bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on RGB image optimization technology based on cluster analysis and improved Hibbard algorithm
Authors: Hou Jiaxin , Wang Zhiyue , Xie Yiling , Zhou Feichen
Published: July 2023

One of the most crucial components of a Bayer mosaic pattern image from a charge-coupled device is image interpolation. There are two common problems when processing the images. First one is false colouring when erroneously interpolating across an edge rather than along it results in sudden or unexpected colour changes. The other is Zipper effect caused by the demosaicing algorithm's propensity to average pixel values along edges, particularly in the red and blue planes, which blurs edges. This paper proposed two image optimization algorithms to solve the aforementioned problems: Hibbard-based edge improvement algorithm and Clustering-based colour interpolation. The improved Hibbard algorithm is used in this paper together with variance comparison, diagonal gradient computation, and clustering approach to complete image optimization. In this experiment, the edge interpolation effect yields a better result. The experimental show that the algorithm can eliminate the zipper effect of feature edges better and obtain clearer edge features.
Stock price prediction with long short-term memory
Authors: He Jialuo
Published: July 2023

Stock forecasting aims to predict future stock prices based on past price changes in the market, playing an essential role in the field of financial transactions. However, since the stock market is highly uncertain, stock prediction is complex and challenging. This paper uses the long short-term memory (LSTM) model to predict the stock market and compares it with the current stock prediction algorithm. Firstly, we preprocessed the raw dataset and normalized data into the range from 0 to 1. Secondly, we introduced the LSTM model and improved its performance by tuning four parameters: learning rate, number of hidden layers, number of epochs, and batch size. Finally, we use four evaluation metrics to evaluate models: mean average error (MAE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error percentage (MAPE). Our LSTM model performs better than the previous model in experiments in terms of MAE, RMSE, R2, and MAPE.
Commercial video recognition system for short video (TikTok) based on machine learning
Authors: Fang Mingyuan
Published: July 2023

Short video has the features of short duration and high information carrying capacity, which is more in accordance with modern netizens' mobile phone using patterns. With the continual increase of the user scale of smart mobile terminals, many mobile phone users may make full use of the fragmented time to shoot and view short movies. Numerous Internet behemoths are fighting to invest in creating short video platforms since the amount of video user traffic generates enormous commercial prospects. For speeding up the audit teams effectiveness, video classification technology needs to be constantly developed and updated. The article proposed a commercial video detection model with a wide range of data analysis and processing. More specifically, Principal Component Analysis (PCA), feature selection by random forest and discretization using decision trees would be involved in order to transform the original data into features that better express the nature of the problem. The application of these features to Random Forest Model can improve the model prediction accuracy of data. Experimental results demonstrate that the recognition system fulfills outstanding performance. The model achieves 0.90 precision and 0.96 AUC score (area under ROC curve) of excellent evaluation in the corresponding test set.
Generate handwritten images based on DCGAN
Authors: Huo Yu
Published: July 2023

Nowadays, more and more machines are applied to artificial intelligence, and generate something itself has become more and more available. To augment databases to get a more accurate result from training a model, generating data is necessary. This paper introduces how to generate new handwritten images by training the existing database. The Generative Adversarial Networks model is used as the basic model. Deep Convolutional Generative Adversarial Networks and the MINIST database are applied as the method and training dataset respectively in this research. After 50 epochs by training 256 images each epoch, the new handwritten-number images are generated.
Handwritten English character recognition using convolutional neural network
Authors: Liu Hanqi
Published: July 2023

Converting paper material into electronic material is still a necessary work nowadays, however, recognition of handwritten characters still has limitations in their recognition rate, owing to the presence of various shapes, scales, and formats in different peoples handwritten characters. Machine learning has significant value in reducing human power. A Convolutional Neural Network model that is revised from LeNet-5, is used for handwritten letter recognition. This study uses the EMINST dataset to train the model, and the final recognition rate is about 93.44%.
A heuristic technique for music recommendation using Haar cascade classifier through facial expressions
Authors: Karrothu Aravind , Lavanya T , Harish V. K. , Ramesh N. K.
Published: July 2023

