bluuebunny/crossref_metadata_2025 huggingface dataset:
Compact Amplitude-Only Direction Finding Based on a Deep Neural Network with a Single-Patch Multi-Beam Antenna
Authors: Cha Seung Gook , Kim Donghyun , Yoon Young Joong
Published: June 2023

In this paper, a compact direction-finding system based on a deep neural network (DNN) with a single-patch multi-beam antenna is proposed. To achieve multiple beams, the patch is divided into four sectors by metal vias, and the pattern is tilted in the theta direction due to the coupled mode of the divided patch structure. This design allows a single-patch multi-beam antenna to generate eight beams using a combination of four excitation ports assigned to the four-divided sectors. This approach increases the amount of training data required for DNN-based direction finding without requiring multiple antennas, thus improving the accuracy of estimation probability. Furthermore, compared to our previous work, the parasitic elements are applied to improve the estimation probability by reducing the beamwidth of the antenna. The size of the antenna for the proposed direction-finding system is 0.44λ × 0.44λ × 0.008λ with a 97.7% estimation probability. The direction-finding performance has been validated and compared through the experiment to show higher accuracy with compactness than previously studied works.
Mechanical and Energy Evolution Characteristics of Sandstone under True Triaxial Cyclic Loading
Authors: Dong Chunliang , Fan Chaotao , Lu Xiaoyu , Zhao Guangming , Qi Minjie , Qin Ruipeng
Published: June 2023

To study the mechanical and energy evolution characteristics of sandstone under true triaxial cyclic loading, a sandstone mechanical test with different intermediate principal stress under true triaxial loading was conducted using the rock true triaxial disturbance unloading test system. The influence of axial load on the deformation, energy evolution, and macroscopic failure characteristics of sandstone under different intermediate principal stress in a true triaxial test was systematically analyzed, and the damage evolution law of sandstone under true triaxial cyclic load was revealed. Results showed that the failure mode of sandstone under true triaxial compression changed from tension–shear composite failure to tension failure. Grading cyclic load σ1 greatly influenced maximum principal strain ε1 and minimum principal strain ε3 but had little influence on intermediate principal strain ε2. Under the same σ2 condition, the input energy and elastic energy in σ1, σ2, and σ3 directions increased nonlinearly. Under different σ2 conditions, the dissipated energy in σ1, σ2, and σ3 directions decreased with the increase in σ2. With the increase in σ2, graded cycles σ1, ε2, and ε3 decreased considerably, and the failure mode changed from tensile failure to shear failure. When the cyclic loading rate increased, the σ1, ε1, ε2, ε3, and volume strain εv of sandstone failure decreased, but the expansion point increased. Under true triaxial grading cyclic loading and unloading, the total dissipated energy of sandstone increased exponentially. The larger σ2 was, the smaller the damage variable was.
Dynamic Prediction of Performance Degradation Characteristics of Direct-Drive Electro-Hydraulic Servo Valves
Authors: Mi Juncheng , Huang Guoqin
Published: June 2023

Direct-drive electro-hydraulic servo valves are widely used in the aerospace industry, in the military, and in remote sensing control, but there is little research and discussion on their performance degradation and service life prediction. Based on previous research, erosion wear is the primary physical failure form of direct-drive electro-hydraulic servo valves, and parameters such as opening, oil contamination, and pressure difference are used as influencing factors of direct-drive electro-hydraulic servo valves. Pressure gain and leakage are used as performance degradation indicators of servo valves, and multiple types of sensors are used for data monitoring. Experimental benches are arranged and verified through experiments. Based on the data and laws obtained from the experiments, the exponential smoothing algorithm and the ARIMA model algorithm were used to establish a prediction model for the servo valve, and the dynamic prediction of the performance indexes was carried out. The error calculation and analysis of the prediction results and the experimental results were then carried out using the Copula function and other mathematical knowledge to verify the accuracy and applicability of this prediction model. This study provides theoretical support and practical guidance for applying and designing direct-drive electro-hydraulic servo valves in industrial applications such as aerospace, sensor experiments, and remote sensing control.
Analysis and Design of Direct Force Control for Robots in Contact with Uneven Surfaces
Authors: Rosales Antonio , Heikkilä Tapio
Published: June 2023

