bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery
Authors: Hassan Ietezaz Ul , Panduru Krishna , Walsh Joseph
Published: May 2024

Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns and maintaining operational safety. In this review, we look at different methods of vibration data processing in the context of vibration-based condition monitoring for heavy machinery. We divided primary approaches related to vibration data processing into three categories–signal processing methods, preprocessing-based techniques and artificial intelligence-based methods. We highlight the importance of these methods in improving the reliability and effectiveness of heavy machinery condition monitoring systems, highlighting the importance of precise and automated fault detection systems. To improve machinery performance and operational efficiency, this review aims to provide information on current developments and future directions in vibration-based condition monitoring by addressing issues like imbalanced data and integrating cutting-edge techniques like anomaly detection algorithms.
Implementation of a Smart Teaching and Assessment System for High-Quality Cardiopulmonary Resuscitation
Authors: Huang Li-Wen , Chan Yu-Wei , Tsan Yu-Tse , Zhang Qi-Xiang , Chan Wei-Chang , Yang Han-Hsuan
Published: May 2024

The purpose of this study is to develop a smart training and assessment system called SmartCPR, for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during CPR meet the standard of high-quality CPR by simply using a device such as a smart phone. Through the system, trainees are able to obtain real-time feedback on the quality of compressions so that they can adjust the cycle, depth, frequency, and posture of compressions to meet the standard of high-quality CPR. In addition, the SmartCPR system is convenient for CPR trainers. Trainers can instantly and accurately assess whether the trainee’s compressions meet the standard of high-quality CPR, which reduces the risk of manual assessment errors and also reduces the trainer’s teaching pressures. Therefore, the SmartCPR system developed in this study can be an important tool for CPR teaching and training for physicians, which can provide training and guidance for high-quality CPR maneuvers and enable trainees to become more proficient in CPR and self-training.
IMPA-Net: Interpretable Multi-Part Attention Network for Trustworthy Brain Tumor Classification from MRI
Authors: Xie Yuting , Zaccagna Fulvio , Rundo Leonardo , Testa Claudia , Zhu Ruifeng , Tonon Caterina , Lodi Raffaele , Manners David Neil
Published: May 2024

Deep learning (DL) networks have shown attractive performance in medical image processing tasks such as brain tumor classification. However, they are often criticized as mysterious “black boxes”. The opaqueness of the model and the reasoning process make it difficult for health workers to decide whether to trust the prediction outcomes. In this study, we develop an interpretable multi-part attention network (IMPA-Net) for brain tumor classification to enhance the interpretability and trustworthiness of classification outcomes. The proposed model not only predicts the tumor grade but also provides a global explanation for the model interpretability and a local explanation as justification for the proffered prediction. Global explanation is represented as a group of feature patterns that the model learns to distinguish high-grade glioma (HGG) and low-grade glioma (LGG) classes. Local explanation interprets the reasoning process of an individual prediction by calculating the similarity between the prototypical parts of the image and a group of pre-learned task-related features. Experiments conducted on the BraTS2017 dataset demonstrate that IMPA-Net is a verifiable model for the classification task. A percentage of 86% of feature patterns were assessed by two radiologists to be valid for representing task-relevant medical features. The model shows a classification accuracy of 92.12%, of which 81.17% were evaluated as trustworthy based on local explanations. Our interpretable model is a trustworthy model that can be used for decision aids for glioma classification. Compared with black-box CNNs, it allows health workers and patients to understand the reasoning process and trust the prediction outcomes.
Comparison of Two X-ray Analyses for Estimating the Prognosis of Eruption of Impacted Mandibular Third Molars
Authors: Hadzhigeorgieva-Kanazirska Petya G. , Kanazirski Nikolay D. , Stoeva Iliyana L.
Published: May 2024

