bluuebunny/crossref_metadata_2025 huggingface dataset:
Review on unmanned retail recommender system based on deep learning user portrait
Authors: Li Chunlin
Published: July 2023

As a data analysis tool, user portrait and recommendation system can deeply analyze and describe customers and commodity resources in unmanned retail industry. The situational fusion of them can provide a new idea for resource aggregation of unmanned retail recommendation system. By way of reading and sorting of the existing literature of unmanned retail recommendation system, the literature of unmanned retail recommendation system based on deep learning user portrait is systematically classified, described and evaluated from the aspect of convolutional neural network, providing a basic literature reference for the combination of deep learning user portrait and unmanned retail recommendation system. In the upcoming new retail industry, artificial intelligence connects the supply side and the demand side and will create a new retail system, which requires a lot of data and algorithms to support, but the resulting social problems also need to be solved. This paper aims at this social phenomenon and hopes to be helpful to subsequent readers.
How deep learning techniques are relevant in a COVID-19 world
Authors: Liu Lucas Jia Chen
Published: July 2023

The diagnosis of COVID19 using neural networks has established itself as a promising field within the modern world of applying deep learning algorithms to problems we are currently facing. Most research has been based on feeding Computed Tomography (CT) scan images into an image classification AI to find pneumonia caused by the disease. Within recent months, the research regarding the diagnosis of COVID19 with AI has substantially broadened, with new ideas and applications proving success. Despite these studies showing incredible success, there has been little review or external support in order to verify this research and potentially lead it into clinical trials. In this study, we review one of the earlier studies on CT diagnosis, and compare this to the relatively new studies that use Chest X Rays (CXR) and the auditory samples. We not only introduce the relevance and significance of several recent models but also conclude several potential challenges for the task and the field as a whole.
Artificial potential field method for obstacle avoidance and lane keeping
Authors: Wu Chenjie
Published: July 2023

This paper uses artificial potential field method to solve obstacle avoidance and lane keeping of autonomous driving safety assistance systems. It focuses on the lane line, direction, car obstacle, and static obstacle, which the car must pay attention to on the road. The artificial potential field method can assign different potential functions to different types of obstacles and road structures. This method also considers safety distance, limited speed, traffic rules, and safety problems. Superimposing these conditional functions will eventually result in a system that will continue to return the safety and appropriate next point until reaching the set goal point. Experiments used PyCharm to simulate a one-way, two-lane environment. The final results showed that regardless of the vehicle's initial position, the vehicle could return to the center of the regular lane and effectively avoid static and dynamic obstacles, in addition to controlling its speed to maintain a safe distance from obstacles. The result of experiments also proved that the artificial potential field method could be effectively used in the obstacle avoidance and lane keeping of automatic driving, which means that such a simple algorithm can also be installed on more lightweight navigation devices that require real-time dynamic obstacle avoidance.
Optimization of coverage path planning algorithm for robots
Authors: Wu Yue
Published: July 2023

Abstract. In the era of technology gradually into the home, sweeping robots have become a must for many families. However, due to the lack of algorithms in the robot itself, there are still many problems with sweeping robots. In order to save cost and improve efficiency, it is necessary to improve the algorithm of robot coverage path planning. In this paper, we study the robot to complete full-area coverage path planning using sensors in an unknown environment. In this paper, the wavefront algorithm is combined with the traditional zigzag full-area path planning algorithm. The authors find that the combined algorithm speeds up the sweeping and reduces the repetition rate. Combining several different algorithms together like this can have more different applications in real life.
Performance comparison of different convolutional neural networks for vegetable and fruit recognition
Authors: Chen Yuxuan , Pan Shengkai , Wang Haoyu
Published: July 2023

Image classification, a significant research problem in the computer vision community, aims to assign different types of images to a certain category in a fixed category set according to different information features reflected in images. With the continuous improvement of living standards, people's daily demand for accurate identification of food categories (such as vegetable, fruit, etc.) is growing. Early vegetable and fruit recognition mostly relied on manual features and machine learning algorithms, with low recognition accuracy and weak generalization ability. Thanks to the rapid evolution of the convolutional neural network, vegetable and fruit recognition based on depth learning has made breakthroughs in accuracy and speed. In this paper, three representative convolutional neural networks are introduced around vegetable and fruit recognition, and their performance differences are quantitatively compared on different data sets to explore the boundaries of their applications. In addition, we summarized the research problems in vegetable and fruit recognition and discussed its future development direction.
Comparison of dynamic programming and greedy algorithms and the way to solve 0-1 knapsack problem
Authors: Wu Yitian
Published: July 2023

