ESD devices can clamp transient high voltages earlier, with low parasitic capacitance and leakage current suitable for ESD protection of high-speed ports up to 1.5 V under normal operating conditions.
A Low-Power Continuous-Time Delta-Sigma Analogue-to-Digital Converter for the Neural Network Architecture of Battery State Estimation
Authors: Shiue Muh-Tian , Ou Yang-Chieh , Li Guan-Shum
Published: September 2024

Electric vehicle systems and smart grid systems are setting stringent development targets to respond to global trends in energy saving, carbon reduction, and sustainable environmental development. In the field of batteries, there has been extensive discussion on the estimation of battery charge. In battery management systems (BMSs) and charging/discharging systems, the accuracy of the measurement of battery physical parameters is critical, as it directly affects the system, alongside the algorithm’s estimation and error correction. Therefore, this paper proposes incorporating a low-power continuous-time delta-sigma analogue-to-digital converter into a battery measurement system to support deep learning algorithms for battery state estimation. This approach aims to maintain the accuracy of battery state estimation while reducing latency and overall system power consumption. Implemented using the UMC 0.18 μm CMOS 1P6M process, the proposed design achieves a measured signal-to-noise distortion ratio (SNDR) of 78.42 dB, an effective number of bits (ENOB) of 12.73 bits, and a power consumption of approximately 15.97 μW. The chip layout area is 0.67 mm × 0.56 mm. By applying delta-sigma modulators to energy management systems, this solution aims to increase the total number of battery monitoring units while reducing overall power consumption and construction costs.
Study on Nighttime Pedestrian Trajectory-Tracking from the Perspective of Driving Blind Spots
Authors: Zhao Wei , Ren Congcong , Tan Ao
Published: September 2024

With the acceleration of urbanization and the growing demand for traffic safety, developing intelligent systems capable of accurately recognizing and tracking pedestrian trajectories at night or under low-light conditions has become a research focus in the field of transportation. This study aims to improve the accuracy and real-time performance of nighttime pedestrian-detection and -tracking. A method that integrates the multi-object detection algorithm YOLOP with the multi-object tracking algorithm DeepSORT is proposed. The improved YOLOP algorithm incorporates the C2f-faster structure in the Backbone and Neck sections, enhancing feature extraction capabilities. Additionally, a BiFormer attention mechanism is introduced to focus on the recognition of small-area features, the CARAFE module is added to improve shallow feature fusion, and the DyHead dynamic target-detection head is employed for comprehensive fusion. In terms of tracking, the ShuffleNetV2 lightweight module is integrated to reduce model parameters and network complexity. Experimental results demonstrate that the proposed FBCD-YOLOP model improves lane detection accuracy by 5.1%, increases the IoU metric by 0.8%, and enhances detection speed by 25 FPS compared to the baseline model. The accuracy of nighttime pedestrian-detection reached 89.6%, representing improvements of 1.3%, 0.9%, and 3.8% over the single-task YOLO v5, multi-task TDL-YOLO, and the original YOLOP models, respectively. These enhancements significantly improve the model’s detection performance in complex nighttime environments. The enhanced DeepSORT algorithm achieved an MOTA of 86.3% and an MOTP of 84.9%, with ID switch occurrences reduced to 5. Compared to the ByteTrack and StrongSORT algorithms, MOTA improved by 2.9% and 0.4%, respectively. Additionally, network parameters were reduced by 63.6%, significantly enhancing the real-time performance of nighttime pedestrian-detection and -tracking, making it highly suitable for deployment on intelligent edge computing surveillance platforms.
Electromagnetic Fields Calculation and Optimization of Structural Parameters for Axial and Radial Helical Air-Core Inductors
Authors: Wu Jinguo , Zhang Yujie , Yang Bin , Li Sihan , Song Haipeng
Published: September 2024