The objective of this study was to compare the results of the measurements made using two methods for determining the retromolar eruption spaces and the mesiodistal inclinations of impacted mandibular third molars. These are the main parameters based on which the eruption of these teeth can be predicted. A Sirona GALILEOS Compact/Comfort CBCT scanner was used for the study. A total of 127 patients were included in the study. We made the measurements using our integrated method and the standard method used in the dental practice for determining the eruption space and the mesiodistal inclination of these teeth, and then we compared the results. The mean difference between the two methods for estimating the retromolar space deficiency on the left was 1.70 mm and standard deviation (SD) 2.95; mean error of the mean was 0.29; and Student t-test (paired t-test) = 5.86, significant level of the correlation was 0.001, <0.05. Regarding the teeth on the right, it was mean 1.59 mm and standard deviation (SD) 2.98; mean error of the mean was 0.31. The t-test performed found a statistically significant difference between the methods in determining the retromolar eruption spaces (t-test (paired t-test) = 5.13; significant level of the correlation 0.001; p < 0.05). The mean difference (in degrees) between the measurements of the inclinations of the teeth on the left using the two methods was 3, 50°; SD = 7.25; mean error of the mean = 1.81; t-test = 2.481; significant level of the correlation 0.025; and p > 0.05. As for the teeth on the right, it was 2.41°, SD = 9.57, mean error of the mean = 2.39, t-test 0.175, significant level of the correlation = 0.863, and >0.05. No statistically significant difference was found between the two methods in measuring the inclinations of impacted third molars. The conclusion of our study is that the determination of the mesiodistal inclination of the teeth and the available eruption space using the method developed by us is more accurate compared to the standard method, because constant points and planes are used. This method allows for predicting the eruption of impacted mandibular third molars.
Simplified Assessment of Radioiodine Biokinetics for Thyroid Cancer Patients: A Practical Approach Using Continuous External Radiation Monitoring
Authors: Tsai Yao-Kuang , Lin Li-Fan , Cheng Cheng-Yi , Wong Ching-Yee Oliver , Wang Wei-Hsung , Shen Daniel Hueng-Yuan , Su Sui-Lung , Chen En-Shih , Chen Tzai-Yang , Chen I-Feng
Published: May 2024

Introduction: The biokinetics of radioiodine (RAI) in thyroid cancer patients are complex. This study aims to develop a practical approach for assessing RAI biokinetics to predict patient discharge time and estimate radiation exposure to caregivers. Methods: We retrospectively reviewed data from patients with differentiated thyroid carcinoma undergoing RAI treatment. Serial radiation dose rates were dynamically collected during hospitalization and fitted to a biexponential model to assess the biokinetic features: RAI uptake fraction of thyroid tissue (Ft) and effective half-life of extra-thyroid tissue (Tet). Correlations with 99mTc thyroid uptake ratio (TcUR), radiation retention ratio (RR), renal function, and body mass index (BMI) were analyzed. Results: Thirty-five patients were enrolled. The derived Ft was 0.08 ± 0.06 and Tet was 7.57 ± 1.45 h. Pearson’s correlation analysis revealed a significant association between Ft and both TcUR and RR (p < 0.05), while Tet correlated with renal function and BMI (p < 0.05). Conclusion: This novel and practical method assessing RAI biokinetics demonstrates consistency with other parameters and related studies, enhancing the model reliability. It shows promise in predicting an appropriate discharge time and estimating radiation exposure to caregivers, allowing for modifications to radiation protection precautions to follow ALARA principle and minimize the potential risks from radiation exposure.
Late Outcomes of Undiagnosed Unilateral Condylar Hyperplasia and Reoccurrence of Mandibular Asymmetry
Authors: Nelke Kamil , Pawlak Wojciech , Łuczak Klaudiusz , Janeczek Maciej , Pasicka Edyta , Nienartowicz Jan , Gogolewski Grzegorz , Dobrzyński Maciej
Published: May 2024

Unilateral condylar hyperplasia (UCH) is a rare cause of asymmetrical mandibular overgrowth because of the presence of an atypical growth in the affected condyle. SPECT (single-photon emission computed tomography) can easily establish the presence of an atypical, prolonged growth exceeding far beyond normal condylar growth and activity. A CT, CBCT, or LDCT (computed tomography, cone-beam computed tomography, or low-dose computed tomography) can confirm the diagnosis by evaluating the scope of bone overgrowth, mandibular basis/ramus asymmetry, tendency to condylar head enlargement, changes in bone density, and occurrence of differences in condylar head shapes, size, and bone structure. In most cases, a condylectomy is the procedure of choice in growing cases of UCH to remove the pathological condyle and reduce asymmetry levels. Sometimes, the growth is very slow and progressive over time, causing slowly growing asymmetry with similar symptoms to any other mandibular asymmetry, and this causes some troublesome procedures in UCH diagnostics, resulting in patients being underdiagnosed; it can even lead to some relapses in mandibular asymmetry and skeletal malocclusion after previously performed orthodontic and surgical treatment of such discrepancies. When the source of asymmetry is not identified in time, possible inadequate treatment protocols can be used. If any relapse of facial and mandibular asymmetry re-occur, SPECT and CT evaluation are necessary to evaluate if condylar hyperplasia is present and to establish what kind of surgical intervention should be used in each case.
Texture-Based Classification to Overcome Uncertainty between COVID-19 and Viral Pneumonia Using Machine Learning and Deep Learning Techniques
Authors: Farghaly Omar , Deshpande Priya
Published: May 2024

