bluuebunny/crossref_metadata_2025 huggingface dataset:
IoT based real time air and noise pollution monitoring and controlling
Authors: Sivaprakash P. , Purushothaman A. , M. Sandeep Kumar , J. Prabhu
Published: July 2023

Air and sound pollution is the biggest problem nowadays. So It is required to monitor air quality and noise levels to keep it control for a better future and happy living for all. In order to monitor the pollution, we propose an air pollution as well as sound pollution monitoring system that allows us to monitor and automatically control the air quality and sound pollution in particular areas through IoT. Our proposed system used air sensors to sense presence of harmful pollutants in the fresh air and simultaneously transmits this data to the microcontroller and also device keeps measuring the sound level and reports it to the cloud storage through Internet. These sensors interfaced with microcontroller which senses this data and transmits it to the internet. Our system also interfaced with GPS module to find the latitude and longitude of different places. This methodology allows authorities and people to monitor the air pollution and noise pollution in different areas and take action against it. If system detects the air quality and noise issues, it alerts authorities and also living people. Once the system exceeds the threshold levels of air and noise pollution, controlling action will automatically take by its own. People are dont need to worry about this. Pollution automatically monitored and controlled by our system.
Applications of deep neural networks on music emotion recognition
Authors: Du Yi
Published: July 2023

Music Emotion Recognition (MER) is a subfield of Music Information Retrieval (MIR) that focuses on finding a relationship between music and human emotions by applying machine learning and signal processing techniques. In recent years, neural networks have achieved great success in a large number of areas, such as speech recognition and image processing, sparking many attempts to utilize neural networks in the MER task. Although new models for MER are constantly emerging, there are few systematic reviews in this field that involves the latest models and datasets. Therefore, in this paper, we provide a detailed review of this task. Our work first expounds on the practical significance and research status of the MER problem. Then, we encapsulate the background research and contributions of predecessors in both machine learning and psychology fields. Our work also includes a thorough analysis of several important datasets and their mathematical principles. Finally, we summarize four novel models from an application perspective and conclude a few potential challenges for the task.
Depression prediction using machine learning algorithms
Authors: Chen Wenhui , Fan Yizhi , Zhou Jing
Published: July 2023

Nowadays, depression is one of the most common negative emotions between people. The goal of our paper is to predict whether the individuals has suffered depression according to the provided 23 features. This study classifies the characteristics and studies their impact on depression by random forest, naive bayes, Support Vector Machine (SVM) and neural network. The authors test all those methods on the public dataset, which conclude that random forest and neural network achieve the same top-1 accuracy (which is 86.8%). Furthermore, the authors also conduct the feature correlation to exploit the most important factors. Results show that the most two influential features leading to depression is age and education level, which means the group of teenagers is the easiest members to have depression emotion. The study also analyses how can people sort the situation and decrease the possibility of suffering from depression.
Investigation of influence of additional convolutional and max-pooling layers in general adversarial network
Authors: Deng Zhibin
Published: July 2023

General Adversarial networks (GANs) are distinctively sensitive models to variation of parameters. Thus, a variable-control measurement is necessary to evaluate the performance of changing the parameters (layers) in GANs. This paper focuses on the performance of DCGANs. It reviews authors experiment about the performance of DCGAN when applying the model to small datasets (MNIST, fashion- MNIST, cifar10, and human faces). This paper aims to discuss the influence of additional convolutional layers, max-pooling layers, and MLP in the generator and discriminator when dealing with small datasets. Using variable controlling, it is noteworthy that more convolutional layers can bring more accuracy for RGB images but not for single-channel images. MLP generators or discriminators are prone to model crashes. The respective analysis of each datasets result is provided later, which proposes that the layers influence is unstable among single-channel pictures. However, due to the complexity it brings, more layers can potentially increase the stability and convergence of RGB images. Some discussions for improving current experiments are listed, and some future research directions utilizing more advanced and matured methods are proposed.
Migration of adversarial example in image classification
Authors: Gu Dongrun
Published: July 2023