To improve the current density distribution and electromagnetic performance of air-core inductors, a structural optimization method combining back-propagation(BP) neural network and genetic algorithm(GA) is proposed for the study of axial and radial spiral multi-winding inductors. The Monte Carlo method was used to extract the structural size samples of the inductors, and the training dataset was obtained through the finite element calculation of electromagnetic fields. Based on BP neural networks, nonlinear mapping models between the inductance value, volumetric inductance density, current distribution non-uniformity coefficient, and inductor structural parameters were constructed. A sensitivity analysis of the inductor inductance value affected by the structural parameters was conducted using the Sobol index calculation. Using the current distribution non-uniformity coefficient as the fitness function and the volumetric inductance density as the constraint condition, a genetic algorithm was applied to globally optimize the structural parameters of the inductor. The optimization results were verified through a finite element comparison. The results show that, under the requirement of satisfying the volumetric inductance density, the current distribution non-uniformity coefficient of the Axial Helical Inductor (AHI)-type inductor was reduced by 4.57% compared with the best sample in the sampling, while that of the Radial Helical Inductor (RHI)-type inductor was reduced by 5.33%, demonstrating the practicality of the BP-GA joint algorithm in the structural optimization design of inductors.
Grapher: A Reconfigurable Graph Computing Accelerator with Optimized Processing Elements
Authors: Deng Junyong , Lu Songtao , Zhang Baoxiang , Jia Yanting
Published: September 2024

In recent years, various graph computing architectures have been proposed to process graph data that represent complex dependencies between different objects in the world. The designs of the processing element (PE) in traditional graph computing accelerators are often optimized for specific graph algorithms or tasks, which limits their flexibility in processing different types of graph algorithms, or the parallel configuration that can be supported by their PE arrays is inefficient. To achieve both flexibility and efficiency, this paper proposes Grapher, a reconfigurable graph computing accelerator based on an optimized PE array, efficiently supporting multiple graph algorithms, enhancing parallel computation, and improving adaptability and system performance through dynamic hardware resource configuration. To verify the performance of Grapher, this paper selected six datasets from the Stanford Network Analysis Project (SNAP) database for testing. Compared with the existing typical graph frameworks Ligra, Gemini, and GraphBIG, the processing time for the six datasets using the BFS, CC, and PR algorithms was reduced by up to 39.31%, 35.43%, and 27.67%, respectively. The energy efficiency has also been improved by 1.8× compared to Hitgraph and 4.7× compared to ThunderGP.
Rapid CU Partitioning and Joint Intra-Frame Mode Decision Algorithm
Authors: Song Wenjun , Li Congxian , Zhang Qiuwen
Published: September 2024

H.266/Versatile Video Coding (VVC) introduces new techniques that build upon previous standards, proposing a nested multi-type tree quadtree (QTMT). The introduction of this structure significantly enhances video coding efficiency; additionally, the number of directional modes in H.266 has increased by 32 compared to H.265, accommodating a greater variety of texture patterns. However, the changes in the related structures have also led to a significant increase in encoding complexity. To address the issue of excessive computational complexity, this paper proposes a targeted rapid Coding Units segmenting approach combined with decision-making for an intra-frame modes algorithm. In the first phase of the algorithm, we extract different features for CU blocks of various sizes and input them into the decision tree model’s classifier for classification processing, determining the CU partitioning mode to prematurely terminate the partitioning, thereby reducing the encoding complexity to some extent. In the second phase of the algorithm, we put forward an intra-frame mode decision strategy grounded in gradient descent techniques with a bidirectional search mode. This maximizes the approach to the global optimum, thereby obtaining the optimal intra-frame mode and further reducing the encoding complexity. Experimentation has demonstrated that the algorithm achieves a 54.53% reduction in encoding time. In comparison, the BD-BR (Bitrate-Distortion Rate) only increases by 1.38%, striking an optimal balance between the fidelity of video and the efficacy of the encoding process.
Machine Learning-Based Beam Pointing Error Reduction for Satellite–Ground FSO Links
Authors: Maharjan Nilesh , Kim Byung Wook
Published: September 2024

