bluuebunny/crossref_metadata_2025 huggingface dataset:
Anatomical and Histological Studies of anogenital scent glands of jerboa Jaculus orientis
Authors: Abusrer Salma A.
Published: July 2023

تم الحصول على غدد الرائحة من الجربوع المصري الكبير Jaculus Orientis الذي يتواجد في المناطق التي تقع بين أجدابيا وبنغازي شرق ليبيا، للكشف عن تراكيبها المورفولوجية والنسيجية. نظرًا لندرة الدراسات حول هذا الموضوع. تم وصف التفاصيل المورفولوجية لغدد المنطقة الشرجية التناسلية للذكور والإناث البالغين للـ Jaculus Orientis  في هذه الدراسة, وللدراسة النسيجية تم تشريح الحيوان المخدر بإيثر ثنائي إيثيل ثم نزعة الجلد حول المنطقة الشرجية التناسلية مع استئصال ثلث القنات الشرجية وتثبيتها في ألدهيد فورمالين بنسبة 10٪ ، جففت العينة في الكحول الاتيلي التصاعدي, الترويق بالزايلين، وتم إدخالها في شمع البارافين، ثم تم قطعها بشكل متسلسل بسمك حوالي 5 ميكرون. صبغت المقاطع بالإيوسين والهيماتوكسيلين والتريكروم للفحص المجهري.
يحتوي Jaculus orientis على تجمع محيطي ضخم من الغدد الدهنية الحويصلية المتفرعة، المتحورة الكبيرة, المتخللة في الطبقة تحت المخاطية و ايضا المغروسة في داخل طبقة العضلة العاصرة الداخلية للقناة الشرجية، تصب هذه الغدد إفرزاتها عن طريق القنوات الإفرازية التي تمر إلى فتحة القناة الشرجية. بالإضافة إلى الغدد حول منطقة العانة وهي غدد القلفة في الذكور وغدد البظر في الإناث. ظهرت هذه الغدد تشريحيًا على شكل غدد جرابية صغيرة, منفصلة, متناثرة, مختلفة الأحجام, ونسيجيا عبارة عن غدد حويصلية دهنية متفرعة متحورة, محاطة بحافظة من النسيج ضام وتصب افرازاتها مع الشعرة في المنطقة الجلدية للعانة عن طريق قنوات مبطنة بخلايا طلائية حرشفية متقرنة. علي كل حال من خلال هذه النتائج قد تلقي هذه الدراسة بعض الضوء على دور هذه الغدد علي وظائف الترقيم بالرائحة وايضا مشاركت هذه الغدد في سلوك الذكور والإناث.
تعريف مسبب مرض جرب التفاح المعزول من أشجار التفاح النامية بحقول الجبل الأخضر، ليبيا
Authors: Mohamed Nwara A. , Albarani Majduldeen F. , Saeed Mohammed A.
Published: July 2023

استهدفت هذه الدراسة التعرف على مسبب مرض جرب التفاح المعزول من أوراق وثمار مصابة الظاهر عليها أعراض جرب التفاح خلال شهر سبتمبر من موسم 2018، نقلت العينات للمعمل لعزل الفطر الممرض منها، كما نُمي الفطر على عدة أوساط غذائية شملت بطاطس دكستروز أجار (PDA)، بيئة الثمان خضروات V8))، بيئة أجار التفاح agar) Apple)، بيئة اجار تشابك دوكس (Czapek’s medium)، كما أجريت اختبارات القدرة الإمراضية على الشتول الحديثة والأوراق المفصولة كلاً على حدا،  وأكدت نتائج العزل والوصف الشكلي والقياسات الميكروسكوبية  أن الفطر المعزول  المرضي هو Venturia inaequalis.

حيث أعطت نتائج العدوى بالفطر المعزول استجابة أوراق التفاح صنف Red delicious من خلال ظهور التبقعات، سجلت نتائج فحص الأجزاء النباتية المصابة وجود الجراثيم البنية زيتونية، مقسمة، نهايتها طرفية ، متباينة الأحجام (جراثيم كبيرة أبعادها   13 x 5)، متوسطة ( 9.5 x 4.5) وصغيرة الحجم ( 7 x 3.7) ميكروميتر، بالإضافة الى الأكياس الأسكية   الاسطوانية  الشكل الحاوية على عدد 8 جراثيم متوسط  طولها 40.43 ميكروميتر وعرضها 3.94، ومن جهة أخري أوضحت نتائج العزل وجود ميسليوم مقسم سمكه (0.43± 3.105 ميكروميتر) مع  تباين أحجام الجراثيم على الوسط الغذائي PDA الذي سجل إختلافه عن البيئات الغذائية الأخرى  وكان أكبر تعداد للجراثيم على بيئة (V8).
خرائط تساوي قيم البخر نتح المرجعي في ليبيا
Authors: Ikhneifir Ali Alagab , Bubareek Hafid M. Y. , Balomi Fouad M. H
Published: July 2023

