bluuebunny/crossref_metadata_2025 huggingface dataset:
Motor Fault Diagnosis Based on Convolutional Block Attention Module-Xception Lightweight Neural Network
Authors: Xie Fengyun , Fan Qiuyang , Li Gang , Wang Yang , Sun Enguang , Zhou Shengtong
Published: September 2024

Electric motors play a crucial role in self-driving vehicles. Therefore, fault diagnosis in motors is important for ensuring the safety and reliability of vehicles. In order to improve fault detection performance, this paper proposes a motor fault diagnosis method based on vibration signals. Firstly, the vibration signals of each operating state of the motor at different frequencies are measured with vibration sensors. Secondly, the characteristic of Gram image coding is used to realize the coding of time domain information, and the one-dimensional vibration signals are transformed into grayscale diagrams to highlight their features. Finally, the lightweight neural network Xception is chosen as the main tool, and the attention mechanism Convolutional Block Attention Module (CBAM) is introduced into the model to enforce the importance of the characteristic information of the motor faults and realize their accurate identification. Xception is a type of convolutional neural network; its lightweight design maintains excellent performance while significantly reducing the model’s order of magnitude. Without affecting the computational complexity and accuracy of the network, the CBAM attention mechanism is added, and Gram’s corner field is combined with the improved lightweight neural network. The experimental results show that this model achieves a better recognition effect and faster iteration speed compared with the traditional Convolutional Neural Network (CNN), ResNet, and Xception networks.
Supertransient Chaos in a Single and Coupled Liénard Systems
Authors: Kingston S. Leo , Kumarasamy Suresh , Chudzik Agnieszka , Wojewoda Jerzy , Kapitaniak Tomasz
Published: September 2024

We report the appearance of supertransient chaos in a single and two-coupled Liénard system with the influence of external periodic force. The existence of transient dynamics in a model is significantly long before it settles into the asymptotic steady state of periodic dynamics understood as supertransient chaos. The two diffusively coupled forced Liénard systems exhibit extremely long transient dynamics when their frequencies of the external forcing are slightly mismatched. Additionally, the coupled system signifies supertransient hyperchaotic dynamics for a specific set of system parameters. This study involves different numerical characterizations, statistical analysis, and hardware implementation using an analog electronic circuit.
Assessing the Pandemic Aviation Crisis: Speculative Behavior, Government Bail Outs, and Accommodative Monetary Policy
Authors: Costa Viviana , Oliveira Maria Alberta , Santos Carlos
Published: September 2024

The COVID-19 pandemic was a health, economic, and financial crisis. The aviation sector was one of the most severely hit. Despite the extensive literature on this, COVID-Finance has been focused on stock returns, neglecting what could be learnt from the spreads of airlines’ credit default swaps (CDSs). This would seem of the utmost importance, given the epicenter of the crisis within the credit market. In this paper, an in-depth analysis of airlines’ CDS spreads is conducted. It is found that they were severely hit, for all airlines studied. However, the results of the PSY test showed that speculative trading led the surge, as explosive roots were found in the spreads of all these aviation firms. The dramatic increase in CDS spreads has contributed to already high borrowing costs for airlines. Our results suggest that aviation bail outs have helped to mitigate spreads’ explosiveness. Monetary policy measures have also limited, albeit indirectly, the funding risk posed by the government bail outs. By the end of March 2021, spreads were no longer explosive, and were approaching, at highly heterogeneous paces, their pre-pandemic values. Notwithstanding, airlines’ stock prices have been notably resistant to recovery.
Impact of Energy Crises on Income Inequality: An Application of Piketty’s Hypothesis to Pakistan
Authors: Hussain Jibran , Siyal Saeed , Ahmad Riaz , Abbas Qaiser , Yitian Yu , Jin Liu
Published: September 2024

