bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study on Professional Identity of Teachers of Chinese International Education
Authors: Wang Jingyan
Published: July 2023

With the continuous development of the globalization of the Chinese language craze, the profession of teaching Chinese as a foreign language is more and more well-known. As Chinese teachers who teach in a multicultural environment, their professional identity has gradually become a topic of concern. This paper takes full-time teachers of Chinese international education as the research object. With the help of existing survey data in the academic field, this paper makes an in-depth discussion and interpretation of their development status. It found that the professional identity of teachers of Chinese international education is in a relatively negative state in all dimensions. Based on the current situation, the paper gives a reasonable analysis of the reasons, and believes that social evaluation, salary level, working environment, career development path and other aspects have a negative impact on the professional identity of teachers of Chinese international education. Based on the reasons, the paper also gives suggestions on feasible measures from the government, society and other aspects, so as to provide a reasonable path for the future career construction of teachers of Chinese international education.
Factors Affecting the Educational Achievement of Chinese College Students
Authors: Wang Junhua
Published: July 2023

China has achieved tremendous economic success over the past three decades. Meanwhile, privatisation and marketisation policies and corresponding implementations also greatly legitimise educational inequality in higher education, namely College Entrance Examinations (CEE), the extra-score policy and the recommendations system. This article comprehensively investigates the causes behind educational underachievement in China’s higher education. This article aims to understand the relationship between social stratification and educational inequality. It surveyed 120 Chinese undergraduate students through an online questionnaire with 15 questions related to the students’ basic information. The results show that urban-rural distinction in household registration and annual income, housing conditions, educational expectations and teacher professionalism are the main factors affecting educational achievement and enrolment in universities. The above issues are worthy of consideration in education policymaking, and the widening gap between advantaged and disadvantaged groups of students still exists. In general, middle-class, and urban students benefit the most in their educational experiences.
Application and Research of Prediction Model Based on ARIMA-LSTM and Multi-Sequence LSTM Algorithm - Taking Wordle as an Example
Authors: Li Zhengze , Zhang Hui
Published: July 2023

The article aims to explore the relationship between the changing trends in the number of Wordle players and the distribution of players' attempts at the game based on word and time attributes. This paper employs wavelet analysis and ARIMA-LSTM model to predict the number of players, and uses canonical correlation analysis and multi-sequence LSTM model to respectively determine the correlation and classification results of word attributes and player attempt distribution. The results indicate that on March 1st, 2023, the number of Wordle players is 16,352. If the solution word on March 1st is 'EERIE', the distribution of player attempts will be [0,8,19,25,23,17,8].
Image enhancement method based on multi algorithm fusion
Authors: Gao Xinyu
Published: July 2023

This research suggests an image enhancement method based on multi algorithm fusion. The illumination component and reflection component are obtained by morphological closure. On the one hand, multiple inputs of the estimated illumination and the corresponding weights are fused by multi-scale fusion technology. On the other hand, space conversion technology is used. Two adjusted images are obtained based on multi-scale Gaussian filter and adaptive brightness enhancement algorithm. Then details are extracted from these two images through PCA fusion strategy. The reflection component is improved significantly after above operations. Finally, combine the illumination and reflection components. Compared with the classical algorithm, our method can simultaneously achieve brightness improvement, naturalness maintenance and contrast enhancement.
Letter2vec+Freq2vec: A Comprehensive Approach to Text Vectorization and Predicting Word Difficulty
Authors: Li Songru , Cheng Yuan , Lu Hanbin
Published: July 2023

This paper introduces the self-developed word embedded algorithm Letter2vec+Freq2vec. It combines with biKmeans and Stacking algorithm to judge word difficulty in Wordle. The algorithm combines Letter2vec, which models letter interactions and context, and Freq2vec, which calculates letter frequencies at different positions of words to produce a more comprehensive text vectorization method. The results show that this algorithm can get better results than BERT algorithm in small sample size when vectorizing a single word. Overall, this paper provides insights into the development and application of new algorithms for text vectorization and the  prediction of word difficulty.
Word difficulty classification prediction based on ARIMA and DBSCAN
Authors: Li Xilun , Sun Lingyu , Li Jigang
Published: July 2023

Wordle is an online word-guessing game, where players are given a five-letter word each day and are asked to guess the word within six tries. Since the launch of the game, it has been loved by a wide range of players. To classify the experiment data and point the ‘EERIE’ at an appropriate label, the article uses unsupervised learning (with DBSCAN and ARIMA), cluster “sum(x)” and “rank(y)” and obtain the values (5.18185152, 8.83924243). Meanwhile, according to the image ‘The word positions by DBSCAN’, we define the ‘EERIE’ in moderately easy.
Research on the Prediction of the Number of Nuclear Weapons in the World in the Future Based on NAR Neural Network Model
Authors: Niu Xiaomin , Niu Xiaoqing
Published: July 2023

