bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Multiple Numerical Solutions to the Duffing Equation Using Machine Learning
Authors: Wang Yi-Ren , Chen Guan-Wei
Published: September 2023

This study addresses the problem of predicting convergence outcomes in the Duffing equation, a nonlinear second-order differential equation. The Duffing equation exhibits intriguing behavior in both undamped free vibration and forced vibration with damping, making it a subject of significant interest. In undamped free vibration, the convergence result oscillates randomly between 1 and −1, contingent upon initial conditions. For forced vibration with damping, multiple variables, including initial conditions and external forces, influence the vibration patterns, leading to diverse outcomes. To tackle this complex problem, we employ the fourth-order Runge–Kutta method to gather convergence results for both scenarios. Our approach leverages machine learning techniques, specifically the Long Short-Term Memory (LSTM) model and the LSTM-Neural Network (LSTM-NN) hybrid model. The LSTM-NN model, featuring additional hidden layers of neurons, offers enhanced predictive capabilities, achieving an impressive 98% accuracy on binary datasets. However, when predicting multiple solutions, the traditional LSTM method excels. The research encompasses three critical stages: data preprocessing, model training, and verification. Our findings demonstrate that while the LSTM-NN model performs exceptionally well in predicting binary outcomes, the LSTM model surpasses it in predicting multiple solutions.
Cuckoo Bloom Hybrid Filter: Algorithm and Hardware Architecture for High Performance Satellite Internet Protocol Route Lookup
Authors: Zhang Yi , Qiao Lufeng , Hu Lin , Xu Xin , Chen Qinghua
Published: September 2023

The next-generation satellite Internet Protocol (IP) router is required to achieve tens of millions of route lookups per second, since satellite Internet services based on low Earth orbit (LEO) constellations have become a reality. Due to the limitation of hardware resources on satellites and the high reliability requirements for equipment, a new satellite IP route lookup architecture is proposed in this paper. The proposed architecture uses a Bloom and cuckoo filter-based structure called cuckoo Bloom hybrid filter (CBHF), which guarantees only one off-chip memory access per lookup, to accelerate the Prefix-Route Trie (PR-Trie) algorithm. The proposed architecture has been evaluated through both a behavioral simulation in C++ language and a hardware implementation in Verilog hardware description language (HDL). Our simulation and implementation results show that the proposed satellite IP route lookup architecture can achieve a single-port throughput beyond 13 Gbps on a field programmable gate array (FPGA) board with a single DDR3 memory chip when operating at 200 MHz. In addition, the resource utilization in the FPGA shows that the proposed architecture also supports triple modular redundancy (TMR) to enhance reliability.
The Influences of Self-Introspection and Credit Evaluation on Self-Organized Flocking
Authors: Zhao Qiang , Luan Yu , Li Shuai , Wang Gang , Xu Minyi , Wang Chen , Xie Guangming
Published: September 2023

For biological groups, the behaviors of individuals will have an impact on the alignment efficiency of the collective movement. Motivated by Vicsek’s pioneering research on self-organized particles and other related works about flocking behaviors, we propose two mathematical models based on the local information of individuals to include more realistic details in the interaction mechanism between individuals and the rest of the group during the flocking process. The local information of the individual refers to the local consistency, representing the degree of alignment with its neighbors. These two models are the self-introspection model, where the process of orientation adjustment of one individual is ruled by the degree of local consistency with the neighborhood, and the credit evaluation model, where the average orientation of the neighborhoods is weighed using the local consistency of the interacting individuals. Different metrics are calculated to analyze the effects of the model parameters and flocking parameters on groups. Simulation calculations indicate that the two improved models have certain advantages in terms of alignment efficiency for the group. Finally, the optimal model parameters are determined, and the effects of random noise on groups with a single behavior and mixed behaviors are analyzed. The results confirm that individuals with mixed behaviors still possess robustness against noise. This research would contribute to the further interdisciplinary cooperation that involves biology, ethology, and multi-agent complex systems.
Examination of a Human Heart Fabricating Its 3D-Printed Cardiovascular Model and Employing Computational Technologies
Authors: Charalampous Paschalis , Kladovasilakis Nikolaos , Zoumaki Maria , Kostavelis Ioannis , Votis Konstantinos , Petsios Konstantinos , Tzetzis Dimitrios , Tzovaras Dimitrios
Published: September 2023