The 0-1 knapsack problem is widely used in reality and it belongs to NP-hard problems. Starting from the basic ideas and time complexity of the algorithm, this paper analyzes how to choose a strategy to solve the problem. This paper aims to solve practical problems and analyzes the 0-1 knapsack problem in combination with the real-life cargo delivery problem. Dynamic programming and greedy algorithms are used to tackle the problem respectively, and the advantages and disadvantages of two strategies are discussed, so as to analyze how to decide which strategy to adopt to solve the problem when encountering the 0-1 knapsack problem in an actual situation. In the face of large-scale problems, this paper suggests choosing greedy algorithm because it will save a lot of time. In the face of small-scale problems that require absolute solutions, this paper suggests choosing dynamic programming to solve the problem.
Comparison and analysis of the machine learning in the movie subtitle document classification
Authors: Chu Buxiao , Xu Dannong , Wu Dongze
Published: July 2023

With the rapid iteration of film and technology, the quantity of movie related subtitle document has been rapidly expanded. However, the issue of classifying these documents is becoming non-negligible. This paper aims to compare different methods of classification of every machine learning algorithm on movie subtitle document and then analyse the result of each method. For the classification of movie subtitle document, this paper mostly does the following features of the work: firstly, retrieve of movie subtitle document and pre-processing step of text document. Secondly, deletion of the irrelevant text and extraction of the related texts, the methods are unsupervised machine learning approach and supervised machine learning approach respectively. Furthermore, analyse each classifier in various model which applied with different approach. Lastly, compare the result of different approach and each model then display the results.
Comparison on machine learning based algorithms for face expression recognition
Authors: Bai Jiayi , He Jiawei , Liu Fengyuan
Published: July 2023

With the development of machine learning methods, most of tasks has benefited from it. Numerous works have been done to improve the accuracy and robustness of these methods. In this paper, the authors aim to exploit the effectiveness of machine learning algorithms in face expression recognition (FER) task. Three methods, including support vector machine, random forest and convolutional neural network are introduced to test the performance in FER task. Three methods are tested in the public dataset, FER2013. The conclusion is drawn as follow. All these methods can handle the image recognition, which is a 7-class classification task. Furthermore, nerual network achieves the best performance, which achieves 65% accuracy. The paper exploit the potential of machine learning methods and give a brief attempt on applying the popular algorithms into real-world image recognition tasks.
Deep Chnet 1-D CNN using raw audio waveform for recognizing traditional Chinese musical instrument
Authors: Wang Bote
Published: July 2023

The classification of music information using various deep learning models is increasingly popular in the field of Music Information Retrieval research. However, as most proposed works focus on western music and musical instruments, little attention is given to traditional Chinese music. This paper proposes a 1-D Convolutional Neural Network (1-D CNN) using only raw audio waveform as input, to undertake the task of traditional Chinese musical instruments classification. This paper starts with a review of the current state of research on the related field, then discuss the proposed model and its data in detail, followed by its performance metrics and then a conclusion on the experiment. The result shows that 1-D CNN provides competitive and even superior results when compared to its 2-D versions as well as when compared to traditional models.
Big data in COVID-19 prevention and control: Modeling and analysis report
Authors: Wang Haolun
Published: July 2023

The COVID-19 epidemic has brought great external impact to China. China is facing complex internal and external environmental challenges. SIR epidemic model is a classical partition model, which is widely used to predict the progress of COVID-19. Although the SIR model may be useful in simulating multiple epidemics, it may not be sufficient to describe the spread of COVID-19. Therefore, some modifications were made and used to study the spread and control of COVID-19 epidemic on the SIR model of COVID-19 disease. Expand it by increasing the link between tracking and other interventions. By studying the SEIR model considering the interaction between human and infectious source. In this paper, we will use the classical SIR model to simulate and predict the spread of COVID-19. By distinguishing between confirmed and undiagnosed individuals, the development of COVID-19 is characterized by phased changes. Based on the preliminary data analysis of the epidemic on various industries, the actual impact of the epidemic on society was quantitatively analyzed.
Exploring mobile robotics: Historical applications and future
Authors: Cong Zhaofeng
Published: July 2023

This paper reviews the history, current research status and future prospects of intelligent mobile robotics. The paper analyzes in detail the automatic navigation of mobile robots and identifies its advantages and disadvantages. Bionic robotics, artificial intelligence, machine learning, and deep learning are further analysed.
Deep learning for comment spam and scams
Authors: Liu Jiangchuan
Published: July 2023