The issues connected with nuclear weapons are closely related to the survival of mankind. Nuclear weapons pose a great threat to world security, human health, the Earth's environment, and other aspects. This paper conducted an investigation and mathematical analysis on the research and development of nuclear weapons in each country, its stockpiles, the number of nuclear tests and the attitude of each country towards nuclear weapons research and development. In order to predict the number of nuclear-weapon countries in the next 100 years, this study can use the number of nuclear-weapon countries in 1938 to 2022 and the number of nuclear weapons in each country in the inventory table to establish a simple sequence prediction neural network, and use the prediction method of NAR neural network model to finally get the change trend of the number of nuclear-weapon countries and the number of nuclear weapons.Finally, it was concluded that the countries with nuclear weapons in 100 years would still be the nine countries with nuclear weapons today, and the total number of nuclear weapons dropped to 8,787. China, France, India, Israel, North Korea, Pakistan, Russia, the United Kingdom, and the United States respectively have 312, 271, 135, 74, 16, 151, 3,745, 161, and 3,922 nuclear weapons in 2123.
Design of information security transmission scheme and related authentication services based on Kerberos
Authors: Du Puyuan , Yan Xinlong , Feng Peiyao
Published: July 2023

A plethora of cloud service companies have sprung up in response to the growing amount of data. Data security protection solutions have been proposed by service providers. As a result, a rising number of people are using the cloud to store their own data. However, as technology advances and hackers' capabilities increase, service providers' protection mechanisms become less effective, and data security cannot be guaranteed. We review the logic of the Kerberos algorithm and discuss the work of each part in this paper. This document also includes and discusses difficulties with Kerberos, as well as improvement strategies.The main focus will be on strengthening the security of the Kerberos secure communication process, which will serve as a reference for Kerberos security transmission and authentication research. It solves the problem of the symmetric encryption algorithm's relatively low security in the authentication process by replacing it with the RSA one-time encryption algorithm; in addition, the Kerberos authentication protocol is added between the client and the cloud service provider to secure the data transmission process. Furthermore, this paper explains some basic authentication principles.
Short-term wind speed prediction based on combinatorial prediction model
Authors: Li Mingguang , Huang Runyi , Yang Yumiao
Published: July 2023

Improving the accuracy of wind speed forecast can increase wind power generation and better achieve wind energy grid connection. Therefore, a two-stage wind speed prediction model based on Ensemble Empirical Modal Decomposition (EEMD) and the combination prediction of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), eXtreme Gradient Boosting (XGBOOST), Gate Recurrent Unit (GRU),  Temporal Convolutional Network (TCN) is proposed. First, the original wind speed series is separated into Intrinsic Mode Functions (IMFs) using EEMD. Then, RNN, LSTM, XGBOOST, GRU, TCN multiple prediction models are established to learn features from each subsequence and superimpose the prediction results of subsequences. Finally, Particle Swarm Optimization (PSO) is applied to the results of multiple prediction models to assign weights, combined with weight superimposing sequences to achieve higher accuracy and more robust wind speed prediction. Simulation analysis using data from St. Thomas, Virgin Islands wind measurement station to validate the validity of the combined prediction model. The experimental simulation results show that the model proposed in this paper has a good result on increasing wind speed prediction accuracy.
Extreme rainfall forecasting based on wavelet analysis and M-K test
Authors: Wang Zijian
Published: July 2023

In recent years, many places in China have encountered extremely rare heavy rainfall weather, and the north has also encountered rare blizzard weather, which poses a serious threat to the personal and property safety of local people. On July 20th, 2021 , with the maximum hourly rainfall reaching 201.9 mm, Zhengzhou suffered the extremely heavy rainfall once in a thousand years. The 7.20 heavy rain in Zhengzhou caused many communities and roads in Zhengzhou to be flooded by the rain and thus drew attention to and recognized the extraordinary precipitation. In this paper, a correlation analysis is made on the interannual variation of precipitation characteristics in Zhengzhou area, and concrete quantitative analysis is made on flood events; In addition, the characteristics and losses of precipitation in Shaanxi and Zhengzhou are compared Finally, put forward the long-term planning and construction of cities under the condition of extreme heavy rainfall in the future.
Preface: 2nd International Conference on Image Processing and Intelligent Information Systems (IPIIS 2023)
Authors: Macaulay Geoffrey , Feng Jin
Published: July 2023

The 2023 2nd International Conference on Image Processing and Intelligent Information Systems (IPIIS 2023) was successfully held during June 17-18, 2023 in Zhengzhou, China. In the context of the conference, scholars, and experts in the fields of image processing and intelligent information systems, involved both in experimental and theoretical research, were invited from various universities and institutions.