The SARS-CoV-2 virus, responsible for COVID-19, often manifests symptoms akin to viral pneumonia, complicating early detection and potentially leading to severe COVID pneumonia and long-term effects. Particularly affecting young individuals, the elderly, and those with weakened immune systems, the accurate classification of COVID-19 poses challenges, especially with highly dimensional image data. Past studies have faced limitations due to simplistic algorithms and small, biased datasets, yielding inaccurate results. In response, our study introduces a novel classification model that integrates advanced texture feature extraction methods, including GLCM, GLDM, and wavelet transform, within a deep learning framework. This innovative approach enables the effective classification of chest X-ray images into normal, COVID-19, and viral pneumonia categories, overcoming the limitations encountered in previous studies. Leveraging the unique textures inherent to each dataset class, our model achieves superior classification performance, even amidst the complexity and diversity of the data. Moreover, we present comprehensive numerical findings demonstrating the superiority of our approach over traditional methods. The numerical results highlight the accuracy (random forest (RF): 0.85; SVM (support vector machine): 0.70; deep learning neural network (DLNN): 0.92), recall (RF: 0.85, SVM: 0.74, DLNN: 0.93), precision (RF: 0.86, SVM: 0.71, DLNN: 0.87), and F1-Score (RF: 0.86, SVM: 0.72, DLNN: 0.89) of our proposed model. Our study represents a significant advancement in AI-based diagnostic systems for COVID-19 and pneumonia, promising improved patient outcomes and healthcare management strategies.
Tuberculous Spondylodiscitis with Psoas Abscess Descending into the Anterior Femoral Compartment Identified Using 2-deoxy-2-[18F]fluoroglucose Positron Emission Tomography Computed Tomography
Authors: Scherer Julian , Kotze Tessa , Mdiza Zintle , Lawson Andrew , Held Michael , Thienemann Friedrich
Published: May 2024

A 24-year-old immunocompetent woman underwent whole-body 18F-FDG PET/CT for the evaluation of MRI-suspicious tuberculous spinal lesions. The PET/CT results showed no pathological uptake in either lung, and there were no pathological changes on CT. There was increased uptake in the right psoas muscle, extending continuously down anterior to the right hip joint, posterior to and around the trochanteric region of the right femur, and into the right thigh, with an SUVmaxbw of 17.0. Subsequently, the patient underwent CT-guided biopsy as per protocol, which revealed drug-sensitive Mycobacterium tuberculosis, and the patient was started on standard tuberculosis treatment for 12 months.
Is Incisor Compensation Related to Skeletal Discrepancies in Skeletal Class III? A Retrospective Cephalometric Study
Authors: Mathapun Jirath , Charoemratrote Chairat
Published: May 2024

This study investigated compensation in skeletal Class III subjects to compare various severities of abnormal jaws. A retrospective analysis of 137 skeletal Class III cephalograms (63 males and 74 females) was conducted, with cephalometric assessments determining skeletal and dental values. The results were compared with Class I cephalograms. Incisor compensation was examined by pairing normal jaws with varied abnormal jaws, classified by severity using one standard deviation (SD). Statistical analyses included Kruskal–Wallis tests, Bonferroni tests, Spearman’s correlations, and multiple linear regression. Four skeletal Class III groups were identified: OMx+PMd, RMx+OMd, OMx+OMd, and PMx+PMd (P = prognathic; O = orthognathic; R = retrognathic; Mx = maxilla; Md = mandible.). The upper central incisor (U1) showed proclination, and the lower central incisor (L1) showed retroclination across all groups except for U1 in PMx+PMd and L1 in OMx+OMd, which exhibited normal inclination. U1 exhibited limited compensation even with progressive maxillary retrognathism, while L1 showed limited compensation after one SD of mandibular prognathism. Maxilla (SNA) and jaw discrepancy (ANB) were inversely related to the U1 degree, whereas only jaw discrepancy (ANB) was positively related to the L1 degree. U1 in PMx+PMd and L1 in OMx+OMd showed no incisor compensation. U1 had limited compensation even with progressive maxillary retrognathism while L1 showed limited compensation after one SD mandibular prognathism.
Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment
Authors: Shia Wei-Chung , Kuo Yu-Hsun , Hsu Fang-Rong , Lin Joseph , Wu Wen-Pei , Wu Hwa-Koon , Yeh Wei-Cheng , Chen Dar-Ren
Published: May 2024