In this paper, an innovative approach concerning the investigation of the human heart is introduced, employing state-of-the-art technologies. In particular, sophisticated algorithms were developed to automatically reconstruct a 3D model of a human heart based on DICOM data and to segment the main parts that constitute it. Regarding the reconstructed 3D model, a diagnosis of the examined patient can be derived, whereas in the present study, a clinical case involving the coarctation of the aorta was inspected. Moreover, numerical approaches that are able to simulate flows on complex shapes were considered. Thereupon, the outcomes of the computation analysis coupled with the segmented patient-specific 3D model were inserted in a virtual reality environment, where the clinicians can visualize the blood flow at the vessel walls and train on real-life medical scenarios, enhancing their procedural understanding prior to the actual operation. The physical model was 3D-printed via the MultiJet 3D printing process utilizing materials possessing an adequate mechanical response replicating the mechanical properties and the geometrical characteristics of the human heart. The presented tools aim at the creation of an innovative digital environment, where gaining surgical experience and developing pre-operative strategies could be achieved without the risk and anxiety of actual surgery.
Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising
Authors: Wang Shuo , Zhu Zhibin , Liu Yufeng , Zhang Benxin
Published: September 2023

Hyperspectral imaging (HSI) has been used in a wide range of applications in recent years. But in the process of image acquisition, hyperspectral images are subject to various types of noise interference. Noise reduction algorithms can be used to enhance the quality of images and make it easier to detect and analyze features of interest. To realize better image recovery, we propose a weighted group sparsity-regularized low-rank tensor ring decomposition (LRTRDGS) method for hyperspectral image recovery. Tensor ring decomposition can be utilized by this approach to investigate self-similarity and global spectral correlation. Furthermore, weighted group sparsity regularization can be employed to depict the sparsity structure of the group along the spectral dimension of the spatial difference image. Moreover, we solve the proposed model using a symmetric alternating direction method multiplier with the addition of a proximity term. The experimental data verify the effectiveness of our proposed method.
Vermicast Analysis with the Earthworm Species Pheretima losbanosensis (Crassiclitellata: Megascolecidae): Bacterial Profiles for Potential Applications in Agriculture
Authors: Mapile Maria Reynalen F. , Aspe Nonillon M. , Obusan Marie Christine M.
Published: September 2023

In the Philippines, the use of non-native earthworm species in vermicomposting is popular. Given that the country is a vital geographical resource for earthworm diversity, the study of earthworm species to establish the potential of their vermicasts in agricultural applications is essential. In this study, the bacteria associated with the vermicasts of the recently described indigenous species, Pheretima losbanosensis, were investigated using next-generation sequencing, community-level physiological profiling, and NPK activity screening. The results showed diverse bacterial species belonging to the phyla Proteobacteria, Bacteroidetes, Acidobacteria, Actinobacteria, Chloroflexi, Firmicutes, Gemmatimonadetes, Nitrospirae, Planctomycetes, Spirochaetes, Thermodesulfobacteria, and Verrucomicrobia. Higher diversity and carbon substrate utilization (p < 0.05) of amines and amides, phenolic compounds, polymers, and carboxylic and acetic acids were exhibited by the bacterial communities of P. losbanosensis compared to those of Eudrilus eugeniae. Likewise, bacteria (n = 25) isolated from P. losbanosensis vermicasts had higher nitrogen fixation and phosphate and potassium solubilization activities (p < 0.05) than the bacteria (n = 20) isolated from E. eugeniae vermicasts. Overall, our results indicate that the diverse bacterial communities inhabiting the vermicasts of P. losbanosensis have nutrient mineralization and carbon substrate utilization activities that may have applications in sustainable agriculture as a potential organic input to promote plant growth and improve soil substrate.
Immersive Phobia Therapy through Adaptive Virtual Reality and Biofeedback
Authors: Moldoveanu Alin , Mitruț Oana , Jinga Nicolae , Petrescu Cătălin , Moldoveanu Florica , Asavei Victor , Anghel Ana Magdalena , Petrescu Livia
Published: September 2023

Phobias are a type of anxiety disorder characterized by severe fear of objects and situations. In recent years, virtual reality exposure therapy has emerged as a safer and more convenient method of treating phobias, with the same rate of success as classical therapy, the in vivo exposure to stimuli. Following extensive research concerning the available technologies and after studying the state of the art in the field, we developed the PhoVR (Phobia therapy through Virtual Reality) system during a 2-year long project that involved the participation of a technical university, a biology faculty, and an industry partner specialized in designing 3D applications. Our final prototype incorporates scenarios for acrophobia, claustrophobia, and fear of public speaking therapy, in which the user is required to perform gamified tasks and navigate virtual reality environments with biophysical data acquisition (electrodermal activity and heart rate), automatic anxiety level classification, biofeedback integrated into the scenes’ elements, the dynamic adaptation of the virtual environments, and relaxation techniques made available at any moment. The control panel is an application dedicated to psychotherapists for managing patients’ profiles and therapy sessions. The feedback obtained in a qualitative survey with subjects and psychotherapists who evaluated the prototype validated the system and provided suggestions for refinement.
A Lightweight Mitigation Approach against a New Inundation Attack in RPL-Based IoT Networks
Authors: Rouissat Mehdi , Belkheir Mohammed , Alsukayti Ibrahim S. , Mokaddem Allel
Published: September 2023

Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels.
The Maintenance Effect of Diaphragm-to-Rib Fatigue Cracks in Orthotropic Steel Deck via Steel Plate Reinforcement
Authors: Dai Xihua , Zhang Xinmin , Meng Cheng , Guo Yawei , Zhu Yaoyu , Yuanzhou Zhiyuan
Published: September 2023

To investigate the maintenance effect of diaphragm-to-rib fatigue cracks via steel plate reinforcement, finite element models of different-shaped steel plates were established. Stress intensity factors at the crack tip before and after reinforcement were obtained, and the variations in stresses were studied. Polygonal steel plate reinforcements were then carried out on a real bridge based on the finite element calculation results. The changes in stress amplitudes and fatigue damage degrees at the crack tip were analyzed via the rain-flow counting method and Miner’s linear damage accumulating theory. The results show that both the polygonal steel plate and the rectangular steel plate could effectively eliminate the stress concentration and restrain the propagation of cracks. The stresses in other parts of the arc notch increased after reinforcement and polygonal steel plates had less influence. After the reinforcement in a real bridge, the cycle number of high stress amplitudes at the crack tip decreased significantly. The fatigue damage degree of the repaired part reduced by 70.1%, which verified the maintenance effect via polygonal steel plate reinforcement on diaphragm-to-rib fatigue cracks.
Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
Authors: Yan Xuewen , Bai Yuanyuan , Zhang Wenwen , Cheng Chen , Liu Jihong
Published: September 2023

This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.
Thermophysical Property Measurements with the Finite Bar
Authors: Ellenrieder Christoph , Kaufmann André , Reick Benedikt , Geimer Marcus
Published: September 2023

Knowledge of thermophysical properties of materials is important in the design process to meet the ambitious targets with respect to reliability and performance of many modern machinery. In this paper a simple method for the measurements of thermophysical material properties is presented. A bar of the sample material is heated at one end by a constant heat source and temperature sensors on or in the sample material at different locations record the temperature response. In the limit of small Fourier-Numbers the temperature will not rise at the adiabatic end and the comparison to the theoretical curve allows to extract thermophysical data. In the case of large Fourier-Numbers a quasi steady temperature profile in the bar allows to extract all relevant thermophysical properties simultaneously. Apart from the theory some measurement results are presented and the errors due to diabatic boundary conditions are discussed.
Developing a Fuzzy Expert System for Diagnosing Chemical Deterioration in Reinforced Concrete Structures
Authors: Farahani Atiye , Naderpour Hosein , Konstantakatos Gerasimos , Tarighat Amir , Peymanfar Reza , Asteris Panagiotis G.
Published: September 2023

The widespread application of reinforced concrete structures in different environmental conditions has underscored the need for effective maintenance and repair strategies. These structures offer numerous advantages, but are not impervious to the deleterious effects of chemical deterioration. The outcomes of this research hold significant implications for the management system of reinforced concrete structures. This study proposes the utilization of a fuzzy expert system as a means of enhancing the diagnosis of chemical deterioration in reinforced concrete structures that is a valuable tool for engineers and decision-makers involved in the maintenance and repair of these structures. The fuzzy expert system serves as an intelligent tool that can incorporate various symptoms of deterioration and inspection data to improve the accuracy and reliability of the diagnostic process. By integrating these inputs, the system evaluates 21 different data points, each representing a specific aspect of deterioration, on a scale ranging from 0 to 100. This numerical representation allows for a quantification of the level of deterioration, with 0 denoting minimal deterioration and 100 indicating severe deterioration. The effectiveness of the fuzzy expert system lies in its ability to process the vast amount of data and apply fuzzy operations on 352 fuzzy rules. These rules define the relationships between the inspection data, the type of deterioration, and its extent. Through this computational process, the fuzzy expert system can provide valuable insights into 10 distinct types of chemical deterioration, facilitating a more precise and comprehensive diagnosis. The implementation of the fuzzy expert system has the potential to revolutionize the field of diagnosing chemical deterioration in reinforced concrete structures. By addressing the limitations of traditional methods, this advanced approach can significantly improve the clarity and accuracy of the diagnostic process. The ability to obtain more precise information regarding the type and extent of deterioration is vital for developing effective maintenance and repair strategies. Ultimately, the fuzzy expert system holds great promise in enhancing the overall durability and performance of reinforced concrete structures in various environments.
Genetic-Algorithm-Driven Parameter Optimization of Three Representative DAB Controllers for Voltage Stability
Authors: Du Wenjie , Chen Wenjie
Published: September 2023