The main goal of IPIIS 2023 was to present the related research work, to promote the cooperation of experts and scholars, to share the research achievements both on a national and international level. This collection of proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the editorial committee. Due to the large number of attendees, the conference was separated into main and parallel sessions. Each plenary speaker gave an oral presentation of 30-40 minutes accompanied by his slides' material, allocated for questions and discussion.

The organizing and editorial committees of IPIIS 2023 hope you can enjoy the reading of this volume. Additionally, we want to thank all authors and participants for providing their valuable contributions for these proceedings, as well as the reviewers for their constructive recommendations and feedback aiding to improve the presented articles.

IPIIS Organizing Committee

Zhengzhou, China
The Derivation and Some Explanations of the Uncertainty Principle
Authors: Huang Haoran
Published: July 2023

This paper is a small discussion about the uncertainty principle. With the great discoveries and researches made by predecessors, I try to answer some questions within the uncertainty principle. Using the definition of momentum, the momentum in de Broglie wave, the property of cosine function, the Euler’s formula, the basic property of plane wave, the law of derivative, the energy of quantum discovered by Max Planck, the photon energy discovered by Albert Einstein, the energy equation of a system and the Hamiltonian operator, I figure out the Schrodinger equation successfully. With the help of Schrodinger equation, the possibility density of a wave function, the concept of normalization, the average value of position and momentum, the Fourier transform, the integration by parts and the limit of normalized wave functions, I find the position operator and the momentum operator. Then using some knowledge of the standard deviation and the variance, the concept of inner product of vectors, some property of Dirac symbol, the law of calculation of complex number and the law of operator calculation, I obtain the general uncertainty relationship of abstract mathematical quantities. According to the general relationship, I get the inequality of uncertainty principle, and discuss the specific relationship between the position and the momentum. After getting the inequality, I use Heisenberg’s experiment of measuring an electron by using the γ ray to hit the electron to explain why we cannot measure the position and the momentum simultaneously. Based on Heisenberg’s experiment, I give my own explanation by discussing the earth and a spacecraft. Transforming the perspective, we can easily see how the accuracies of position and momentum change. In addition, I attempt to figure out why the uncertainty exists. However, I can only explain it by discussing the time and frequency in mathematical method and have no idea to work it out in a pure physics approach.
Uncertainty principle and complementary variables
Authors: Cao Zhaozhong
Published: July 2023

The two most important areas in modern physics are quantum mechanics and the theory of relativity. Unlike classical physics, where Newton's mechanics dominates, these two areas change human beings' fundamental view of the universe. One of the theories that build up the base of quantum mechanics is Heisenberg's Uncertainty Principle. Starting from a thought experiment, Heisenberg's microscope in the setting of classical physics, Werner Heisenberg built a bridge between classical and quantum physics by presenting a counterintuitive outcome in the thought experiment. Since then, the observer of a physics phenomenon is no longer a bystander. The behavior of observation became a part of the physical experiment. To come up with a mathematical expression that can describe such a new discovery, Heisenberg came up with matrix mechanics and the concept of complementary variables. There is a trade-off between a pair of complementary variables. When one of them is measured precisely, meaning the information of that variable is known on a large scale, the other variable can not be measured precisely, meaning there is no way to know enough information about the other variable. The principle indicates a fundamental limit on what human beings can know about the unknown variables. The discoveries of other complementary variables help physicists know the new image of the physics world under new rules.
Optimization of credit scorecard combinations based on quantum annealing algorithm
Authors: He Bingze , Ji Xingyi , Lv Jiaxin
Published: July 2023

With the development of the times, the usage of loan-related business is increasing. However, most of the credit score card portfolio methods used nowadays are manually designed and supplemented with computer calculations, so the bank yields are low. Therefore, it is of great interest to study the application of quantum computers in credit score card portfolio optimization for better rating of customer credit ratings. Based on the given credit score card data, a linear programming model is developed. Firstly, the data is processed into two sets of one-dimensional data by Python and the working range of the two sets of data is found. Secondly, after deriving the objective function expressions according to the given conditions, the data were visualized by building a 3D grid matrix with the pass rate and bad debt rate as the XOY plane and the final benefit to the bank as the Z axis. Subsequently, the decision variables in the linear programming model are converted into binary variables, and the original model is transformed into a QUBO model by converting the constraints into binary variables. After analyzing the QUBO model, the quantum annealing method is chosen to solve the optimal solution of the objective function. And based on this, the case of three selected credit score card combinations and any three credit score cards are considered.
Finite Square Well and its Applications
Authors: Wang Yifan
Published: July 2023

