bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges to Treat Complex Zinc Concentrate and Latest Technical Development
Authors: Tan Pengfu 
Published: April 2024

Abstract
The complex zinc concentrate containing high Pb, Cu, SiO2, Na and K, including the Zn-Pb bulk concentrate, cannot be treated in the fluidized bed roaster. The Imperial Smelting Process (ISP) is the main technology to treat this type of concentrate. However, ISP consumes a large amount of coke, which is not only expensive, but also has high CO2 emission.
Several metallurgical processes, which can treat the type of complex zinc concentrate and have low CO2 emission, have been reviewed and compared.
Recently, a new technology “Flash Burner Roasting Process” has been tested successfully at the industrial scale suspension roaster, and the tests demonstrate that this technology can treat the complex zinc concentrate containing high contents of Pb, Cu and SiO2. The thermodynamic modelling for this process has been done. The industrial test results, the modelling results and the further development are presented in this paper.
A deep learning-based intelligent online warning method for gear wear damage based on oil vibration
Authors: Guo Boyang , Chen Jing 
Published: April 2024

Abstract
Gearboxes, as essential connecting and transmission components in mechanical equipment, have been widely used in modern industrial development. Gearboxes are prone to malfunction or even failure due to complex structures and harsh working environments. This article takes online monitoring of gear wear and damage as the research object and studies the fault diagnosis method of gear multi-source heterogeneous parameters for oil monitoring and vibration monitoring. The Yolov5 model is used to identify multi-objective wear particles. The experimental outcomes suggest that the optimized detection method can sensitively reflect the evolution process of gear wear.
Style transfer and recognition of infrared images of transformers under different lighting based on CycleGAN
Authors: Du Xin , Qi Jun , Kang Jiyi , Xie Jun , Wang Chunxin , Wang Jiaxin 
Published: April 2024

Abstract
In order to solve the problem that the transformer in the infrared image of the transformer is difficult to be accurately detected due to strong light irradiation under different lighting conditions, a method for unifying the illumination and equipment identification of the infrared image of the transformer based on CycleGAN and yolov3 is proposed. CycleGAN is used to unify the illumination intensity of the infrared image of the transformer, and an adversarial loss function and a cycle consistency loss function are introduced to ensure high-quality generation of infrared images, and the expanded infrared sample library is richer. Finally, the yolov3 network is trained using the uniformly illuminated image set to achieve accurate identification of transformers. The experimental results show that the infrared image of the transformer that has been unified by illumination can identify the transformer with an accuracy of 95.2%, which is 3.1% higher than before enhancement.
Classification of wafer map defect based on ResNeSt
Authors: Chen Shouhong , Zhu Ziren , Hou Xingna 
Published: April 2024

Abstract
With the increasing circuit complexity of chip design, wafers are prone to defects in the production process. Since different defects have their causes, post-analysis is important to wafer production. However, the sample size is not balanced, and the effect of classical residual networks is limited. This article uses a Split-Attention network (ResNeSt) with which feature maps will be input into k-base groups, each consisting of r groups. Thanks to the large receptive field, image features can be obtained better. We add channel attention and give higher weight to the effective feature channels to improve network performance. Compared with the classical residual network, the new network has an improvement of 43.06%, 32.73%, and 10.86% in handling Scratch, Near-full, and Local defects, respectively.
Research on garbage sorting robotic arm based on image vision
Authors: Huang Youran 
Published: April 2024

Abstract
This paper aims to investigate the application of artificial intelligence in robotic arm automation control for the accurate identification of different types of garbage using deep learning algorithms. The goal is to enable the robotic arm to autonomously classify and handle garbage. The appropriate garbage classification dataset was selected and subjected to data preprocessing in this study. After comparing various well-established convolutional neural network models, including VGG16, InceptionResNetV2, Xception, and InceptionResNetV3, in terms of performance and suitability on the target dataset, the Xception model, which exhibited the best performance metrics, was selected for this research. Subsequently, the paper optimized the model by incorporating self-attention mechanisms, self-optimization strategies for learning rate, learning weight adjustments, and unfreezing of pre-trained layers, resulting in a predictive accuracy of 96.9% on the test set and an AUC area of 0.9989. Additionally, the paper simulated the robotic arm in a simulated environment and successfully achieved the objective of automatic garbage identification and classification using the developed model.
A limited labeled data bearing fault diagnosis method based on self-supervised learning
Authors: Yang Yunji , Wei Yadong , Liang Gaoqi , Guo Liang 
Published: April 2024