In the process of integrating renewable energy sources into DC microgrids, the isolated bidirectional bridge plays a crucial role. Under load disturbances, voltage fluctuations in the microgrid can affect system stability. This study focuses on using a Genetic Algorithm to optimize the parameters of three typical DAB controllers (PI controller based on pole placement, sliding mode controller, and model predictive controller) with the aim of improving voltage stability, especially during sudden load drops. The results demonstrate that controllers optimized using Genetic Algorithm outperform the methods of pole placement and traditional manual tuning significantly. For the PI controller, the maximum drop rate reduced from 8.00% to 4.00%. The phase margin increased from 123° to 126°. In the case of the sliding mode controller, the maximum drop rate decreased from 7.50% to 5.00%. The phase margin increased from 127° to 155°. As for the model predictive controller, the maximum drop rate reduced from 1.00% to 0.70%. The gain margin increased from 25.8 dB to 26.2 dB. These results highlight the potential of using the Genetic Algorithm in optimizing control parameters, offering the prospect of improving the performance and stability of DC–DC converters.
Full-Scale Model Test of Subway Contact Channel under Mechanical Construction
Authors: Fan Liping , Sun Xibo , Ye Xinfeng , Niu Shuai , Dong Zibo , Deng Yuebao , Zhu Yaohong
Published: September 2023

The mechanical construction method for a subway contact channel has the advantages of a short construction period and high safety, and is more and more being applied in coastal soft-soil areas. In order to explore the suitability of this method in inland areas, a full-scale model test platform is used to simulate the shield-cutting construction process of the subway contact channel. The convergence deformation of the segments and the strain of the reinforcement and concrete are tested, so as to analyze the internal force and deformation law of the tunnel structure during cutting construction. The influence of the steel ring on the deformation of the subway contact channel is also studied. It is found that the segment convergence at the top is less than that at the waist position, and the convergence deformation of the waist is less than 30 mm; the internal force of the segment redistributes and the axial force mainly decreases during the cutting process; the stress state may change from compression to tension. The segment structure of the main tunnel, the supporting structure in the tunnel, and the stiffness of the steel-ring-lined composite pipe segment have little influence on the cutting force of the contact channel. The research results provide the corresponding technical indexes for the construction of a contact channel by the mechanical method, as well as a reference for the design and optimization of a steel-ring-lined composite pipe segment.
Glycerol Steam Reforming in a Pilot Plant: Effect of Temperature and Catalyst Load
Authors: González Juan Félix , Álvarez Murillo Andrés , González Arroyo Álvaro , Nogales-Delgado Sergio
Published: September 2023

One of the main environmental problems is the use of oil for fuel and plastic production, implying the increase in pollutants that might contribute to the greenhouse gas effect, among others. Thus, the use of vegetable oils to produce biodiesel can be interesting, as it is biodegradable and less pollutant compared to diesel fuels, presenting higher flash and combustion points. Moreover, biodiesel production could take part in a biorefinery concept, to produce products such as biolubricants and obtain interesting byproducts that can be reused (such as methanol) or upgraded for energy or pharmaceutical purposes (like glycerol). Consequently, the valorization of these byproducts could contribute to the higher energy efficiency of the process, improving the atom economy. The aim of this work was to assess the effect of the temperature and the amount of catalyst on glycerol steam reforming to produce hydrogen at a semi-industrial level, regarding some aspects like gas production, hydrogen mole ratio and power, using a Ni-based catalyst. In conclusion, the best results found in this experiment, allowing a plant to work continuously for 9 h, were obtained with the following conditions: S/C ratio: 0.7; inlet flow: 40 mL/min; temperature: 850 °C; La2O3 and NiO percentage: 5 and 12%, respectively.
Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices
Authors: Ameen Mostafa , Elwageeh Mohamed , Abdelaziz Ahmed , Bonduà Stefano , Elkarmoty Mohamed
Published: September 2023