Human emotion assumes a fundamental part lately. Feeling depends on human sentiments which can be both communicated or not. Feelings communicate the human's singular way of behaving which can be in various structures. Extraction of the emotion states people individual condition of conduct. The goal of this task is to separate element from human face and identify feeling and to play music as indicated by the emotion identified. In this task, fostering a model to suggest dynamic music proposal framework in view of human feelings is our principal perspective. From the genuine face the emotion is recognized and when the mind-set is gotten from input picture, particular melodies for the state of mind would be played to hold the clients. In this methodology, the application gets associated with human sentiments accordingly giving an individualized touch to the clients. Our projected framework focuses on distinguishing the human face using Haar Cascade classifier. A few stages like face location utilizing webcam, facial feature extraction, look identification, with that interfacing with a music player will be followed. For exploratory outcomes, Haar cascade calculation and Computer vision along with CNN (Convolution Neural Network) for feeling discovery and music suggestion.
Analysis on airport security of baggage screening using deep learning
Authors: Suraj Chakrapanda , Malyada Amuktha , Anjali Burada , Bhagavan Rama , Vardhan Harsha , Karrothu Aravind
Published: July 2023

Airports are the places where landings and take offs of the flights take place. In general, airports are preferred for traveling over large distances. While traveling in flights we carry our baggage which consists of various items, but the items that are prohibited in the baggage are scissors, knives, gun, and the electronic devices which cannot be switched off. Carrying dangerous items may lead to hijacking of flights and it even led to unusual situations in the airport. To prevent public from threat, airports generally have security inspection in which you will be required to pass your luggage through an X-ray machine where it will be checked for dangerous items. This analysis deals with software side of the security inspection and it is a brief study on various state-of-art proposed deep learning intended methods that are used to detect threat in baggage screening, advantages of their methodology, limitations, and gaps to be implemented.
Question & answering system based on retrieval
Authors: Fan Ziyi , Jiang Mingkan , Li Wei , Weng Zihan
Published: July 2023

The principle of the question and answering system is nowadays one big branch of natural language processing. When a user enters a query, the system should find the question that is most similar to the entered question stored in the datasets. Then directly return the answer corresponding to the found question. Two models are implemented to better calculate the similarity between entered questions and questions in the corpus: TF-IDF and Word2Vec. After one hundred experiments to test the accuracy of these two models, the result shows that the Word2Vec has 81% accuracy and TF-IDF has only 49% accuracy. It is concluded that Word2Vec has a better accurate performance than TF-IDF. There is also an optimized storage method by using the inverted list for the corpus to improve the models efficiency.
Image denosiong based on python
Authors: Shen Rundong
Published: July 2023

This paper introduces the common image restoration technology, discusses the image denoising technology based on python, and analyzes the advantages and disadvantages of this method.
Review on the development and optimization of steering systems for autonomous vehicles
Authors: Liu Jiayi
Published: July 2023

The steering system of an automobile is an extremely important part. The development of unmanned driving technology has put forward new requirements for the steering system. This paper summarizes the three mainstream steering systems in the market, combines them with the requirements of automatic driving technology, and compares and analyses their strengths and disadvantages. The optimal solution of a steering system in the field of automatic driving is given, and research on the development of steering systems in the future is focused. The conclusion is that hydraulic steering system is not suitable for the use of autonomous vehicles. At present, most autonomous vehicles use EPS, but EPS has limitations. The steer-by-wire system under research will be widely used in the field of autonomous driving in the future.
Pneumonia image detection based on convolutional neural network
Authors: Zhao Yijin
Published: July 2023

In recent years, with the continuous increase of various types of data sets and the acceleration of computer hardware, the field of artificial intelligence has achieved unprecedented development. As a core driving technology of artificial intelligence, deep learning has a significant impact on human life. With the emergence of the novel coronavirus and its rapid development into a global new infectious disease, the intelligent medical treatment of pneumonia image detection has become very important. However, due to the particularity and limitation of medical images, medical image segmentation is more challenging than other computer vision tasks. Therefore, it is urgent to carry out research on medical image segmentation. This paper takes convolutional neural network as the research direction, reasonably designs and improves the model, and improves the detection and segmentation performance of pneumonia images. The main research contents are as follows: Aiming at the problems of cumbersome steps and low accuracy in traditional image detection methods and poor detection effect and efficiency in existing deep learning methods, a detection network model based on Mask R-CNN is proposed. The experimental results show that: first, the random weighting algorithm can improve the detection accuracy and training speed of pneumonia; Second, by using ResNet with different layers, it is proved that reducing the number of layers can effectively improve the detection accuracy and training speed of pneumonia.
Analysis of the application of facial expression recognition in psychotherapy
Authors: Wan Shipeng
Published: July 2023