يعتبر البخر نتح جزءًا هامًا من دورة المياه السطحية للأرض ويلعب دورًا رئيسيًا في إدارة الموارد المائية. بسبب التنمية الاقتصادية وتغير المناخ، أصبح نقص الموارد المائية أزمة تلوح في الأفق و تهدد أمن البلدان واستقرارها الاجتماعي. لذا، فإن فهم نمط التوزيع المكاني والزماني لتغير البخر نتح المرجعي أمراً ضرورياً للإدارة الصحيحة للموارد المائية و تحديد أولويات استصلاح الأراضي الزراعية. للحصول على قيم البخر نتح المرجعي تغطي جميع مساحة ليبيا تم اختيار إحداثيات 174 موقع مقسم بين كل خط طول وعرض ابتداءً من أول نقطة قريبة من ساحل البحر الأبيض المتوسط. تم الحصول على البيانات المناخية للفترة من 1990 إلى 2020 من موقع وكالة ناسا الذي يتمتع بميزة التغطية المكانية والزمانية على نطاق عالمي وكبديل للبيانات المرصودة التي يتعذر الوصول إليها. وتم تقدير البخر نتح المرجعي لهذه المواقع باستخدام نموذج جامعة عمر المختار لتقدير الاحتياجات المائية للري Omu-ET model.1.0.0. و رسم خرائط لمتوسط البيانات المناخية السنوية (درجات الحرارة والرطوبة النسبية وسرعة الرياح والإشعاع الشمسي قصير الموجة) وأيضاً رسم خرائط تساوي قيم البخر نتح المرجعي على أساس شهري وسنوي. وقد لوحظ من خلال الخرائط أن توزيع قيم البخر نتح المرجعي تتبع نفس نمط توزيع درجات الحرارة والإشعاع الشمسي قصير الموجة وبشكل عكسي الرطوبة النسبية. تكون الخرائط التي تم الحصول عليها مفيدة للغاية للإدارة الزراعية والبيئية والموارد المائية في ليبيا. تم إعداد نموذج حاسوبي باللغتين العربية والإنجليزية باستخدام لغة البرمجة فيجوال بيسك اكسل، يوفر هذا البرنامج سهولة الحصول على قيم البخر نتح المرجعي الشهرية والسنوية في كامل ربوع ليبيا والذي لا يتطلب سوى احداثيات المنطقة فقط (خطي الطول والعرض).
Increasing step size of the 4-link robotic simulator in stable state
Authors: Ji H
Published: July 2023

In this research, the 4-Link Robotic Simulator program in MATLAB is utilized to find the relation between the length of the links and the length of the two feet. While a few previous research focused on the zero-moment point (ZMP) of the robot geometry, this neglects the change of the position of ZMP in the processes of locomotion. The simulation model is introduced to present an integrated locomotion process of the robot. The testing method was developed based on the comparison between the simulation results from MATLAB. Three different scenarios are analyzed and compared to the original outcome. The main finding of this research is concluded as the length of L5 and L6 should not be smaller than the length of L2 and L3. The critical length of the L5 and L6 was determined as 3.0065.
Application technology of intelligent weak current engineering system in intelligent building
Authors: Liu Qinglin
Published: July 2023

The popularization of electronic equipment makes the weak current system more and more important. This paper mainly describes the development of intelligent weak current engineering system in China and the application technology of intelligent weak current engineering system in the field of intelligent building in today's advanced science and technology and people's increasingly high demand for housing. On this basis, the development direction of the intelligent weak current engineering system in China is expounded, and a concrete example is given to illustrate how the intelligent weak current engineering system can realize the comfort and safety of people's houses in real life. It is believed that with the economic development of China and the improvement of intelligent weak current engineering system technology, people's working environment in intelligent buildings will become better and more convenient.
Artificial-Intelligence integrated circuits: Comparison of GPU, FPGA and ASIC
Authors: Wang Yujie
Published: July 2023

In the recent years, the boom in technology industries has been greatly accelerated by the development of artificial intelligence (AI). AI, which is based on machine learning (ML), can only be developed rapidly because of the continuously increasing computational capacity of AI processors. Compared to general-purpose processors (GPPs), AI processors have specially designed architectures to accelerate the operations of AI applications, such as convolution, matrix, and massive parallel computing. The objectives of this paper are: (1) to illustrate the differences between general-purpose processors and AI processors; (2) to summarise the characteristic three mainstream AI processors: GPU, FPGA and ASIC, and draw a comparison among them. It shows that GPUs provide very competitive performance with high power consumption; FPGAs can offer high efficiency at low cost; and AISCs provide the highest performance with the lowest power consumption, but cost the most.
Researches advanced in image recognition
Authors: Zhuang Yixian
Published: July 2023