The relation between finite square well and laser is paramount in quantum mechanics. This paper focuses on establishing the relationship between finite square well and laser through the most fundamental theory of quantum mechanics. This paper first introduced the importance of finite square well and the Kronig-Penney model in the development of the theory of laser and Quantum mechanics briefly. Then, this paper gives a detailed derivation of energy level finite square well through the Schrodinger equation. In the application section, we first introduced Bloch’s Law, a vital theorem for the following subsection. It describes the form of wave function under periodic potential. Then, the energy level of the finite square well is put into the application of the Kronig-Penney model, which is an infinite repetition of the finite square well. This part focus on the derivation of the energy level of the Kronig-Penney model. It applies both the energy level of the finite square well and Bloch’s Law. In the end, we note that the energy level of the Kronig-Penny model exhibits a band gap. It is an important phenomenon for lasers.
Research on the Investment Value of National Economy Stocks based on Factor Analysis
Authors: Li Wei
Published: July 2023

Scientific and technological innovation is a hot topic today, and companies on the STAR Board also have good performance and development prospects in the market. Many investors regard STAR Board stocks as key investment modules. This paper analyzes the factors influencing the investment value of the stocks of STAR Board enterprises listed from June to December 2021 in the first half of 2022. In this paper, nine indicators are selected to describe the market condition of each enterprise and the factors are narrowed down to four principal factors by factor analysis. Combined with the actual data analysis of the performance of each STAR Board enterprises in these four principal factors, score it to analyze the advantages and weaknesses of the corresponding enterprises of each stock, so as to provide investment reference for investors with investment needs. The dimension reduction and transformation function in SPSS are mainly utilized in entire research.
Modeling User Behavior Online: A Comparison of Pareto/NBD and BG/NBD Models
Authors: Ding Haochen
Published: July 2023

This study employs user activity data from Steam, which is the largest PC gaming website globally, to compare the effectiveness of two commonly used probabilistic models, the Pareto/NBD and BG/NBD. The primary aim of the research is to evaluate online search engines and assess the vitality of online communities by developing models that simulate customer behavior and calculate the likelihood of customer engagement. The study examines the assumptions and benefits of both models and their limitations. To collect data, user activity data for a six-month period, from January to June 2022, was collected, including user ID, game title, behavior name, and value. The study focuses on the behaviors of "Purchase" and "play," and the value represents the intensity of each action. The research then evaluates the effectiveness of the two models in predicting user behavior, highlighting their strengths and weaknesses. The study concludes by recommending modifications to the BG/NBD model to enhance its accuracy and overcome its limitations. In summary, the study provides valuable insights into the potential applications of the two models and their strengths and limitations in predicting user behavior in online communities.
Prediction Of Medical Insurance Cost Through Linear Regression Model
Authors: Cao Chengwei
Published: July 2023

Nowadays health care problem has become an increasingly important topic, which means that more and more people choose to pay for health care insurance. This article is mainly about constructing a model which is used to predict a person’s health care insurance cost as a reason that it is usually hard to find the total amount of cost from people on health care insurance area. The author found a data set about health care insurance cost and used r to analyze it. In the end, 2 linear regression models which seem to fit the data set are built to predict the cost of heal care insurance. After the comparison of these 2 models, a better one is chosen. The model has also been checked to see if it is accurate and proper. The particular significance of this study is that it builds a model which can predict hard-to-collect data from some data which is easy to collect like BMI and age.
A Systematic Analysis of The UART Transceiver Theory and Application
Authors: Zhang Hang
Published: July 2023

This article introduces the theoretical foundation and application of Universal Asynchronous Receiver/Transmitter (UART) in four chapters, including the history and structure of UART transceivers, and so on. UART is a kind of communication hardware using serial Asynchronous communication, which has the advantages of simple structure and good stability. It can convert serial data and parallel data to each other and transmit data between devices. It is widely used on various devices. In the 50 years since its invention, it has undergone many improvements and improvements. In the end, UART transceivers became one of the extremely important hardware in modern electronic technology. At present, UART transceivers are applied in many fields, including healthcare, the Internet of Things, and avionics. Its application in the fields of healthcare and the Internet of Things is of great significance, providing more possibilities for a more convenient life for humanity. Meanwhile, researchers are also attempting to continue improving the transmission speed of UART transceivers and reducing their power consumption.
Analysis of the baud rate of the UART to affects the data
Authors: Huang Yichen , Lin Hao , Zhang Di
Published: July 2023

This article explores the impact of different baud rates on Universal Asynchronous Receiver/Transmitter (UART) asynchronous serial communication. It discusses the frame format and calculation method of baud rate error, as well as the main reasons for error, such as clock error, sampling error, line noise, data bit error, and software delay. To ensure reliable communication, it is essential to test and debug the actual baud rate and adjust it accordingly. The study found that higher expected baud rates resulted in higher baud rate errors, affecting transmission distance, speed, reliability, and system complexity. The appropriate baud rate should be selected based on specific application scenarios and requirements. Higher baud rates are needed for high-speed transmissions, while lower baud rates are required for long transmission distances or high reliability requirements. However, further research is needed to determine the impact of unallocated baud rates on data. Overall, this article provides valuable insights into the development of UART.
Protostellar Outflows
Authors: Lin Shuping
Published: July 2023