Abstract
The key focus of this study revolves around several crucial issues concerning bearing fault diagnosis, and presents an innovative solution. Conventional labeled approaches for bearing fault diagnosis often necessitate labeled data sets, which can be time-consuming or infeasible to obtain. To address this problem, an increasing amount of research has started to explore fault diagnosis methods that utilize limited labeled data. In our study, we introduce a framework for bearing fault diagnosis that incorporates wavelet transform and self-supervised learning techniques. The framework leverages vibration signals and transforms them into time-frequency spectrograms as inputs. To extract features, we employ the Swin Transformer as an encoder. Furthermore, we present a self-supervised learning approach named MoBy to address the challenge of limited labeled samples. Encouragingly, our approach achieves a diagnostic accuracy of 96.4% by utilizing only 1% labeled samples, through a well-trained encoder and a simple linear classification layer. This demonstrates outstanding performance in utilizing limited labeled data. To validate the superiority of our proposed approach, we conducted experiments on two rolling bearing fault datasets and achieved significant results.
Fine-grained partial discharge pattern recognition method based on substation equipment knowledge graph
Authors: Gao Bo , Yan Zhenhua , Li Xuefeng , Xie Chenhao , Geng Shaosheng 
Published: April 2024

Abstract
Discharge-type faults are one of the main types of faults in substation equipment, and partial discharge (PD) faults are an important cause of equipment discharge faults. When different types of PD faults occur in equipment, their fault handling plans also vary accordingly. Therefore, the identification of PD types in equipment is very important. Aiming at the problem that the recognition accuracy of the existing methods is not high enough due to the low quality of PD data and unbalanced samples in the process of equipment PD pattern recognition, we propose a fine-grained method to recognize PD patterns based on substation equipment knowledge graph. This model combines a visual encoder with a knowledge graph and uses the fine-grained attribute features and the association feature information of the PD entity provided by the knowledge graph for the research of equipment PD pattern recognition. After experimental verification, the accuracy of PD pattern recognition in our proposed method is significantly higher than that of previous methods.
Kinematic performance analysis of a robot climbing steps without movable tracks on a swing arm
Authors: Chen Wei , Zhang Zhian 
Published: April 2024

Abstract
A lightweight tracked robot with no moving swing arm is designed, which is mainly used in exploring narrow spaces. The mathematical model of the position of the robot’s center of mass with the elevation Angle of the vehicle body and the Angle between the swinging arm is established. The kinematics of the robot in different typical terrains, such as ramp, ditch, and step, is analyzed. By analyzing the critical state of the change of the position of the center of mass, the maximum Angle and width of climbing and crossing are calculated under ideal conditions. The maximum height of the robot is also calculated when using different ways to overcome obstacles, corresponding to the position attitude under the maximum climbing height. It provides a data theory basis for robot motion control strategy.
Analysis and solution of sparking fault in IGBT connection copper bar of compact driver
Authors: Ma Yuhua , Wu Di , Mao Xueyu 
Published: April 2024

Abstract
With the high-speed development of modern industrial automation, the demand for miniaturization of the high-current driver is higher and higher, which makes the space inside the driver more and more compact. According to the traditional installation form, in the production process, there will be screw deviation or loose during the use of the problem. Then, this can lead to final product quality problems such as fire. To solve the mechanical connection problems, based on the Datsan of the ignition problem, this paper puts forward some solutions, such as operation, Torque Detection, and Mark re-checking. A method is proposed to improve the reliability and save the cost of the drive product is recommended.
Transformer fault analysis based on rough sets and improved gravity algorithms
Authors: Cheng Chenyang , Wang Xihuai 
Published: April 2024

Abstract
Transformer is one of the most important equipment of power grid, in order to ensure the safe and reliable operation of power grid, the correct diagnosis of transformer fault is very important. Rough set theory is a new field of mathematical science, and it is applicable to many fields, including pattern recognition, machine learning, decision support, process control, and predictive models. Considering the incompleteness and complexity of transformer fault diagnosis data, a fault diagnosis method based on rough set theory and improved gravity algorithm is proposed in this paper.
Analysis of the influence of gear root chamfer radius on the strength of climbing gears
Authors: Liang Tongxin , Ling Aijun 
Published: April 2024

Abstract
For self-elevating units, the lifting locking system is one of the most critical equipment and has important applications in the field of marine engineering. The reliability of the jacking and fixation system is crucial for the safety of the platform, and the jacking and fixation system, the strength of the climbing gear and rack directly affects the reliability of the system. Therefore, optimizing the structural parameters of the climbing gear and rack to enhance their strength has always been a research hotspot in the field of marine engineering. This article uses ANSYS simulation software to model the climbing gear and rack, in order to analyze the impact of gear root chamfer radius on the strength of the climbing gear.
Modeling and simulation of magnetostrictive actuators based on ΔE effect Preisach model
Authors: Gao Xiaohui , Feng Zhipeng , Zhu Haokuan , Tong Saisai 
Published: April 2024