Image recognition has always been a current study issue in the field of computer vision, which aims at training models to automatically predict the categories of objects contained in a given image. Early image recognition mainly relied on manual features, whose recognition accuracy is far from meeting the actual application needs. Thanks to the rapid development of deep learning technology, the image recognition algorithms based on convolutional neural network have made breakthroughs in recognition accuracy and speed. Nowadays, Image recognition has been widely used in various fields, such as security, medicine, aerospace. Through detailed literature analysis and investigation, this paper first introduces the representative image recognition algorithms; Secondly, we introduce the common data sets and evaluation indicators in the field of image recognition, and quantitatively compare the accuracy differences of different recognition are summarized and the future development of this field is predicted.
Study on chess game based on artificial intelligence
Authors: Lee Yuanfeng
Published: July 2023

With the development of technology and the growth of humans needs, artificial intelligence appears to the sight of the public and wields apparent influence in this world piece by piece, especially under the scheme of automation. There are many applications of artificial intelligence, such as computer games, software design, remote controlling, online meeting and so forth. This research will base on chess and card games, and make corresponding research via pattern matching, Bayesian network and TD algorithm application, making certain analysis to mentality of human and intelligent system. A large quantity of findings has been discovered that many researchers at home and abroad are keenly interested in this field through searching for assortments of relative references, looking through previous research results and inciting part of them. Throughout the world, Alan Turing once discussed the relationship between the next stage and machine intelligence with the paper chess machine. And Arthur Samuel, one of the founders of artificial intelligence, initiated a powerful Checkers Program when he studied and worked in this field. A lot of evidence shows the fact that machine games are one of the most riveting and challenging researches in artificial intelligence. Lots of scholars make some researches for it through kinds of matches and then draw corresponding conclusions.
Review on CNN-based violent detection method
Authors: Liang Guanjie
Published: July 2023

Violent behaviors seriously endanger peoples life and property safety, and also undermine social stability and development. In order to monitor the occurrence of violent behavior, the monitoring system plays a vitally important role. As the surveillance is used widely in daily life and video data is growing rapidly, it is increasingly unrealistic to manually detect violent behavior in surveillance, because it will consume excessive manpower. Therefore, it is necessary to establish an automated system for detecting violent behavior. And the convolutional neural network (CNN) plays a leading role in automatic detection. This paper has done a lot of research to study and understand the development of CNN-based violent behavior recognition. First, the original CNN is introduced and the related work is mentioned; Then, the two different improvement paths on CNN, 2DCNN+RNN and 3DCNN, are utilized to enhance the accuracy of violence detection or reduce the calculation. And finally, the discussion about advantages and disadvantages of these two improvement paths is shown in this paper and conclusion is presented. At present, the development of potential of CNN remains to be exploited and the development of CNN is expected.
Garbage image recognition and classification based on CNN
Authors: Chen Yuhui , He Yan , Lin Jinghan , Sun Siyu
Published: July 2023

The trend in today's society is to pursue a higher quality of life. Trash classificationn obviously needs to be taken into account. In reality, however, many people have problems with sorting, making them so inefficient that the time and effort spent on it outweighs the rewards. A model program based on a Convolutional Neural Network (CNN) machine learning approach is employed to perform the job of identifying and classifying to solve the problem. By dividing the data set into different uses and performing processes such as mirroring images, the recognition capability of the model is enhanced, and its accuracy can be verified. The number of layers is set at a reasonable level and various parameters are initialized. The learning rate set by the project adjusts itself to avoid overshooting and too low efficiency, but it also leads to fluctuations in cost during convergence. After a careful comparison of the pros and cons, the ReLu function was chosen as the activation function to ensure proper operation of the program and to speed up training and reduce computation. The experimental test results prove that the model outperforms its predecessors. The convolution of CNN compresses the data well, and the number of iterations ensures the accuracy of the model prediction. It could go even further, if more samples and more powerful hardware could be used to solve the problems of over-fitting and insufficient computing power.
A secret image sharing solution for autonomous vehicles
Authors: Li Zuyu
Published: July 2023

With the improvement of personal privacy awareness and government agencies' awareness of national security information protection, data access and data security of smart cars is a thorny problem. The fundamental reason is that the key to access and store data is kept in the hands of smart car companies. Although many scholars have made great progress in the research of secret image sharing solutions before, such as progressive visual secret sharing, polynomial based secret image sharing, etc., these encryption schemes are single key encryption and cannot solve the technical problems encountered by the current autonomous vehicle. Therefore, based on the research of previous scholars and taking into account the social problems in solving the existing image sharing, this paper proposes two secret image sharing solutions. One is key sharing solution; The other is Images Sharing solution. Through the theoretical analysis of the application scenario, we can conclude that these two solutions can well solve the problems of whether users, smart car companies or third-party institutions infringe on users' privacy, whether it is due to the security hidden dangers brought by smart car technology, and whether they violate the national information security law.
An overview of image enhancement dehazing algorithms
Authors: Liu Feilu
Published: July 2023