Neural network technology has made remarkable achievements in computer vision, speech recognition, natural language processing, and other fields. However, the problem of the interpretability of the neural network model makes its application in real situations have potential security risks. In recent years, many studies have pointed out that using Adversarial example technology to make extremely weak perturbations of the input sample can mislead most mainstream neural network models, such as fully connected neural networks and convolutional neural networks, to make wrong judgments. This phenomenon reveals that the existing neural network technology lacks security and robustness. The study of adversarial example technology is of great significance to improve the safety and robustness of neural network models and to promote the researcher's understanding of the learning process of neural network models deeply. Studying adversarial examples of migration is an important research field in adversarial attacks. Researchers attempt to summarize the rules of adversarial attacks by exploring the migration of adversarial examples, thus establishing a robust model in the deep learning area. In this paper, the migration of adversarial examples in image classification is studied to provide analytical data for summarizing the characteristics of adversarial examples.
Performance analysis of sentiment classification based on deep learning methods
Authors: Zhao Yuting
Published: July 2023

When it comes to natural language processing, the textual differentiation task is one of the classical and important research problems. Recently, the deep learning model has increasingly become one of the main methods to solve text classification problems. Common deep learning text classification models are convolutional neural networks (CNN), recurrent neural networks (RNN), the BERT model. For comparing the manifestation of various deep learning models in textual differentiation tasks horizontally, the thesis tests the classification accuracy of different deep learning models under the same experimental configuration. The experimental results show that using pre-trained word vectors helps to improve the classification accuracy of deep learning models. In addition, the reasonable design of more complex and larger deep learning models is helpful in enhancing the study capability of the specific model on text data. The experimental results indicate that the text classification model using pre-trained word vectors could gain higher accuracy than the model without pre-trained word vectors. In addition, in the comparison experiment of feedforward neural network (FNN), CNN, RNN and BERT model, BERT model performs best, and the text classification accuracy reaches 0.9232. Compared with a 1-layer FNN, the accuracy rate is increased by about 16%.
Malware detection using different supervised learning methods
Authors: Lyu Kailin , Yang Fengning , Zhang Luning
Published: July 2023

Malware that can threaten cyber security is now a problem that needs to be addressed. Malware detection has been significantly developed through the use of machine learning techniques. However, simple supervised learning is rarely used for malware detection. The goal of this paper is to compare the accuracy of different supervised learning models in malware detection. The experiment will load a dataset with malware and corresponding characteristics, train four models separately using a decision tree, logistic regression, and Nave Bayes, and compare their train scores, test scores, and test time. The experimental result shows that the decision tree model has the best in malware detection, following the Logistic regression model and the Gaussian Nave Bayes model which has similar scores. In contrast, the Multinomial Nave Bayes model has lower scores. Moreover, the logistic regression model is significantly slower than other models. The results of the experiments reflect the different efficiency and accuracy of different supervised learning methods when used for malware detection. A possible reason for the differences between the methods is due to the complex dataset, and in conclusion, supervised learning could play a crucial role in cyber security.
Analysis of homogeneous charge compression ignition combustion strategy and its current application
Authors: Miao Junwei
Published: July 2023

Under the pressure of energy and environmental problems, peoples demand for high-efficiency and low-pollution power sources is becoming more and more urgent. In this case, the HCCI combustion engine was developed. A new combustion method combining premixed combustion gas and low-temperature combustion was developed: it relies on a uniform formed mixture by premixed combustion gas and the lower cylinder temperature when combustion happened to reduce PM and NOX emissions simultaneously. The HCCI combustion adopts high compression ratio ignition and multi-point combustion in the cylinder, which makes its lean mixture have high thermal efficiency, and the engine performance can reach a better condition.The research and development goal of HCCI technology is to surpass compression ignition and spark ignition engines in performance and emissions. Because HCCI engine has the advantages of the first two, and its emission control system only needs to rely on its own operating characteristics and emission characteristics to reduce pollutant emissions.So if HCCI combustion can be truly mature and commercialized on a large scale, it will be a major innovation in the development history of internal combustion engines.
Application of deep learning in smart agriculture research
Authors: Hu Jiangfeng
Published: July 2023