Background: The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often cannot be provided in a timely manner. To address this challenge, digital breast tomosynthesis technology was utilized to generate detailed cross-sectional images of the breast tissue and integrate deep learning algorithms for image segmentation, achieving an assessment of tumor margins during surgery. Methods: this study utilized post-operative tissue samples from 46 patients who underwent breast-conserving treatment, and generated image sets using digital breast tomosynthesis for the training and evaluation of deep learning models. Results: Deep learning algorithms effectively identifying the tumor area. They achieved a Mean Intersection over Union (MIoU) of 0.91, global accuracy of 99%, weighted IoU of 44%, precision of 98%, recall of 83%, F1 score of 89%, and dice coefficient of 93% on the training dataset; for the testing dataset, MIoU was at 83%, global accuracy at 97%, weighted IoU at 38%, precision at 87%, recall rate at 69%, F1 score at 76%, dice coefficient at 86%. Conclusions: The initial evaluation suggests that the deep learning-based image segmentation method is highly accurate in measuring breast tumor margins. This helps provide information related to tumor margins during surgery, and by using different datasets, this research method can also be applied to the surgical margin assessment of various types of tumors.
Frequency of the Main Human Leukocyte Antigen A, B, DR, and DQ Loci Known to Be Associated with the Clearance or Persistence of Hepatitis C Virus Infection in a Healthy Population from the Southern Region of Morocco: A Preliminary Study
Authors: Machraoui Safa , Errafii Khaoula , Oujamaa Ider , Belghali Moulay Yassine , Hakmaoui Abdelmalek , Lamjadli Saad , Eddehbi Fatima Ezzohra , Brahim Ikram , Haida Yasmine , Admou Brahim
Published: May 2024

Hepatitis C Virus (HCV) infection represents a significant global health challenge, with its natural course largely influenced by the host’s immune response. Human Leukocyte Antigen (HLA) molecules, particularly HLA class I and II, play a crucial role in the adaptive immune response against HCV. The polymorphism of HLA molecules contributes to the variability in immune response, affecting the outcomes of HCV infection. This study aims to investigate the frequency of HLA A, B, DR, and DQ alleles known to be associated with HCV clearance or persistence in a healthy Moroccan population. Conducted at the University Hospital Center Mohammed VI, Marrakech, this study spanned from 2015 to 2022 and included 703 healthy Moroccan individuals. HLA class I and II typing was performed using complement-dependent cytotoxicity and polymerase chain reaction-based methodologies. The results revealed the distinct patterns of HLA-A, B, DRB1, and DQB1 alleles in the Moroccan population. Notably, alleles linked to favorable HCV outcomes, such as HLA-DQB1*0301, DQB1*0501, and DRB1*1101, were more prevalent. Conversely, alleles associated with increased HCV susceptibility and persistence, such as HLA-DQB1*02 and DRB1*03, were also prominent. Gender-specific variations in allele frequencies were observed, providing insights into genetic influences on HCV infection outcomes. The findings align with global trends in HLA allele associations with HCV infection outcomes. The study emphasizes the role of host genetics in HCV infection, highlighting the need for further research in the Moroccan community, including HCV-infected individuals. The prevalence of certain HLA alleles, both protective and susceptibility-linked, underscores the potential for a national HLA data bank in Morocco.
Multi-Device Security Application for Unmanned Surface and Aerial Systems
Authors: Leon Andre , Britt Christopher , Hale Britta
Published: May 2024

The use of autonomous and unmanned systems continues to increase, with uses spanning from package delivery to simple automation of tasks and from factory usage to defense industries and agricultural applications. With the proliferation of unmanned systems comes the question of how to secure the command-and-control communication links among such devices and their operators. In this work, we look at the use of the Messaging Layer Security (MLS) protocol, designed to support long-lived continuous sessions and group communication with a high degree of security. We build out MAUI—an MLS API for UxS Integration that provides an interface for the secure exchange of data between a ScanEagle unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) in a multi-domain ad-hoc network configuration, and experiment on system limits such as the ciphersuite set-up time and message handling rates. The experiments in this work were conducted in virtual and physical environments between the UAV, USV, and a controller device (all of different platforms). Our results demonstrate the viability of capitalizing on MLS’s capabilities to securely and efficiently transmit data for distributed communication among various unmanned system platforms.
Multi-Target Optimization Strategy for Unmanned Aerial Vehicle Formation in Forest Fire Monitoring Based on Deep Q-Network Algorithm
Authors: Liu Wenjia , Lyu Sung-Ki , Liu Tao , Wu Yu-Ting , Qin Zhen
Published: May 2024

Forest fires often pose serious hazards, and the timely monitoring and extinguishing of residual forest fires using unmanned aerial vehicles (UAVs) can prevent re-ignition and mitigate the damage caused. Due to the urgency of forest fires, drones need to respond quickly during firefighting operations, while traditional drone formation deployment requires a significant amount of time. This paper proposes a pure azimuth passive positioning strategy for circular UAV formations and utilizes the Deep Q-Network (DQN) algorithm to effectively adjust the formation within a short timeframe. Initially, a passive positioning model for UAVs based on the relationships between the sides and angles of a triangle is established, with the closest point to the ideal position being selected as the position for the UAV to be located. Subsequently, a multi-target optimization model is developed, considering 10 UAVs as an example, with the objective of minimizing the number of adjustments while minimizing the deviation between the ideal and adjusted UAV positions. The DQN algorithm is employed to solve and design experiments for validation, demonstrating that the deviation between the UAV positions and the ideal positions, as well as the number of adjustments, are within acceptable ranges. In comparison to genetic algorithms, it saves approximately 120 s.
Research on Improved YOLOv5 Vehicle Target Detection Algorithm in Aerial Images
Authors: Yang Xue , Xiu Jihong , Liu Xiaojia
Published: May 2024