This dissertation is an overview of Image dehazing algorithm that is utilized to process hazy images through certain technologies to remove the image haze occlusion and interference, improve the visual effect of image. For example, the contrast, color and detail and other aspects. The research method is literature review. The image enhancement algorithms mainly include histogram equilibrium, homomorphic filtering, wavelet transformation and Retinex method. The of these algorithms will be discussed detailly in the following sections of the article. The conclusion is that due to the error of the parameter information in the image with fog, the current defogging algorithm is still unable to achieve perfect results.
Explainable artificial intelligence and its practical applications
Authors: Liu Yijun
Published: July 2023

With the continuous development of the times, the artificial intelligence industry is also booming, and its presence in various fields has a huge role in promoting social progress and advancing industrial development. Research on it is also in full swing. People are eager to understand the cause-and-effect relationship between the actions performed or the strategies decided based on the black-box model, so that they can learn or judge from another perspective. Thus the Explainable AI is proposed, it is a new generation of AI that allows humans to understand the cause and give them a decision solution, so that every outcome has its own basis for decision. Although some considerable results have been achieved in the application of explainable AI, it is still at the beginning stage and there are still some challenges to be solved. From the transportation industry, which facilitates people's access to autonomous driving, to the medical industry, which saves people's lives, to the financial industry, which is a huge industry, and even in the education industry, which is accessible to all people, it has a presence. This paper talks about the current situation and problems of explainable AI through its application in various aspects. Explainable AI can serve not only developers but also users by satisfying their interest-related needs. The transparency of explainable AI is important when it is used in socially relevant applications, which is why we have conducted extensive research on explainable AI.
Image semantic segmentation using deep learning technique
Authors: Fan Yifei
Published: July 2023

With the deepening research on image understanding in many application fields, including auto drive system, unmanned aerial vehicle (UAV) landing point judgment, virtual reality wearable devices, etc., computer vision and machine learning researchers are paying more and more attention to image semantic segmentation (ISS). In this paper, according to the different region generation algorithms, the regional classification image semantic segmentation methods are classified into the candidate region method and the segmentation mask method, according to different learning methods, the image semantic segmentation methods based on super pixels are divided into fully supervised learning method and weakly supervised learning method. The typical algorithms in these various categories are summarized and compared. In addition, this paper also systematically expounds the role of DL technology in the field of ISS, and discusses the main challenges and future development prospects in this field.
RS on video games based on item-based collaborative filtering algorithm
Authors: Zhu Hongyun
Published: July 2023

With the rapid development of the Internet and e-commerce, recommender systems have received great attention and wide application in this environment. Because it is difficult for people to choose the one that they like in the face of the dazzling array of items on the Internet, and these e-commerce sites also need to consider how to improve efficiency, the recommendation system is an excellent solution. This paper mainly reviewed the development of recommender systems, focusing on the research and experiments of a recommender system based on an item-based collaborative filtering algorithm. According to the experimental results and some previous studies, summarizing the advantages and disadvantages of this method, proposing some solutions, and pointing out some problems that will be faced by future researches on recommendation systems.
A novel treatment program for adolescents with post traumatic stress disorder with virtual reality technology
Authors: Zhao Chuanyi
Published: July 2023

Recent advancements in the study of posttraumatic stress disorder (PTSD) have led to the discovery of innovative improvements to therapies that have already received empirical validation. The purpose of this paper is to investigate the theoretical feasibility and expected effects of a new treatment approach for the adolescent PTSD patient population that combines VR virtual reality technology with traditional treatment modalities by referring to relevant studies, literature, and survey feedback from the relevant groups. The main focus is on the use of virtual reality technology to address the reluctance of the adolescent patient population to accept treatment and to explore other possibilities for the development of a relevant target population. The limitations and drawbacks of current VR systems in the treatment of psychological disorders are also discussed, but theoretical solutions are also given. The specific role of the senses in the theoretical model and the role and usefulness for patients, physicians, and others, respectively, are also given.
Based on natural language processing, human-computer dialogue, image recognition, and machine learning analysis whether artificial intelligence will surpass the human brain
Authors: MU Chunxu
Published: July 2023

With the popularization and development of the concept of artificial intelligence, the application of artificial intelligence has also begun to deepen into people's lives. While bringing convenience to people, it has also made some people worry about whether artificial intelligence will replace humans. Therefore, In order to make people understand the current development status and bottlenecks of artificial intelligence more intuitively, as well as the difference between artificial intelligence and human brain, this article will turn from speech recognition and natural language processing, human-computer dialogue, image recognition, and machine learning ability, that is, machine listening, reading, and thinking four aspects of research and discussion, and finally summarize why artificial intelligence cannot completely surpass humans.
A review of the application of CNN-based computer vision in auto-driving
Authors: Zhang Tongwei
Published: July 2023