Robots executing contact tasks are essential in a wide range of industrial processes such as polishing, welding, debugging, drilling, etc. Force control is indispensable in these type of tasks since it is required to keep the interaction force (between the robot and the environment/surface) within acceptable values. In this paper, we present a methodology to analyze and to design the force control system needed to regulate the force as close as possible to the desired value. The proposed methods are presented using a widely used generic contact task consisting of exerting a desired force on the normal direction to the surface while a desired velocity/position is tracked on the tangent direction to the surface. The analysis considers environments/surfaces with certain uneven characteristics, i.e., not perfectly flat. The uneven characteristic is studied using ramp or sinusoidal signals disturbing the position on the normal direction to the surface, and we present how the velocity on the tangent direction is related with the slope of the ramp or the frequency of the sinusoidal disturbance. Then, we provide a method to design the force controller that keeps the force error within desired limits and preserves stability, despite the uneven surface. Furthermore, considering the relation between the disturbance (ramp or sinusoidal) and the tangent velocity, we present a method to compute the maximum velocity for which the task can be executed. Simulations exemplifying and verifying the proposed methods are presented.
A Microscopic Traffic Model Considering Time Headway and Distance Headway
Authors: Ali Faryal , Khan Zawar Hussain , Altamimi Ahmed B. , Khattak Khurram Shehzad , Gulliver Thomas Aaron
Published: June 2023

A microscopic traffic model is presented which employs differences in velocity to characterize driver behavior. The Intelligent Driver (ID) model is based on an acceleration constant which cannot capture different traffic conditions. Further, it is not based on traffic physics and so can produce inaccurate results. The proposed model is an improved ID model and both are evaluated on a 2000 m circular road. The results obtained show that the proposed model can appropriately characterize traffic flow and density. Further, the variations in flow and velocity are smoother than with the ID model. This is because the proposed model is based on actual traffic parameters rather than an unrealistic traffic exponent.
The 20th Century Wall Paintings in the Chapel of the Fallen in Parma Cathedral (Italy): Scientific Investigations for a Correct Conservation Project
Authors: Bergamonti Laura , Graiff Claudia , Simeti Silvia , Casoli Antonella
Published: June 2023

In this work, we present a diagnostic study carried out on 20th century wall paintings in the Chapel of the Fallen of the Great War in the Cathedral of Parma (Italy). The Chapel was painted in the two-year period of 1921–1922 and has been recently restored. The paintings were investigated in order to study the technique used by the painter, Biagio Biagetti (Porto Recanati, 1877–Macerata, 1948) and their state of conservation. A total of twelve micro-fragments of the painting layers were sampled and investigated using different techniques. Raman spectroscopy revealed the large amount of different pigments used for each hue, many of them being synthetic materials. SEM/EDS analysis showed that the mortar was an aerial lime obtained from calcium carbonate mixed with a small amount of clay. Organic materials were identified by FTIR spectroscopy. GC/MS investigations revealed lipid and proteinaceous materials in the examined specimens; the lipid fraction, however, is not attributable to the presence of drying oils. From the determination of the amino acid content, it became apparent that the proteinaceous fraction is due to the combination of egg and animal glue; this allowed us to infer the use of “a secco” techniques, confirming the visual impressions of the restorer. The results obtained have contributed to the definition of the conservation project in its various phases.
Sequential Data Processing for IMERG Satellite Rainfall Comparison and Improvement Using LSTM and ADAM Optimizer
Authors: Toh Seng Choon , Lai Sai Hin , Mirzaei Majid , Soo Eugene Zhen Xiang , Teo Fang Yenn
Published: June 2023

This study introduces a systematic methodology whereby different technologies were utilized to download, pre-process, and interactively compare the rainfall datasets from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) satellite and rain gauges. To efficiently handle the large volume of data, we developed automated shell scripts for downloading IMERG data and storing it, along with rain gauge data, in a relational database system. Hypertext pre-processor (pHp) programs were built to visualize the result for better analysis. In this study, the performance of IMERG estimations over the east coast of Peninsular Malaysia for the duration of 10 years (2011–2020) against rain gauge observation data is evaluated. Moreover, this study aimed to improve the daily IMERG estimations with long short-term memory (LSTM) developed with Python. Findings show that the LSTM with Adaptive Moment Estimation (ADAM) optimizer trained against the mean square error (MSE) loss enhances the accuracy of satellite estimations. At the point-to-pixel scale, the correlation between satellite estimations and ground observations was increased by about 15%. The bias was reduced by 81–118%, MAE was reduced by 18–59%, the root-mean-square error (RMSE) was reduced by 1–66%, and the Kling–Gupta efficiency (KGE) was increased by approximately 200%. The approach developed in this study establishes a comprehensive and scalable data processing and analysis pipeline that can be applied to diverse datasets and regions encountering similar domain-specific challenges.
Prediction of Reinforcement Connection Loads in Geosynthetic Reinforced Segmental Retaining Walls Using Response Surface Method
Authors: Zhang Wan , Chen Jianfeng
Published: June 2023