As the popularity of online social networks has increased, so has the manner in which people purchase online. Online reviews on the purchasing of items or the provision of services have become the primary source of user opinion. When a substantial body of knowledge assesses a product or service, that body's effect on the market is significant. Because of this, concerns have been raised among manufacturers and merchants, who frequently compose rubbish reviews to promote or denigrate the quality of specific items or services in order to make a profit or maintain their reputation. You may choose to promote or denigrate certain products or services that have been targeted. This kind of commentary is known as "trash remark." Comment spam violates the interests of enterprises to a significant level, and it possesses a defensive mechanism that combines the "Deepfake" of artificial intelligence technology, which renders conventional security measures inapplicable. Deep learning is the approach that will be most effective in resolving this issue. Researchers assess this research according on how they extract features from the commentary dataset and how they extract features from the commentary dataset, employing a variety of methodologies and strategies to find solutions to the problems. The authors also looked at the key deep learning technologies that have been suggested as a solution to the issue of spam detection in the primary machine, as well as the performance of various deep learning technologies. At the same time, the fact that there is not a lot of spam online review data makes this sort of research a highly significant one. The purpose of this article is to offer an insightful and all-encompassing analysis of comparative research on the topic of spam detection that has recently been conducted.
Educational inequities in New York city by thematic analysis of school website reviews
Authors: Deng ShenHua
Published: July 2023

With the widening gap between the rich and the poor, social inequality has emerged in all aspects. The issue of inequity in education needs more attention, because it is related to the development of national quality. There have been many studies on the use of online housing advertisements for feature extraction and semantic analysis, and the use of machine learning methods to construct models to predict socioeconomic status. This study considers the influence factors of education, and conducts Bayesian classification and LDA model analysis on all reviews of New York schools on the largest school rating and recording website in the United States to explore the primary factors associated with educational imbalance in a society. Results show that various requirements for teachers, such as teaching ability and student management ability, were the most important factors that appeared in the reviews. Gender issues are also very important in education. In terms of the overall parts of speech, the emotions are all positive, indicating that the current level of education can satisfy parents as a whole. However, there are still many potential problems of educational inequality that need to be discovered and solved, and the methods of inquiry need to be expanded and upgraded.
Correlation between fine-grained neighborhood socioeconomic status distribution and crime rates in New York city based on machine learning
Authors: Qin Xuefei
Published: July 2023

Urban crime poses a serious challenge to urban sustainability and livability. Many studies have been conducted to explore the patterns and causes of urban crime, as well as prevention techniques. Studies have found that neighborhood socioeconomic status affects the incidence of urban crime, but studies on this topic are limited due to data limitations. To fill this gap, this study designed an approach for Brooklyn, USA, that collects publicly available data from housing advertising sites and the Open Street Map and trains a machine learning model to predict fine-grained neighborhood socioeconomic status. The experimental results show that the gradient boosting decision tree regression model has the best prediction accuracy. Then, we verified the predicted significant correlation between fine-grained neighborhood socioeconomic status and criminal activity in the precinct by using a geographically weighted regression model, that is, criminal activity has a higher incidence in disadvantaged neighborhoods. It was also found that neighbourhood socioeconomic status was the best predictor of harassment and burglary.
The comment trial and comparison of urban vitality based on POI data: A case study of Shenzhen
Authors: Deng Cefang , Li Zimo
Published: July 2023

Urban vitality is defined as the zing of cities providing citizens with the ability to live, and it is important to offer an essential basis for estimating urban growth and spatial balance. Shenzhens rapid development has made remarkable achievements in a brief period of forty years as a special economic zone since reform and openness. This study takes Shenzhen as an example, uses 21 POI data obtained from Baidu Maps, and discusses the relationship between urban vitality and time and space and the influence factors of urban vitality. Results show that urban vitality has a close relationship with time and space and high-vitality areas are judged based on the production activities of urban people. Besides, amusement activities do not have a clear influence on urban vitality. Furthermore, convenient transportation and ordered life-supporting services play a stressful role in pushing urban vitality. Based on the above three elements, it is necessary to pay attention to taking production activities, creating convenient transportation, and providing ordered life-supporting services to increase a citys urban vitality.
Analysis on the application and development of microelectronic technology
Authors: Fan Wenbo
Published: July 2023