Free space optical (FSO) communication, which has the potential to meet the demand for high-data-rate communications between satellites and ground stations, requires accurate alignment between the transmitter and receiver to establish a line-of-sight channel link. In this paper, we propose a machine learning (ML)-based approach to reduce beam pointing errors in FSO satellite-to-ground communications subjected to satellite vibration and weak atmospheric turbulence. ML models are utilized to find the optimal gain, which plays a crucial role in reducing pointing error displacement in a closed-loop FSO system. In designing the FSO environment, we employ several system model parameters, including control and system matrix components of the transmitter and receiver, noise parameters for the optical channel, irradiance, and the scintillation index of the signal. To predict the gain matrix of the closed-loop system, ML methods, such as tree-based algorithms, and a 1D convolutional neural network (Conv1D) are applied. Experimental results show that the Conv1D model outperforms other ML methods in gain value prediction, helping to maintain the beam position centered on the receiver aperture, minimizing beam pointing errors. When constructing a closed-loop system based on the Conv1D model, the error variance of the pointing error displacement was obtained as 0.012 and 0.015 in clear weather and light fog conditions, respectively. In addition, this research analyzes the impact of input features in a closed-loop FSO system, and compares the pointing error performance of the closed-loop setup to the conventional open-loop setup under weak turbulence.
Ship-VNet: An Algorithm for Ship Velocity Analysis Based on Optical Remote Sensing Imagery Containing Kelvin Wakes
Authors: Gao Mingming , Fang Shaojun , Wan Ling , Kang Wenchao , Ma Lei , He Ya , Zhao Kai
Published: September 2024

Extracting ship velocity vectors from optical remote sensing images is a very challenging task, and ship wakes are the only motion features of ships. However, because the sensor’s field of view is not sufficiently bright and the brightness is not uniform, the image contains noise, which makes it difficult to define and extract the wake of the ship. Velocity analysis of the extracted wake makes the whole process complicated and slow. Therefore, considering the above problems, this paper proposes Ship-VNet, an optical remote sensing image ship velocity analysis algorithm based on Kelvin wakes. In this model, the rotating target detection algorithm is used to detect the ship, and then, the classical relationship between the kinematic characteristics of the ship’s Kelvin wake and the velocity of the ship is studied and experimentally analyzed in the frequency domain. In addition, based on optical remote sensing images and corresponding real AIS data, a ship dataset with Kelvin wakes marked with heading velocity was constructed to verify the effectiveness of the proposed method. Compared with the ship velocity analysis method based on the frequency domain, which was also used in the previous research, the experiment demonstrates the superiority of the method in terms of analysis accuracy.
Anti-Spectral Interference Waveform Design Based on High-Order Norm Optimized Autocorrelation Sidelobes Properties
Authors: Mao Xinrong , Fu Yaoqiang , Xia Meng , Yang Lichao
Published: September 2024

This paper introduces a robust waveform design method aimed at reducing the impact of electromagnetic interference in radar systems, thereby enhancing target detection accuracy. We propose utilizing a high-order p-norm to characterize the peak sidelobe level (PSL) of the waveform. Additionally, the method incorporates spectral zero-trapping within known interfering frequency bands to mitigate interference effects. A unified optimization objective function is developed to ensure optimal correlation properties of waveforms for dual-use in radar and communication systems. By employing the AdamW algorithm for dynamic adjustment of the iteration factor, combined with a gradient descent search, this method refines both the autocorrelation of the waveform and its resilience to known disturbances. Experimental results demonstrate that our approach significantly improves autocorrelation performance over randomly generated initial waveforms. Moreover, the introduction of spectral zero-trapping notably enhances interference suppression in targeted frequency bands, thereby boosting overall signal performance. Our method effectively balances interference rejection with the minimization of sidelobe levels, offering a pragmatic waveform solution for complex radar environments.
RGMeta: Enhancing Cold-Start Recommendations with a Residual Graph Meta-Embedding Model
Authors: Zhao Fuzhe , Huang Chaoge , Xu Han , Yang Wen , Han Wenlin
Published: September 2024