In Pakistan, the majority of people have access to energy supplies. However, people who are underprivileged, below the extreme poverty line, or part of the middle class often spend disproportionate portions of their income on energy supplies and services, to some extent because of higher upfront prices for energy supplies, expensive products, and expensive imported appliances. The nonavailability of low-cost energy supplies is mainly affecting underdeveloped regions that have mostly low-income households. We used the dynamic ordinary least squares method to look at the impact of the energy crisis on income inequality from 1997 to 2021. The results show that the energy crisis exacerbates income inequality as low-income groups end up spending more significant shares of their income on energy products, supplies, and services than higher-income groups. Fair and equal access to energy supplies and services is less likely to reduce income inequality if prices are not cost-efficient. Cautious deliberation regarding the structure of energy tariffs is inevitable; at the same time, safety nets and social security programs for the poorest groups need to be expanded. At this stage, the aim is to target energy prices that will achieve the objectives of reducing polarity and increasing real income.
Investigating the Impact of STEM Inquiry-Based Learning Activities on Secondary School Student’s STEM Career Interests: A Gender-Based Analysis Using the Social Cognitive Career Framework
Authors: Ribeirinha Teresa , Baptista Mónica , Correia Marisa
Published: September 2024

Gender disparity within science, technology, engineering, and mathematics (STEM) fields is a persistent global issue, with women being consistently underrepresented. Recent research indicates that inquiry-based practices may enhance students’ interest in STEM careers and challenge gender-related STEM stereotypes. The aim of this study is to evaluate the impact of STEM inquiry-based learning activities on students’ interest in STEM careers, as well as on the social cognitive career theory (SCCT) dimensions (self-efficacy, personal goals, outcome expectations, interests, contextual support, and personal inputs), with a specific focus on gender. A quantitative approach was employed, whereby pre- and post-test measures were administered to a cohort of 190 Portuguese secondary school students, using the STEM Career Interest Survey. The results showed that, while the STEM inquiry-based learning activities did not alter the gender-based patterns of interest in STEM subjects and careers, they did contribute to a significant increase in students’ interest in these areas. It was found that these activities were particularly effective in promoting female students’ interest in STEM careers. A significant increase was observed in all SCCT dimensions, particularly in engineering, for female students. Additionally, no gender disparities were observed in self-efficacy in STEM areas. The results suggest that STEM inquiry-based learning activities are an effective approach to building students’ confidence in STEM fields and stimulating their interest in STEM careers. This study reinforces the importance of inquiry-based practices in promoting a more equitable STEM education landscape.
High Quality Factor Miniaturized CMOS Transmission Lines Using Cascaded T-Networks for 60 GHz Millimeter Wave Applications
Authors: Barakat Adel , Pokharel Ramesh K.
Published: September 2024

This work presents an on-chip 60 GHz lumped element transmission line (TL) using cascaded T-networks with a high Quality factor. Each T-network consists of a series inductance with a shunt capacitance at its center. We have derived the design equations for this TL configuration and proved that a TL whose electrical length ≥ 60° achieves additional miniaturization when implemented with three or more cascaded T-networks. The inductance is implemented using a straight wire model with no ground below it, while the capacitance is implemented using a parallel plate model with a grounding pedestal. Both configurations guarantee maximum inductance per unit length and capacitance per unit area to further improve the miniaturization level. A quarter wavelength TL with three cascaded T-networks is fabricated as proof of concept. The measured and simulated results of the fabricated TL have good agreement, and the measured quality factor is 17 at 60 GHz.
Lightweight Bearing Fault Diagnosis Method Based on Improved Residual Network
Authors: Gong Lei , Pang Chongwen , Wang Guoqiang , Shi Nianfeng
Published: September 2024

A lightweight bearing fault detection approach based on an improved residual network is presented to solve the shortcomings of previous fault diagnostic methods, such as inadequate feature extraction and an excessive computational cost due to high model complexity. First, the raw data are turned into a time–frequency map using the continuous wavelet transform, which captures all of the signal’s time- and frequency-domain properties. Second, an improved residual network model was built, which incorporates the criss-cross attention mechanism and depth-separable convolution into the residual network structure to realize the important distinction of the extracted features and reduce computational resources while ensuring diagnostic accuracy; simultaneously, the Meta-Acon activation function was introduced to improve the network’s self-adaptive characterization ability. The study findings indicate that the suggested approach had a 99.95% accuracy rate and a floating point computational complexity of 0.53 GF. Compared with other networks, it had greater fault detection accuracy and stronger generalization ability, and it could perform high-precision fault diagnostic jobs due to its lower complexity.
A Robust Generalized Zero-Shot Learning Method with Attribute Prototype and Discriminative Attention Mechanism
Authors: Liu Xiaodong , Luo Weixing , Du Jiale , Wang Xinshuo , Dang Yuhao , Liu Yang
Published: September 2024