Peoples psychological problems are increasing with the increasing influence of COVID-19. Psychological problems can greatly affect our daily lives, so we must take this problem seriously. Traditional psychotherapy has many disadvantages. To address these shortcomings, a new technology that combines artificial intelligence is gradually emerging in the psychotherapy industry. Use image recognition in artificial intelligence to help consultants do this job better. This article discusses how technologies in artificial intelligence such as facial expression recognition, chatbots, and other technologies can improve these shortcomings and presents hypotheses about the future of psychotherapy and artificial intelligence based on relevant The results show that although facial expression recognition has broad prospects, there are still many problems, such as fewer micro expression data sets and insufficient training models for model generalization ability.
The development of transformer in vision
Authors: Shao Jialong
Published: July 2023

Transformer is a deep neural network that utilizes a self-attentive technique to process data in parallel. The area of vision was neural network based before Transformer, with great frameworks like faster-RNN, YOLO, etc. Transformer, which was developed to stop this phenomena, is discussed in this article together with its accomplishments in the field of vision during the past few years and its projections for the future in order to provide some references for more research.
Research on human-computer interaction and game design
Authors: Wang Runzhe
Published: July 2023

Game design and human-computer interaction are two popular research fields, close cooperation connects rapidly in between. This paper focuses on the mechanisms between these two fields and how they influence each other during the collaboration. Literature review and qualitative research are used in this paper to analyze human-computer interaction and game design, as well as their connections. The game Diablo 2 Resurrected as a practical example of the theories shows that both theories of gaming and user interfaces affect the success of a game, only when these 2 fields work together will a game become attractive to the players.
An analysis of the fans of Chinese virtual idols represented by A-SOUL
Authors: Fu Wei
Published: July 2023

This study aims to analyse A-SOUL fans, the top virtual idol group in China. Public data from 6,778 fans were ultimately selected, collecting data related to their gender, platform account levels, and whom they follow. It was found that fans of these virtual idols prefer five areas: Japanese anime, games, life, music and knowledge. A general direction for the operation of virtual idol projects is proposed to help the companies concerned reduce the number of failures caused by meeting the needs of fans.
A new method for employee promotion detection based on the hybrid ANN-SVM model
Authors: Gong Shanshan
Published: July 2023

A companys ability to achieve its ultimate long-term goal highly depends on the potential and motives of its employees. The Human Resource Management team must be able to accurately evaluate a workers ability in quality work and incentivize him/her using promotion. While it has been quite difficult to measure an employees aptness for promotion, previously, machine learning classification methods has been researched upon. The aim of this study is to analyze a newly proposed hybrid classification model in detecting potential employee for promotion. The hybrid model combines the strength in feature selection of Artificial Neural Network model and the binary classification power of Support Vector Machine. The model is tested and trained after its hyperparameters are tuned. Afterward, the model is evaluated on the accuracy, precision, recall and F1 score, and compared to the individual SVM and ANN models for performance. The predictions of this proposed model have proven to exceed those two models with a range of 0.15% to 7.26% increase in performances. Note, the precision of ANN model is somewhat higher than the hybrid model. In summary, the hybrid ANN-SVM model has proven to work effectively with both imbalanced and balanced datasets.
Facial expression recognition for a seven-class small and medium-sized dataset based on transfer learning CNNs
Authors: Chu Zixuan
Published: July 2023

As one of the most common biological information for human to express their emotions, facial expression plays an important role in biological research, psychological analysis and even human-computer interaction in the computer field. It is very important to use the efficient computing and processing power of computers to realize automatic facial expression recognition. However, in the research process of this field, most people's technology and model achievements are based on large datasets, which lack the universality of small and medium-sized datasets. Therefore, this project provides an optimization model on a specific seven-class small and medium face image dataset and provides a possible technical optimization reference direction for facial expression recognition models on similar small and medium-sized datasets. During the experiment, the training performance of VGG16 and MobileNet is compared. A comparative experiment is set up to observe the effect of transfer learning mechanism on training results. The results show that transfer learning has a significant effect on the performance of the model, and the accuracy of the optimal test set is more than 90%. Regardless of whether transfer learning mechanism is used, the training performance of VGG16 model structure is better than that of MobileNet structure in the same dataset.
Research on live detection technology in wireless charging of electric vehicles based on milli-meter wave radar
Authors: Kang Haolan , Lang Xuhan , Gao Mengyang
Published: July 2023

Recently, milli-meter wave radar has been recognized as efficient solution that has the capacity to contribute to live detection in wireless charging of electric vehicles. We discuss the importance of the live detection in wireless charging of electric vehicles and traditional solution like infrared sensor, ultrasonic detector and camera. This article proposes to apply milli-meter-wave radar to improve the reliability and accuracy of live detection. At the end of the article, the software and hardware design ideas of the scheme are proposed. It can effectively solve the safety and reliability problems in the existing electric vehicle wireless charging scenarios. At the same time, it can eliminate people's concerns about the safety of wireless charging technology and lay a good foundation for the promotion and further research of electric vehicle wireless charging technology.
A review of research on directional off-road path planning algorithms
Authors: Sun Peiyuan
Published: July 2023