Traditional recommendation models grapple with challenges such as the scarcity of similar user or item references and data sparsity, rendering the cold-start problem particularly formidable. Meta-learning has emerged as a promising avenue to address these issues, particularly in solving the item cold-start problem by generating meta-embeddings for new items as their initial ID embeddings. This approach has shown notable success in enhancing the accuracy of click-through rate predictions. However, prevalent meta-embedding models often focus solely on the attribute features of the item, neglecting crucial user information associated with it during the generation of initial ID embeddings for new items. This oversight hinders the exploitation of valuable user-related information to enhance the quality and accuracy of the initial ID embedding. To tackle this limitation, we introduce the residual graph meta-embedding model (RGMeta). RGMeta adopts a comprehensive approach by considering both the attribute features and target users of both old and new items. Through the integration of residual connections, the model effectively combines the representation information of the old neighbor items with the intrinsic features of the new item, resulting in an improved initial ID embedding generation. Experimental results demonstrate that RGMeta significantly enhances the performance of the cold-start phase, showcasing its effectiveness in overcoming challenges associated with sparse data and limited reference points. Our proposed model presents a promising step forward in leveraging both item attributes and user-related information to address cold-start problems in recommendation systems.
Fake Base Station Detection and Link Routing Defense
Authors: Purification Sourav , Kim Jinoh , Kim Jonghyun , Chang Sang-Yoon
Published: September 2024

Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment’s connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. We designed and built a defense scheme which detects and blacklists a fake base station and then, informed by the detection, avoids it through link routing for connectivity availability. For detection and blacklisting, our scheme uses the real-time information of both the time duration and the number of request transmissions, the features of which are directly impacted by the fake base station’s threat and which have not been studied in previous research. Upon detection, our scheme takes an active measure called link routing, which is a novel concept in mobile/4G/5G networking, where the user equipment routes the connectivity request to another base station. To defend against a Sybil-capable fake base station, we use a history–reputation-based link routing scheme for routing and base station selection. We implemented both the base station and the user on software-defined radios using open-source 5G software (srsRAN v23.10 and Open5GS v2.6.6) for validation. We varied the base station implementation to simulate legitimate vs. faulty but legitimate vs. fake and malicious base stations, where a faulty base station notifies the user of the connectivity disruption and releases the session, while a fake base station continues to hold the session. We empirically analyzed the detection and identification thresholds, which vary with the fake base station’s power and the channel condition. By strategically selecting the threshold parameters, our scheme provides zero errors, including zero false positives, to avoid blacklisting a temporarily faulty base station that cannot provide connectivity at the time. Furthermore, our link routing scheme enables the base station to switch in order to restore the connectivity availability and limit the threat impact. We also discuss future directions to facilitate and encourage R&D in securing telecommunications and base station security.
The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation
Authors: Vladov Serhii , Kovtun Viacheslav , Sokurenko Valerii , Muzychuk Oleksandr , Vysotska Victoria
Published: September 2024

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).
Multi-View Graph Learning for Path-Level Aging-Aware Timing Prediction
Authors: Bu Aiguo , Li Xiang , Li Zeyu , Chen Yizhen
Published: September 2024

As CMOS technology continues to scale down, the aging effect—known as negative bias temperature instability (NBTI)—has become increasingly prominent, gradually emerging as a key factor affecting device reliability. Accurate aging-aware static timing analysis (STA) at the early design phase is critical for establishing appropriate timing margins to ensure circuit reliability throughout the chip lifecycle. However, traditional aging-aware timing analysis methods, typically based on Simulation Program with Integrated Circuit Emphasis (SPICE) simulations or aging-aware timing libraries, struggle to balance prediction accuracy with computational cost. In this paper, we propose a multi-view graph learning framework for path-level aging-aware timing prediction, which combines the strengths of the spatial–temporal Transformer network (STTN) and graph attention network (GAT) models to extract the aging timing features of paths from both timing-sensitive and workload-sensitive perspectives. Experimental results demonstrate that our proposed framework achieves an average MAPE score of 3.96% and reduces the average MAPE by 5.8 times compared to FFNN and 2.2 times compared to PNA, while maintaining acceptable increases in processing time.
Utilizing TGAN and ConSinGAN for Improved Tool Wear Prediction: A Comparative Study with ED-LSTM, GRU, and CNN Models
Authors: Shah Milind , Borade Himanshu , Dave Vipul , Agrawal Hitesh , Nair Pranav , Vakharia Vinay
Published: September 2024