In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the generalization ability and efficiency of GZSL models by integrating a Convolutional Block Attention Module (CBAM). The CBAM blends channel-wise and spatial-wise information to emphasize key features, thereby improving the model’s discriminative and localization capabilities. Additionally, the method employs a ResNet101 backbone for systematic image feature extraction, enhanced contrastive learning, and a similarity map generator with attribute prototypes. This comprehensive framework aims to achieve robust visual–semantic embedding for classification tasks. The proposed method demonstrates significant improvements in performance metrics in benchmark datasets, showcasing its potential in advancing GZSL applications.
Timed Genetic Process Mining for Robust Tracking of Processes under Incomplete Event Log Conditions
Authors: Effendi Yutika Amelia , Kim Minsoo
Published: September 2024

In process mining, an event log is a structured collection of recorded events that describes the execution of processes within an organization. The completeness of event logs is crucial for ensuring accurate and reliable process models. Incomplete event logs, which can result from system errors, manual data entry mistakes, or irregular operational patterns, undermine the integrity of these models. Addressing this issue is essential for constructing accurate models. This research aims to enhance process model performance and robustness by transforming incomplete event logs into complete ones using a process discovery algorithm. Genetic process mining, a type of process discovery algorithm, is chosen for its ability to evaluate multiple candidate solutions concurrently, effectively recovering missing events and improving log completeness. However, the original form of the genetic process mining algorithm is not optimized for handling incomplete logs, which can result in incorrect models being discovered. To address this limitation, this research proposes a modified approach that incorporates timing information to better manage incomplete logs. By leveraging timing data, the algorithm can infer missing events, leading to process tracking and reconstruction which is more accurate. Experimental results validate the effectiveness of the modified algorithm, showing higher fitness and precision scores, improved process model comparisons, and a good level of coverage without errors. Additionally, several advanced metrics for conformance checking are presented to further validate the process models and event logs discovered by both algorithms.
Dual-Stream Feature Collaboration Perception Network for Salient Object Detection in Remote Sensing Images
Authors: Li Hongli , Chen Xuhui , Mei Liye , Yang Wei
Published: September 2024

As the core technology of artificial intelligence, salient object detection (SOD) is an important approach to improve the analysis efficiency of remote sensing images by intelligently identifying key areas in images. However, existing methods that rely on a single strategy, convolution or Transformer, exhibit certain limitations in complex remote sensing scenarios. Therefore, we developed a Dual-Stream Feature Collaboration Perception Network (DCPNet) to enable the collaborative work and feature complementation of Transformer and CNN. First, we adopted a dual-branch feature extractor with strong local bias and long-range dependence characteristics to perform multi-scale feature extraction from remote sensing images. Then, we presented a Multi-path Complementary-aware Interaction Module (MCIM) to refine and fuse the feature representations of salient targets from the global and local branches, achieving fine-grained fusion and interactive alignment of dual-branch features. Finally, we proposed a Feature Weighting Balance Module (FWBM) to balance global and local features, preventing the model from overemphasizing global information at the expense of local details or from inadequately mining global cues due to excessive focus on local information. Extensive experiments on the EORSSD and ORSSD datasets demonstrated that DCPNet outperformed the current 19 state-of-the-art methods.
Assessing the Impact of Artificial Intelligence Tools on Employee Productivity: Insights from a Comprehensive Survey Analysis
Authors: Necula Sabina-Cristiana , Fotache Doina , Rieder Emanuel
Published: September 2024

This study provides a nuanced understanding of AI’s impact on productivity and employment using machine learning models and Bayesian Network Analysis. Data from 233 employees across various industries were analyzed using logistic regression, Random Forest, and XGBoost, with 5-fold cross-validation. The findings reveal that high levels of AI tool usage and integration within organizational workflows significantly enhance productivity, particularly among younger employees. A significant interaction between AI tools usage and integration (β = 0.4319, p < 0.001) further emphasizes the importance of comprehensive AI adoption. Bayesian Network Analysis highlights complex interdependencies between AI usage, innovation, and employee characteristics. This study confirms that strategic AI integration, along with targeted training programs and ethical frameworks, is essential for maximizing AI’s economic potential.
Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis
Authors: Halkiopoulos Constantinos , Gkintoni Evgenia
Published: September 2024

This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes.
Iterative Removal of G-PCC Attribute Compression Artifacts Based on a Graph Neural Network
Authors: He Zhouyan , Yang Wenming , Li Lijun , Bai Rui
Published: September 2024