Aerial photoelectric imaging payloads have become an important means of reconnaissance and surveillance in recent years. However, aerial images are easily affected by external conditions and have unclear edges, which greatly reduces the accuracy of imaging target recognition. This paper proposes the M-YOLOv5 model, which uses a shallow feature layer. The RFBs module is introduced to improve the receptive field and detection effect of small targets. In the neck network part, the BiFPN structure is used to reuse the underlying features to integrate more features, and a CBAM attention mechanism is added to improve detection accuracy. The experimental results show that the detection effect of this method on the DroneVehicle dataset is better than that of the original network, with the precision rate increased by 2.8%, the recall rate increased by 16%, and the average precision increased by 2.3%. Considering the real-time problem of target detection, based on the improved model, the Clight-YOLOv5 model is proposed, by lightweighting the network structure and using the depth-separable convolution optimization module. After lightweighting, the number of model parameters is decreased by 71.3%, which provides a new idea for lightweight target detection and proves the model’s effectiveness in aviation scenarios.
Model Selection for Exponential Power Mixture Regression Models
Authors: Jiang Yunlu , Liu Jiangchuan , Zou Hang , Huang Xiaowen
Published: May 2024

Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions for FMLR models via an exponential power error distribution, which includes normal distributions and Laplace distributions as special cases. Under some regularity conditions, the consistency of order selection and the consistency of variable selection are established, and the asymptotic normality for the estimators of non-zero parameters is investigated. In addition, an efficient modified expectation-maximization (EM) algorithm and a majorization-maximization (MM) algorithm are proposed to implement the proposed optimization problem. Furthermore, we use the numerical simulations to demonstrate the finite sample performance of the proposed methodology. Finally, we apply the proposed approach to analyze a baseball salary data set. Results indicate that our proposed method obtains a smaller BIC value than the existing method.
Validation Study of the Spanish Version of the Measure of Happiness (MH) Questionnaire
Authors: Rizzato Matteo , Miraglia Laura , Arnau Mollà Antonio Francisco , Romero Naranjo Francisco Javier , Di Dio Cinzia , Antonelli Michele , Donelli Davide
Published: May 2024

This study aims to validate the Spanish version of the Measure of Happiness (MH), a questionnaire that identifies the specific areas of an individual’s life that are related to experienced happiness. The sample consisted of 458 Spanish native speakers (65.7% women, 34.3% men; mean age = 24.14, SD = 8.45) and was divided into two groups: Sample 1 (N = 226) underwent Exploratory Factor Analysis (EFA), and Sample 2 (N = 232) underwent Confirmatory Factor Analysis (CFA). The convergent and discriminant validity of the Spanish version of the MH and its subscales was assessed by calculating Pearson correlations between the MH factors and the Subjective Happiness Scale, the State–Trait Anxiety Inventory, the Beck Depression Inventory, and the WHOQOL-BREF. The EFA and CFA confirmed the original five-factor structure of the MH questionnaire. The only difference in the Spanish version of the MH is that Item 4, which was originally assigned to Factor 2 “Financial Status”, was reallocated to Factor 1 “Psychophysical Status”. The reliability of the Spanish version of the MH questionnaire was also confirmed, and the factors correlated in the expected direction with the constructs of quality of life, happiness, anxiety, and depression. The MH questionnaire demonstrated excellent psychometric properties among Spanish respondents.
Quality of Life and Clinical Impairment in Spanish Adolescent Anorexia Nervosa Patients
Authors: Neipp Marie-Carmen , Ruiz Álvaro , Manchón Javier , León-Zarceño Eva , Quiles María José , Quiles Yolanda
Published: May 2024

Eating disorders have serious physical, mental and social consequences that can affect the quality of life of the sufferer. This study aimed to evaluate the relationship between the severity of ED-related psychopathology and clinical impairment in adolescents with anorexia nervosa (AN) as well as their perception of health-related quality of life. Eighty-six Spanish young women with AN completed a set of questionnaires assessing eating disorder pathology, clinical impairment, and quality of life. The set included the following instruments: the Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, Short Form-12 Item Health Survey, and the Eating Disorder-Specific Heath-Related Quality of Life instrument. Descriptive and regression analyses were applied to identify associations between variables. Higher scores on clinical impairment domains were associated with greater impairment of mental and physical health. Moreover, clinical impairment domains and concerns due to ED were related to a lower quality of life. In conclusion, adolescents with AN have a poor quality of life. Moreover, the findings suggest that the clinical features of impairment may serve as severity indicators of quality of life.
Selecting the Best Permanent Magnet Synchronous Machine Design for Use in a Small Wind Turbine
Authors: Lefik Marcin , Firych-Nowacka Anna , Lipian Michal , Brzozowska Malgorzata , Smaz Tomasz
Published: May 2024

