Ebook: Electronic Engineering and Informatics

The 5th International Conference on Electronic Engineering and Informatics (ICEEI 2023) took place as a hybrid event from 23 to 25 June 2023 in Wuhan, China, attracting about 150 delegates from around the world.

ICEEI 2023 brought together leading academics, scientists, researchers and practitioners to present and discuss recent innovations, trends, and concerns in the domain of interest, together with practical challenges encountered and solutions adopted around the world. The conference also gave delegates the opportunity to share their experiences and research results and exchange views on all aspects of electronic engineering and informatics.

ICEEI 2023 was the premier interdisciplinary platform for the presentation of new advances and research results in relevant fields. Prospective authors were encouraged to contribute to and help shape the conference by submitting research abstracts, papers, posters and high-quality research contributions describing the original and unpublished results of conceptual, constructive, empirical, experimental or theoretical work in all areas of electronic engineering and informatics, and speakers were invited to present these at the conference.

Thanks to the participation of outstanding delegates from all over the globe, the meeting gave us a comprehensive overview of this fascinating field and of future scenarios. Associate Professor Ghamgeen Izat Rashed from Wuhan University, China presented his report during the keynote speech session, and interacted proactively with the participants on questions raised, and the sharing of knowledge and experience sparked heated discussion during the Conference.

Last but not least, we would like to express our sincere thanks to all the plenary and invited speakers, the members of the Conference General Chair, the Technical Program Committee, and the Organizing Committee for the success of the conference which has given rise to this volume of selected papers. Special thanks also to the members of IOS Press for publishing these proceedings.

The Committee of ICEEI 2023

Note about the Editor

Ghamgeen Izat Rashed is an Associate Professor at Wuhan University, China. His research domains include AI application in power systems, Smart Grid, reliability of power systems, FACTS devices, renewable energy, etc. He has published more than 50 SCI and EI papers in core journals and international conferences, participated in more than 30 high-level international academic conferences and delivered keynote speeches.

The classical electric field theory does not indicate the effect of temperature on the proton and electron electric fields or explain the differences between the proton and electron electric fields. The E_pe value determines the radius r and the electron’s velocity Ve of the hydrogen atom and the electromagnetic energy photon ( E_e × E_p) emitted by the hydrogen atom. Bohr’s theory of the hydrogen atom model and Planck’s theory of hydrogen atom binding energy are apparently flawed. These theories do not consider that energy is a key factor in generating electric fields. According to research, the E_p and E_e data indicate that materials in an electric field have mass. The energy particle ε is the basic unit of electric field material. The mass is 2.8 × 10^-78 (kg.ε^-1). The light (E_e × E_p) emitted by hydrogen atoms also has mass.

With the progress of science and technology, the microwave oven has been widely used for heating and thawing, but there are still uneven heating conditions in microwave heating at present. This paper designs two-dimensional and three-dimensional heating simulation experiments, discusses the effect of the rotation angle of the rotating body on the electric field distribution in the cavity in the two-dimensional simulation, and verifies it in the three-dimensional simulation. The results show that when the cross-rotary body rotates at 22.5 deg, the magnetic field distribution is more uniform, the heating average value is the highest, and the standard deviation is the lowest.

Technical indicator factors can quickly reflect the transformation of current market behavior. The application system in quantitative trading has become increasingly mature in recent years. Back testing is widely used in factor validity tests because of its validity. However, the current research on the effectiveness of technical indicator factors has ignored the adaptability to the model timing strategy, and the use of factors is not differentiated enough. How to carry out reasonable and effective factor validity research has become a difficult problem for many scholars to discuss. This paper first selected representative technical indicators as the research object and crawled the trading data of Chinese A-share listed companies through Python. It then calculated the sample data using computer databases such as Pandas and NumPy. Furthermore, this paper confirms the optimal interval of each factor with the method of back testing. On this basis, it tests the income distribution of each factor and the maximum pullback. It introduces the timing method of the simple moving average for comparison and discusses the feasibility of using a technical indicator strategy to conduct stock selection trading.