As a compression standard, Geometry-based Point Cloud Compression (G-PCC) can effectively reduce data by compressing both geometric and attribute information. Even so, due to coding errors and data loss, point clouds (PCs) still face distortion challenges, such as the encoding of attribute information may lead to spatial detail loss and visible artifacts, which negatively impact visual quality. To address these challenges, this paper proposes an iterative removal method for attribute compression artifacts based on a graph neural network. First, the geometric coordinates of the PCs are used to construct a graph that accurately reflects the spatial structure, with the PC attributes treated as signals on the graph’s vertices. Adaptive graph convolution is then employed to dynamically focus on the areas most affected by compression, while a bi-branch attention block is used to restore high-frequency details. To maintain overall visual quality, a spatial consistency mechanism is applied to the recovered PCs. Additionally, an iterative strategy is introduced to correct systematic distortions, such as additive bias, introduced during compression. The experimental results demonstrate that the proposed method produces finer and more realistic visual details, compared to state-of-the-art techniques for PC attribute compression artifact removal. Furthermore, the proposed method significantly reduces the network runtime, enhancing processing efficiency.
Meta-Hybrid: Integrate Meta-Learning to Enhance Class Imbalance Graph Learning
Authors: Ran Liming , Sun Hongyu , Gao Lanqi , Dong Yanhua , Lu Yang
Published: September 2024

The class imbalance problem is a significant challenge in node classification tasks. Since majority class samples dominate imbalanced data, the model tends to favor the majority class, resulting in insufficient ability to identify minority classes. Evaluation indicators such as accuracy may not fully reflect the model’s performance. To solve these undesirable effects, we propose a framework for synthesizing minority class samples, GraphSHX, to balance the number of samples of different classes, and integrate the XGBoost model for node classification prediction during the training process. Conventional graph neural networks (GNNs) yielded unsatisfactory results, possibly due to the limited number of newly generated nodes. Therefore, we introduce a meta-mechanism to deal with small-sample problems, and employ the meta-learning approach to enhance performance on small-sample tasks by learning from a large number of tasks. An empirical evaluation of node classification on six publicly available datasets demonstrated that our balanced data set method outperforms existing optimal loss repair methods and synthetic node methods. The addition of the XGBoost model and meta-learning improves the accuracy by more than 5% to 10%, with the overall accuracy of the improved model being 15% higher than that of the baseline method.
OAR-UNet: Enhancing Long-Distance Dependencies for Head and Neck OAR Segmentation
Authors: Peng Kuankuan , Zhou Danyu , Gong Shihua
Published: September 2024

Accurate segmentation of organs at risk (OARs) is a crucial step in the precise planning of radiotherapy for head and neck tumors. However, manual segmentation methods using CT images, which are still predominantly applied in clinical settings, are inefficient and expensive. Additionally, existing segmentation methods struggle with small organs and have difficulty managing the complex interdependencies between organs. To address these issues, this study proposed an OAR-UNet segmentation method based on a U-shaped architecture with two key designs. To tackle the challenge of segmenting small organs, a Local Feature Perception Module (LFPM) is developed to enhance the sensitivity of the method to subtle structures. Furthermore, a Cross-shaped Transformer Block (CSTB) with a cross-shaped attention mechanism is introduced to improve the ability of the model to capture and process long-distance dependency information. To accelerate the convergence of the Transformer, we designed a Local Encoding Module (LEM) based on depthwise separable convolutions. In our experimental evaluation, we utilized two publicly available datasets, SegRap2023 and PDDCA, achieving Dice coefficients of 78.22% and 89.42%, respectively. These results demonstrate that our method outperforms both previous classic methods and state-of-the-art (SOTA) methods.
Construction and Explanation Analysis of a Hypotension Risk Prediction Model in Hemodialysis Based on Machine Learning
Authors: Zhang Mingwei , Zhang Tianyi
Published: September 2024