Developing precise deep learning (DL) models for predicting tool wear is challenging, particularly due to the scarcity of experimental data. To address this issue, this paper introduces an innovative approach that leverages the capabilities of tabular generative adversarial networks (TGAN) and conditional single image GAN (ConSinGAN). These models are employed to generate synthetic data, thereby enriching the dataset and enhancing the robustness of the predictive models. The efficacy of this methodology was rigorously evaluated using publicly available milling datasets. The pre-processing of acoustic emission data involved the application of the Walsh-Hadamard transform, followed by the generation of spectrograms. These spectrograms were then used to extract statistical attributes, forming a comprehensive feature vector for model input. Three DL models—encoder-decoder long short-term memory (ED-LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN)—were applied to assess their tool wear prediction capabilities. The application of 10-fold cross-validation across these models yielded exceptionally low RMSE and MAE values of 0.02 and 0.16, respectively, underscoring the effectiveness of this approach. The results not only highlight the potential of TGAN and ConSinGAN in mitigating data scarcity but also demonstrate significant improvements in the accuracy of tool wear predictions, paving the way for more reliable and precise predictive maintenance in manufacturing processes.
Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions
Authors: Zhang Tantan , Liu Haipeng , Wang Weijie , Wang Xinwei
Published: September 2024

Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios and provide quick feedback, while datasets offer valuable real-world driving data for algorithm training and optimization. However, existing research often provides brief and limited overviews of simulators and datasets. Additionally, while the role of virtual autonomous driving competitions in advancing autonomous driving technology is recognized, comprehensive surveys on these competitions are scarce. This survey paper addresses these gaps by presenting an in-depth analysis of 22 mainstream autonomous driving simulators, focusing on their accessibility, physics engines, and rendering engines. It also compiles 35 open-source datasets, detailing key features in scenes and data-collecting sensors. Furthermore, the paper surveys 10 notable virtual competitions, highlighting essential information on the involved simulators, datasets, and tested scenarios involved. Additionally, this review analyzes the challenges in developing autonomous driving simulators, datasets, and virtual competitions. The aim is to provide researchers with a comprehensive perspective, aiding in the selection of suitable tools and resources to advance autonomous driving technology and its commercial implementation.
Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning
Authors: Hao Zhongwen , Zhang Deli , Honarvar Shakibaei Asli Barmak
Published: September 2024

This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system’s performance and reliability. The research first implements object detection on the VOC2007 dataset using the Detection Transformer (DETR) and achieves ideal detection scores. The particle swarm optimization algorithm and 3-5-3 polynomial interpolation methods were utilized for trajectory planning, creating a unique dataset through simulation. This dataset contains randomly generated trajectories within the workspace, fully simulating actual working conditions. Significantly, the Bidirectional Long Short-Term Memory (BILSTM) model was improved by substituting its traditional Multilayer Perceptron (MLP) components with Kolmogorov–Arnold Networks (KANs). KANs, inspired by the K-A theorem, improve the network representation ability by placing learnable activation functions on fixed node activation functions. By implementing KANs, the model enhances parameter efficiency and interpretability, thus addressing the typical challenges of MLPs, such as the high parameter count and lack of transparency. The experiments achieved favorable predictive results, indicating that the KAN not only reduces the complexity of the model but also improves learning efficiency and prediction accuracy in dynamic visual servoing environments. Finally, Gazebo software was used in ROS to model and simulate the robotic arm, verify the effectiveness of the algorithm, and achieve visual servoing.
Dense Feature Pyramid Deep Completion Network
Authors: Yang Xiaoping , Ni Ping , Li Zhenhua , Liu Guanghui
Published: September 2024

Most current point cloud super-resolution reconstruction requires huge calculations and has low accuracy when facing large outdoor scenes; a Dense Feature Pyramid Network (DenseFPNet) is proposed for the feature-level fusion of images with low-resolution point clouds to generate higher-resolution point clouds, which can be utilized to solve the problem of the super-resolution reconstruction of 3D point clouds by turning it into a 2D depth map complementation problem, which can reduce the time and complexity of obtaining high-resolution point clouds only by LiDAR. The network first utilizes an image-guided feature extraction network based on RGBD-DenseNet as an encoder to extract multi-scale features, followed by an upsampling block as a decoder to gradually recover the size and details of the feature map. Additionally, the network connects the corresponding layers of the encoder and decoder through pyramid connections. Finally, experiments are conducted on the KITTI deep complementation dataset, and the network performs well in various metrics compared to other networks. It improves the RMSE by 17.71%, 16.60%, 7.11%, and 4.68% compared to the CSPD, Spade-RGBsD, Sparse-to-Dense, and GAENET.
Scalable Ni12P5-Coated Carbon Cloth Cathode for Lithium–Sulfur Batteries
Authors: Suzanowicz Artur M. , Abeywickrama Thulitha M. , Lin Hao , Alramahi Dana , Segre Carlo U. , Mandal Braja K.
Published: September 2024