Military orienteering can cultivate the skills of map reading, map use and military charting, and has gradually become a regular military training subject. A good track design plan is the basis for the smooth running of the competition. The current research on cross-country courses is mainly focused on single-track optimization, and there is very little research on multi-track planning, and design. However, in specific events, there are often multiple tracks running at the same time, and there are many restrictions, single track optimization design solutions are often not applicable. In this paper, we review multiple path planning algorithms and summarize the problems of these algorithms. On this basis, the advantages and disadvantages of static and dynamic path planning algorithms are analyzed, and future research trends and methods of path planning algorithms in orienteering are proposed.
Sound-based animal recognition model
Authors: Zhang Hengjian
Published: July 2023

Conservation of biological and biological diversity is the goal of joint efforts worldwide, and protecting wild creatures requires various monitoring equipment with identification capabilities. In this case, this article will introduce a model with animal call recognition ability designed using edge impulse. A two-layers convolutional neural network (CNN) is selected and trained with the input features generated by Mel Frequency Cepstral Coefficient, Spectrogram and Mel-Spectrogram. The model hoped not only to focus on the recognition accuracy rate of the model but also on the inferencing time, peak RAM usage and other performance to cope with the poor performance of the tiny equipment used in the real wild. Using five classes consisting of over 4,000 audio samples as the input and after several round experiments. Finally, the MFCC&CNN model is chosen because it has an accuracy rate of over 85.6%, the swiftest inferencing time and the lowest peak RAM usage.
Analysis of reinforce learning in medical treatment
Authors: Zhang Ningyan
Published: July 2023

As human approaches the big data period, artificial intelligence becomes dominating in almost every domain. As part of machine learning, reinforcement learning (RL) is intended to utilize mutual communication experiences around the world and assess feedback to strengthen human ability in decision-making. Unlike traditional supervised learning, RL is able to sample, assess and order the delayed feedback decision-making at the same time. This characteristic of RL makes it powerful when it comes to exploring a solution in the medical field. This paper investigates the wide application of RL in the medical field. Including two major parts of the medical field: artificial diagnosis and precision medicine, this paper first introduces several algorithms of RL in each part, then states the inefficiency and unsolved difficulty in this area, together with the future investigation direction of RL. This paper provides researchers with multiple feasible algorithms, supported methods and theoretical analysis, which pave the way for future development of reinforcement learning in medical field.
Comparison between Bayesian and SVM model for breast cancer risk prediction
Authors: Jiang Jiatong , Li Haodan , Qi Houhua , Wu Wenbo
Published: July 2023

Breast cancer ranks first in the incidence of cancer worldwide. It is a kind of malignant disease with incidence increasing year by year. Recently, machine learning algorithms widely used in cancer prediction and other fields to relieve the burden of doctors and accelerate the diagnostic process. In this work, two representative models, the support vector machine (SVM) and Bayesian classification algorithm are leveraged for breast cancer risk prediction. It is found that the effect and accuracy of the two algorithms are very different when they are used for breast cancer prediction. However, there is still a research gap in the comparison of the two algorithms in practical application. Therefore, the research topic of this paper is the comparison between the Bayesian classification algorithm and the SVM algorithm in breast cancer prediction. The research methods of this paper are as follows: firstly, the dataset is collected, then applying the Bayesian classification to process the dataset, and then the SVM algorithm is used to process the dataset. Finally, the processing results of the two algorithms are compared to comprehensively analyze and compare which algorithm is more efficient and more suitable for actual breast cancer prediction. The test results support that the SVM outperforms the Bayesian classification algorithm in the actual target tracking problem. Therefore, it is suggested to choose the SVM classification algorithm in the actual target tracking problem to boost the accuracy and efficiency of prediction results to the greatest extent.
Multi chronic disease prediction: A survey
Authors: Anilkumar Chunduru , Kanchana Seepana , Kumar Sasapu Bharath , Pravallika Reddy , Mrudula Surapureddi
Published: July 2023