Mode I fracture toughness (KIC) and rock brittleness are important properties that influence many rock engineering applications. Due to the difficulties in determining KIC experimentally, previous studies have investigated the relationship between KIC and rock brittleness indices. However, only rock brittleness indices (based on strength parameters) and KIC obtained from Chevron Bend and Short Rod test methods were considered. In this paper, regression analysis was carried out to investigate the relationship between KIC and rock brittleness using literature data collected from different rock types and core KIC test methods under level I and static test conditions. Rock brittleness was assessed using ten indices based on strength and pre-peak elastic parameters. The results showed that elastic-based indices were not good predictors of KIC, while strength-based indices correlated well with KIC. A comparison with previous studies revealed that the correlations between KIC and strength-based indices were significantly sensitive to the rock type, i.e., soft or hard, and the KIC test method. However, a brittleness index, based on both strength and pre-peak elastic parameters, was found to be the best index to predict KIC because of its lower sensitivity to the test method and rock type.
Novel Paintings from the Latent Diffusion Model through Transfer Learning
Authors: Wang Dayin , Ma Chong , Sun Siwen
Published: September 2023

With the development of deep learning, image synthesis has achieved unprecedented achievements in the past few years. Image synthesis models, represented by diffusion models, demonstrated stable and high-fidelity image generation. However, the traditional diffusion model computes in pixel space, which is memory-heavy and computing-heavy. Therefore, to ease the expensive computing and improve the accessibility of diffusion models, we train the diffusion model in latent space. In this paper, we are devoted to creating novel paintings from existing paintings based on powerful diffusion models. Because the cross-attention layer is adopted in the latent diffusion model, we can create novel paintings with conditional text prompts. However, direct training of the diffusion model on the limited dataset is non-trivial. Therefore, inspired by the transfer learning, we train the diffusion model with the pre-trained weights, which eases the training process and enhances the image synthesis results. Additionally, we introduce the GPT-2 model to expand text prompts for detailed image generation. To validate the performance of our model, we train the model on paintings of the specific artist from the dataset WikiArt. To make up for missing image context descriptions of the WikiArt dataset, we adopt a pre-trained language model to generate corresponding context descriptions automatically and clean wrong descriptions manually, and we will make it available to the public. Experimental results demonstrate the capacity and effectiveness of the model.
Analysis of Heuristic Optimization Technique Solutions for Combined Heat-Power Economic Load Dispatch
Authors: Singh Nagendra , Chakrabarti Tulika , Chakrabarti Prasun , Panchenko Vladimir , Budnikov Dmitry , Yudaev Igor , Bolshev Vadim
Published: September 2023

Thermal power plants use coal as a fuel to create electricity while wasting a significant amount of energy as heat. If the heat and power plants are combined and used in cogeneration systems, it is possible to reuse the waste heat and hence enhance the overall efficiency of the power plant. In order to minimize production costs while taking system constraints into account, it is important to find out the optimal operating point of power and heat for each unit. Combined heat and power production is now widely used to improve thermal efficiency, lower environmental emissions, and reduce power generation costs. In order to determine the best solutions to the combined heat and power economic dispatch problem, several traditional as well as innovative heuristic optimization approaches were employed. This study offers a thorough analysis of the use of heuristic optimization techniques for the solution of the combined heat and power economic dispatch problem. In this proposed work, the most well-known heuristic optimization methods are examined and used for the solution of various generating unit systems, such as 4, 7, 11, 24, 48, 84, and 96, taking into account various constraints. This study analyzes how various evolutionary approaches are performed for various test systems. The heuristic methodologies’ best outcomes for various case studies with restrictions are contrasted.
A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List
Authors: Zhao Haiwu , Zhao Shuai , Shang Xiwu , Wang Guozhong
Published: September 2023

Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list (MPPPL)and pixel content similarity is proposed. Firstly, the MPPPL is constructed by using the average texture complexity difference of the sub-coding unit under different partition modes. Then, the sub-block pixel mean difference is used to decide the best partition mode or shorten the MPPPL. Finally, the selection rules of the reference lines in the intra prediction process are counted and the unnecessary reference lines are skipped by using the pixel content similarity. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 52.26% of the encoding time, and the BDBR (Bjontegarrd delta bit rate) only increases by 1.23%.
Impact Force Mechanism of a Trimaran Model and Its Experimental Validation
Authors: Li Jiaqi , Sun Shili
Published: September 2023