Objective. To establish a risk prediction model for intradialytic hypotension (IDH) in maintenance hemodialysis (MHD) patients and to analyze the explainability of the risk prediction model. Methods. A total of 2,228,650 hemodialysis records of 1075 MHD patients were selected as the research objects. Thirteen important clinical features including demographic features and clinical features were screened, the blood pressure measured before hemodialysis was collected, then an IDH risk prediction model during hemodialysis was established based on a machine learning algorithm. The contribution of each feature to the risk prediction of IDH was measured based on the Gini evaluation index. The TreeSHAP method was used to provide global and individual explanations for the IDH risk prediction model. Results. Hemodialysis duration, pre-dialysis mean arterial pressure, and pre-dialysis systolic blood pressure were the most important predictive variables for the occurrence of IDH during hemodialysis in MHD patients. The best IDH risk prediction model based on machine learning had an accuracy of 0.92 (95% CI 0.90–0.94) and an AUC of 0.95 (95% CI 0.94–0.96), indicating that machine learning has a good effect on the prediction of IDH during hemodialysis treatment. Our research innovatively achieved IDH risk prediction during the entire hemodialysis period based on blood pressure before the start of hemodialysis and other clinical features, thus enabling the medical team to quickly adjust hemodialysis prescriptions or initiate treatment for timely management and prevention of IDH. Global and individual explanations of the IDH risk prediction model can help hemodialysis medical staff understand the overall prediction mechanism of the model, discover prediction outliers, and identify potential biases or errors in the model. Conclusions. The IDH risk prediction model has definite clinical value in actual hemodialysis treatment.
Evaluation of Haptic Textures for Tangible Interfaces for the Tactile Internet
Authors: Tzimos Nikolaos , Voutsakelis George , Kontogiannis Sotirios , Kokkonis Georgios
Published: September 2024

Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information about the texture and the shape of objects. In this paper, we enhance X3D 3D graphics files with haptic features to create 3D objects with haptic feedback. We propose haptic attributes such as static and dynamic friction, stiffness, and maximum altitude that provide the optimal user experience in a virtual haptic environment. After numerous optimization attempts on the haptic textures, we propose various haptic geometrical textures for creating a virtual 3D haptic environment for the tactile Internet. These tangible geometrical textures can be attached to any geometric shape, enhancing the haptic sense. We conducted a study of user interaction with a virtual environment consisting of 3D objects enhanced with haptic textures to evaluate performance and user experience. The goal is to evaluate the realism and recognition accuracy of each generated texture. The findings of the study aid visually impaired individuals to better understand their physical environment, using haptic devices in conjunction with the enhanced haptic textures.
Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables
Authors: Omran Hesham
Published: September 2024

Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance.
Improved Control Strategy for Dual-PWM Converter Based on Equivalent Input Disturbance
Authors: Huang Zixin , Wang Wei , Yu Chengsong , Lu Junjie
Published: September 2024

Aiming at the problems of jittering waveforms and poor power quality caused by external disturbances during the operation of a dual-pulse-width-modulation (PWM) converter, an improved terminal sliding mode control and an improved active disturbance rejection control (ADRC) are investigated. The method is based on mathematical models of grid-side and machine-side converters to design the controllers separately, and the balance between the two sides is maintained by the capacitor voltage. An improved terminal fuzzy sliding mode control and equivalent input disturbance (EID)-error-estimation-based active disturbance rejection control are presented on the grid side to improve the voltage response rate, and an improved support vector modulation (SVM)–direct torque control (DTC)–ADRC method is developed on the motor side to improve the robustness against disturbances. Finally, theoretical simulation experiments are built in MATLAB R2023a/Simulink to verify the effectiveness and superiority of this method.
Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study
Authors: Ekambaram Dilliraj , Ponnusamy Vijayakumar
Published: September 2024

In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna .v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately tracks and evaluates individualized rehabilitation exercises for patients suffering from low back pain using features such as exercise visualization and guidance, real-time feedback and monitoring, and personalized exercise plans. This dashboard was very effective for tracking rehabilitation progress. We recruited 32 individuals to participate in this pilot study. We monitored an individual’s overall performance for one week. Of the participants, 18.75% engaged in rehabilitative exercises less frequently than twice daily; 81.25% did so at least three times daily. The proposed Long Short-Term Memory (LSTM) architecture had a training accuracy score of 98.8% and a testing accuracy of 99.7%, with an average accuracy of 10-fold cross-validation of 98.54%. On the pre- and post-test assessments, there is a significant difference between pain levels, with a p < 0.05 and a t-stat value of 12.175. The proposed system’s usability score is 79.375, indicating that it provides a user-friendly environment for the user to use the PoAna .v1 web application. So far, our research suggests that the Streamlit 1.38.0-based dashboard improves patients’ engagement, adherence, and success with exercise. Future research aims to add more characteristics that can improve the complete care of low back pain (LBP) and validate the effectiveness of this intervention in larger patient cohorts.
Experimental Design of Steel Surface Defect Detection Based on MSFE-YOLO—An Improved YOLOV5 Algorithm with Multi-Scale Feature Extraction
Authors: Li Lin , Zhang Ruopeng , Xie Tunjun , He Yushan , Zhou Hao , Zhang Yongzhong
Published: September 2024