People today deal with a variety of illnesses as a result of their lifestyle choices and the environment. As a result, many people have chronic diseases that go untreated for long periods of time, imposing a tremendous impact on society. Therefore, predicting disease sooner is becoming a crucial duty. in order to systematically evaluate patients' future disease risks using their medical records. But for a doctor, making an accurate forecast based on symptoms is too challenging. The hardest task is making an accurate diagnosis of a condition. For this problem to be resolved, illness detection requires the use of deep learning and machine learning approaches. The amount of data in the medical sciences grows significantly every year. Earlier, health care for patient care has benefited from precise medical data analysis due of the development of information in the medical and healthcare areas. Prior identification and therapy are usually necessary to prevent chronic aeropathy from getting worse. Machine learning and deep learning algorithms are used to predict chronic diseases. Eight illness categories were our predictions. The Random Forest ensemble learning approach fared best overall. Finding the sickness prediction techniques with the highest accuracy and computation efficiency is the aim of this study.
Analysis of object recognition trends based on deep learning
Authors: Cao Siwei
Published: July 2023

With the increasing development and maturity of deep learning, computers have also made world-renowned achievements in the domain of vision, especially in the basic and core branch of object detection, giving birth to many classical algorithms, which are widely used in many fields such as autonomous driving, intelligent medical care, intelligent security, and search entertainment. Before the emergence of deep learning algorithms, traditional algorithms for object detection were usually divided into three stages: region selection, feature extraction, and feature classification. However, with the advent of deep learning algorithms, object detection has taken to another peak, with Single Shot MultiBox Detector (SSD) enabling first-order detection of multi-feature maps and Region-based Convolutional Neural Networks (R-CNN) improving the performance of object detection while enabling instance segmentation. For object detection, this paper investigates the traditional algorithms, R-CNN, SSD, You Only Look Once (YOLO), and diffusion model, which is influential detection algorithms, and compares their differences as well as advantages in object detection to provide a reference for related research.
Lung CT recognition based on convolutional neural network
Authors: Tian Ruilin
Published: July 2023

Lung medical image recognition plays an integral role in today's clinical diagnosis. This process involves finding valid recognition parts in lung CT images and classifying them as normal and abnormal. although many breakthroughs have been made in image classification and lung CT image recognition, current models are mostly for 2D lung images. This paper uses convolutional neural networks (CNN) to extract features for image recognition of infected and uninfected pneumonia virus. Based on the characteristics of the dataset, this paper introduces dynamic learning rate and defines an early stopping strategy to compensate for the traditional manual parameter adjustment methods which are time-consuming and consume a lot of computing power. By comparing with other methods, the model proposed in this paper is able to produce very competitive results in a shorter time, which is exciting.
Analysis of the autonomous driving technology -- Lidar versus visual recognition
Authors: Yu Ziheng
Published: July 2023

With the development of human society, economy and technology, today's automobile industry is in an era of rapidly changing, and the development and application of autonomous driving-related technologies are in full swing. This paper mainly focused on autonomous driving technologies --- the comparison between lidar and computer vision cameras. Lidar and computer vision cameras are completely two different kinds of solutions for auto-drive from all aspects. These two schemes are the main current in the autonomous driving market, and this essay mainly focuses on these two solutions by researching and summarizing existing research and essay online. Through the research, it is clear that these two solutions both have their own advantages and disadvantages even disparate future development orientations.
Recognition of feline images based on deep learning
Authors: Gu Zhuchang
Published: July 2023

As the study of felines continues, image recognition technology is helping researchers identify species more and more. However, the research on cat image recognition based on deep learning is not deep enough in the past, which leads to the low efficiency of researchers and the inability to achieve the purpose of accurate recognition. According to researchers, there are more than 500 million pet cats in the world, of about 40 breeds. Predictably, the popularity of cats has led to an increasing amount of research on cats. On the basis of the existing background, this paper uses the deep learning network based on the PyTorch framework to study this topic and conducts in-depth exploration which finally has high efficiency and accuracy. It is hoped that this study can improve the research efficiency of related researchers.
Research of object detection algorithms based on CNN
Authors: Jiang Jingzhe , Liu Renkai , Zhai Xizhe , Zhao Ziheng
Published: July 2023

This study first presents a taxonomy of region-based and regression-based target identification methods, and then it evaluates each individual technique in turn. The former can extract picture characteristics from the CNN backbone and apply sliding window procedures to candidate areas to lessen computational complexity. The latter immediately takes data from the convolution layer for feature extraction, target classification, and location regression rather than going via the intermediate layer to retrieve candidate areas. Although the accuracy is only marginally poor, this approach can significantly cut the computation time. Next, we categorize and evaluate the method for detecting Anchor-free targets. First, an appropriate illustration of the target identification technique based on intense prediction is shown. It is also clear that this method, when compared to others, has a high rate of calculation and a low rate of accuracy. The newly suggested corner pooling, Cascade corner pooling, and center pooling have significantly increased the calculation accuracy of the target identification method based on key point estimate. Finally, Transformer, which is very inclusive and powerful, can be effectively used in the field of natural language processing and computer vision.
Happiness index prediction using machine learning algorithms
Authors: Liu Anche , Zhang Yaxian
Published: July 2023