The article describes the selection of a permanent magnet synchronous machine design that could be implemented in a small wind turbine designed by the GUST student organization together with researchers working at the Technical University of Lodz. Based on measurements of the characteristics of available machines, eight initial designs of machines with different rotor designs were proposed. The size of the stator, the number of pairs of poles, and the dimensions of the magnets were used as initial parameters of the designed machines. The analysis was carried out about the K-index, the so-called index of benefits. The idea was to make the selected design as efficient as possible while keeping production costs and manufacturing time low. This paper describes how to select the best design of a permanent magnet synchronous generator intended to work with a small wind turbine. All generator parameters were selected keeping in mind the competition requirements, as the designed generator will be used in the author’s wind turbine. Based on the determined characteristics of the generator variants and the value of the K-index, a generator with a latent magnet rotor was selected as the best solution. The aforementioned K-index is a proprietary concept developed for the selection of the most suitable generator design. This paper did not use optimization methods; the analysis was only supported by the K-index.
Enhanced Wild Horse Optimizer with Cauchy Mutation and Dynamic Random Search for Hyperspectral Image Band Selection
Authors: Chen Tao , Sun Yue , Chen Huayue , Deng Wu
Published: May 2024

The high dimensionality of hyperspectral images (HSIs) brings significant redundancy to data processing. Band selection (BS) is one of the most commonly used dimensionality reduction (DR) techniques, which eliminates redundant information between bands while retaining a subset of bands with a high information content and low noise. The wild horse optimizer (WHO) is a novel metaheuristic algorithm widely used for its efficient search performance, yet it tends to become trapped in local optima during later iterations. To address these issues, an enhanced wild horse optimizer (IBSWHO) is proposed for HSI band selection in this paper. IBSWHO utilizes Sobol sequences to initialize the population, thereby increasing population diversity. It incorporates Cauchy mutation to perturb the population with a certain probability, enhancing the global search capability and avoiding local optima. Additionally, dynamic random search techniques are introduced to improve the algorithm search efficiency and expand the search space. The convergence of IBSWHO is verified on commonly used nonlinear test functions and compared with state-of-the-art optimization algorithms. Finally, experiments on three classic HSI datasets are conducted for HSI classification. The experimental results demonstrate that the band subset selected by IBSWHO achieves the best classification accuracy compared to conventional and state-of-the-art band selection methods, confirming the superiority of the proposed BS method.
Wideband Low Phase-Noise Signal Generation Using Coaxial Resonator in Cascaded Phase Locked Loop
Authors: Blatnik Aljaž , Batagelj Boštjan
Published: May 2024

The generation of high-quality wideband frequency sweeps presents a significant challenge, particularly in modern telecommunication, radar, and measurement systems where miniaturization is paramount. While phase-locked loops (PLLs) have become the dominant technique for signal generation, their application in broadband sweeps necessitates fractional-N operation. This, in turn, degrades phase noise and introduces unwanted spurs. This paper proposes a novel approach for broadband signal generation. By cascading two PLLs and utilizing a coaxial resonator, we achieve a high-performance oscillator that operates without the excessive fractional spurs, maintaining their level below −80 dBc across the entire frequency band. The prototype demonstrates non-degraded phase noise performance, reaching −102 dBc/Hz at 100 kHz offset and −121 dBc/Hz at 1 MHz offset for signals at 10 GHz. Despite significant frequency jumps, our design achieves lock times below 41 µs. These results, supported by theoretical analysis, validate the proposed method’s effectiveness in generating low-noise broadband frequency sweeps, ideal for local oscillator applications.
Algebraic Speed Estimation for Sensorless Induction Motor Control: Insights from an Electric Vehicle Drive Cycle
Authors: Neira-García Jorge , Beltrán-Pulido Andrés , Cortés-Romero John
Published: May 2024

Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme.
Detection of False Data Injection Attacks on Smart Grids Based on A-BiTG Approach
Authors: He Wei , Liu Weifeng , Wen Chenglin , Yang Qingqing
Published: May 2024