Integrating artificial intelligence (AI) technology into student training programs is strategically crucial for developing future professionals with both forward-thinking capabilities and practical skills. This paper uses steel surface defect detection as a case study to propose a simulation-based teaching method grounded in deep learning. The method encompasses the entire process from data preprocessing and model training to validation analysis and innovation optimization with the goal of deepening students’ understanding of AI technology and enhancing their ability to apply it to real-world scenarios. We have designed an experimental framework that incorporates the Efficient Multi-Scale Attention (EMA) mechanism into the Backbone network. This approach helps students understand the principles of feature extraction and the core functions of attention mechanisms. Additionally, we introduced a novel architecture—Convolution 3 Dilated Convolution X (C3DX)—into the Neck network. This architecture effectively expands the network’s receptive field, improves its ability to capture multi-scale information, and thus enhances defect detection accuracy. Furthermore, the implementation of the Efficient Intersection over Union (EIoU) loss function optimizes the bounding box predictions, further increasing the model’s accuracy and robustness. Overall, the teaching design not only ensures that the content remains at the cutting edge of technology but also emphasizes its practicality and operability. This approach enables students to effectively apply theoretical knowledge to real-world engineering projects.
Dual-Branch Colorization Network for Unpaired Infrared Images Based on High-Level Semantic Features and Multiscale Residual Attention
Authors: Jiang Tong , Bai Junqi , Xiao Lin , Liu Tingting , Kuang Xiaodong , Liu Yuan , Sui Xiubao , Chen Qian
Published: September 2024

The infrared image colorization technique overcomes the limitation of grayscale characteristics of infrared images and achieves cross-modal conversion between infrared and visible images. Aiming at the problem of lack of infrared-visible pairing data, existing studies usually adopt unsupervised learning methods based on contrastive loss. Due to significant differences between modalities, reliance on contrastive loss alone hampers the learning of accurate semantic features. In this paper, we propose DC-Net, which is a dual-branch contrastive learning network that combines perceptual features and multiscale residual attention for the unsupervised cross-modal transformation of infrared to visible images. The network comprises a patch-wise contrastive guidance branch (PwCGB) and a perceptual contrastive guidance branch (PCGB). PwCGB focuses on discerning feature similarities and variances across image patches, synergizing patch-wise contrastive loss with adversarial loss to adaptively learn local structure and texture. In addition, we design a multiscale residual attention generator to capture richer features and adaptively integrate multiscale information. PCGB introduces a novel perceptual contrastive loss that uses perceptual features from pre-trained VGG16 models as positive and negative samples. This helps the network align colorized infrared images with visible images in the high-level feature space, improving the semantic accuracy of the colorized infrared images. Our unsupervised infrared image colorization method achieves a PSNR of 16.833 and an SSIM of 0.584 on the thermal infrared dataset and a PSNR of 18.828 and an SSIM of 0.685 on the near-infrared dataset. Compared to existing algorithms, it demonstrates substantial improvements across all metrics, validating its effectiveness.
Contact State Recognition for Dual Peg-in-Hole Assembly of Tightly Coupled Dual Manipulator
Authors: Zhang Jiawei , Bai Chengchao , Guo Jifeng , Cheng Zhengai , Chen Ying
Published: September 2024

Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes significantly more complex. Additionally, when a tightly coupled multi-manipulator is required, the estimation errors in the contact forces between pegs and holes make contact state recognition challenging. The current state recognition methods have not been tested in such tasks. This paper tested Support Vector Machine (SVM) and several neural network models on these tasks and analyzed the recognition accuracy, precision, recall, and F1 score. An ablation experiment was carried out to test the contributions of force, image, and position to the recognition performance. The experimental results show that SVM has better performance than the neural network models. However, when the size of the dataset is limited, SVM still faces generalization issues. By applying heuristic action, this paper proposes a two-stage recognition strategy that can improve the recognition success rate of the SVM.
A Smart Contract Vulnerability Detection Method Based on Heterogeneous Contract Semantic Graphs and Pre-Training Techniques
Authors: Zhang Jie , Lu Gehao , Yu Jia
Published: September 2024