Beginning with Tesla, self-driving technology has become commercially available in recent decades. Target recognition and semantic segmentation remain significant obstacles for autonomous driving systems. Given that these two tasks are also part of the primary tasks of computer vision and that deep learning techniques based on convolutional neural networks have made advancements in the field of computer vision, a great deal of research has begun to apply convolutional neural networks to autonomous driving in the past few years. In this paper, we examine recent publications on CNN-based techniques for autonomous driving, classify them, and offer insights into future research directions.
Survey on abstractive text summarization using pretraining models and their developments
Authors: Zhang Yixin
Published: July 2023

In these years, pre-training models gain a lot of attention in the summary generation area and demonstrate new possibilities for improving the sequence-to-sequence attention framework. This survey conducts a comprehensive overview of BERT-based pre-training models that can be used in abstractive summaries. Firstly, the BERT model is introduced as a typical pre-training model, followed by baseline models inspired by it. Then problems and developments of previous models are discussed including some recent SOTA approaches. Apart from that, some datasets used for models are demonstrated with main features. Besides, the commonly used evaluation methods are introduced. Last but not least, several potential research directions are suggested.
Researches advanced in fine-grained image classification based on convolutional neural network
Authors: Xu Shiyao
Published: July 2023

Due to practical needs, fine-grained image classification (FGIC) has been considered for many years as a direction of study in computer vision, which aims to subdivide images belonging to one coarse-grained category into multiple fine-grained classes. Traditional fine-grained image classification algorithms rely heavily on annotations. Recently, convolutional neural networks (CNN) have prefigured unprecedented opportunities for this research direction with the popularity and development in deep learning. To start, this study introduces the development history with various fine-grained image classification algorithms, as well as definition and research significance of the problem. After that, it compares and analyzes the different algorithms respectively in the aspects of strong supervision and weak supervision. This paper also compares the accuracy of these models on frequently used datasets. We conclude the paper by summarizing and evaluating the different aspects of these algorithms, and then discuss possible future research domains and challenges in this field.
The relationship between machine translation and human translation in the era of artificial intelligence machine translation
Authors: Lin Yutao
Published: July 2023

In recent years, neural network machine translation has vastly enhanced translation efficiency and ushered in the era of artificial intelligence for machine translation. In the era of artificial intelligence, technological progress has forced us to reconsider the link between machine translation and human translation. Through a literature analysis, the paper investigates the history and current situation of the development of AI translation, the technical anxiety that AI brings to human translation, and the equilibrium between machine translation and human translation.
Preliminary diagnosis of Parkinsons disease based on convolutional neural network
Authors: Li Fengwan , Luo Nianze , Qin Jiangyu
Published: July 2023

The mainstream examination for Parkinsons disease is still to determine the Unified Parkinsons Disease Rating Scale (UPDRS), it can be inaccurate due to doctors or patients subjectivity. But recent studies have shown that patients of Parkinsons disease will represent a certain extent of dysgraphia in the early stage. Based on this feature, we proposed a method to realize the preliminary diagnosis for Parkinsons disease based on patients hand-drawings. In this paper, after the images that we used are pre-processed based on Threshold Segmentation, we set a 4-layer network for Convolutional Neural Networks (CNNs). First, we design a convolutional layer to learn the local features of hand-drawing, then the image is passed to then Maxpooling to go through the maximum pooling operation to preserve the contour features and to remove extraneous information. We set up two fully connected layers to capture more nonlinear relationships between images and labels. In the last, an accuracy calculation formula is adopted to diagnose Parkinsons disease. Overall, this diagnosis scheme that only requires patients hand-drawing can be completely automatic and more convenient than the traditional examination, the accuracy of result can be further improved if more details in the hand-drawing can be gathered.
Comparing one-stage and two-stage learning strategy in object detection
Authors: Bi Hanqing , Wen Vincent , Xu Zhenyu
Published: July 2023

Object detection plays a vital role in computer social perception and computer vision. It could be applied to computer navigation, video monitoring, industrial detection, and so on. It greatly reduces the human labours by automatically locate and identify objects. Nowadays, the mainstream methods of object detection could be separated into the one- and two-stage method. The one-stage method leverages Convolutional Neural Network (CNN) for obtaining features and directly locate the target objects and their corresponding category probabilities. Different from the two-stage solutions, its accuracy is lower and the recognition speed is higher. The two-stage method is a straight forward solution, which process is mainly completed through a complete CNN, so CNN features will be leveraged to extract the feature description of the target among candidate regions through a CNN. The accuracy of the two-step method has been greatly improved, but the running speed is much slower than the one-step method. While the one-step method is less accurate, it is much faster. In this work, representative works for object detection are conducted and compared. The results could further demonstrate their respective advantages.
Image super-resolution reconstruction algorithm based on EDSR and improved SRGAN
Authors: Song Liehao
Published: July 2023