With the breakthrough of deep neural network research, deep learning technology has developed rapidly and has been widely used in the area of smart agriculture and has achieved great accomplishment in target detection and recognition, numerical weather forecasting and data analysis. This paper summarizes the application of deep learning technology in three aspects: detection and identification of pests and diseases, weather forecast and breeding. At the same time, this paper further discusses the major challenges and future development directions of the application of deep learning technology in the area of smart agriculture, and provides a reference for further research into deep learning in the area of smart agriculture. Accurate identification of crop pests is an important prerequisite for pest prediction and control. Image recognition has recently emerged as the primary technological tool for pest management due to its benefits of high efficiency, cheap cost, and simple operation. Future trends in the realm of pest identification and detection include the fusion of transfer learning and deep learning. Deep learning has great advantages in computing efficiency, accuracy, portability, synergy, flexibility, and ease of use. It will be possible to change the traditional meteorological observation mode, accelerate and improve the processing of meteorological observation data. Future development trends include combining the benefits of deep learning and numerical forecasting. Deep learning has made many advances in the fields of genomics, transcriptomics, proteomics and synthetic biology, and can provide a powerful driving force for the fields of crop breeding and plant genomics. Standardized collection, processing, storage and management of data, and the establishment of an open and shared database are some of the future research directions.
Brain tumor classification using CNN: Difference between optimizers
Authors: Li Yizhou
Published: July 2023

The classification of brain tumor using artificial intelligence has long been a heated topic and never failed to arouse public attention, being both efficient and useful. Each year, around nearly 12000 people are diagnosed with brain tumor, and the AI approach allows a number of specific cases to be identified quickly with few errors, contributing in saving millions of lives. This paper uses CNN (ResNet50 architecture) for classification and evaluates the performances of three kinds of optimizers adaptive moment estimation (Adam), stochastic gradient descent (SGD), and genetic algorithm (GA) when being applied to the model. The resulting accuracy scores are, respectively, 93%, 90%, and 95%, which demonstrates that genetic algorithm performs the best, suggesting a fine choice of utilization in practical scenarios. The results as well show the most precise diagnosis on pituitary tumor and the least on meningioma tumor, providing a direction for future improvement on dataset and training parameters.
Methods for DDoS attacks classification
Authors: Gao Yiting , Li Jiayi , Meng Xianghui
Published: July 2023

At present, many people and companies are attacked by DDoS every day. DDoS attacks will make many large websites inoperable, which will not only affect people's normal use but also cause substantial economic losses. So far, many researchers have found different methods to prevent DDoS attacks, but there is no unified research on the classification of different types of DDoS attacks. If people are familiar with all common types of DDoS attacks and can successfully classify various DDoS attacks, they can take corresponding and effective precautionary measures to reduce risks and offer more protection for their own security. Therefore, the research topic is set to use four different algorithms to identify various DDoS attacks. The research methods of this paper are as follows: First, the CICDDOS2019 dataset is selected as the most basic research foundation. Second, the database is analyzed with K - nearest neighbors, Decision tree, Logistics Regression, and Nave Bayes, then each method's accuracy and precision value are calculated. According to the results, the times that the highest accuracy and precision values of logistic expression are the most, and the times that the lowest accuracy and precision values of the decision tree are the most. Therefore, relative to K nearest neighbors, Decision tree, and Nave Bayes, the most effective way for people and companies is to classify the DDoS attacks by using Logistic Regression.
The advance of recommendation system with graph neural network
Authors: Rao Hui
Published: July 2023

The field of e-commerce provides customers with many kinds of goods to choose from. It is also a challenge for consumers to choose among tens of thousands of product categories. The emergence of recommender systems solves such problems for consumers to a certain extent because it would recommend products that may fulfil their needs for consumers. Finding user preferences or rankings for a specific item can be aided by recommender systems. It uses specialized software and techniques to offer suggestions for items utilized by various users. To increase customer satisfaction or sell different things for e-commerce platforms, recommender systems advise consumers about specific items they may be interested in or desire to utilize. Graph neural networks generalize effectively to new datasets because they can learn the graph structure of various datasets. To put it another way, this makes Graph Neural Networks (GNNs) the perfect recommender systems for various real-world issues. As a result, the growth and usage of neural graph networks in recommender systems and the limitations of graph neural networks will be covered in detail in this paper.
The advance of neural networks generalization performance
Authors: Chen Haoyu
Published: July 2023