The formation of massive stars differs from low-mass stars due to their rapid evolution, relatively low abundance, and their burial within molecular clouds, making observations more challenging. Moreover, the mechanisms give rise to the formation of O-type and B-type stars, in particular, differ from those responsible for the formation of low-mass stars. The paper presents an overview of the two main theories that have been proposed to explain the formation of massive stars: accretion and collision theories. The paper also investigates several mainstream theories of protostellar outflows during the massive star formation phase, and describe the model and their simulation of these theories. These results may provide useful references and guidance for future studies on the formation of massive stars.
A study on the prediction of user participation and game outcome of Wordle guessing game based on Arima time series and BERT neural network model
Authors: Wang Yuanhang
Published: July 2023

Wordle is a popular daily puzzle game in which players try to guess a five-letter word six times or less. This report develops predictive models for the distribution of reported outcomes and the number of reported outcomes reported. The data were first preprocessed, and then an ARIMA(1,1) prediction model was developed by ADF testing and plotting ACF and PACF plots, and calculated that there would be 7,260 reported outcomes on March 1, 2023. In addition, two attributes were extracted, namely the number of repeated letters and the information entropy. Through Pearson correlation analysis, the report investigates the relationship between the attributes and the variance of the scores. It can be seen that entropy is significantly negatively correlated with variance and the number of repeated letters is significantly positively correlated. The report uses the One-hot method to encode words, applies the BERT neural network architecture, and uses the SGD algorithm for optimization to predict the distribution of the reported results. For the word EERIE, the calculated percentages were 1.16%, 5.75%, 20.68%, 32.60%, 24.72%, 12.39%, and 2.70%. The calculated MSE errors indicate the high confidence level of the model. The study provides insight into user behavior and improves game design.
A Time Series Data Regression Analysis based on B-spline Basis Expansion and Distance Covariance Weighted Stacking Framework
Authors: Yan Chenge , Zhu Ziwen , Hong Yuning
Published: July 2023

Aiming at the problem of time series data regression prediction, this paper proposed a methodology for time series prediction based on data augmentation and cosine similarity weighted model average in the case where the predictor and response variable is a time series and the continuous scalar respectively. The constructed method deals with the problems of high dimension and noise of time series through B-spline basis expansion in which Blending algorithm was used to enhance the correlation information between B-spline basis coefficients and response variables, so as to further reduce the influence of noise on prediction models. Next, the cosine similarity model average is used to capture the unknown latent model structure between characteristic and response variables to improve the prediction accuracy of the model. The proposed method can effectively balance the bias and variance of the prediction model. In addition, the regression method in the technique is model-free. The analysis of actual data shows that the proposed method has certain advantages compared with those existing. Eventually, the method can be extended to forecasting applications in the fields of stock price prediction, social science, medicine and so on.
Deep Learning Based Player Identification Via Behavioral Characteristics
Authors: Mai Yunhao
Published: July 2023

Behavioral recognition in game is a fundamental topic in data analysis. With the emerging of deep learning-based method, more and more artificial neural networks are proposed to achieve higher accuracy and robustness. This paper presents a comparison between CNN and RNN-LSTM for player identification based on player behavioral characteristics in a simple game environment. The goal is to detect potential manual cheating in competitions by recognizing unidentified players (cheaters) from their behavioral patterns. We implement a basic game, record player behaviors, and then compare the accuracy of outputs from the CNN and RNN-LSTM models. The conclusion is summarized as follow. CNN obtains 87.5% accuracy and RNN-LSTM achieves 92.3% accuracy in the simulated data. Our results indicate that the RNN-LSTM model outperforms the CNN model in terms of accuracy, making it a more suitable choice for player identification in this contextt.
Preface: 2nd International Forum on Mathematical Statistics, Physical Sciences and Telecommunication System (IFMPT 2023)
Authors: Hobson Montague , Lin Meiqi
Published: July 2023

We are glad to introduce you that the 2023 2nd International Forum on Mathematical Statistics, Physical Sciences and Telecommunication System (IFMPT 2023) was successfully held on June 24-25, 2023 in Guangzhou, China. This forum aims to bring together academic scientists, leading engineers, industry researchers, and scholar students to exchange and share their experiences and research results about all aspects of mathematical statistics, mathematical model, physical sciences, quantum mechanics, astrophysics, 5G networking, and telecommunication system. This forum also discusses the practical challenges encountered and the solutions adopted.

This scientific event brings together more than 50 national and international researchers. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 8-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-40 minutes to hold their speeches.

Lastly, we would like to express our sincere gratitude to the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.