Nowadays Image super-resolution reconstruction algorithm has become the dominant research aspect of computer vision, which is about generate high resolution composite image using low resolution original image. SRCNN(Super-Resolution Convolutional Neural Network) algorithm is the pioneer to apply deep learning to image super-resolution reconstruction, while SRGAN(Super-Resolution using Generative Adversarial Network) further explores the fusion of deep learning domain and image super-resolution reconstruction on grounds of SRCNN algorithm, and uses generative adversarial network to fit the extra information after image resolution improvement. Aiming at the defects of SRResNet generation network in SRGAN algorithm, this paper tries to improve the generator in its generation adversarial network by referring to the idea of ENSR(Enhanced Deep Residual Networks for Single Image Super-Resolution) algorithm, and obtains obvious effect. The improved algorithm has a significant improvement in performance.
Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data
Authors: Bian Haolin
Published: July 2023

With the development of autonomous driving technology, higher-level autonomous driving is hopeful to be applied in vehicles, and drivers control of the car will also be replaced by intelligent algorithms. At the same time, a question has been raised as to whether the traditional steering wheel can be replaced when advanced autonomous driving becomes commonplace. To this end, a survey on steering wheelless cars was conducted to explore the feasibility of autonomous vehicles without the steering wheel. As a result, the operational data of Waymo and Apollo, two Robotaxi (a self-driving car operated by a ridesharing company) companies in the United States and China, is analyzed. The results show that at this stage, autonomous driving cannot fully control the car, and the driver still needs to take over the vehicle in complex situations. Of course, according to the data, Miles per Intervention (MPI) is gradually rising and is expected to reach a reasonable expectation, so steering wheelless cars still have a certain feasibility in the future.
Image super-resolution techniques using deep neural networks
Authors: Guo Meilin
Published: July 2023

Super-resolution (SR) based on deep convolutional neural networks is a rapidly developing field with many real-world applications. In this paper, we examine cutting-edge super-resolution neural networks in-depth using freshly released difficult datasets to test single-image SR. We present a taxonomy that divides existing techniques into six categories, including upsampling, residual, recursive, dense connection, attention-based, and loss function designs. This taxonomy is applicable to deep learning-based SR networks. The comprehensive analysis shows that in the past few years, the accuracy has increased steadily and rapidly, while the complexity of the model and the accessibility of large-scale information have also increased accordingly. It has been noted that the present techniques have greatly outperformed the past techniques that were indicated as benchmarks. On this basis, this paper will put forward some suggestions for future research.
Current research and progress of machine learning in traffic monitoring systems
Authors: Wang Zhengqian
Published: July 2023

In modern traffic environments and road construction, road monitoring is one of the most important safeguards for urban safety. Traditional image processing techniques are not suitable for comprehensive data collection and thus accurate and fast feature extraction and generalization of the massive amount of data generated. Real-time surveillance is both tedious and tiring, but vital to safety. This paper describes the traditional image and video processing techniques that have been used to monitor pavements, as well as todays means of image processing for pavement inspection, and summarize the current development based on existing literature research. The result shows that the application of these methods can lead to a better understanding of complex pavement conditions, improved road planning and design and overall safety, and even derive applications to other aspects of computer vision as well.
Comparison of machine learning algorithms over prediction of Titanic database
Authors: Ma Chunzhu
Published: July 2023

With the popularize of artificial intelligent, algorithms such as machine learning algorithm takes an important part in many fields. In terms of data prediction, it is important to know which machine learning algorithm fits the specific dataset the most. Comparison between machine learning algorithms helps us figure out the advantage and disadvantage of them and thus save time, and money during the working process. In this paper, this paper will be comparing K-nearest neighbours algorithm, Random Forest algorithm, and Support vector machine algorithm mostly focusing on their performance over the survival prediction of Titanic dataset. It could be seen that algorithm with more adjustable hyper parameters could produce more accurate result. However, finding the suitable hyper parameters is time consuming. In the future, this paper may try to explore ensembled algorithms for better result.
Pretraining technology of adversarial generative networks based data selection
Authors: Zhao Ziyi
Published: July 2023

The effectiveness of adversarial generative networks (GANs) has been shown in many works. However, the convergence of the GANs should be granted mathematically with proper hyperparameters and some network structures. Considering the difficulties of constructing a large and clean dataset, GANs are required in many cases to extract enough information from a relatively small dataset. This could result in bad coverage performance of GANs, and one way to tackle this problem is pretraining. Since this method relies on importing external information from the pretraining dataset, this paper does a necessary discussion on the effect that the pretraining dataset has on the model and makes a simple assumption on the standard of choosing a pretraining dataset. Specifically, this paper focuses on the case where the target is to generate animation-style characters, and it has been proven that when providing a pretraining dataset with higher diversity, the performance of the GAN is better. The result of this paper should reveal the possibility that using a specially chosen pretraining dataset can improve the result of GAN.
Handwritten digit recognition using machine learning
Authors: Gao Yiting
Published: July 2023