This paper presents an improved earth pressure method that considers the capacity of a wall toe to carry an earth load to predict the connection loads of GRS segmental walls constructed with cohesionless backfills on competent foundations. In this method, a response surface model (RSM) of the lateral earth pressure coefficient replaces the Coulomb active earth pressure coefficient. The parameters of the RSM were determined from numerical studies on the impacts of toe restraint, wall geometry, and backfill properties on the distribution of earth loads between the toe and reinforcement layers. The unknown coefficients of the RSM were obtained through a regression analysis of 705 reinforcement load values from 65 simulated walls. The proposed method was compared to the earth pressure method and the stiffness method using measured connection loads from field and centrifuge GRS segmental walls. The results show that the predictions of the proposed RSM method are in better agreement with the measurements than those of the stiffness method and the earth pressure method, whether under a typical or poor toe restraint condition.
Methods and Tools Supporting the Learning and Teaching of Mathematics Dedicated to Students with Blindness
Authors: Maćkowski Michał , Kawulok Mateusz , Brzoza Piotr , Spinczyk Dominik
Published: June 2023

Teaching mathematics to blind people is a challenge of modern educational methods. This article presents a method of preparing the adapted material and its usage in the learning process of mathematics by blind people, as well as the results of evaluating the proposed approach. The presented results were obtained based on a mathematical analysis course conducted in two classes—with and without using the developed method. The developed method uses the conceptualization of knowledge as a graph. The learning process is supported by feedback processes that consider the mechanisms of knowledge and error vectors, on which a personalized adaptation of the learning path is made for each particular student. The evaluation process has shown a statistically significant improvement in learning results achieved by blind students. The average final test score in the group working with the platform during learning increased by 14%. In addition, there was an increase in cooperation between blind students who had the opportunity to take on the role of a teacher, which was observed in 27% of the participants. Our results indicate the effectiveness of the developed approach and motivate us to evaluate the method in a broader group of students. The engagement of students indirectly indicates overcoming the barriers known from the state of the art: uncertainty, poor motivation, and difficulties in consolidating the acquired skills.
Periodical Vehicle Inspections with Smart Technology
Authors: Tapak Peter , Kocur Michal , Rabek Matej , Matej Juraj
Published: June 2023

This paper presents the major outcomes of the utilization of smart phone applications in the periodical technical inspection process. Road safety is one of the main topics in transportation. Technical defects of vehicles can play an important role in fatal accidents; therefore, periodical vehicle inspection is a common practice amongst many countries across all continents. The authors observed that advancements in smart technologies presented an opportunity to enhance the efficiency and effectiveness of vehicle inspections. They specifically focused on the Slovak Republic and described the transition of technical inspections towards smart technologies in this country. The goal was to utilize new technologies and improvements without increasing costs dramatically. The paper discusses the outcomes of the first two years of using this app at every technical inspection station in the Slovak Republic. The smart phones were used not only to help with process management but the novelty is that they also collected data from the sensors and used their own sensors for the vehicle dynamics measurements. One of the important factors is the successful implementation of low cost devices at such a large scale, country wise.
Modification of Nonlinear Controller for Asymmetric Marine Vehicles Moving in Horizontal Plane
Authors: Herman Przemyslaw
Published: June 2023

This paper considers a trajectory-tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North-East-Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method for which the application condition is that the vehicle is symmetric with respect to the left and right sides. The proposed control scheme can be applied to a fully asymmetric model, and, therefore, the geometric center can be different from the center of mass in both the longitudinal and lateral directions. A velocity transformation to generalized vehicle equations of motion was used to develop a suitable controller. Theoretical considerations were supported by simulation tests performed for a model with 3 degrees of freedom, in which the performance of the proposed algorithm was compared with that of the original algorithm and the selected control scheme based on a combination of backstepping and integral sliding mode control approaches.
Defect-Induced Modulation of a 2D ZnO/Graphene Heterostructure: Exploring Structural and Electronic Transformations
Authors: Shtepliuk Ivan
Published: June 2023