The Committee of IFMPT 2023

Guangzhou, China
Applications of Embedded Systems in Medicine: Challenges and Future Trends
Authors: Zhao Hanlin
Published: July 2023

With the advancement of science and technology, embedded systems play an essential role in the medical field. They have become an integral part of medical equipment, enabling functions such as patient monitoring, diagnosis, and treatment. Embedded systems also improve the work efficiency and accuracy of medical staff. This paper summarizes and reviews the recent applications of embedded systems in medicine. Firstly, it discusses the advantages and challenges of embedded system applications in medicine. Then, it discusses the development prospects and directions of embedded systems in the medical field. Through analysis, it is found that the application of embedded systems in the medical field is developing towards intelligence, miniaturization, and personalization. In the future, embedded systems will become more significant in the medical field, providing convenience to patients with different medical needs and bringing more innovations and changes to the medical field.
Prevent Cardiovascular Disease to Detection Health Status by Design and Measurement of an Electrocardiogram Circuit
Authors: Wu Zheyu
Published: July 2023

Cardiovascular disease, especially myocardial infarction, is a disease that has gained increasing concern in today's more health-conscious society. Usually, these disorders cause abnormal heart beats. The collection or monitoring of heartbeat behavior is good way to judge people's current heart‘s health status. For getting this kind signal, the application of ECG is extremely important. The manufacture of ECG relies on electronic engineering. The electrical signal produced by the heart is very small. Acquiring tiny signals is always difficult. The reason of difficult is that there is error in the actual collection, and when the input signal is small, the error is relatively large. So the function that the ECG needs to have is to be able to accurately measure and optimize the signal, and in the final obtain a clear and obvious electrocardiogram by optimizing the collected signals. The principle of used on making an ECG is to complete a circuit that can correctly process the input signal. This includes using amplifiers and filters to transfer the signal to the output signal as a waveform. Based on such a combination of the circuit and some components, a fully functional ECG is successfully fabricated. By recording and analyzing the collected ECG, it is possible to discover and treat these kinds of diseases in the human body.
Hardware Implementation for Convolutional Neural Networks in Artificial Intelligence
Authors: Chen Hongxi
Published: July 2023

Artificial Intelligence (AI) has brought great convenience and help to human society by improving efficiency, increasing productivity and reducing cost. As a part of deep learning, convolutional neural networks (CNNs) have been widely concerned by researchers in recent years. In this paper, five parts of the CNN structure, including input layer, convolutional layer, pooling layer, fully connected layer, activation function and output layer are going to be elaborated. Besides, four different kinds of hardware, which can be used in AI implementation, including graphics processing unit (GPU), field programmable gate array (FPGA), application-specific integrated circuit (ASIC) and brain-like chips will be discussed in this paper. Based on the different characteristics of these four groups of hardware, this paper will analyze their feasibility to implement artificial intelligence algorithm. After contrasting their cost, flexibility and power consumption, it is concluded that different hardware has different advantages to implement AI under different circumstances. GPU performs better to handle with parallel operations or construct complex network models of AI. FPGA is able to achieve flexible AI model programming. On the other hand, ASIC is preferred considering its low power consumption and cost to implement AI. Although brain-like chip is not as well developed as the other three chips, it is promising to implement AI in the future.
FPGA-Based Machine Learning: Platforms, Applications, Design Considerations, Challenges, and Future Directions
Authors: Zhao Tianrun
Published: July 2023

Field-Programmable Gate Arrays (FPGAs) have emerged as a promising platform for accelerating machine learning tasks due to their high parallelism, low latency, and hardware customization ability. In this paper, the authors provide an overview of popular FPGA platforms for machine learning and compare the tradeoffs among FPGAs, GPUs, and CPUs for machine learning. The authors also present specific applications of machine learning based on FPGAs, including those in autonomous driving and healthcare. Additionally, the paper explores FPGA design considerations, such as architecture, resource utilization, and power consumption. Nonetheless, obstacles persist in the realm of FPGA-based machine learning that require attention. Identifying the ideal balance between adaptability and performance, considering factors such as space, energy usage, and latency, is still challenging. As the capabilities of FPGAs expand, there is a significant need for devices that have a smaller footprint, reduced power consumption, and minimized delays. The paper emphasizes the necessity of ongoing research in the field of FPGA-based machine learning to address these issues and continue enhancing the performance and effectiveness of machine learning systems.
Machine manufacturing process improvement and optimisation
Authors: Wang Yaoyao , Ma Suchang , Wang Jun , Wang Haifeng , Pu Wenying
Published: July 2023