Abstract
Based on the classical Preisach model, combined with the magneto-mechanical coupling effect of Giant magnetostrictive materials (GMM), this paper introduces the Everett function to optimize the classical Preisach hysteresis model and improve its accuracy. By importing dynamic parameters, the dynamic Preisach hysteresis model is established. Combined with the constitutive model of GMM, a dynamic Preisach hysteretic nonlinear magnetostrictive actuator model based on the ΔE effect is established. Then, the finite element simulation is carried out by Comsol software and compared with the theoretical model simulation. The results show that the error between the magnetostrictive strain of the established hysteresis model and the finite element simulation results is less than 100 ppm, which verifies the accuracy of the proposed model. Finally, the curves of magnetic field intensity, magnetic induction intensity, magnetization, magnetostrictive strain and ΔE effect of GMA are obtained by using this model.
Optimal scheduling of blockchain-based virtual power plants considering distribution network congestion
Authors: Nie Kunyue , Zhao Shengnan , Song Haixia , Wang Zhijun 
Published: April 2024

Abstract
The development of renewable energy has brought new challenges to the power system. Virtual power plant based on blockchain is an effective means to solve the problem of new energy consumption. Solving the internal risk of line overload or voltage over-limit of a virtual power plant can make it play a better role. This paper proposes a scheduling method for a virtual power plant based on blockchain, which can solve the congestion problem. Firstly, the scheduling process of a blockchain-based virtual power plant is designed. Secondly, it is discretized as the basis of the congestion management scheme. Thirdly, the price penalty is introduced into the scheduling process to solve the congestion problem. Finally, an example is designed for simulation. The results show that the method can solve the congestion problem while realizing the virtual power plant scheduling.
The impact of different air gap defects in polypropylene coaxial cables on electric field distortion
Authors: Li Qiang , Yang Xu , Zhang Changhong , Fu Qiang , Li Dong , Zhou Weizhen 
Published: April 2024

Abstract
The internal cable will have different air gaps due to various physical influences, and partial discharge caused by air gap defects is the main cause of cable insulation aging. To avoid serious consequences caused by air gap defects, it is crucial to analyze the extent of the impact of early-stage air gaps on the electric field in the cable. In this paper, COMSOL Multiphysics software is used to build a three-dimensional model of the cable, and the insulation material is polypropylene. Different positions and sizes of air gap defects are designed in the model. The electric field distribution inside the cable is obtained through the finite element algorithm, and the degrees of distortion of the electric field under different air gap sizes, positions, and shapes are compared. The results show that the size of the air gap affects the change in the electric field. Air gaps closer to the copper core position produce greater electric field distortion. Additionally, air gaps with larger curvature also result in greater electric field distortion. Based on the conclusion of this study, the influence degree caused by different air gap defects can be determined and provide a basis for the analysis of such faults.
Exponential amplification of the magnetic field in the primordial star-forming cloud
Authors: Hirano Shingo , Machida Masahiro N. , Basu Shantanu 
Published: April 2024

Abstract
In the study of the initial mass function associated with the first generation of stars, known as Population III (Pop III) stars, a fundamental yet unresolved question pertains to the ultimate destiny of the secondary protostars emerging within the accretion disk – specifically, their likelihood of either merging or persisting as distinct entities. Our research concentrates on the magnetic influences affecting the genesis of these first stars under the conditions set by the cosmological initial magnetic field strength. We employ ideal magnetohydrodynamic simulations, utilizing a stiff equation-of-state (EOS) model, to accurately depict the magnetic field structure interconnecting these protostars. We observe that the magnetic field experiences rapid intensification due to the gas near the protostar completing multiple tens of orbital rotations in the initial decade following the formation of the protostar. Concurrently, as mass accretion continues, the region influenced by the significant magnetic field expands outward. This process of magnetic braking effectively curtails the disk fragmentation that would typically occur without a magnetic field. The resulting exponential augmentation of the magnetic field is posited to facilitate the formation of supermassive first stars.
Modeling the Time Dependent Interaction between the Solar Wind and the Local Interstellar Medium with Kinetic Neutral Hydrogen and Helium Atoms
Authors: Fraternale Federico , Pogorelov Nikolai V. , Bera Ratan K. 
Published: April 2024

Abstract
We present recent advancements in our 3D modeling of the interaction between the solar wind and the local interstellar medium (LISM). The latest model results (Fraternale et al., ApJ, 2023) have raised a question about the electron density of the LISM near the heliopause. We have shown that the presence of helium ions leads to a significant underestimation of this parameter compared to the past simulations and Voyager 1 PWS observations. The latter observations, with over 12 years’ worth of LISM data, offers a robust constraint on our models. Here we present additional simulations in support of the idea that the LISM proton density may need to be revised from approximately 0.054 cm–3 to values around 0.07 cm–3 or higher. Additionally, we have developed and successfully tested a new version of the kinetic code suitable for simulating time-dependent solutions.
Numerical modeling of non-equilibrium neutrino radiation field by solving quantum kinetic equation
Authors: Nagakura Hiroki 
Published: April 2024