Different people are expected to possess varying writing styles that are distinctive to their personalities. There are many aspects that make handwriting differ from person to person, which include spaces, inclination, height, basic patterns, connecting strokes, sizes and widths, markings, and ornaments, among others. However, the challenges come when the handwritten text must be converted into digital form to enhance information sharing and storage. To address this challenge, the recent past has seen the rise of reliance on machines over humans and the subsequent development of machine learning algorithms. Likewise, the recognition of handwritten text is part of the important field of research and development that appear to have a promising future. The handwritten characters recognition has gained considerable attention in the area of machine learning and pattern recognition, most recently. Several techniques have been proposed for recognizing handwritten text. In line with this, many studies have been conducted that explain techniques employed in converting textural substance from paper material to a form that is readable by a machine. Such a digital recognition system has the potential to create a paperless environment by processing existing paper documents and digitization in future. This paper presents two recent techniques based on neural networks, Convolutional Neural Networks (CNNs) and Waikato Environment for Knowledge Analysis (WEKA) and makes a comparative analysis. In this paper, we want to ensure the reliable and effective approaches to the recognition of handwritten digits. The results show that CNN is comparatively more accurate than WEKA, with an accuracy rate as high as 99.59 percent, with the lowest reported degree of accuracy standing at 88.3 percent. On the other hand, the highest report accuracy with regards to WEKA was 82.92 percent, and the lowest stood at 67.45 percent. Although there is a need for more research to be carried out, the information from the two algorithms demonstrates that the existing techniques are increasingly becoming more reliable, and CNN is leading in terms of accuracy.
Gas & steam power systems: Current status and prospects
Authors: Cheng Chuanqi
Published: July 2023

Gas power generation systems, steam power generation systems and combined cycle power plants play an important role and value in industrial production, and have attracted extensive attention from scholars at home and abroad this year. It is not only an important cornerstone of a country's industrial production, but also plays an important role in technological development in other fields. This article is an introduction to gas-power generation system, steam-power generation system and Combined cycle power plant (CCPP), covering their respective principles, working processes and modifications to improve thermal efficiency. Each system has its own advantages and drawbacks which should be considered in a case-to-case scenario. However, both standalone gas/steam power systems are less effective compared with CCPP, which is a more superior system to utilize fossil fuel for electricity generation. Based on the current situation of some countries, CCPP is the optimal way for power generation in the future.
Research of image detection and matching algorithms
Authors: Bai Yufei
Published: July 2023

Image matching, a fundamental computer vision method, serves as a crucial pillar for more complex vision applications. The general adoption of feature-based image registration technologies has been accelerated by advances in computing hardware and vision theory. As the current research in this field is not very sufficient, this paper gives an overview of the relevant aspects. At the beginning, this article first introduces the research background, the research achievements and the application in different fields of image feature detection and matching. The main body discusses the most current advancements in this subject, including feature points, local features, global features, matching, and optimization, after examining the classical detection algorithms from recent decades and referencing the most recent machine learning algorithm headed by depth learning, and shows the advantages and disadvantages of the algorithms. Finally, the paper summarizes and prospects the full text.
Research on the algorithm of image feature detection and matching
Authors: Zhang Jiali
Published: July 2023

In-depth research on feature detection technology affects people's modern life. Modern artificial intelligence can act as the eyes of human beings and efficiently filter out effective information from complex pictures. Corner detection has now evolved into a tool for efficient image scanning. People's increasingly stringent requirements for image processing continue to promote the birth of new technologies. Corner detection methods have been improved and perfected, and have experienced detectors such as Harris, FAST, Scalriant Feature Transform (SIFT), Speeded Up Robust Feature (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), KAZE, AKAZE and Oriented FAST and Rotated BRIEF (ORB). In the face of many mainstream detectors, the main research purpose of this paper is to rely on Python and Computer vision to study the detection efficiency of various detectors in different environments. In this experiment, thirteen groups of pictures were selected, and after being flipped, complicated, and blurred respectively, they were detected by different detectors to obtain the results. Finally, by comparing the feature detection points, detection time and other factors, this study found that the ORB detector can be competent in most situations and is currently the fastest and stable feature point detection and extraction algorithm. On the other hand, the BRISK detector can handle highly blurred images more efficiently.
Using neural networks to explore path planning algorithms for robots
Authors: Zhu Yepeng
Published: July 2023

In the introduction section of this paper, the importance of path planning for automatic self-navigation is outlined, along with the structure and basic model of neural networks, as well as numerous practical examples of using neural networks to solve the problem of traffic prediction and autonomous robot navigation. In the method section, the specific formulations and steps of how neural networks can be used to solve the path planning problem are explained in detail. Additionally, a variety of algorithms for solving the path planning problem with neural networks are introduced, and practical experiments are conducted to compare the results of these algorithms in various environments. In the RESULTS section, the characteristics are summarized and explained by comparing the experimental results. In the CONFUSION section, a fundamental outlook on the future of neural networks for solving path-planning problems is provided, as well as the possibility of cooperation between neural networks and other methods for solving path-planning problems.
Researches advanced in image recognization based on deep learning
Authors: Li Zhizhi
Published: July 2023