For many years, individual welfare and world happiness are an important research point in all over the world. The predictor of life quality is connected closely with the world happiness index. Economic output, social support, life expectancy, freedom, the absence of corruption, and generosity are six major variables that can affect the global happiness index. This paper presents two machine learning methods to analyze the dataset, which is supported vector machine (SVM) and nave bayes. Then, the authors used them to predict the next year's happiness index by observing the images that the authors concluded from these two methods. Thus, the authors can get a clearer picture of peoples life quality as well. In the first step, the authors first perform feature normalization and input the features into the two algorithms. After that, the authors found that SVM was able to achieve better results with 92% and Nave Bayes with 87%. In addition, the authors analyze the significance of the indicators and the authors find that the factors that most affect the happiness index of the country are economic and medical. Those factors are very important things for each country. Moreover, before starting the analysis, the authors made some predictions. In our point of views, the economic, health, and social support should be the largest effect on happiness.
Adversarial attacks in deep learning
Authors: Weng Rouying
Published: July 2023

Accompanying the advancement of deep learning models, which are now used in many different areas such as Nature Language Processing (NLP), Computer Vision (CV), and so on, the computationally exceedingly powerful deep ability of deep learning has outstanding performance in handling various tasks, have become a hot topic of concern. At present, research illustrates that if the input samples are interfered with using the adversarial sample technique, it can make most mainstream neural network models make wrong judgment results. Therefore, it becomes an important issue how to compensate for the shortcomings of existing neural network techniques in terms of security and robustness. This paper first introduces the development of adversarial attack techniques and then describes the theoretical foundations, algorithms, and applications. Then this paper designs an experiment to verify whether ResNet18 can be attacked under adversarial attacks and then subsequently discusses the open problems and challenges deep learning faces.
Current status of turbojets and their future
Authors: Yu Mengzhen
Published: July 2023

As one of the important equipment components, aeroengine reflects the national aviation development level and has an important strategic position and value. At present, there are four main types of aeroengines. This paper focuses on the mechanical structure, energy transfer mode, thrust weight ratio and existing defects of the turbine engine. From the point of view of mechanical design, the motion principle of engine components is discussed. From the point of view of energy, the performance test method of engine afterburner is studied, several feasible test schemes are proposed, and the application of turbine engine in miniaturization is discussed. From the point of view of energy saving, the power, maximum torque and thrust gravity ratio of naturally aspirated engine and turbine engine are compared, and the advantages and disadvantages of turbine engine are analyzed. Finally, the future development direction of aeroengine is studied, such as the wide application of new generation plasma engine and more efficient reburning afterburner.
Analysis of the research status of stationary gas turbine and its material selection
Authors: Xia Yixuan
Published: July 2023

In the industrial development, the position and value of gas turbine is self-evident. It has played an important role in the industrial process and the development of productive forces. At present, with the rapid progress of industrial technology, the traditional gas turbine has been unable to meet the needs of industrial production. Gas turbine is also changing to the direction of cleaner fuel and higher efficiency, which has attracted extensive attention of scholars at home and abroad. Stationary gas turbine is an important source of power generation. They are continuous flow internal combustion engines located in the center of the power plant, burning fuel, converting chemical energy in natural gas or other fuels into mechanical energy, then rotating turbine blades, and then driving generators to generate electricity, which is transmitted to homes and businesses through power lines. In order to complete this process, the fluid working fluid flows through three parts of the gas turbine: compressor, burner and turbine after the thermal Brayton cycle. However, the improvement of gas turbine efficiency has encountered the bottleneck of high temperature resistance, so it is particularly important to develop ceramic materials to replace metal materials.
Performance analysis of sentiment classification based neural network
Authors: Wang Jingyi , Xu Ruijie
Published: July 2023