GaN possesses numerous exceptional properties, such as wide bandgap and high thermal conductivity, which make it an ideal material for fabricating semiconductor devices. The high electric field and current in the channel, coupled with the high power density, make it inevitable that HEMT devices will generate a great deal of heat. Thus, an increase in temperature will unavoidably cause the DC and microwave characteristics of the device to deteriorate. At present, the fourth-generation semiconductor materials with new ultra-broad or ultra-narrow band gaps, mainly Ga₂O₃, GaSb, diamond and AIN, have emerged. In conventional situations, the substrate material will be Si, Sapphire, or the more advanced third-generation material SiC compared to the former, and the passivation layer will use SiN as the material. Our research is in progress to replace the traditional SiN and third-generation SiC with diamond and GaSb.

Peek is a special polymer material known for its exceptional mechanical properties, stable chemical properties, high temperature resistance, self-lubrication, wear resistance, and fatigue resistance. Peek is extensively used in various fields such as aerospace, automotive, electrical and electronic, and medical equipment. It has emerged as the most popular high-performance engineering plastic due to its excellent properties and versatility. Therefore, it is crucial to investigate the impact of process parameters on the quality of peek materials. This research article presents a finite element model that investigates the impact of process parameters on compression moulding density. The model incorporates the compression equation and the Shima-Oyane yield criterion. The model was used to perform finite element simulations of compression moulding with different process parameters. The simulation results show that increasing the compression load within a certain range enhances the top punch’s ability to overcome the interaction and friction between materials, resulting in increased forming density. By extending the holding time and improving the lubricant conditions, it is possible to not only increase the forming density, but also enhance the uniformity of distribution. This study lays the foundation for optimizing pressing process parameters and predicting future moulding results.

Resistive random access memory devices based on the thin film of Graphene Oxide are investigated. Resistive films of different thicknesses are prepared by multi-step spin-coating. Results show that the double spin-coated devices have better performance. The formation of the conductive path and the performance is affected by the thickness of the resistive film. Also, the resistive characteristics of the prepared devices are influenced by the electrodes. Additionally, it is found that the resistive switching process involves the diffusion effect, which has an impact on the device.

This article has been withdrawn from publication at the request of the corresponding author.

In this paper, the effects of environmental factors on snow simulation in civil aircraft ground testing are analyzed and technical support is provided for adaptive testing of civil aircraft in snowfall environments. Snowy weather has a significant impact on the propulsion system, flight control system, and flight performance of civil aircraft. Based on the analysis of natural and artificial snow formation mechanisms and the requirements for snowfall simulation testing, an outdoor simulation system for snowfall in civil aircraft ground testing was established. Tests were conducted to investigate the influence of environmental factors on snowfall simulation. Temperature and humidity ranges were established to achieve snowfall simulation, creating snowfall environments with specific snowfall intensity and density within these ranges. The results in this paper effectively support the adaptive testing of civil aircraft in snowfall environments.

To better improve the productivity of discrete assembly shops and promote the application of intelligent manufacturing technology, the architecture of intelligent manufacturing in discrete assembly shops was put forward, as well as its operation process. The key technologies such as intelligent process design, adaptive assembly and adjustment, and real-time dynamic control were studied. Altogether, the basic composition, operation logic, and optimal control mechanism of an intelligent assembly shop were discussed. Ultimately, the intelligent assembly system of shop level for discrete products was formed. It has some reference value for the development and application of intelligent assembly technology.

There are many limitations in the traditional text-based design method of aircraft power systems, which makes it difficult to meet the challenges of complex product design. In this paper, a civil aircraft airborne system simulation modelling process based on the Arcadia method is proposed, and a modelling and simulation design process such as system analysis and logic analysis is constructed. Through the modelling and simulation application of the power system, it is shown that this method has great advantages in the early R&D and design of the civil aircraft power system.

A multi-strategy switching variability index CFAR (MSVI-CFAR) detector is proposed to further the capability of radar target constant false alarm detection in complex backgrounds. The detector can estimate the clutter background in the reference window. It adaptively selects the optimal detection strategy from the cell-averaging CFAR (CA-CFAR), greatest-of CFAR (GO-CFAR), switching CFAR (S-CFAR), and ordered statistic with cell averaging CFAR (OSCA-CFAR). The results indicate that MSVI-CFAR is beneficial to the detection of SVI-CFAR in the background of uniform background, clutter edge, and multi-target interference and has less CFAR loss and more robust anti-multi-target interference performance.