The advancement of science and technology, as well as the passage of time, have resulted in an increase in the use of high-tech products in our daily lives, with microelectronics playing an important role as a fundamental technology. Today, the microelectronics industry is the world's sunrise industry. China is rapidly expanding its microelectronics industry. Microelectronics technology is one of the world's fastest growing technologies, and it is the foundation of the information industry in the information age. Now, microelectronics technology has become a standard for measuring a country's level of science and technology. This paper, using a method of literature review, focuses on discussing and summarizing the development and application of microelectronics technology in China, as well as providing some possible correct solutions.
The combination and application of virtual reality and artificial intelligence
Authors: Wang Aoze
Published: July 2023

In recent years, virtual reality technology has gradually entered the public's vision. The rapid development of virtual reality technology has caused a great impact on people's cognition. Artificial intelligence (AI) is also a cutting-edge modern technology that can greatly affect human life. The combination of virtual reality (VR) and artificial intelligence is also seen as a new research direction. Based on this situation. By comparing with traditional virtual reality technology, this paper analyses the necessity and development situation of the combination of AI and VR, and expounds the feasibility and importance of AI+VR. At the same time, it explains how artificial intelligence can enhance the fidelity and interactivity of virtual reality technology to a certain extent. In addition, it also studies how AI can help VR in solving the current difficulties faced by VR technology. In addition, it also summarizes the application status of AI+VR, and looks forward to the impact of artificial intelligence and virtual reality technology on our life.
Communication technology and the prospect of 5G
Authors: Lei Songyu
Published: July 2023

Since the turn of the twenty-first century, our civilization's expansion has accelerated dramatically. Science and technology are becoming increasingly refined and intelligent. In the field of communication, five generations have existed. It is utilized in many regions of the globe. A range of modern communication technologies have made it more practical for both individuals and groups. This essay will cover the history of communication as well as the essential concepts underlying the development of crucial 5g technologies like as non-orthogonal multiple access (noma), ultra-dense hetnets and massive mimo(multiple-input multiple-output). The majority of this paper's sections relate to the sections listed in the table of contents: general characteristic, benefit, development, and outlook. Also what the paper covered includes the mimo-noma system, which combines mimo with noma. For the tendency of it,we can continue to investigate this potential method. In its conclusion, the research examines the possibilities for future growth in the communications industry. Despite some disadvantages, these technologies offer numerous merits. As communication technology advances, artificial intelligence, the internet of things, transportation, and other industries will alter.
Opacity verification in discrete event system
Authors: Liu Qingxian
Published: July 2023

The increasing data frequency and size of information flow have aroused peoples concern about their private information safety. For a system, it is necessary to verify whether the system is opaque to external invaders. A plant is said to be opaque if any secret behaviour in the plant cannot be detected by an external invader. This paper focuses on illustrating a method to decide whether a given system is opaque. To solve the question, the system is first abstracted as the model of finite state automata. Then this paper studies the safety property from the perspective of current-state opacity of a given automaton.
Comparison of spam classification methods based on machine learning
Authors: Wu Chengrong , Wang Jianlin
Published: July 2023

Wapid development of the Internet, people's use of mail continues to expand, anti-spam has become a top priority. According to the statistics of relevant departments, in 2006, the total amount of spam mails received by netizens was 50 billion, which caused an economic loss of about 10.431.5 billion yuan to the national economy. In 2007, netizens received 69.4 billion pieces of junk mail, with a loss of 18.84 billion yuan. The growth rate was 38.8 percent. Spam is the main culprit that consumes network resources. Of course, preventing spam is a long way to go. Among the types of spam received by users, the top three are online shopping spam, online money-making spam and sex toys spam, which account for 17.57%, 12.55% and 9.21% respectively. Followed by attack spam, spam containing viruses, etc. At present, anti - spam main technology and method means.
Discussion on how humanoid robots could possibly replace humans at works
Authors: Zhang Zhihe
Published: July 2023

In this paper, it has been briefly discussed that the present situation of humanoid robots utilizations from several aspects and according to the information collected the possibility of robots replacing humans has been analyzed independently. Humanoid robots are considered as invulnerable and durable and these benefits have been utilized in several different areas such as military and space and they are expected to conquer those objective problems that are difficult for a human. Other areas like industry and medicine have included humanoid robots to accomplish mechanical and repetitive work because of their characteristics of precision and energy-saving. Additionally, robots with flexible outlooks give them the ability to take part in advanced medical innovations such as micro-robots for tumor cell killing, and they are expected to make a breakthrough in difficult miscellaneous disease treatment. On the other hand, the shortage of humanoid robots that they cannot generate human emotions has been discussed and the representation of robots in the art field - the AI artwork generators cannot completely replace human artists.
Overweight and overload implementation in Apollo systems
Authors: Li Hairuo
Published: July 2023