Deep learning has more significant advantages for word embedding technology than sentiment analysis. This paper studies the application of deep learning on the word embedding problem in context, mainly discusses the RNN model with Word2Vec and without Word2Vec, then compares and analyzes their performance in the experiment, mainly evaluating the accuracy and test loss of seven models. This paper compares and illustrates the model which gets the different results in experiments, complementing the model and re-running the model, and analyzing the reasons for the difference in the performance of each model. The seven models are a single-layer neural network, multiple-layer (two and three) feed-forward neural networks, Convolutional Neural Network (CNN)- A feedforward neural network, which consists of single or multiple convolutional layers, pooling layers, and a fully connected layer on top, so this model is good at image processing. Long Short Term Memory (LSTM)- A temporal recurrent neural network, the advantage of the model is it could solve the gradient disappearance and explosion problem when it handles the long-sequence problem. Bi-directional Long Short Term Memory (Bi-LSTM)-Composed of forwarding LSTM and backward LSTM, it is very common for sequence labelling tasks that are related to the top and bottom, which are often used to model context information in NLP. Bi-directional Encoder Representation from Transformers (BERT)- A bidirectional language model. Finally, this paper analyses and evaluates these models with a specific illustration and research.
The characters and advantages of different algorithms in multi-robot path planning
Authors: Wang Liwen
Published: July 2023

Multi-agent path finding (MAPF) issue has recently gained popularity, with the goal of planning for several agents who can all follow instructions at the same time without colliding. It is a common difficulty when attempting to identify pathways to several agents while ensuring that each agent reaches its destination without colliding and confusion. Real-world MAPF applications such as warehouse management, food, and beverage distribution, and aviation management are paid a number of attention from people in recent years. This article discusses centralized and decentralized techniques in MAPF challenges. Several traditional algorithms will be quoted and introduced through the help of some past papers and experiments. Finally, it was determined that decentralized strategies generally provide more powerful performance, whereas centralized strategies are generally capable of computing optimal solution sequences with minimal coupling and solving complex problems, ranging from relatively few robot problems with high coupling to many robot problems with low coupling.
Research advanced in object detection based on deep learning
Authors: Li Dongyang
Published: July 2023

In the field of computer vision, identifying specific objects in images and predicting their location and class have long been hot research topics.Algorithms for early object detection rely on traditional handcrafted features, and the speed and precision of their detection cannot meet the needs of real-world applications. Rapid development in convolutional neural networks has accelerated the development of object identification systems based on deep learning.Existing deep learning-based object identification algorithms mostly use two-stage detection and single-stage detection, according to various detection frameworks. In this paper, around the above two types of frameworks, the latest research progress in the field of object detection is systematically introduced. Specifically, we first introduce representative object detection algorithms, including their design ideas, basic processes, and advantages and disadvantages. Second, using widely-used datasets, we objectively compare the effectiveness of several detection techniques. Finally, we summarize the unsolved problems in the detection of objects and talk about the route this topic will take going forward.
A comparison of machine learning techniques in building an intelligent tutoring system
Authors: Chen Mingqian
Published: July 2023

An Intelligent Teaching System (ITS) integrates Artificial Intelligence (AI) to the field of education, in order to dynamically adapts to users with different background and provide optimal teaching methods. Recent advancements in intelligent tutoring have proved its effectiveness in enhancing the achievement and abilities of learners. At the same time, with the rapid development of AI technology, various AI and machine learning algorithms have been applied to the design of ITS, optimizing their performance to varying degrees. This paper provides an overall review of previous ITS research using various techniques of artificial intelligence and machine learning (ML) and provides an overview of ITS and its architecture. In addition, it discusses and summarizes current research efforts and barriers to ITS using AI, as well as some future opportunities. This paper provides an overall comparison of various machine learning techniques that have previously been applied to ITS and an overview of ITS and its architecture. In addition, it discusses and summarizes the current barriers to ITS using AI, as well as an expectation of its future development.
Analysis of Chinas aviation network and the key node
Authors: Kong Tianyu
Published: July 2023

Aviation network plays a crucial role in the economic development.However, over these years, the epidemic of COVID-19 seriously influenced the aviation network, including airport shutdowns and a large number of flight cancellations, which causes serious economic losses. Our team examined the topology traits of Chinas aviation network in our research. The details of the aviation network, including airports as nodes and air routes as edges, are visualised in the study. Essential factors of the network are examined, including degree distribution, clustering coefficient, centrality, etc. Gephi is utilized to visualise the communities of the aviation network. The study analyses these factors and provides a case analysis of how the aviation network will behave if one important node is removed. In the study, it is also discussed the centrality, how the communities change and whether the network collapses to gain a clear understanding of the airports as nodes in the network.
Charging scheme for wireless sensor network based on area scanning and energy detection
Authors: Ye Xiaojie , Zau Kaming
Published: July 2023