The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic losses and data breaches, making pre-deployment vulnerability detection critically important. Traditional smart contract vulnerability detection methods suffer from low accuracy and limited reusability across different scenarios. To enhance detection capabilities, this paper proposes a smart contract vulnerability detection method based on heterogeneous contract semantic graphs and pre-training techniques. Compared to the conventional graph structures used in existing methods, heterogeneous contract semantic graphs contain richer contract information. By integrating these with pre-trained models, our method exhibits stronger vulnerability capture and generalization capabilities. Experimental results show that this method has improved the accuracy, recall, precision, and F1 value in the detection of four widely existing and harmful smart contract vulnerabilities compared with existing methods, which greatly improves the detection ability of smart contract vulnerabilities.
A Detailed Analysis and Gain Derivation of Reconfigurable Voltage Rectifier-Based LLC Converter
Authors: Alaql Fahad , Alfraidi Walied , Alhatlani Abdullah , Al-Shamma’a Abdullrahman A. , Hussein Farh Hassan M. , Allehyani Ahmed
Published: September 2024

In this paper, a complete analysis of an LLC resonant converter with a customized rectifier structure is presented. The converter is intended for wide, low-input, high-output voltage DC bus applications. The performance of the converter is assessed using comprehensive time-domain and fundamental harmonic approximation (FHA), which demonstrates its capacity to operate across an ample range of voltages by precisely adjusting the rectifier structure. The converter’s capability is illustrated by deriving and discussing detailed mode operation, steady-state analysis, and DC gain equations. In order to verify the theoretical analysis, a prototype with a power output of 250 watts is constructed and subjected to testing. The results of the testing demonstrate that the converter is both feasible and effective. The experimental findings illustrate its capacity to manage vast voltage ranges while upholding high efficiency. In addition, the converter utilizes a frequency switching modulation (FSM) to connect with a photovoltaic (PV) panel and control the high output voltage. This demonstrates its adaptability in renewable energy applications. The validation is in accordance with theoretical predictions, demonstrating the converter’s high-efficiency performance and versatility.
Model Assisted Extended State Observer-Based Deadbeat Predictive Current Control for Modular Multilevel Converter
Authors: Yang Xiaowei , Zhang Yongqiang , Liu Yang , Jiang Sheng
Published: September 2024

Aiming at the issues of control delay and circuit parameter mismatch in three-phase modular multilevel converters (MMCs), this paper proposes a model assisted extended state observer-based deadbeat predictive current control (MAESO-based DPCC) strategy to regulate the AC-side current and internal circulating current. The model assisted ESO (MAESO) is employed to estimate the predicted values of the d- and q-axis components of the AC-side current, the internal circulating current, and system disturbance caused by the other certain and uncertain factors (including circuit parameter changes) of MMC at the time instant k + 1, and the required control input at the time instant k + 1 is then calculated based on the deadbeat control principle. The proposed control strategy not only maintains excellent steady-state performance and fast dynamic response characteristics similar to those of the traditional deadbeat predictive current control (DPCC) strategy but also has stronger robustness in the case of circuit parameter changes. The proposed control strategy was ultimately compared with the traditional DPCC strategy via experiments, and the experimental results verify the feasibility and effectiveness of the proposed control strategy.
Nighttime Pothole Detection: A Benchmark
Authors: Ling Min , Shi Quanjun , Zhao Xin , Chen Wenzheng , Wei Wei , Xiao Kai , Yang Zeyu , Zhang Hao , Li Shuiwang , Lu Chenchen , Zeng Yufan
Published: September 2024

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method’s strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety.
Advanced Design and Implementation of a 2-Channel, Multi-Functional Therapeutic Electrical Stimulator
Authors: Lakatem Rujira , Boontaklang Suttipong , Chompoo-inwai Chow
Published: September 2024