Apollo is a high-performance, flexible architecture developed by Baidu, Kinglong, and a consortium of more than 40 companies for the purpose of accelerating the development, testing, and deployment of Autonomous Vehicles. This article first describes Apollo in terms of the functionality of the system and the interaction between its components, and then analyzes the conceptual architecture through system evolution, control and data flow, and concurrency, while illustrating the impact of the division of developer responsibilities on this. Then will go through a detailed overview of the concrete architecture of Baidu Apollo with working on mapping the source code from the Apollo Github Website to our architecture using the diagram drawing tool Scitools Understand. And divided into five parts, the report concludes with five major parts: the process from mapping code to diagram; concrete architecture explanation; unexpected dependencies discovery; subsystem analysis, and sequence diagrams presented in a more accurate and specific way. Finally, this report will propose a specific feature or enhancement to the current concrete architecture. The impact that this feature or enhancement can have on the non-functional requirements and stakeholders of the system will be explored based on the existing architecture and component interactions. In this regard, this report discusses two specific approaches that can be implemented and compares the two approaches through SAAM analysis to determine the best approach.
Animal detection in farms using machine learning
Authors: Thylashri S. , Rajalakshmi N.R. , Karthikeyan P. , Senthamil L.Sahaya
Published: July 2023

Agriculture is extremely important to a countrys development. Agricultural issues have consistently stymied the countrys progress. Farmers face a slew of problems, including a lack of water for irrigation, crops withering due to climatic changes, nutrient-depleted soils, and crop damage from pests and wildlife. In recent decades, innovation has developed to provide effective solutions to a large number of these difficulties. In any event, agricultural protection from wild animals has not been fully addressed until now. The fields have been destroyed by elephants, monkeys, and wild boars. The production of crops is diminished when wild animals trample them or eat them. The presence of wild animals on farmland has always been a contentious issue for farmers. Deer, wild boar, moles, elephants, and monkeys are just some of the animals that might ruin a harvest. When the farmer isn't there, these animals may eat the crops and wander the field, which is bad for the harvest. Because of this, yields might drop significantly, and supplementary financial protection could be necessary to cope with the fallout. This problem has to be solved as soon as possible, and a workable solution needs to be found and put into action. Accordingly, the goal of our programme is to address this problem. Wireless sensors might be used to set off an alarm in the event of an animal intrusion, communicating the situation to the proprietor and forest authorities with an attached photo. This provides an early warning so that measures may be taken according to the intruder type.
Orthogonal frequency division multiplexing technology based on MATLAB
Authors: Yang Jieying
Published: July 2023

The subcarriers of an orthogonal frequency division multiplexing (OFDM) system are not identical in the time domain, and their spectra also overlap. As a result, the spectrum is being used effectively. Its effective use of frequency resources and capacity to combat channel fading have made it the de facto technical standard. In wireless communication, numerous pathways in the channel can lead to interference between symbols, known as inter-symbol interference (ISI). This research supplements OFDM systems with some safeguards to eliminate inter-symbol interference. Most traditionally, OFDM symbols have had their post-event sample points duplicated to their beginnings, a process known as Cyclic prefix (CP) padding in the protection interval. In order to further counteract ISI, a protection interval might be added to the beginning of each symbol. An OFDM system's bit error rate (BER) can be decreased by using cyclic prefixes to attenuate inter-code interference. This study explains how OFDM and CP-OFDM work and compares their performance. The primary topic of this study is a contrast between OFDM and CP-OFDM concerning the performance metrics of average symbol error rate (BER) and signal spectrum diagram (PSD).
Research on smart health monitoring tool
Authors: Luo Qi
Published: July 2023

The following project comprehensively discusses a cloud-based health monitoring system that can collect patients health parameters. The data is then transmitted to the internet, where healthcare personnel can access it. The logic behind the project is that data can be transmitted and accessed on a platform on the internet. Thus, facilitating ease of coordination between patients and practitioners. The topic is an intelligent monitoring health tool that collects and transmits data to the cloud. The research method, in this case, combines a literature study and statistical analysis of the collected experimental data. The aim is to assess the consistency of the results produced by the medical devices. In the review, it is concluded that the main weakness of the project is the ability to produce consistent results. For that matter, the project can be improved by using an efficient processor other than the Arduino. Further, highly sensitive sensors should be used to collect accurate data. The aim is to process the data on time while ensuring data consistency is transmitted so it can be confirmed and verified.
Research on turnout system diagnosis based on group decision making
Authors: Wang Binghong
Published: July 2023