Abstract
Numerical modeling of neutrino quantum kinetics is a new frontier in the study of core-collapse supernova (CCSN) and binary neutron star merger (BNSM). The noticeable feature in the quantum kinetics is neutrino flavor conversion driven by neutrino self-interactions. Although there remain many unresolved issues in their non-linear properties of flavor conversions, rapid progress has been made in the last few years. In this article, we introduce the current status for the study of collective neutrino oscillations, which are representative characteristics in self-induced flavor conversions, and then we show our recent results: global quantum kinetic simulations in CCSN and BNSM environments.
Advancing parabolic operators in thermodynamic MHD models II: Evaluating a Practical Time Step Limit for Unconditionally Stable Methods
Authors: Caplan Ronald M. , Johnston Craig D. , Daldoff Lars K. S. , Linker Jon A. 
Published: April 2024

Abstract
Unconditionally stable time stepping schemes are useful and often practically necessary for advancing parabolic operators in multi-scale systems. However, serious accuracy problems may emerge when taking time steps that far exceed the explicit stability limits. In our previous work, we compared the accuracy and performance of advancing parabolic operators in a thermodynamic MHD model using an implicit method and an explicit super time-stepping (STS) method. We found that while the STS method outperformed the implicit one with overall good results, it was not able to damp oscillatory behavior in the solution efficiently, hindering its practical use. In this follow-up work, we evaluate an easy-to-implement method for selecting a practical time step limit (PTL) for unconditionally stable schemes. This time step is used to ‘cycle’ the operator-split thermal conduction and viscosity parabolic operators. We test the new time step with both an implicit and STS scheme for accuracy, performance, and scaling. We find that, for our test cases here, the PTL dramatically improves the STS solution, matching or improving the solution of the original implicit scheme, while retaining most of its performance and scaling advantages. The PTL shows promise to allow more accurate use of unconditionally stable schemes for parabolic operators and reliable use of STS methods.
Complex quantum networks: a topical review
Authors: Nokkala Johannes , Piilo Jyrki , Bianconi Ginestra 
Published: April 2024

Abstract
These are exciting times for quantum physics as new quantum technologies are expected to soon transform computing at an unprecedented level. Simultaneously network science is flourishing proving an ideal mathematical and computational framework to capture the complexity of large interacting systems. Here we provide a comprehensive and timely review of the rising field of complex quantum networks. On one side, this subject is key to harness the potential of complex networks in order to provide design principles to boost and enhance quantum algorithms and quantum technologies. On the other side this subject can provide a new generation of quantum algorithms to infer significant complex network properties. The field features fundamental research questions as diverse as designing networks to shape Hamiltonians and their corresponding phase diagram, taming the complexity of many-body quantum systems with network theory, revealing how quantum physics and quantum algorithms can predict novel network properties and phase transitions, and studying the interplay between architecture, topology and performance in quantum communication networks. Our review covers all of these multifaceted aspects in a self-contained presentation aimed both at network-curious quantum physicists and at quantum-curious network theorists. We provide a framework that unifies the field of quantum complex networks along four main research lines: network-generalized, quantum-applied, quantum-generalized and quantum-enhanced. Finally we draw attention to the connections between these research lines, which can lead to new opportunities and new discoveries at the interface between quantum physics and network science.
Preface
Authors: Mangindaan Dave 
Published: April 2024

-Ladies and gentlemen,
-Rector of Bina Nusanatara University, Dr. Nelly, S.Kom., M.M., CSCA
-Vice Rector of Research and Technology Transfer, Prof. Dr. Juneman Abraham, S.Psi, M.Psi, CIRR
-Distinguished keynote speakers:
Prof. Pablo La Roche, Cal Poly Pomona University
Prof. Ir. Dr Mohd Khairol Anuar Bin Mohd Ariffin, Universiti Putra Malaysia
Prof. Dr. Ir. Oktovian Berty Alexander Sompie M.Eng, Sam Ratulangi University (UNSRAT), Indonesia
Prof. Julian Cox, University of South Wales, Australia
-Presenters, and all participants.
With a thankful heart, it is my pleasure to welcome you to the 7th International Conference on Eco Engineering Development (7th ICEED 2023). I would like to express my greatest appreciation to your participation, as well as your contribution to the 7th ICEED 2023. As the seventh iteration of ICEED that is organized by Faculty of Engineering, Bina Nusantara University, this year’s ICEED bears the theme of “Eco Engineering Development to Achieve Sustainable Development Goals”. This issue is critical because United Nations targeted the fulfillment of Sustainable Development Goals (SDGs) in 2030. Bina Nusantara University supports completely all actions to achieve the SDGs, where one of them is this 7th ICEED 2023.
List of Organizing Committee are available in this pdf.