As a better alternative to lithium-ion batteries (LIBs), lithium–sulfur batteries (LSBs) stand out because of their multi-electron redox reactions and high theoretical specific capacity (1675 mA h g−1). However, the long-term stability of LSBs and their commercialization are significantly compromised by the inherently irreversible transition of soluble lithium polysulfides (LiPS) into solid short-chain S species (Li2S2 and Li2S) and the resulting substantial density change in S. To address these issues, we used activated carbon cloth (ACC) coated with Ni12P5 as a porous, conductive, and scalable sulfur host material for LSBs. ACC has the benefit of high electrical conductivity, high surface area, and a three-dimensional (3D) porous architecture, allowing for ion transport channels and void spaces for the volume expansion of S upon lithiation. Ni12P5 accelerates the breakdown of Li2S to increase the efficiency of active materials and trap soluble polysulfides. The highly effective Ni12P5 electrocatalyst supported on ACC drastically reduced the severity of the LiPS shuttle, affected the abundance of adsorption–diffusion–conversion interfaces, and demonstrated outstanding performance. Our cells achieved near theoretical capacity (>1611 mA h g−1) during initial cycling and superior capacity retention (87%) for >250 cycles following stabilization with a 0.05% decay rate per cycle at 0.2 C.
Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8
Authors: Xiang Siyu , Chang Zhengwei , Liu Xueyuan , Luo Lei , Mao Yang , Du Xiying , Li Bing , Zhao Zhenbing
Published: September 2024

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment.
Characteristic Analysis of Electromagnetic Force in an HTS Field Coil Using a Performance Evaluation System
Authors: Go Byeong-Soo
Published: September 2024

A performance evaluation system (PES) can experimentally test the structural stability and magnetic field effects of HTS coils against high magnetic fields and electromagnetic forces before mounting the HTS coils on a large-capacity rotating machine. This paper deals with the characteristic analysis of electromagnetic force in an HTS field coil for a 10 MW Class HTS Wind Power Generator using PES. Based on the designed 10 MW class HTS wind power generator, the HTS coils are manufactured and installed in the PES by a support structure, which is designed considering the electromagnetic force (torque) and heat loads in the HTS coil. To check the stress and deformation in the support structure caused by the electromagnetic force generated from the coil, strain gauge sensors were attached to the support structure and measured under full-load conditions. As a result, the maximum magnetic field and electromagnetic force are 2.8 T and 71 kN, respectively. Compared to the analysis results, the magnetic field and generated electromagnetic force in the HTS coil were the same under no-load and full-load conditions. These results will be effectively used to study and fabricate high magnetic field coils for HTS applications, as well as the PES being fabricated.
Forecasting of Daily Heat Production in a District Heating Plant Using a Neural Network
Authors: Maryniak Adam , Banaś Marian , Michalak Piotr , Szymiczek Jakub
Published: September 2024

Artificial neural networks (ANNs) can be used for accurate heat load forecasting in district heating systems (DHSs). This paper presents an application of a shallow ANN with two hidden layers in the case of a local DHS. The developed model was used to write a simple application in Python 3.10 that can be used in the operation of a district heating plant to carry out a preliminary analysis of heat demand, taking into account the ambient temperature on a given day. The model was trained using the real data from the period 2019–2022. The training was sufficient for the number of 150 epochs. The prediction effectiveness indicator was proposed. In the considered case, the effectiveness of the trained network was 85% and was better in comparison to five different regression models. The developed tool was based on an open-source programming environment and proved its ability to predict heating load.
Multi-Objective Energy Management in Microgrids: Improved Honey Badger Algorithm with Fuzzy Decision-Making and Battery Aging Considerations
Authors: Alanazi Mohana , Alanazi Abdulaziz , Memon Zulfiqar Ali , Awan Ahmed Bilal , Deriche Mohamed
Published: September 2024