Safety has always been the primary consideration in high-speed railway operations. Turnouts are one of the most safety-critical components in the railway system, yet its fault diagnosis still relies on manual inspections, which is time-consuming and can lead to missing reports of turnout malfunctions. This paper proposes an automatic turnout fault diagnosis method based on group decision making. By assembling three individual classification algorithms, including the k-nearest neighbors algorithm, naive Bayes classifier, and deep neural network, this algorithm aims to automate turnout fault diagnosis and reduce the possibility of missing reports. Experiments to compare the performance of the group decision making algorithm and the three individual classifiers based on datasets generated by real turnout systems in simulated fault conditions are carried out. The result shows that the recall, i.e., the sensitivity to turnout fault of this algorithm is superior to the individual classifiers without losing overall accuracy, indicating that the missing report rate can be reduced through the group decision making process.
Introduction to machine leaning in electronic design automation (EDA)
Authors: Tian Zeyu
Published: July 2023

The rapid growth of the intergraded circuit industry as predicted by Moores law has significantly increased the importance of efficient design processes. In addition, due to physical constraint, we will soon reach the limit of how small the size of the transistor can become, and the design processes will become more complex than ever. In order to cope with those challenges and introduce the product to the market within the time to market, the industry has been developing ways to apply machine learning to concurrent EDA tools. This paper will aim to introduce how machine learning is applied to varies processes of electronic design and how they improve the current EDA tools. We will also show the limitations and opportunities of ML based EDA tools and provide a rough idea of the potential future to those who are looking into this area.
Application of virtual reality technology in the psychological field
Authors: Liu Zhishan
Published: July 2023

Virtual Reality is now widely used in various fields, which includes entertainment, education, working and telecommuting. With the popularization of virtual reality technology, there are more and more fields that VR is able to apply to, such as medical care and corresponding therapies. In this article, a new idea of using virtual reality in psychological field will be provided and scrutinized. By literature review and quantitative analysis, virtual reality technology can actually contribute to psychological consulting by transform the counselling environment, combining with other technologies, together stated in this passage with other potential usage and ethical issues.
Overview of definition, evaluation, and algorithms of serendipity in recommender systems
Authors: Sun Ning
Published: July 2023

Over time, recommendation systems are playing an important role in an increasingly wide range of areas, such as paper retrieval sites that can recommend papers or books to users, and shopping sites that can recommend products to users. With the development of recommendation systems, there are many different metrics to measure a good recommendation system, including serendipity. This paper summarizes the definition of serendipity, a review of the metrics for measuring serendipity, and several major serendipity-oriented algorithms and presents conjectures for future research on serendipity. Through the research of some papers, for how to delimit and evaluate recommender systems, experts have mostly focused on the unexpected, and most of them use and optimize collaborative filtering algorithms to achieve and improve serendipity.
5G technology to help the development of smart grids: A review
Authors: Liao Hongyi
Published: July 2023

The continuous advancement of technology has led to an increase in electricity consumption by residential, industrial, commercial, and other users in all aspects, and the demand for electricity quality is also increasing. In such a context, the smart grid is regarded as a new generation of power systems. The smart grid uses advanced communication and information processing, remote automatic control, and other technologies to achieve precise control of fault removal, three-dimensional monitoring of the system and optimal dispatch of electrical energy; while ensuring that the reliability of the power supply can be maintained even when a variety of uncontrollable renewable energy sources and a large number of new electricity loads are connected. Smart upgrades require strong communication systems to support them, and the characteristics of 5G technology perfectly match the various needs of smart grids for wireless communication, which is why 5G is widely studied. This paper is a review of the use of 5G technology in smart grids. Firstly, an overview of wireless communication in smart grids is given, and then the three main scenarios for the use of 5G and various grid services are adapted. This is followed by a summary of the problems and under-researched areas in the use of 5G in smart grids. In the end, the current advanced solutions are summarised and analyzed, and it is determined that the most urgent issues to be addressed in the use of 5G in smart grids are the confidentiality of critical data, network anti-attack capability, and other security guarantees. There is also further value in the use of big data to calculate the optimization of energy dispatch solutions. In general, its development prospects are quite optimistic.
PID controller combined with intelligent algorithm
Authors: Sun Yuxuan
Published: July 2023