The allowable value test is an important part of the strength test of civil aircraft interiors. In this article, a certain type of interior connection of civil aircraft is explored, general disposal methods and ideas are sorted out, and the principle of selecting the allowable value test configuration of civil aircraft interior insert and test support and loading schemes is put forward. Taking the allowable value of the sidewall panel insert in the cabin of a certain civilian aircraft as an example, it is demonstrated in the article that the proposed method has certain engineering application significance.

The pulse source technology that can generate high repetition frequency pulse signals is being used in more and more fields, such as electromagnetic interference, biomedicine, precision instrument control, radar detection, etc. Developing pulse signals with high repetition frequency and narrow pulse width parameters is a challenging aspect of this technology. However, in this paper, using avalanche transistors as fast-switching devices helps to address this challenge. The authors have developed a pulse source based on the Marx circuit by optimizing various parameters of the circuit components, improving the circuit layout, and designing impedance matching. This pulse source can generate a negative pulse signal with specific characteristics of a falling edge of 140 ps, an amplitude of 620 V, and a half-peak pulse width of 170 ps when operating under a matched load of 50 Ω. To ensure stable operation and prolonged circuit life, a combination of water cooling and a semiconductor heat sink is utilized for heat dissipation. This allows the pulse source to work reliably at a repetition frequency of 300 kHz for more than 30 minutes.

Flower classification is a crucial task for understanding biodiversity, tracking climate changes, and protecting endangered plants. In this paper, we propose a deep learning approach using a convolutional neural network (CNN) architecture for accurate and efficient flower classification. Our methodology includes preprocessing the dataset, implementing the CNN architecture, and training the model using stochastic gradient descent with cross-entropy loss. Our results demonstrate that our approach achieves an accuracy of 91.73% on the test set, which is comparable to or better than other sophisticated models. Ablation studies reveal the importance of each component of our CNN architecture, while our data preprocessing step improves the model’s generalization performance and prevents overfitting. Our study provides a reliable and effective deep learning approach for flower classification that can be used in various applications, including botany, agriculture, and ecology.

UAV cluster is a powerful tool for the effective execution of complex tasks in the era of intelligence, and the merits of the cluster network topology control method directly affect the performance of the network and the efficiency of cluster task execution. In this paper, a distributed cluster virtual backbone network construction algorithm (DCBNCA) is proposed for the topology control of the UAV cluster network. In this particular approach, the construction of the neighbor set for each node is accomplished through broadcasting instructions. Furthermore, a weight function is designed for network edges, considering physical attributes such as node degree, energy, and bandwidth. The final virtual backbone network is obtained by establishing connectivity based on the weight values within the independent set of the network. This process enables rapid convergence for topology control. Empirical results indicate that the aforementioned algorithm effectively manages the topology of UAV cluster networks, exhibiting significant performance improvements when compared to the original algorithm.

Foreign objects on power lines especially on high-voltage power lines can threaten transmission safety. Therefore inspection of power lines is very important. In the inspection process, people often face low-illumination situations, which could cause great difficulties in the inspection. In this paper, I conducted this study to use the improved Retinex based on HSV color space for image enhancement and then use the improved YOLOv5 with SimAM attention mechanisms for power line foreign object detection. Evaluation of the algorithm through multiple datasets shows that the algorithm can effectively detect power line foreign objects and has application value for power line inspection tasks.

In this paper, the stability analysis and control of cavity flow in the field of aircraft design are carried out. By solving the time-averaged flow field and combining it with the linear instability theory, the eigenmodes and the instability growth rate of cavity flow are studied. The results show that there are multiple unstable modes in the flow before the control is applied, and the flow is passively controlled by introducing the plate configuration, By changing the velocity profile in the shear layer, the unstable eigen mode tends to be stable, and the flow field is well controlled.