Image recognition is a fundamental problem in the field of computer vision community, which aims to understand the content of an image to predict the class of the image. Traditional image recognition methods rely heavily on the quality of handcrafted features, and the recognition accuracy and generalization ability cannot meet practical application requirements. Thanks to the speedy progress of deep learning theory and technology, convolutional neural networks can adaptively learn image semantic features through high-dimensional nonlinear changes, which sensibly rises the exactness of image recognition. At present, image recognition has been generally applied to many fields such as commodity circulation, smart retail, pest identification, medical image analysis and so on. In this paper, we introduce the latest research progress in deep learning-based image recognition. Specifically, following the temporal clues of technological development, we first introduce representative image recognition networks, including their design ideas, basic network structures, advantages and disadvantages, etc. Second, we quantitatively compare the performance of different image recognition networks. Finally, we summarize the existing problems in the sphere of image recognition research and discuss its possible future directions.
Research advanced in deep learning-based image restoration
Authors: Xie Chengyue
Published: July 2023

In the computer vision community, which tries to address issues with image data change, loss, and missing that may arise in the process of image collecting, transmission, and preprocessing, image restoration has always been a hot study topic.Learning the contextual information of the visible regions in an image is the basic idea of image restoration algorithms, which perform noise removal, missing region repair and transformation on the regions to be restored to reproduce the original image content. Most of the early image restoration algorithms rely on the design of manual features, whose accuracy and generalization ability can not meet the practical application requirements. Due to the rapid advancement of CNN, in recent years, image repair accuracy and effect have made breakthroughs, and have been widely used in old photos, movies, videos and other repair scenes. In this paper, three restoration algorithms (sequence-based restoration, CNN-based restoration and GAN-based restoration) are comprehensively introduced while the research progress of image restoration based on depth learning is reviewed. Secondly, we quantitatively compare the performance of different repair algorithms and analyze their advantages and disadvantages. Finally, we summarize the challenges facing the area of image restoration research and evaluate its potential future directions.
Analysis on the differences in information dissemination laws between online and offline social networks
Authors: Wang Xinyang
Published: July 2023

Social networks are ubiquitous, which has been one of the important topics of many scholars. With the rapid development of information technology in recent years, people's social platforms are not limited to offline, and online social networks have gradually become an indispensable part of our lives. However, there is still a lack of research on the differences between online and offline social networks. Therefore, this paper probes into the differences between online and offline social network information dissemination by collecting data from online and offline double-layer coupled networks, and comparing and analyzing the data. Research shows that there are some differences between online and offline social networks in the law of information dissemination, which will have a certain impact on real life. Therefore, three suggestions are put forward: 1. At the initial stage of promotion, there are few audience groups, so offline methods can be adopted. With the expansion of information dissemination scope, gradually turning to online promotion, the best promotion effect will be achieved at the fastest speed. 2. The speed and effect of information dissemination can't be ignored for people who have no value in social network information dissemination. 3. Based on the complexity of the public opinion information dissemination network, users need to constantly cultivate their ability to identify information, the management of major social platforms earnestly perform their regulatory responsibilities, and the relevant functional departments of the government gradually establish a public opinion guidance mechanism. If joint efforts can be formed, the public opinion prevention and control effect will be more significant.
Application of machine learning on depression prediction and analysis
Authors: Lyu Hongbin
Published: July 2023

In this paper, machine learning methods based on Python are applied to predict and analysis the possibility of depression, which provide the possibility whether the individuals suffer depression. Four kinds of machine learning methods, including, supported vector machine, nave Bayes, random forest and neural network, that make predictions are all based on 23 types of features from the public depression dataset and after processing the data, some relevant figures have also been drawn. The results show that the random forest obtains the best performance among all the methods. Random forest achieves 86.8% accuracy, which validates the effectiveness of machine learning based depression prediction. Furthermore, the important factors which lead to depression are also analysed. Among the predictions of the depression dataset, these figures show that the most influential features are the age, level of education, living expenses and the gender does not have any effect.
Stock prediction in machine learning: Comparison and analysis
Authors: Bai Shengmao
Published: July 2023

In recent years, the rapid development of machine learning has made it go into the field of stocks prediction. There have already been a few mature algorithms in the world that could help analyze the futural movements of the stocks market. In this paper, the author aims to exploit the effectiveness of machine learning methods in stock prediction. The utilized algorithms contain SOM(Self-organizing Maps) and SVR(Support vector regression) SVM, RNN, LSTM as well as work on several papers from others to estimate the probable advantages or disadvantages of the algorithms. After these experiments and testing, the author finds out some conclusions about the algorithms and realizes that the SOM algorithm has the advantage of exploiting the neighbor relationship on the cluster centroids is beneficial to the interpretation of the clustering results while having the disadvantage that users must select parameters, neighbor function, grid type and number of centroids.