A multi-objective energy management and scheduling strategy for a microgrid comprising wind turbines, solar cells, fuel cells, microturbines, batteries, and loads is proposed in this work. The plan uses a fuzzy decision-making technique to reduce pollution emissions, battery storage aging costs, and operating expenses. To be more precise, we applied an improved honey badger algorithm (IHBA) to find the best choice variables, such as the size of energy resources and storage, by combining fuzzy decision-making with the Pareto solution set and a chaotic sequence. We used the IHBA to perform single- and multi-objective optimization simulations for the microgrid’s energy management, and we compared the results with those of the conventional HBA and particle swarm optimization (PSO). The results showed that the multi-objective method improved both goals by resulting in a compromise between them. On the other hand, the single-objective strategy makes one goal stronger and the other weaker. Apart from that, the IHBA performed better than the conventional HBA and PSO, which also lowers the cost. The suggested approach beat the alternative tactics in terms of savings and effectively reached the ideal solution based on the Pareto set by utilizing fuzzy decision-making and the IHBA. Furthermore, compared with the scenario without this cost, the results indicated that integrating battery aging costs resulted in an increase of 7.44% in operational expenses and 3.57% in pollution emissions costs.
A Hybrid Forecasting Model for Electricity Demand in Sustainable Power Systems Based on Support Vector Machine
Authors: Li Xuejun , Jiang Minghua , Cai Deyu , Song Wenqin , Sun Yalu
Published: September 2024

Renewable energy sources, such as wind and solar power, are increasingly contributing to electricity systems. Participants in the energy market need to understand the future electricity demand in order to plan their purchasing and selling strategies. To forecast the electricity demand, this study proposes a hybrid forecasting model. The method uses Kalman filtering to eliminate noise from the electricity demand series. After decomposing the electricity demand using an empirical model, a support vector machine optimized by a genetic algorithm is employed for prediction. The performance of the proposed forecasting model was evaluated using actual electricity demand data from the Australian energy market. The simulation results indicate that the proposed model has the best forecasting capability, with a mean absolute percentage error of 0.25%. Accuracy improved by 74% compared to the Support Vector Machine (SVM) electricity demand forecasting model, by 73% when compared to the SVM with empirical mode decomposition, and by 51% when compared to the SVM with Kalman filtering for noise reduction. Additionally, compared to existing forecasting methods, this study’s accuracy surpasses LSTM by 63%, Transformer by 47%, and LSTM-Adaboost by 36%. The simulation of and comparison with existing forecasting methods validate the effectiveness of the proposed hybrid forecasting model, demonstrating its superior predictive capabilities.
Recognition of Artificial Gases Formed during Drill-Bit Metamorphism Using Advanced Mud Gas
Authors: Leon Janaina Andrade de Lima , Penteado Henrique Luiz de Barros , Ellis Geoffrey S. , Milkov Alexei , Filho João Graciano Mendonça
Published: September 2024

Drill-bit metamorphism (DBM) is the process of thermal degradation of drilling fluid at the interface of the bit and rock due to the overheating of the bit. The heat generated by the drill when drilling into a rock formation promotes the generation of artificial hydrocarbon and non-hydrocarbon gas, changing the composition of the gas. The objective of this work is to recognize and evaluate artificial gases originating from DBM in wells targeting oil accumulations in pre-salt carbonates in the Santos Basin, Brazil. For the evaluation, chromatographic data from advanced mud gas equipment, drilling parameters, drill type, and lithology were used. The molar concentrations of gases and gas ratios (especially ethene/ethene+ethane and dryness) were analyzed, which identified the occurrence of DBM. DBM is most severe when wells penetrate igneous and carbonate rocks with diamond-impregnated drill bits. The rate of penetration, weight on bit, and rotation per minute were evaluated together with gas data but did not present good correlations to assist in identifying DBM. The depth intervals over which artificial gases formed during DBM are recognized should not be used to infer pay zones or predict the composition and properties of reservoir fluids because the gas composition is completely changed.
Probabilistic Analysis of Green Hydrogen Production from a Mix of Solar and Wind Energy
Authors: Dudziak Agnieszka , Małek Arkadiusz , Marciniak Andrzej , Caban Jacek , Seńko Jarosław
Published: September 2024