A false data injection attack (FDIA) is the main attack method that threatens the security of smart grids. FDIAs mislead the control center to make wrong judgments by modifying the measurement data of the power grid system. Therefore, the effective and accurate detection of FDIAs is crucial for the safe operation of smart grids. However, the current deep learning-based methods do not fully exploit the short-term local characteristics and long-term dependencies of power grid data and have poor correlation with past and future time series information, resulting in a lack of credibility in the detection results. In view of this, an FDIA detection model combining a bidirectional temporal convolutional network and bidirectional gated recurrent unit with an attention mechanism (A-BiTG) was proposed. The proposed model utilizes a bidirectional time convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) to consider past and future temporal information in the grid. This enhances the ability of the model to capture long-term dependencies and extract features, while also solving the model’s problem of exploding and vanishing gradients. In addition, an attention mechanism (AM) was added to dynamically assign weights to the extracted feature information and retain the most valuable features to improve the detection accuracy of the model. Finally, the proposed method was compared with existing methods on the IEEE 14-bus and IEEE 118-bus test systems. The results show that the proposed detection model is more robust and superior under different noise environments and FDIA signals with different intensities.
Unstructured Document Information Extraction Method with Multi-Faceted Domain Knowledge Graph Assistance for M2M Customs Risk Prevention and Screening Application
Authors: Tian Fengchun , Wang Haochen , Wan Zhenlong , Liu Ran , Liu Ruilong , Lv Di , Lin Yingcheng
Published: May 2024

As a crucial national security defense line, the existing risk prevention and screening system of customs falls short in terms of intelligence and diversity for risk identification factors. Hence, the urgent issues to be addressed in the risk identification system include intelligent extraction technology for key information from Customs Unstructured Accompanying Documents (CUADs) and the reliability of the extraction results. In the customs scenario, OCR is employed for M2M interactions, but current models have difficulty adapting to diverse image qualities and complex customs document content. We propose a hybrid mutual learning knowledge distillation (HMLKD) method for optimizing a pre-trained OCR model’s performance against such challenges. Additionally, current models lack effective incorporation of domain-specific knowledge, resulting in insufficient text recognition accuracy for practical customs risk identification. We propose a customs domain knowledge graph (CDKG) developed using CUAD knowledge and propose an integrated CDKG post-OCR correction method (iCDKG-PostOCR) based on CDKG. The results on real data demonstrate that the accuracies improve for code text fields to 97.70%, for character type fields to 96.55%, and for numerical type fields to 96.00%, with a confidence rate exceeding 99% for each. Furthermore, the Customs Health Certificate Extraction System (CHCES) developed using the proposed method has been implemented and verified at Tianjin Customs in China, where it has showcased outstanding operational performance.
Optimization Decomposition of Monthly Contracts for Integrated Energy Service Provider Considering Spot Market Bidding Equilibria
Authors: Wu Chen , Wei Zhinong , Jiang Xiangchen , Huang Yizhen , Fan Donglou
Published: May 2024

Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the integrated energy service provider (IESP) participates in the spot market. It helps to avoid the price risk of the spot market. Additionally, it promotes the optimization of the operation of the regional energy day-ahead scheduling. At the present stage, most of the medium- and long-term contract decomposition methods focus on the decomposition of a single power and take less consideration of the bidding space in the spot market. This limitation makes it challenging to achieve efficient interaction and interconnection among multi-energy resources and smooth integration between the medium- and long-term market and the spot market. To address these issues, this paper proposes an optimal monthly contract decomposition method for IESPs that takes into account the equilibrium of spot bidding. First, the linking process and rolling framework of multi-energy transactions between the medium- and long-term market and the spot market are designed. Second, an optimal decomposition model for monthly contracts is constructed, and a daily decomposition method for monthly medium- and long-term contracts that accounts for the spot bidding equilibrium is proposed. Then, the daily preliminary decomposition result of medium- and long-term multi-energy contracts is used as the boundary condition of the day-ahead scheduling model, and the coupling characteristics of the multi-energy networks of electricity, gas, and heat are taken into account, as well as the operational characteristics. Then, considering the coupling characteristics and operating characteristics of electricity, gas, and heat networks, the optimal scheduling model of a multi-energy network is constructed to minimize the sum of cumulative daily operating costs, and the monthly final contract decomposition value and daily spot bidding space are derived. Finally, examples are calculated to verify the validity of the decomposition model, and the examples show that the proposed method can reduce the variance in spot energy purchase by about 4.64%, and, at the same time, reduce the cost of contract decomposition by about USD 0.33 million.
Design of a New Neuro-Generator with a Neuronal Module to Produce Pseudorandom and Perfectly Pseudorandom Sequences
Authors: Rivas Becerra María de Lourdes , Panduro Juan José Raygoza , Ortega Cisneros Susana , Becerra Álvarez Edwin Christian , Rios Arrañaga Jaime David
Published: May 2024