This paper presents a theoretical study on the effects of selected defects (oxygen vacancies and substitutional FeZn atoms) on the structural and electronic properties of a 2D ZnO/graphene heterostructure. Spin-polarized Hubbard- and dispersion-corrected density functional theory (DFT) was used to optimize the geometrical configurations of the heterostructure and to analyze the equilibrium distance, interlayer distance, adhesion energy, and bond lengths. Charge density difference (CDD) analysis and band structure calculations were also performed to study the electronic properties of the heterostructure. The results show that the presence of defects affects the interlayer distance and adhesion energy, with structures including oxygen vacancies and FeZn substitutional atoms having the strongest interaction with graphene. It is demonstrated that the oxygen vacancies generate localized defect states in the ZnO bandgap and lead to a shift of both valence and conduction band positions, affecting the Schottky barrier. In contrast, Fe dopants induce strong spin polarization and high spin density localized on Fe atoms and their adjacent oxygen neighbors as well as the spin asymmetry of Schottky barriers in 2D ZnO/graphene. This study presents a comprehensive investigation into the effects of graphene on the electronic and adsorption properties of 2D ZnO/graphene heterostructures. The changes in electronic properties induced by oxygen vacancies and Fe dopants can enhance the sensitivity and catalytic activity of the 2D ZnO/graphene system, making it a promising material for sensing and catalytic applications.
Smart Mobility with Big Data: Approaches, Applications, and Challenges
Authors: Lee Dohoon , Camacho David , Jung Jason J.
Published: June 2023

Many vehicles are connected to the Internet, and big data are continually created. Various studies have been conducted involving the development of artificial intelligence, machine learning technology, and big data frameworks. The analysis of smart mobility big data is essential and helps to address problems that arise as society faces increased future mobility. In this paper, we analyze application issues such as personal information leakage and data visualization due to increased data exchange in detail, as well as approaches focusing on analyses exploiting machine learning and architecture research exploiting big data frameworks, such as Apache Hadoop, Apache Spark, and Apache Kafka. Finally, future research directions and open challenges are discussed.
Detecting and Evaluating Displacements of Paving Blocks Adjacent to Deep Excavation Sites Using Terrestrial Photogrammetry
Authors: Kim Taesik , Choi Ho , Jung Jinman , Min Hong , Jung Young-Hoon
Published: June 2023

In urban areas, deep excavation-induced ground deformations may damage adjacent existing structures and are conventionally evaluated by levelling at installed settlement points. However, a small number of measurements cannot represent the total changes in ground deformations adjacent to excavation sites. Furthermore, significant local subsidences may occur in places where settlement points have not been installed and only noticed after an accident. For deep excavation sites located in urban areas, paved pedestrian sidewalks are often located adjacent to sites, and construction activities can cause these paving blocks to become displaced. This study introduces a method to detect paving block displacements adjacent to deep excavation sites using terrestrial photogrammetry. A digital camera creating point cloud data (PCD) and an acquisition method satisfying the frontal and side overlap requirements were demonstrated. To investigate the displacement detections and measurement capabilities by PCD analysis, an experimental program was conducted, including a PCD comparison containing the uplift, settlement, and horizontal paving block displacement and reference data. The cloud-to-cloud distance computation algorithm was adopted for PCD comparisons. Paving block displacements were detected for displacements of 5, 7.5, and 10 mm in the uplift, settlement, and horizontal directions; however, the horizontal displacements were less clear. PCD analysis enabled satisfactory measurements between 0.024 and 0.881 mm for the vertical-displacement cases, but significant errors were observed for the horizontal-displacement cases owing to the cloud-comparison algorithm. The measurement blind spot of limited settlement points was overcome by the proposed method that detected and measured paving block displacements adjacent to excavation sites.
Building Energy Flexibility Assessment in Mediterranean Climatic Conditions: The Case of a Greek Office Building
Authors: Chantzis Georgios , Giama Effrosyni , Papadopoulos Agis M.
Published: June 2023

The EU energy and climate policy has set quantitative goals for decarbonization based on the energy efficiency and the evolution of energy systems. The utilization of demand side flexibility can help towards this direction and achieve the target of higher levels of penetration in regard to intermittent renewable energy production and carbon emission reduction. This paper presents a simulation-based assessment of thermal flexibility in a typical office building in Greece, which is a representative Mediterranean country. The use of variable speed heat pumps coupled with hydronic terminal units was evaluated. The research focused mainly on the evaluation of energy flexibility offered by energy stored in the form of thermal energy by utilizing the building’s thermal mass. The demand response potential under hourly CO2eq intensity and energy prices was investigated. The flexibility potential was evaluated under different demand response strategies, and the effect of demand response on energy consumption, operational costs, CO2eq emissions and thermal comfort was analyzed and discussed. The results showed that both control strategies based on both the CO2eqintensity signal and spot price signal have, in some cases, the potential for cost and emission savings, and in other cases, the potential to depreciate in terms of emissions and cost the increase of energy consumption due to load shifting.
Visual–Inertial Navigation System Based on Virtual Inertial Sensors
Authors: Cai Yunpiao , Qian Weixing , Zhao Jiaqi , Dong Jiayi , Shen Tianxiao
Published: June 2023