This article describes an example of using the measurement data from photovoltaic systems and wind turbines to perform practical probabilistic calculations around green hydrogen generation. First, the power generated in one month by a ground-mounted photovoltaic system with a peak power of 3 MWp is described. Using the Metalog family of probability distributions, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is calculated. Identical calculations are performed for the simulation data, allowing us to determine the power produced by a wind turbine with a maximum power of 3.45 MW. After interpolating both time series of the power generated by the renewable energy sources to a common sampling time, they are summed. For the sum of the power produced by the photovoltaic system and the wind turbine, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is again calculated. The presented calculations allow us to determine, with probability distribution accuracy, the amount of hydrogen generated from the energy sources constituting a mix of photovoltaics and wind. The green hydrogen production model includes the hardware and the geographic context. It can be used to determine the preliminary assumptions related to the production of large amounts of green hydrogen in selected locations. The calculations presented in this article are a practical example of Business Intelligence.
A Grouping and Aggregation Modeling Method of Induction Motors for Transient Voltage Stability Analysis
Authors: Liang Zhaowen , Liu Yongqiang , Mo Lili , Zhang Yan
Published: September 2024

Induction motors are the most common type of motor in power systems, constituting approximately 70–90% of the dynamic loads, making them significant contributors to system dynamics. In transient voltage stability analysis, dynamic equivalent models are commonly used to simplify the representation of a group of induction motors. This paper presents a method for the grouping and aggregation of induction motors at a common bus. Firstly, grouping rules are provided for clustering induction motors into several subgroups based on the mechanical principles of rotor force and motion, and aggregation rules are provided for aggregating a motor subgroup into a single-unit model based on the relationship between voltage drop and power transmission in distribution networks. Secondly, guided by the grouping rules, high-speed remaining electromagnetic torque and low-speed remaining electromagnetic torque are defined as new clustering indicators, and an adaptive K-means clustering method using silhouette coefficient verification is introduced to obtain the optimal motor subgroups. Thirdly, guided by the aggregation rules, a dynamic equivalent method is further introduced to obtain the equivalent single-unit model from a motor subgroup. Lastly, a transient voltage stability simulation in a typical distribution network is presented to illustrate that the proposed clustering and equivalent methods are more reasonable, accurate, and effective than traditional methods, as the obtained model has better dynamic characteristics and can more accurately reproduce the process of voltage collapse.
Unlocking Energy Efficiency: Debunking Myths on the Road to Decarbonization
Authors: Nandy Paulomi , Guo Wei , Wenning Thomas
Published: September 2024

Energy efficiency is widely recognized as the foundational and most critical strategy for decarbonizing the manufacturing sector. Misconceptions surrounding energy efficiency measures often hinder their widespread adoption. This article aims to debunk five common myths and provides data and resources to help implement efficiency projects faster and more effectively to achieve greater decarbonization. First, the article challenges the myth that organizations have exhausted all possible energy efficiency opportunities by achieving voluntary energy intensity goals or energy performance certification. Second, it also addresses the misconceptions that efficiency projects are capital-intensive, require many qualified specialists, and have long investment return periods. By presenting real-world case studies and referencing commonly found efficiency opportunities, the article illustrates that energy-savings opportunities are ubiquitous. Organizations can use various contracting mechanisms as well as financial and technical resources from utility companies and government programs to lessen their burden. The notion that efficiency measures can be implemented solely in proprietorship facilities is dispelled. This article emphasizes the importance of green leases and explains that aligning decarbonization goals between the lessor and lessee can help drive savings for both parties. Finally, using unbundled renewable energy certificates as the sole pathway to decarbonization is strongly discouraged. By debunking these prevalent myths, this article aims to foster a deeper understanding of energy efficiency’s potential as a cornerstone of decarbonization efforts and to embrace it as a critical pathway toward a sustainable future.