In recent years, trimarans have been used in high-speed transport and in warships, but studies of them have made little progress. We designed a trimaran model and used it to perform a water-entry experiment to find a way to reduce the slamming pressure. The process of the model entering the water was divided into several steps, and different theoretical models were designed for each step, together with their force analysis. Relying on our experimental platform, we designed three flexible coating thicknesses and six drop heights as the different working conditions. With an analysis of the results under these experimental conditions, the experiment clearly indicates that the flexible cladding on the model can decrease the impact force when the model slams into water. Furthermore, the theoretical models and their corresponding force analyses are validated by the experimental results.
A Novel Risk Assessment for Cable Fires Based on a Hybrid Cloud-Model-Enabled Dynamic Bayesian Network Method
Authors: Gao Shenyuan , Huang Guozhong , Xiang Zhijin , Yang Yan , Gao Xuehong
Published: September 2023

The fire risk of cables constantly changes over time and is affected by the materials and working conditions of cables. To address its internal timing property, it is essential to use a dynamic analysis method to assess cable fire risk. Meanwhile, data uncertainty resulting in the deviation of risk values must also be considered in the risk assessment. In this regard, this study proposes a hybrid cloud model (CM)-enabled Dynamic Bayesian network (DBN) method to estimate the cable fire risk under uncertainty. In particular, the CM is initially applied to determine the membership degrees of the assessment data relative to different states of the root nodes; then, these degrees are considered the prior probabilities of DBN, where the dynamic risk profiles are reasoned. Subsequently, the Birnbaum and Fussell–Vesely importance measures are constructed to identify the key nodes for risk prevention and control, respectively. Moreover, a case study of the Chongqing Tobacco Logistics Distribution Center is conducted, the computational results of which indicate the proposed method’s decision-making effectiveness. Finally, a comparison of the reasoning results between the proposed and traditional methods is performed, presenting strong evidence that demonstrates the reliability of the proposed method.
Face Keypoint Detection Method Based on Blaze_ghost Network
Authors: Yu Ning , Tian Yongping , Zhang Xiaochuan , Yin Xiaofeng
Published: September 2023

The accuracy and speed of facial keypoint detection are crucial factors for effectively extracting fatigue features, such as eye blinking and yawning. This paper focuses on the improvement and optimization of facial keypoint detection algorithms, presenting a facial keypoint detection method based on the Blaze_ghost network and providing more reliable support for facial fatigue analysis. Firstly, the Blaze_ghost network is designed as the backbone network with a deeper structure and more parameters to better capture facial detail features, improving the accuracy of keypoint localization. Secondly, HuberWingloss is designed as the loss function to further reduce the training difficulty of the model and enhance its generalization ability. Compared to traditional loss functions, HuberWingloss can reduce the interference of outliers (such as noise and occlusion) in model training, improve the model’s robustness to complex situations, and further enhance the accuracy of keypoint detection. Experimental results show that the proposed method achieves significant improvements in both the NME (Normal Mean Error) and FR (Failure Rate) evaluation metrics. Compared to traditional methods, the proposed model demonstrates a considerable improvement in keypoint localization accuracy while still maintaining high detection efficiency.
Carbon Dioxide Uptake by Brazilian Cement-Based Materials
Authors: da Silva Rego Joao Henrique , Sanjuán Miguel Ángel , Mora Pedro , Zaragoza Aniceto , Visedo Gonzalo
Published: September 2023

The worldwide cement industry plays an important role in addressing the climate change challenge. Brazil’s cement industry currently has 91 cement plants with an installed production capacity of 94 million tons per year and has started to calculate the net CO2 emissions to achieve a carbon-neutral cement sector by 2050. Accordingly, the carbon dioxide uptake due to mortar and concrete carbonation is subtracted from the carbon dioxide emitted by the chemical reaction for the calcination of lime, i.e., the calcination process performed during clinker production. Now-adays, the Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas (GHG) Inventories to report the GHG emissions do not include any calculation procedure to consider the mortar and concrete carbonation. However, the Intergovernmental Panel on Climate Change (IPCC)’s Sixth Assessment Report (AR6) recognizes the physico-chemical process known as carbonation. Brazilian net carbon dioxide emissions of cements produced from 1990 to 2019 are estimated considering the carbon dioxide uptake during the service-life and end-of-life and secondary usage stages (Tier 1). This is a fundamental scientific and technological novelty that changes the current approach to estimate the carbon dioxide emissions due to the Portland cement clinker production. Even considering the relative novelty of this approach, it should be promoted in the future and included in the national inventory report (NIR). The carbon dioxide uptake by mortar and concrete carbonation for 30 years is about 140 million tons. Within this thirty-year period about 483 million tons have been released due to the calcination process.
Comparison of Two Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider
Authors: Klyukhin Vyacheslav , Ball Austin , Berriaud Christophe Paul , Curé Benoit , Dudarev Alexey , Gaddi Andrea , Gerwig Hubert , Hervé Alain , Mentink Matthias , Riegler Werner , Wagner Udo , Ten Kate Herman
Published: September 2023