In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system’s positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments.
Investigation of Modal Identification of Frame Structures Using Blind Source Separation Technique Based on Vibration Data
Authors: Meng Fanhao , Ma Yong , Xia Yongjun , Ma Yimin , Jiang Ming
Published: June 2023

This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification procedures include two novel blind source separation (BSS) algorithms, complexity pursuit method (CP) and generalized eigen decomposition method (GED), based on modern signal processing technology. Here, the frequency response function (FRF) method is introduced as an important reference to verify the effectiveness of the CP algorithm and GED algorithm. The effectiveness and accuracy of both types of algorithms are verified by numerical simulations and experiments on a suspension bridge. In addition, an engineering application of these two BSS methods is successfully implemented in an overhead transmission line-crossing frame. The results show that the two novel BSS learning rules (CP and GED) are capable of successfully identifying modal parameters of the civil structure under ambient excitation.
Enhancing Feature Selection for Imbalanced Alzheimer’s Disease Brain MRI Images by Random Forest
Authors: Wang Xibin , Zhou Qiong , Li Hui , Chen Mei
Published: June 2023

Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribution, especially for high-dimensional longitudinal data structures. In the medical field, the imbalance of data problem is more common, and correctly identifying samples of the minority class can obtain important information. Moreover, class imbalance in imbalanced AD (Alzheimer’s disease) data presents a significant challenge for machine learning algorithms that assume the data are evenly distributed within the classes. In this paper, we propose a random forest-based feature selection algorithm for imbalanced neuroimaging data classification. The algorithm employs random forest to evaluate the value of each feature and combines the correlation matrix to choose the optimal feature subset, which is applied to imbalanced MRI (magnetic resonance imaging) AD data to identify AD, MCI (mild cognitive impairment), and NC (normal individuals). In addition, we extract multiple features from AD images that can represent 2D and 3D brain information. The effectiveness of the proposed method is verified by the experimental evaluation using the public ADNI (Alzheimer’s neuroimaging initiative) dataset, and results demonstrate that the proposed method has a higher prediction accuracy and AUC (area under the receiver operating characteristic curve) value in NC-AD, MCI-AD, and NC-MCI group data, with the highest accuracy and AUC value for the NC-AD group data.
A Transformer-Based Approach to Authorship Attribution in Classical Arabic Texts
Authors: AlZahrani Fetoun Mansour , Al-Yahya Maha
Published: June 2023

Authorship attribution (AA) is a field of natural language processing that aims to attribute text to its author. Although the literature includes several studies on Arabic AA in general, applying AA to classical Arabic texts has not gained similar attention. This study focuses on investigating recent Arabic pretrained transformer-based models in a rarely studied domain with limited research contributions: the domain of Islamic law. We adopt an experimental approach to investigate AA. Because no dataset has been designed specifically for this task, we design and build our own dataset using Islamic law digital resources. We conduct several experiments on fine-tuning four Arabic pretrained transformer-based models: AraBERT, AraELECTRA, ARBERT, and MARBERT. Results of the experiments indicate that for the task of attributing a given text to its author, ARBERT and AraELECTRA outperform the other models with an accuracy of 96%. We conclude that pretrained transformer models, specifically ARBERT and AraELECTRA, fine-tuned using the Islamic legal dataset, show significant results in applying AA to Islamic legal texts.
Microbial and Chemical Changes during Fermentation of Coconut Water Kefir Beverage
Authors: Limbad Mansi , Gutierrez-Maddox Noemi , Hamid Nazimah , Kantono Kevin , Liu Tingting , Young Tim
Published: June 2023

This study investigated changes in the microbial growth and chemical characteristics of coconut water kefir (CWK) during fermentation. The carbohydrate profile, in terms of glucose and sucrose consumption, production of carboxylic acids, and changes in amino acid profile, was determined during CWK fermentation over a period of 96 h. The results showed that the kefir grains were able to utilise both glucose and sucrose and produce significant quantities of carboxylic acids after 96 h of fermentation. The total titratable acidity significantly increased throughout 96 h of incubation at 30 °C, which correlated to a significant drop in pH to 2.8 for CWK supplemented with 12 g/L of sucrose. In addition, this was accompanied by a significant increase in lactic acid, acetic acid, and pyruvic acid. During fermentation, a total of eighteen amino acids were generated, with a notable decline observed across all amino acids. Among them, glutamic acid exhibited a higher concentration compared to the other amino acids. The Scanning Electron Microscope (SEM) results confirmed a higher density of lactic acid bacteria (LAB) and acetic acid bacteria (AAB), with fewer yeast cells through morphological identification. Overall, the findings support the notion that coconut water fermented with kefir could be used as a potential functional starter to produce other fermented food products or a refreshing beverage.
Current Research in Future Information and Communication Engineering 2022
Authors: Yu Yun Seop , Kim Kwang-Baek , Jo Dongsik , Kim Hee-Cheol , Seo Jeongwook
Published: June 2023