With the development of wireless sensor networks, the demand for wireless sensors increased sharply. At the same time, there is a problem that the storage of energy is relatively low due to the small size of wireless sensors, which means that it needs to replace when sensors are out of charge. It will cost huge finance and pollute the environment. Consequently, many scholars propose using wireless sensors that can recharge instead of primary wireless ones. In this case, choosing a suitable route to charge wireless sensors has become a significantly critical problem as there is a relationship between energy loss and the charging path. Since it is unstable or unsuitable to use solar energy and wind energy to charge, charging wireless sensors using drones, proposed by Kulaea T. Pauu1, Han Xu, and Bang Wang [1], has become a feasible solution. However, this solution still has some drawbacks as it only uses energy to charge the point with the lowest energy level by drones without considering the priority, the rest of the power, and the efficiency of time and energy. This report will use the data about the utilization rate of energy and make some comparisons to improve this solution.
The performance analysis of Adam and SGD in image classification and generation tasks
Authors: Song Ci
Published: July 2023

Optimization problems have a very important leading position in machine learning. A great deal of machine learning algorithms ends up solving optimization problems. Among all the optimization algorithms, gradient methods are the simplest and most commonly used compared to algorithms like Particle Swarm Optimization and Ant Colony Optimization. In the gradient methods, Adaptive Moment Estimation (Adam) and stochastic parallel gradient descent (SGD) are both outstanding algorithms that have helped solve all kinds of deep learning tasks. But which one is better in some certain conditions is still unknown, which means programmers need to try many of the optimizers to have the best choice. Based on some previous researches, this paper study the impact of L2 regularization and weight decay in Adam and SGD with momentum, which turns out in adaptive methods, L2 regularization is not as effective as it is in SGD. It gives the intuition that SGD should outperform Adam in image classification tasks. However, this paper finds things go the other way around by running an experiment using Lenet-5 on MINST. Besides, this paper describes an experiment on Fashion-MINST using DCGAN with both Adam and SGD as optimizers in Generator and Discriminator. The result shows that the generator with SGD produces fake images with higher quality.
Wiener filter and wavelet transform of noisy image analysis based on MATLAB
Authors: Liu Zhonghao
Published: July 2023

In recent years, how to filter noise more effectively has been a topic of great interest, and improvements have been made based on various filtering algorithms in the hope of obtaining the most optimal filtering effect with the least amount of computation. However, there is a considerable research gap in how to achieve this goal. This article is based on this research. The motif of this study paper is the analysis of images containing Gaussian noise based on Wiener filter and wavelet transform, and an attempt to improve the filtering algorithm. The SNR and PSNR obtained by applying Wiener filtering analysis are 46.7864 and 76.0247, respectively, and the SNR and PSNR results of wavelet transform are 14.0851 and 49.7426. this article implements Wiener filtering based on Wiener method, and the results are almost consistent with the function of invoked system. It is found that the Wiener filter is more suitable for handling processing Gaussian noise. The SNR and PSNR values of both sets of experimental Wiener filter are larger than those of wavelet transform, then it indicates that Wiener filter is more beneficial to suppress Gaussian noise under the same condition.
Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms
Authors: Ma Shaowen
Published: July 2023

Image compression technology is very popular in the field of image analysis because the compressed image is convenient for storage and transmission. In this paper, the Huffman algorithm and Lempel-Ziv-Welch (LZW) algorithm are introduced. They are widely used in the field of image compression, and the compressed image results of the two algorithms are calculated and compared. Based on the four dimensions of Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), the applicable conditions of the two algorithms in compressing small image files are analysed. The results illustrated that when the source image files are less than 300kb, the Compression Ratio (CR) of Huffman algorithm was better than that of LZW algorithm. However, for Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), which are used to represent the compressed images qualities, LZW algorithm gave more satisfactory results.
Graph convolutional network with position and global information improves relation extraction
Authors: Chen Jingxuan
Published: July 2023

With the continuous improvement of the related relation extraction model, researchers focused on optimizations of the input of the sentence expression structures, such as dependency trees, and slight improvements and integrations of basic models applied to this task. This paper utilizes position-aware attention mechanisms to highlight critical information and makes good use of global information generated by the bidirectional LSTM, absorbing characteristics and advantages of those related models to propose a new Position-Aware Graph Convolutional Network (PA-GCN) model and describe it in three steps. Moreover, this paper trials the model on the RACRED dataset to evaluate its performance and suitability. The experiment results demonstrate that the PA-GCN model outperformed others listed in this paper, achieving an F_1 score of 66.6%, which verifies an improvement of the model and the effectiveness of adding an attention-aware mechanism.