The improvement and optimisation of machine manufacturing processes is crucial to the development of modern manufacturing. By improving processes, introducing advanced technologies and increasing production efficiency, companies can achieve lower production costs, higher quality products and shorter production times. This issue of the journal focuses on practical experience and technological exploration of process improvement and optimisation in machine manufacturing, with the aim of providing readers with the latest research and developments in this field. This article explores the application of automation technology, artificial intelligence and machine learning, data analytics and big data, and 3D printing technology in machine manufacturing process improvement. Through case studies, we will showcase some successful examples of machine manufacturing process improvement across different industries and company sizes. At the same time, we will explore the challenges of machine manufacturing process improvement and propose solutions. Finally, we will look at future trends and developments in machine manufacturing process improvement to provide readers with an in-depth understanding of the field.
A combined model of Hawkes-ICEEMDAN-Catboost-Holt for interval-valued carbon price forecasting
Authors: Wang Muziyan , Zou Qingqing , Shi Qian , Luo Rui
Published: July 2023

Accurate carbon price forecasting is crucial for efficiently operating both carbon trading and financial markets. This study proposes a Hawkes-ICEEMDAN-Catboost-Holt combined interval forecasting model to improve the accuracy of carbon price forecasts. First, the t-SNE-LLE second-order dimensionality reduction was performed on the collected search index data to quantify the attention of netizens. Subsequently, the attention series was converted into an interval series using the Hawkes exponential decay model, and further decomposed into radius and center series. In addition, the radius and center series were decomposed using ICEEMDAN and then reconstructed. Finally, the carbon price interval combined forecast was based on the Catboost–Holt model. The proposed model exhibits high accuracy and strong stability in comparison to other forecast models. The Hawkes exponential decay of netizens’ attention is creatively included in the influence mechanism of carbon price fluctuations, effectively improving forecast accuracy. The interval decomposition and reconstruction methods provide an innovative development path for interval forecasting research. The residual and trend series obtained from interval reconstruction can be forecasted using Catboost and Holt, respectively, thus significantly improving the accuracy and stability of interval forecasts and enhancing the decision-making process for carbon trading and financial markets.
Research And Analysis of Intelligent Construction Platform Technology for High-Rise Buildings
Authors: Luo FaJiang , Li Shiwei , Yu Ziqiang , Meng Zhaohu , Gu Yuke , Han Guangzhao , Yang Rong , Liu Heng , Yan Li , Gao Jinliang , Jiang Xuezhi , Zhang Lidong
Published: July 2023

In recent years, with the rapid development of China's society and the continuous improvement of scientific and technological power, construction technology has been constantly innovated, from the traditional scaffolding to slipform, and then to climbing mold, lifting mold, construction mechanization, standardization and efficiency has been increasing. The high-rise intelligent integrated construction platform came into being as a "mobile factory" in the air, reducing the labor intensity of workers and improving the overall safety and operational efficiency of the construction. This paper starts from the basic principle of intelligent integrated construction platform, combined with Lanzhou Poly-Tianhui project, which is studied and analyzed from the aspects of design, construction and safety assurance, and systematically expounds the technology related to intelligent integrated construction platform of high rise.
Intelligent Handling Robot Based on Mecanum Wheel
Authors: Zhang Chenghanyu , Hu Jie
Published: July 2023

This paper proposes an intelligent handling robot based on Mecanum wheel, which can be set to recognize and handle the goods in the inherently known site, and can be adapted to the logistics sorting and other scenarios. The robot to NXP's RT1064 microcontroller as the main control, the use of RT-Thread embedded system development, the Mecanum wheel as a framework, the independent construction of the car model structure, equipped with a Raspberry Pi and robotic arm structure, as well as the MT9V032 total drilling wind camera to build the hardware circuit. The robot can recognize the position of the handling object through the off-site camera, then the upper computer for path planning, through the PID parameter adjustment and vehicle attitude solving to control the movement of the handling robot, to reach the handling location and then by the total drilling wind camera for detailed position adjustment. The robot equipped with Openart mini can recognize the type of objects to be handled, pick them up and place them on the pallet through robotic arm attitude solving, and correct the deviation through remote manual control, and finally realize fast omni-directional handling through Mecanum wheels to deliver the objects to the end point.
Analysis of underground garage roof slab no-return roof structure in the construction phase travel route area
Authors: Peng Li , Meng Zhaohu , Li Jianning , Han Guangzhao , Ding Luo , Zhang Lidong , Yan Li , Wang Zhigang , Lei Ye , Li Longfu , Pan Li
Published: July 2023

This paper adopts the calculation method of finite element combined with manual analysis to analyze the structural deformation of the underground garage roof slab without back roofing in the construction stage of the traffic route part, and proposes the structural slab reinforcement method combined with the actual construction situation, which shortens the construction period and reduces the cost compared with the traditional full scaffolding back roofing method and has a strong promotion, which can provide the theoretical reference basis for the subsequent works.
PELAKSANAAN WIRID REMAJA SEBAGAI PEMBENTUKAN AKHLAKUL KARIMAH SISWA SMA N 01 KOTA SALAK DHARMASRAYA
Authors: Winanda Tri , Kamal Muhiddinur , Maryani Eka
Published: July 2023