The conceptual design study of a Future Circular hadron-hadron Collider (FCC-hh) to be constructed at CERN with a center-of-mass energy of the order of 100 TeV requires superconducting magnetic systems with a central magnetic flux density of an order of 4 T for the experimental detectors. The developed concept of the FCC-hh detector involves the use of an iron-free magnetic system consisting of three superconducting solenoids. A superconducting magnet with a minimal steel yoke is proposed as an alternative to the baseline iron-free design. In this study, both magnetic system options for the FCC-hh detector are modeled with the same electrical parameters using Cobham’s program TOSCA. All the main characteristics of both designs are compared and discussed.
Investigating Polarization-Sensitive Transmission and Reflection Metasurfaces for Advanced Wavefront Manipulation
Authors: Ahmad Ashfaq , Ali Jawad , Choi Dong-You
Published: September 2023

This paper presents the design and optimization of a dual-band polarization-dependent metasurface capable of dynamically switching transmission and reflection characteristics. The metasurface is composed of three metallic patterns, with the bottom layer governing the reflection and transmission phase for both TE-polarization and TM-polarization states. The middle and top layers are strategically employed to ensure optimal transmission and reflection performance. The results confirm that the metasurface enables the transformation of the transmission band into a complete reflection band, and vice versa, through variations in the incident wave polarization. Remarkable transmission and reflection characteristics are achieved within the frequency ranges of 6.1–6.55 GHz and 8.9–9.3 GHz, respectively. The proposed metasurface offers promising applications in advanced communication systems and radar technology, enabling dynamic manipulation of electromagnetic waves.
Research on Multi-Sensor Data Fusion Positioning Method of Unmanned Ships Based on Threshold- and Hierarchical-Capacity Particle Filter
Authors: Shen Yi , Zhao Zeyu , Yuan Mingxin , Wang Sun
Published: September 2023

To improve the positioning accuracy of unmanned ships, a multi-sensor system including ZigBee, a Global Positioning System (GPS), and BeiDou Navigation Satellite System (BDS) is constructed, and an adaptive multi-sensor data fusion positioning method based on the threshold and hierarchical capacity particle filter (TCPF) is designed. First, the ZigBee-GPS/BDS multi-sensor measurement data is preprocessed to achieve a consistent space–time reference and transformed into the same coordinate system by projection. Then, the fault data is weighted and corrected through the consistency inspection of ZigBee-GPS/BDS multi-sensor positioning data, and the corresponding confidence factor is given according to the confidence distance of the positioning data; furthermore, the confidence factor is associated with stratified sampling. After that, the multi-sensor positioning data is filtered and denoised using a basic particle filter. Finally, a TCPF data fusion algorithm is designed, and the navigation positioning data of the unmanned ship is fused and filtered to obtain its positioning information. Numerical tests show that compared with other filtering algorithms, the mean square root error and standard deviation of the proposed TCPF algorithm decrease by an average of 25.0% and 28.0%, respectively, which verifies its high filtering accuracy and its advantages in suppressing particle degradation and avoiding sample scarcity. The experimental tests show that compared with other fusion algorithms, the proposed TCPF algorithm can not only realize the precise positioning during unmanned ship navigation, but also in the positioning and fault tolerance test, the average positioning error, root-mean-square error, and standard deviation of the former decrease by 36.0%, 38.0%, and 37.0%, respectively, and the corresponding performance indicators of the latter decrease by an average of 20.0%, 19.5%, and 17.5%, which verifies that it has the advantages of high data reliability and good filtering fault tolerance, and helps to improve the positioning accuracy of unmanned ships.
Non-Contact Monitoring of Operating Conditions for Solar Cells in a Photovoltaic Module Using a Surface Potential Meter for Detecting the Risk of Fire
Authors: Shimizu Ryo , Ota Yasuyuki , Nagaoka Akira , Araki Kenji , Nishioka Kensuke
Published: September 2023