PID controller has a wide range of applications in industrial control due to its suitability for a variety of system types, easy operation, and effective results. With the advancement of technology, however, industrial production is placing a greater emphasis on automation, intelligence, and PID controllers, as it is difficult to adapt linear time-invariant systems to the control requirements of current industrial production. This research examines the optimization and transformation of PID controller using a range of clever algorithms by conducting a literature review. PID controller paired with an intelligence algorithm has superior transient and steady-state performance compared to standard PID when dealing with nonlinear systems that are more complex and difficult to predict. This paper's research reveals the general optimization concept and direction of self-tuning PID with an intelligent algorithm.
Light-weight YOLO based object detection algorithm for unmanned aerial vehicle
Authors: Hu Disen
Published: July 2023

Object detection algorithms based on deep learning usually have good results in terms of speed and accuracy on GPU- based computing platforms. However, as this kind of algorithm is not perfectly supported for CPU-based Unmanned aerial vehicle(UAV), the object detection algorithm usually used in UAV has the problem of slow detection speed, which will lead to traffic accidents, traffic congestion, and other problems. To solve this problem, we proposed an object detection algorithm based on YOLOv5. Firstly, aiming at the problem of light- weight model architecture, mobilenetv3 was added to YOLOv5 to replace the original backbone. Secondly, in order to maintain a high detection accuracy, omni-dimensional dynamic convolution was added in the feature fusion part of the network as a replacement for stander convolution. Through the architecture analysis, the proposed algorithm solves the problem in the UAV traffic monitoring system.
Performance comparison of machine learning algorithms for credit card fraud detection
Authors: Chen Zhendong
Published: July 2023

Digital payment methods like credit cards have increased in popularity and usage dramatically in the past two decades along with the whole world transforming to digital and the emergence of E-commerce like Amazon, Shopify, and eBay that completely changed peoples spending habits. There is no doubt that online shopping and credit cards have brought huge conveniences to everyones daily lives, however, it also makes committing payment fraud easier than ever before. Payment fraud can be categorized into a variety of types and the research is focused on fraud where a credit card gets accessed by unknown individuals or groups with unauthorized transactions. The research consists of three main parts: 1. Determine a suitable metric to measure how well different machine learning algorithms work in terms of the ability to correctly identify fraudulent transactions and the practicability when being utilized in real-life scenarios. 2. Compares the performance of the algorithms including Gaussian Naive Bayes, Logistic Regression, SVM, Random Forest, and Sequential Neutral Network. 3. Explores techniques like random undersampling and smote oversampling that handle imbalanced data and evaluates their impacts on the performance of the algorithms. Throughout the research, it is shown that the recall and precision for fraudulent transactions should be the priority metric to measure how capable the machine learning algorithms are in detecting credit card fraud. Random forest performed the best overall compared to 4 other algorithms examined in the research. In addition, random undersampling made the overall performance of the algorithms worse, and smote oversampling had positive impacts on the recall for the Random Forest and Sequential Neural Network algorithms but with a slight drop in precision.
Multi-modal sentiment analysis based on graph neural network
Authors: Li Boao
Published: July 2023

Thanks to popularity of social media, people are witnessing the rapid proliferation of posts with various modalities. It is worth noting that these multi-modal expressions share certain characteristics, including the interdependence of objects in the posted images, which is sometimes overlooked in previous researches as they focused on single image-text posts and pay little attention on obtaining the global features. In this paper, a neural network with multiple channels for image-text sentiment detection is proposed. The first step is to encode text and images to capture implicit tendencies. Then the introduction of this model obtains multi-modal expressions by collecting the shared characteristics of the dataset. Finally, the attention mechanism provides reliable predictions of the sentiment tendencies of the given pairs of image-text data. The results of experiments conducted on two publicly available datasets crawled from Twitter prove the reliability of the model on multi-modal sentiment detection, since the model precedes previously proposed models in the main evaluating criteria.
Intelligent network management model based on Bayesian network
Authors: Shi Haoqiang
Published: July 2023

Nowadays, with the vigorous promotion of digital applications, major enterprises have established their own enterprise networks, in which there are many network equipment, communication lines, servers and various applications. To ensure the stable running of the network system, the enterprise establishes a special network management system to monitor the network running status. The network management system can detect all kinds of network faults, such as line interruption, port down, and service application exception alarms. However, the usual network management system can only discover and deal with the faults that have occurred, and lack to predict and judge the equipment or application system that may have problems. This paper discusses the establishment of an intelligent network management model that links the log system and the alarm monitoring system based on the Bayesian network. By collecting and processing the logs of all devices and applications in the network, based on the alarm information, people can calculate the probability of network devices, communication lines, and service applications that may be faulty to predict network faults in advance and improve the security performance of the network system.
Sentiment analysis to COVID-19 vaccination based on bert and LSTM
Authors: Fang Shuyu
Published: July 2023