The gradient magnetic field has been widely used in biomedicine and other fields. Electrical coils are often used in engineering to generate the required magnetic field. In practical application, the uniformity of the magnetic gradient coil has a great influence on the accuracy of measuring point positioning of the magnetic sensor. In this paper, the gradient uniformity is taken as the optimization goal, and the sequential quadratic programming (SQP) algorithm is creatively used to study the coil design and optimization method. Firstly, based on the design of the standard Helmholtz coil configuration, the parameters to be optimized are determined. The objective function of the magnetic field gradient uniformity is then established, and the objective function is iteratively optimized by the SQP optimization algorithm. Numerical simulation results show that the gradient uniformity of the magnetic field is greatly improved by optimizing the coil configuration.

The rise of deep learning technology has significantly improved the recognition rate of voiceprint recognition technology, such as the success of the X-vector architecture, which utilizes Time Delay Neural Networks (TDNN) to transform variable-length speech segments into fixed-length outputs. However, the current popular voiceprint recognition models have significantly decreased applicability in noisy environments. To address this issue, this study investigates the limitations of the X-vector architecture and proposes an improved speaker verification model based on TDNN. This model incorporates Long Short-Term Memory (LSTM) to model the input speech features while retaining information related to previous time steps. Similar to the ECAPA-TDNN model, we introduce a one-dimensional Res2Net module with a channel attention mechanism (SE-Res2Block) at the frame level, which enhances channel correlation and rescales channels based on recorded global properties, thereby extending the temporal context of the frame layer. Finally, the model’s feature representation capacity is enhanced through multi-layer aggregation. The results show that the recognition performance of this system reaches 96.32% in a 15 dB noise environment. Furthermore, this system outperforms the commonly used ECAPA TDNN model, demonstrating good accuracy and robustness.

In view of the problems such as high labor intensity, low processing efficiency and great potential danger during the baking process of Xinjiang Naan, an electric tunnel-type Naan baking stove was designed. By the COMSOL software, the thermal simulation of tunnel-type Naan baking stove drawn by SolidWorks was made, so as to find out the heat distribution, heat source loss position and the Naan sizes suitable for processing in Naan baking stoves. In the workshop, a baking experiment was made on the prototype in order to realize the baking of Naan. The simulation results show that the heat dissipation at the inlet and outlet of the baking oven is serious, and the large distance between the heating rods is not suitable for heating Naan. The test results show that the baking stove has good baking effect and stable baking performance under the test conditions of determining the transmission speed of 6m/s and the heating tube temperature of 260 °C by adjusting various horizontal factors and parameters, which can achieve the expected design objectives and has certain value for popularization and application.

Aiming at the problems of complex structure and lack of feature information of skin lesions, an image segmentation method based on attention mechanism and double coding network is proposed. Firstly, the dual-coded branch network is used to extract image feature information to improve the ability of network information capture. Secondly, the dual attention module is used to encode the global context information, which suppresses irrelevant features and highlights relevant features. Finally, in the coding part, depth separable convolution and cooperative attention are used to reconstruct the image. Experimental results show that on ISIC2018 data set, the accuracy, Dice similarity coefficient and Jaccard index of this method reach 96.59%, 92.98% and 82.65%, respectively. Compared with other networks, the accuracy and boundary segmentation effect are obviously improved.

Eye health is essential to national health and involves all age groups throughout the lifespan. Visual impairment seriously affects people’s physical and mental health and quality of life, increases the burden on families and society, and is a public health and social problem involving people’s well-being. Our method of combining acupuncture and physiotherapy to stimulate acupoints was found to relieve eye muscle fatigue and reduce eye tendon contracture and blood stasis. It also causes bio-stimulation within the tissues and improves local blood circulation. On this basis, using a laser instead of a silver needle was improved to stimulate the acupoints. The laser is low-intensity laser irradiation through the laser beam deep inside the tissue, which can play the proper bio-thermal effect and does not damage the body’s normal biological tissues.

In response to the demand for low-cost 3D heterogeneous integrated RF front-end and phase-controlled arrays, an integration Ka-band phased array RF front-end module based on 3D-integrated packaging is proposed in this paper. It can realize the miniaturization of the RF front-end system, greatly reduce the volume and weight of the radar system, and effectively control the total cost. The proposed module consists of final power amplification, drive amplification, low noise reception, phase shift, and attenuation control. The electronic scan range of this module is greater than 15° within the 32-38 GHz frequency passband.