The digital revolution has transformed the way we communicate, access information, and interact with technology [...]
Hydropower Functional Zoning with Crowdsourced Geospatial Data: A Case Study in Sichuan Province
Authors: Ju Li , Luo Maosheng , Luo Han , Ma Zelong , Lu Xiping , Jiang Guoxin
Published: June 2023

Hydro-electric development has received increasing attention due to its characteristics of ecological and environmental protection. In addition, aquatic ecological functional zoning plays a key role in the aquatic ecological management in the basin because of its ecological background and basic unit. However, hydropower function has seldom been considered in aquatic ecological functional zoning. This research proposes a framework for hydropower functional zoning on the aquatic-and-terrestrial-coupled ecosystem function with crowdsourced geospatial data and the spatial-clustering method. Sichuan Province was selected as the research area due to its critical hydroelectric position in China, and it is divided into 53 level 3 zones, 27 level 2 aquatic ecological functional zones, and 17 level 1 ecological functional zones. Focusing on the results of the hydropower functional zoning, the ecological-environmental problem of each zoning and the hydroelectric development in the future are discussed. The soil-erosion area in Sichuan Province did not overlap with the hydroelectric-construction-affected zones. Further, water pollution occurred in construction zones and core affected zones of the Fu River Basin and the Jialing River Basin. In the next 10 years, the middle and upper reaches of the trunk of the Ya-lung River will become key areas for hydropower-engineering projects. This research provides new insight into the development of various regional hydropower projects and the sustainable management of watersheds, which is helpful for the construction of new hydroelectric-energy development.
High Frequency Transformers for Solid-State Transformer Applications
Authors: Santos Nuno , Chaves Miguel , Gamboa Paulo , Cordeiro Armando , Santos Nelson , Pinto Sónia Ferreira
Published: June 2023

This paper focuses on the study of the high frequency transformer incorporated in solid- state transformers, specifically on the development of the steps that enable the design of an optimized high frequency transformer and its equivalent model based on the desired characteristics. The impact of operating a transformer at high frequency and the respective solutions that allow this impact to be reduced are analyzed, alongside the numerous advantages that the utilization of these transformers has over traditional 50/60 Hz transformers. Furthermore, the power scheme of the solid-state transformer is outlined, focusing on the power converters, which are immediately before and after the high frequency transformer (HFT). We also investigate a control technique that allows for correct operation and the existence of power bidirectionality. In a novel approach, this paper demonstrates the systematic steps for designing an HFT according to the desired specifications of each given project, helping students and engineers achieve their objectives in power-electronic applications. Moreover, this paper aims at increasing the knowledge of this area of power electronics and facilitating the development of new topologies with high power density, which are very important to the integration of renewable power sources and other applications. Finally, a simulation is presented to validate a high frequency transformer and its control technique.
Classification of Speed Sensor Faults Based on Shallow Neural Networks
Authors: Jankowska Kamila , Dybkowski Mateusz , Petro Viktor , Kyslan Karol
Published: June 2023

This paper presents a novel speed sensor fault detection, classification, and compensation mechanism in a permanent magnet synchronous motor (PMSM) drive system. Application is based on state variable observers and shallow neural networks (NN). Classical fault detection mechanism based on state variable observers has been extended with neural networks. This enables improved detection efficiency and increases immunity to false alarms. In addition, the use of neural networks allowed for the classification of the failure type. Three types of failures are considered in the paper: signal loss, scaling error, and signal interference. The detection efficiency of the proposed solution is about 97%. On the other hand, the classification of the worst type of failure—signal loss—was achieved at the level of 100%. Other considered failure types are classified at the level of 80–90%. In addition, tests were carried out for two types of observers—model reference adaptive system and sliding mode observer—to compare the results. The work presents experimental results carried out for various operating conditions of the drive system. The failure classification times in the experimental tests were achieved at a level of less than 30 ms.
Dark-Center Based Insulator Detection Method in Foggy Environment
Authors: Liu Lisang , Ke Chengyang , Lin He
Published: June 2023