Fires in photovoltaic modules are caused by hot spots, which are typically monitored by thermal images. This method helps visualize the hot spot, but it is affected by the environment (solar irradiance, wind, ambient temperature) and is not reproducible. Assessing the heat dissipation of the hot cell can be used for alternative assessment of the fire risk. This method was validated by comparing the value measured by the surface potential meter and the module potential measured directly by adding a bypass measurement circuit. The substantial reverse-bias voltage caused by mismatching or partial shading (depending on the operating conditions) leads to local heat consumption of the partially shaded solar cells and potentially causes fire. The fire risk can be assessed in the worst-case conditions (ex. 1380 W/m2 solar irradiance) by non-contact measurement of the reverse-bias voltage and calculating the heat dissipation and temperature rise. This work suggested that −13 V is the criterion and was close to the known value of reverse voltage for Si cells. The current technology inspects solar cells before assembly to the module, and there is no way of inspecting in the product test or detecting after degradation that can be covered by the proposed method in this work.
V2G Strategies to Flatten the Daily Load Curve in Seoul, South Korea
Authors: Choi Sangbong , Kim Changsoo , Sung Backsub
Published: September 2023

In order to meet the increasing demand for electricity to maintain electric vehicles (EVs) worldwide, this paper aims to improve our understanding of the impact of the load on the power grid generated by the charging and discharging of electric vehicles. The rapid development of the electric vehicle (EV) industry offers new economic and environmental benefits, such as mitigating global warming by reducing carbon dioxide. On the other hand, however, we will face the reality that the emergence of such large-scale EVs will undoubtedly put additional strain on the power grid. In this context, solving the problem of excessive power usage associated with charging large electric vehicles and reducing the impact on the grid is paramount. Accordingly, in order to meet the increasing demand for electricity to maintain electric vehicles (EVs) worldwide, this paper aims to improve our understanding of the impact of the load on the power grid generated by the charging and discharging of electric vehicles. A V2G strategy is presented with the goal of flattening the daily load curve by considering the charge and the discharge positions of EVs. First, in this paper, based on the estimated share of electric vehicles, we set the assumption that EVs travel to work in the morning and leave work in the afternoon. Second, we develop an efficient V2G strategy to equalize the daily load curve due to charging and discharging of electric vehicles in Seoul by applying a system marginal price (SMP) and time-of-use (TOU) rate system. The EV charging/discharging load and existing load using V2G modeling are added up, all daily load curves are calculated and analyzed based on the 2030 and 2040 EV share scenarios for Seoul, and the grid load is leveled. The analysis suggests measures to minimize the impact of EV loads on the power grid according to the V2G strategy based on charging and discharging plans. Overall, this paper aims to smooth the grid’s daily load curve and avoid grid overload by applying appropriate SMP and TOU plans; we also present an efficient V2G strategy, established through charge and discharge modeling and EV charge and discharge management techniques, in order to minimize grid expansion.
Camellia oleifera Fruit Detection Algorithm in Natural Environment Based on Lightweight Convolutional Neural Network
Authors: Li Zefeng , Kang Lichun , Rao Honghui , Nie Ganggang , Tan Yuhan , Liu Muhua
Published: September 2023

At present, Camellia oleifera fruit harvesting relies on manual labor with low efficiency, while mechanized harvesting could result in bud damage because flowering and fruiting are synchronized. As a prerequisite, rapid detection and identification are urgently needed for high accuracy and efficiency with simple models to realize selective and intelligent harvesting. In this paper, a lightweight detection algorithm YOLOv5s-Camellia based on YOLOv5s is proposed. First, the network unit of the lightweight network ShuffleNetv2 was used to reconstruct the backbone network, and thereby the number of computations and parameters of the model was reduced to increase the running speed for saving computational costs. Second, to mitigate the impact of the lightweight improvement on model detection accuracy, three efficient channel attention (ECA) modules were introduced into the backbone network to enhance the network’s attention to fruit features, and the Concat operation in the neck network was replaced by the Add operation with fewer parameters, which could increase the amount of information under features while maintaining the same number of channels. Third, the Gaussian Error Linear Units (GELU) activation function was introduced to improve the nonlinear characterization ability of the network. In addition, to improve the ability of the network to locate objects in the natural environment, the penalty index was redefined to optimize the bounding box loss function, which can improve the convergence speed and regression accuracy. Furthermore, the final experimental results showed that this model possesses 98.8% accuracy, 5.5 G FLOPs computation, and 6.3 MB size, and the detection speed reached 60.98 frame/s. Compared with the original algorithm, the calculation amount, size, and parameters were reduced by 65.18%, 56.55%, and 57.59%, respectively. The results can provide a technical reference for the development of a Camellia oleifera fruit-harvesting robot.