The novel coronavirus (COVID-19) was defined as a pandemic in March 2020, which has brought great harm to the overall economy and people's health. Efforts to suppress and end the COVID-19 have led to an unprecedented vaccine rush, stemming from extensive research by experts and authoritative institutions. Previous research has found that significant numbers of people are hesitant to get vaccinated, worrying there may be some hurt for heath. To this end, a timely understanding of people's sentiments about the COVID-19 vaccination is crucial to the popularization of vaccines. Thanks to the popularity of the mobile Internet, people have become accustomed to expressing their opinions by posting comments on the Internet, which provides a quick way to obtain data for analyzing people's sentiment changes on the COVID-19 vaccine. In this paper, to gain an intuitive understanding of people's acceptance of a particular vaccine, we propose a tweet sentiment analysis method based on Bert and LSTM. Using N-grams to evaluate the model results, an accuracy rate of 74.14% can be obtained, which verifies the effectiveness of the proposed method. Extensive experiments show that our method can provide some new insight for the later vaccination policy promotion to a certain extent.
The application of big data and the analysis of advantages and disadvantages in its development
Authors: Yang Shiyue
Published: July 2023

Today, all Internet technologies are being constructed on the basis of big data, the so-called Internet of Everything, which is said to be a comprehensive service for individuals, facilitating peoples life, enriching peoples brain, and improving the quality of life and work efficiency. On the other hand, Restrictive laws and regulations have not been developed and improved along with big data, but rather lag behind. This has resulted in loopholes in the big data industry, giving all the bad people the opportunity to take advantage of personal data reselling, privacy disclosure, and even the use of big data to influence personal habits, such as daily work and rest, shopping and consumption, education and cultural beliefs. This paper lists the benefits of big data to various groups, as well as the hidden problems in the development of this technology. Through literature analysis, this paper finds that big data is a great threat to the privacy and security of the masses. At the end of the paper, some constructive suggestions are put forward to protect public privacy.
Monte-Carlo tree search with Epsilon-Greedy for game of amazons
Authors: Tian Chuan
Published: July 2023

The Game of the Amazons is an abstract strategy board game. It has a high computational complexity similar to the game of Go. Due to its NP-complete nature and large branching factor of game tree, finding the optimal move given a specific game state is infeasible and it is not trivial to design a computer algorithm that is competitive to an expert in the game of amazons. One way to tackle this problem is to leverage the Monte-Carlo Tree Search by using random simulations. In this article, a computationally cheap heuristic function is proposed and use together with Monte-Carlo Tree Search algorithm with Epsilon-Greedy policy aiming to design a competitive AI for the Game of the Amazon. The effectiveness of the -greedy based Monte-Carlo algorithm is compared to the widely used MCTS with Upper Confidence Bound and other classical tree search method such as breadth-first search, depth-first search, minmax search and alpha-beta pruning.
Networking technologies in online gaming: Current status and future development
Authors: Yuan Victor
Published: July 2023

The markets for cloud gaming and online games are growing fast. Although the development opens up a lot of possibilities, making decisions in related fields demands a basic comprehension of the current state of networking technologies used in the gaming industry today. In this article, the functionalities, technical designs, and social impacts of network technologies on modern online gaming are introduced and discussed. To assess the current worth of online gaming technology, a SWOT analysis was conducted. Based on our extensive research into related subjects, we offered several forecasts regarding the future development of online gaming. It can be concluded that technologies in the current online gaming industry are undergoing a major transformation, mostly due to the standardisation of QUIC and the rise of cloud and VR gaming, which determine the future trend of the gaming industry in the next decade.
Analog circuit fault diagnosis based on RBF
Authors: Zhou Jingyi
Published: July 2023

Analog circuit fault diagnosis is growing common using techniques based on artificial intelligence (AI). Among these, defect diagnosis based on radial basis function (RBF) has recently received attention and has demonstrated a respectable level of accuracy. This paper's goal is to demonstrate this method's thought process, methodology, and specific actions. Researchers can refer to the reference circuit diagram, experimental table, and analytic process in the publication to evaluate the efficacy of this approach. Additionally, the elements that can be changed and improved upon are highlighted, offering a path for future study.