Tempat pelaksanaan wirid remaja ini dilakukan dengan cara menepati masjid atau mushalla dengan pembina masing-masing guru yang mengajar disekolah, yang telah ditunjuk oleh kepala sekolah SMA N 01 Koto Salak yaitu ditempat domisili masing-masing siswa. Dengan hal yang demikian tidak semua masjid atau mushallah yang berada didomisili melaksanakannya dengan maksimal, masih banyak lagi yang tidak bisa datang pada kegiatan wirid remaja terutama bagi siswa. Dari semua segi efektifitas, ditemukan banyak sekali para remaja yang tidak mengikuti kegiatan wirid remaja, dikarenakan akibat pergi main berkeliaran dimalam minggu.. Dampak wirid remaja dan relevannya terhadap pembentukkan akhlak siswa SMA N 01 Koto Salak, didapatkan hasinya bahwa wirid remaja ini memberikan hal-hal yang bersifat positif terhadap akhlak peserta bagi remaja, baik itu akhlak kepada Allah Swt maupun kepada sesama manusia, serta juga agar bisa selalu meningkatkan  keimanan dan ketakwaan para remaja dan pembina kepada Allah Swt.
A New Endemic Species of Milariconus Tucker & Tenorio, 2009 (Gastropoda: Conidae) from Western Australia
Authors: Maxwell Stephen , Berschauer David
Published: July 2023

A new species of Miliariconus from tropical Western Australia, is described based on morphological differences with known taxa from that region. The new species differs from known Australian species in sculptural form and colour. This species further highlights the endemism of much of the new taxa that is being discovered in remote northern Australia.
A Review of Neodilatilabrum Dekkers, 2008 (Mollusca: Neostromboidae: Strombidae) Resulting in a New Species from Japan
Authors: Maxwell Stephen
Published: July 2023

The genus Neodilatilabrum contains taxa that are both morphologically stable across their ranges, while others are highly plastic, making the choice of discerning characteristics critical to achieving taxonomic stability. A new species of Neodilatilabrum from Japan is presented here. Neodilatilabrum tasminae n. sp. lacks the axial shoulder rib on the body whorl found in N. robustum and N. sowerbyorum. The angulate shoulders of N. succinctum differentiate that taxon from N. tasminae. Neodilatilabrum simanoki is placed within the synonymy of N. sowerbyorum, and N. boucheti is placed under the synonymy of N. robustum. This revision provides the basis from which more detailed studies into the complex variability of species, both locally and across their ranges, to be undertaken.
Description of a new species of Darioconus from Mozambique
Authors: Aiken Roy
Published: July 2023

A new species of Darioconus is described here, from Mozambique.
A New Genus and Species of Chiton from the Aleutian Islands
Authors: Clark Roger
Published: July 2023

An unusual new genus and species of chiton, Boreacanthus griphus n. sp. is described from the shallow subtidal depths of the central Aleutian Islands, and is tentatively placed in the family Mopaliidae Dall, 1889. The tail valve of the new taxon has a caudal sinus typical of the family Mopaliidae, but differs from all other known members of Mopaliidae in possessing pustulose tegmental sculpture and spike-like, calcareous girdle elements.
Emotion Recognition Based on the Human's Brain
Authors: Tang Liqi
Published: July 2023

There are several different algorithms to train models in machine learning and for purpose of enhancing the accuracy of the models for emotion recognition systems by using EEG datasets, we can compare different algorithms and methods. To experience more intuitively the different impacts caused by different algorithms and methods, Emotion classification by applying Convolutional Neural Networks, Sparse Auto-encoder, Deep Neural Network, and ZTW-based epoch selection algorithm, these three models of emotion recognition will be mentioned and compared in this essay to find out a way to improve the accuracy of the emotion recognition models based on EEG datasets, which also include the process and final results of these models.
Comparison of Brain Tumor Segmentation Methods Based on Different Algorithms Using MRI Images
Authors: Yong Jiaying
Published: July 2023

Brain tumor is a serious disease for human beings. MRI is the most widely used method for its innocuousness since people do not need to be exposure to radioactivity. The segmentation on MRI images is a vital step in tumor detection. To improve efficiency and accuracy of the segmentation, scientists apply different algorithms in this process. This paper focuses on three particular algorithms including Connected component label algorithm (CCLA), Watershed algorithm (WSA) and Fuzzy C-means clustering algorithm (FCCA). The principles and applied procedures of these three algorithms are introduced. Basing on this background information, algorithms are compared from three aspects. Fuzzy C-means clustering algorithm is considered as more efficient and accurate among these three algorithms. All algorithms have good research prospects, and the segmentation result can be improved through the improvement on algorithms.