In foggy environments, outdoor insulator detection is always with low visibility and unclear targets. Meanwhile, the scale of haze simulation insulator datasets is insufficient. Aiming to solve these problems, this paper proposes a novel Dark-Center algorithm, which is a joint learning framework based on image defogging and target detection. Firstly, the dark channel prior algorithm is used to calculate the foggy sky image transmittance and then transpose it to the original image to generate a foggy-simulated insulator dataset; secondly, the defogging and restoration modules and an optimized defogging module are combined to improve the robustness of the defogging algorithm; then, for small insulator detection, the CenterNet network structure is improved to enhance the feature extraction capability for small targets; finally, the target detection accuracy in foggy environments is improved by jointly learning the structure details and color features recovered in image defogging via the defogging model and the target detection model, which effectively learn the structure details and color features recovered in image defogging. The experimental results on the CPILD dataset show that the proposed Dark-Center algorithm based on image defogging and target detection can effectively improve the performance of the target detector in foggy scenes, with a detection accuracy of 96.76%.
Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
Authors: Zhang Yaru , Liu Jiantao , Zhang Tong , Zhao Zhibiao
Published: June 2023

In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo image pairs also have an important impact on the reconstruction accuracy in the process of network learning. Therefore, aiming at the information interaction and stereo consistency of stereo image pairs, a cross-view attention interaction fusion stereo super-resolution algorithm is proposed. Firstly, based on parallax attention mechanism and triple attention mechanism, an attention stereo fusion module is constructed. The attention stereo fusion module is inserted between different levels of two single image super-resolution network branches, and the attention weight is calculated through the cross dimensional interaction of the three branches. It makes full use of the ability of single image super-resolution network to extract single view information and further maintaining the stereo consistency between stereo image pairs. Then, an enhanced cross-view interaction strategy including three fusion methods is proposed. Specifically, the vertical sparse fusion method is used to integrate the interior view information of different levels in the two single image super-resolution sub branches, the horizontal dense fusion method is used to connect the adjacent attention stereo fusion modules and the constraint between stereo image consistency is further strengthened in combination with the feature fusion method. Finally, the experimental results on Flickr 1024, Middlebury and KITTI benchmark datasets show that the proposed algorithm is superior to the existing stereo image super-resolution methods in quantitative measurement and qualitative visual quality while maintaining the stereo consistency of image pairs.
A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media
Authors: Alotaibi Fahad M.
Published: June 2023

Machine learning frameworks categorizing customer reviews on online products have significantly improved sales and product quality for major manufacturers. Manually scrutinizing extensive customer reviews is imprecise and time-consuming. Current product research techniques rely on text mining, neglecting audio, and image components, resulting in less productive outcomes for researchers and developers. AI-based machine learning frameworks that consider social media and online buyer reviews are essential for accurate recommendations in online e-commerce shops. This research paper proposes a novel machine-learning-based framework for categorizing customer reviews that uses a bag-of-features approach for feature extraction and a hybrid DNN framework for robust classification. We assess the performance of our machine learning framework using AliExpress and Amazon e-commerce product review data provided by customers, and we have achieved a classification accuracy of 91.5% with only 8.46% fallout. Moreover, when compared with state-of-the-art models, our proposed model shows superior performance in terms of sensitivity, specificity, precision, fallout, and accuracy.
Wheat Transformation with ScTPS1-TPS2 Bifunctional Enzyme for Trehalose Biosynthesis Protects Photosynthesis during Drought Stress
Authors: Romero-Reyes Andrea , Valenzuela-Avendaño Juan Pablo , Figueroa-Soto Ciria G. , Mascorro-Gallardo José O. , Iturriaga Gabriel , Castellanos-Villegas Alejandro , Rivera-Domínguez Marisela , Valenzuela-Soto Elisa M.
Published: June 2023