This research introduces the design, implementation, and rigorous evaluation of a novel 2-channel, multi-functional therapeutic electrical stimulator, meticulously engineered to meet the stringent demands of contemporary clinical applications. The device integrates a high-speed R-2R ladder DAC and a sophisticated pulse generator unit, capable of producing twelve essential current waveforms with fully adjustable parameters, including pulse amplitude, pulse duration, and pulse repetitive frequency. The proposed driving stage unit ensures precise voltage-to-current conversion, delivering stable and accurate output currents even under varying load conditions, which effectively simulate the diverse impedance characteristics of human tissue. Extensive testing confirmed the compliance with international medical standards, notably IEC 60601-1, IEC 60601-1-2, and IEC 60601-2-10. The experimental results underscore the device’s consistent operation within prescribed safety and performance thresholds, with all deviations in pulse parameters remaining well below the permissible limits. Furthermore, the proposed electrical stimulator demonstrated exceptional stability across variable load conditions, as evidenced by minimal amplitude errors and high correlation between waveform characteristics. These findings highlight the proposed device’s robustness and its potential as a versatile tool for a wide range of therapeutic applications, including pain management, muscle stimulation, and nerve rehabilitation, thus marking a significant advancement in the field of therapeutic electrical stimulation.
Determination of the Temperature Development in a Borehole Heat Exchanger Field Using Distributed Temperature Sensing
Authors: Bertermann David , Suft Oliver
Published: September 2024

The use of geothermal borehole heat exchangers (BHEs) in combination with ground-source heat pumps represents an important part of shallow geothermal energy production, which is already used worldwide and becoming more and more important. Different measurement techniques are available to examine a BHE field while it is in operation. In this study, a field with 54 BHEs up to a depth of 120 m below ground level was analyzed using fiber optic cables. A distributed temperature sensing (DTS) concept was developed by equipping several BHEs with dual-ended hybrid cables. The individual fiber optics were collected in a distributor shaft, and multiple measurements were carried out during active and inactive operation of the field. The field trial was carried out on a converted, partly retrofitted, residential complex, “Lagarde Campus”, in Bamberg, Upper Franconia, Germany. Groundwater and lithological changes are visible in the depth-resolved temperature profiles throughout the whole BHE field.
Continuous Control Set Predictive Control with Affine Registration Technique for Permanent Magnet Synchronous Motor Drive
Authors: Zhao Wentao , Ye Jianxiong , Yang Min
Published: September 2024

This article introduces an affine registration (AR) algorithm to improve the performance of ultra-local (UL) model-free predictive control (MFPC) methods. The UL model is extensively utilized in continuous control set (CCS)-MFPC to estimate lumped parameters. However, in these cases, variations in inductance during operation can significantly impact the input gain coefficient of the UL model. Little work has focused on this aspect, which makes the motivation and novelty of this paper. The AR technique, derived from image processing methodologies, is employed to establish a mapping between predicted states and actual states during the commissioning process. Subsequently, during actual operation, the resolved voltage reference is modified based on the AR matrix to compensate for errors in the UL model. The theoretical control model is rigorously derived, and experimental results are presented to validate the superior performance of the proposed method.
An Optimized Power-Angle and Excitation Dual Loop Virtual Power System Stabilizer for Enhanced MMC-VSG Control and Low-Frequency Oscillation Suppression
Authors: Yang Mu , Wu Xiaojie , Yu Dongsheng , Loveth Maxwell Chiemeka , Yu Samson S.
Published: September 2024

Modular Multilevel Converter Virtual Synchronous Generator (MMC-VSG) technology is gaining widespread attention for its ability to enhance the inertia and frequency stability of the power grid integrated with converter-interfaced renewable energy sources. However, the excitation voltage regulation in the MMC-VSG can generate equivalent negative damping torque and cause low-frequency oscillation problems similar to those in synchronous machines. This article aims to improve the system’s damping torque and minimize low-frequency oscillations by introducing a Virtual Power System Stabilizer (VPSS) into the power control loop. Building on the study of dynamic interactions between various control links of the MMC, this research establishes a reduced-order model (ROM) and a Phillips–Heffron state equation for the MMC-VSG single machine infinite bus system, using a hybrid modeling approach and a zero-pole truncation method. It also analyzes the mechanism of low-frequency oscillations in the MMC-VSG system through the damping torque method. The analysis reveals that the negative damping torque produced during the excitation voltage regulation process causes changes in the virtual power angle, which in turn increases the risk of low-frequency oscillation in the MMC-VSG. To address this issue, the article proposes an optimized control method for the MMC-VSG dual power loop architecture (power-angle/excitation) VPSS. This strategy compensates for the inadequate damping torque of a single loop VPSS and effectively suppresses low-frequency oscillations in the system.