This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distribution. The circuit is connected to a neuronal module with six impulse neurons with different behaviors: spike frequency adaptation, phasic spiking, mixed mode, phasic bursting, tonic bursting and tonic spiking. This module aims to generate a non-periodic signal that becomes the clock signal for one of the LFSRs Linear Feedback Shift Register that the neuro-generator has. To verify its correct operation, the neuro-generator was subjected to a series of tests where the frequencies of the impulse neurons were modified. This modification allows the generation of a greater number of pulses at the output of the neuronal module, to obtain sequences with different characteristics that pass different NIST statistical tests (National Institute of Standards and Technology of U.S.). The results show that the new neuro-generator maintains pseudo-randomness in the sequences obtained with different frequencies and it can be implemented on a reconfigurable FPGA Field Programmable Gate Array Virtex 7 xc7vx485t-2ffg1761 device. Therefore, it can be used for applications such as biological systems.
High-Resolution Millimeter-Wave Radar for Real-Time Detection and Characterization of High-Speed Objects with Rapid Acceleration Capabilities
Authors: Richter Yair , Balal Nezah
Published: May 2024

In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of the reflected signal from the moving target, enabling the extraction of motion-related attributes such as velocity, position, and physical characteristics of the object. The use of a 94 GHz carrier frequency allows for high-resolution velocity detection with a velocity resolution of 6.38 m/s, achieved using a short integration time of 0.25 ms. This high-frequency operation also results in minimal atmospheric absorption, further enhancing the efficiency and effectiveness of the detection process. The proposed system utilizes cost-effective and less complex equipment, including compact antennas, made possible by the low sampling rate required for processing the intermediate frequency signal. The experimental results demonstrate the successful detection and characterization of high-speed moving objects with high acceleration rates, highlighting the potential of this approach for various scientific, industrial, and safety applications, particularly those involving targets with rapidly changing velocities. The detailed analysis of the micro-Doppler signatures associated with these objects provides valuable insights into their unique motion dynamics, paving the way for improved tracking and classification algorithms in fields such as aerospace research, meteorology, and collision avoidance systems.
Thermodynamic Analysis and Optimization of Binary CO2-Organic Rankine Power Cycles for Small Modular Reactors
Authors: Kindra Vladimir , Maksimov Igor , Patorkin Daniil , Rogalev Andrey , Rogalev Nikolay
Published: May 2024

Small nuclear power plants are a promising direction of research for the development of carbon-free energy in isolated power systems and in remote regions with undeveloped infrastructure. Improving the efficiency of power units integrated with small modular reactors will improve the prospects for the commercialization of such projects. Power cycles based on supercritical carbon dioxide are an effective solution for nuclear power plants that use reactor facilities with an initial coolant temperature above 550 °C. However, the presence of low temperature rejected heat sources in closed Bryton cycles indicates a potential for energy saving. This paper presents a comprehensive thermodynamic analysis of the integration of an additional low-temperature organic Rankine cycle for heat recovery to supercritical carbon dioxide cycles. A scheme for sequential heat recovery from several sources in S-CO2 cycles is proposed. It was found that the use of R134a improved the power of the low-temperature circuit. It was revealed that in the S-CO2 Brayton cycle with a recuperator, the ORC add-on increased the net efficiency by an average of 2.98%, and in the recompression cycle by 1.7–2.2%. With sequential heat recovery in the recuperative cycle from the intercooling of the compressor and the main cooler, the increase in efficiency from the ORC superstructure will be 1.8%.
Jerusalem Artichoke as a Raw Material for Manufacturing Alternative Fuels for Gasoline Internal Combustion Engines
Authors: Bembenek Michał , Melnyk Vasyl , Karwat Bolesław , Hnyp Mariia , Kowalski Łukasz , Mosora Yurii
Published: May 2024

The Jerusalem artichoke (Helianthus tuberosus) is a high-yield crop, and a great source of fermentable sugars, which gives the plant the potential to be used as raw material for economical fuel alcohol production. In this article, the authors focus on the technological aspect of the biofuel manufacturing process and its properties. First, the fuel alcohol manufacturing process is described, afterwards assessing its characteristics such as kinematic viscosity, density and octane number. The amount of fuel alcohol obtained from 10 kg of biomass equals to 0.85 L. Afterwards, the mixtures of gasoline and obtained fuel alcohol are prepared and studied. Optimal alcohol and gasoline mixtures are determined to obtain biofuels with octane ratings of 92, 95 and 98. The kinematic viscosity of obtained mixtures does not differ significantly from its values for pure gasoline. The obtained biofuel mixture with 25% alcohol content yielded a decrease of sulfur content by 38%, an increase of vaporized fuel amount by 17.5% at 70 °C and by 10.5% at a temperature of 100 °C, which improves engine startup time and ensures its stable operation in comparison to pure gasoline. The alcohol obtained can be successfully used as a high-octane additive for gasolines.