Wheat cultivation makes an important contribution to human nutrition. Trehalose synthesis plays a role in the tolerance to drought stress. A bifunctional TPS-TPP enzyme gene from yeast was used to obtain transgenic wheat plants to increase trehalose synthesis. Mature wheat embryos were transformed using pGreen rd29A::TPS1-TPS2 or pGreen 35S::TPS1-TPS2 constructs. The transgene presence in mature leaves of T3 plants was confirmed by sequencing a PCR fragment of the inserted transgene. Transgenic and NT plants were submitted to drought stress for eight days. Transformed wheat lines retained a higher relative water content than NT plants during drought stress, and the Rubisco activity was unaffected. Plants transformed with the 35S construct showed a lower photosynthetic rate and lower fructose 1–6-bisphosphatase (FBPase) activity during drought, suggesting that constitutive trehalose and sucrose synthesis caused a reduced ribulose 1,5-bisphosphate (RuBP) regeneration. Lines transformed with the rd29A promoter showed a higher photosynthetic rate after eight days of drought, as the RuBP regeneration was unaffected. Transgenic wheat plants had higher biomass and grain weight than NT plants after drought. These results suggest that trehalose synthesis improves photosynthesis during stress and induces changes in the activity of some Calvin-cycle enzymes, reflected in plant metabolism and growth.
Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
Authors: Hu Chunwei , Liu Xianfeng , Wu Sheng , Yu Fei , Song Yongkun , Zhang Jin
Published: June 2023

Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for crowd inflow and outflow prediction in urban areas. Firstly, as the attention layer, the spatio-temporal attention module (SA) is employed for capturing the spatial relationship between the target node and the multi-order adjacent nodes by processing the features of the human flow data. Secondly, a conditional convolution module (SCondConv) is used to enhance the model’s capacity for learning about the shifting characteristics of crowd flow to obtain spatial dependence. Finally, we train the model with the Huber loss function to lower the model’s sensitivity to outliers and achieve optimal convergence. In two public datasets, the mean absolute error (MAE) of the proposed model is improved by 5.2% and 9.4%, respectively, compared to the baseline models, and the root mean square error (RMSE) is improved by 4.8% and 8.8%, confirming the model’s usefulness for crowd flow prediction tasks.
Numerical Simulation on Thermoelectric Cooling of Core Power Devices in Air Conditioning
Authors: Wang Jiang , Hu Kai , Tang Kechen , Xing Yubing , Xiao Yani , Liu Yutian , Yan Yonggao , Yang Dongwang
Published: June 2023

Air conditioning has become a necessity in people’s daily life. The performance of the compressor determines the energy efficiency ratio of this electrical equipment, but the heat generated during the operation of its internal core power components will greatly limit its performance release, so it is urgent to carry out research on the heat dissipation of power devices. In this work, we explore the application of thermoelectric coolers (TECs) in the field of power device heat dissipation through finite element simulation. First, we geometrically modeled the structure and typical operating conditions of core power devices in air conditioners. We compared the temperature fields in air-cooling and TEC active cooling modes for high-power-consumption power devices in a 319 K operating environment. The simulation results show that in the single air-cooling mode, the maximum temperature of the 173.8 W power device reached 394.4 K, and the average temperature reached 373.9 K, which exceeds its rated operating temperature of 368.1 K. However, the maximum and average temperature of the power device dropped to 331.8 K and 326.5 K, respectively, at an operating current of 7.5 A after adding TECs, which indicates that TEC active cooling has a significant effect on the temperature control of the power device. Furthermore, we studied the effect of the TEC working current on the temperature control effect of power devices to better understand the reliability of the TECs. The results show that TECs have a minimum working current of 5 A, which means it has no significant cooling effect when the working current is less than 5 A, and when increasing the current to 10 A, the average temperature of the power device can be reduced to 292.9 K. This study provides a meaningful exploration of the application of TECs in chip temperature control and heat dissipation, providing a new solution for chip thermal management and accurate temperature control.
Static Analysis of Mobile Pump Truck Frame for Four Typical Working Conditions
Authors: Li San-Ping , Lin Hai-Bin , Zhang Yu-Liang , Cheng Liang
Published: June 2023

In order to verify the safety and reliability of the mobile pump truck, this paper takes the frame of a certain type of mobile pump truck as the research object. Through the establishment of a finite element model, four kinds of materials including Q345 and other types of steel are used to define the body parts, the four typical motion situations of the mobile pump truck are analyzed statically, and the maximum stress and deformation of the mobile pump truck under four working conditions are obtained. The results show that the stress and deformation generated by the mobile pump truck under full load bending, emergency turning, and emergency braking conditions are relatively small; while they generate significant stress and deformation under torsional conditions, they all meet the strength design requirements. Among them, the maximum stress and maximum displacement under the full load bending condition are 71.76 MPa and 2.11 mm; the maximum stress and maximum displacement under the full load torsion condition are 352.68 MPa and 18.18 mm; the maximum stress and maximum displacement under emergency turning conditions are 79.718 MPa and 2.68 mm; and the maximum stress and maximum displacement under emergency braking conditions are 74.907 MPa and 2.81 mm. The analysis results can provide a reference basis for the design of the mobile pump truck frame in the future.