A big mystery in deep learning is the promising generalization performance generated by massive neural networks. While over-parameterization increases the tendency of overfitting in other machine learning models, neural networks seem to magically overcome this hurdle and achieve minor test errors in various tasks. Researchers are motivated to resolve this enigma through a variety of aspects and methods, both theoretically and empirically. This paper aims to comprehensively review the explanations for the generalization power of deep networks. Firstly, the review compares various types of generalization bounds under PAC-Bayes analysis and non-PAC-Bayesian settings. Then, works of regularizers, both explicit (e.g., dropout) and implicit (e.g., batch normalization), and algorithms-caused regularizations are reviewed in this work. Some researchers also explore networks generalization ability from other perspectives, and this review talks about works that investigate the relationship between images and generalization performance. Additionally, works of adversarial examples are included in this review, since adversarial attacks have challenged networks power to generalize well and have become an important field in understanding deep learning. By collecting works from different viewpoints, this paper finally discusses some possible directions in the future.
Path planning based on reinforcement learning
Authors: Lin Jin
Published: July 2023

With the wide application of mobile robots in industry, path planning has always been a difficult problem for mobile robots. Reinforcement learning algorithms such as Q-learning play a huge role in path planning. Traditional Q-learning algorithm mainly uses - greedy search policy. But for a fixed search factor -greedy. For example, the problems of slow convergence speed, time-consuming and many continuous action transformations (such as the number of turns during robot movement) are not conducive to the stability requirements of mobile robots in industrial transportation. Especially for the transportation of dangerous chemicals, continuous transformation of turns will increase the risk of objects toppling. This paper proposes a new method based on - greedy 's improved dynamic search strategy is used to improve the stability of mobile robots in motion planning. The experiment shows that the dynamic search strategy converges faster, consumes less time, has less continuous transformation times of action, and has higher motion stability in the test environment.
Iterative pseudo-labelling with SoftMax probability in text classification
Authors: Wang Jiyu
Published: July 2023

Semi-supervised learning is one of the potential research fields in text classification. In this paper, semi-supervised pseudo-label training experiments are conducted using the BERT model that has been pre-trained as a baseline. Only 20% of the original dataset is used for the new training set after segmenting the training set. The raw corpus used for pseudo-label training consists of the remaining 80% of data after labels are removed, while the original test set is still utilized. The results indicate that the key to the semi-supervised pseudo-labelling method is the performance of the original model and reasonable data filtering techniques. Even though the SoftMax value used for data filtering is not precisely equivalent to model prediction accuracy, experimental results show it can somewhat reduce the error propagation problem of the model. This is consistent with earlier research. However, using SoftMax as the threshold for data screening can't bring enough benefits to the model training and make it surpass the training performance of the original data set. As a result, future studies will focus on improving the accuracy of pseudo-labelling with a more suitable data selection method to better the model's performance.
The impacts of TOD on sustainability based on the livability prism model
Authors: Chen Jun
Published: July 2023

The universal issue for authorities is to plan land use more effectively and efficiently, and to provide more sustainable mobility in urban areas. The sustainable prism model is proposed to achieve the requirements for sustainable development. Transit-oriented Development (TOD) is a city planning method that coordinates the mass transit system and the land use pattern. This article analyzes the sustainable prism and discusses the effect of TOD on sustainabilities in ecology, economy and equality to provide the city planner insight into the urban development pattern. Generally, TOD can reduce the energy consumption in the transport sector and related infrastructure, contribute to the air quality from the environmental aspect, facilitate employment and attract investment in the economic aspect and improve discrepancy in employment availability and transport burden among different groups from the social equality aspect. However, the noise pollution in the environment, the value capture effect in the economic aspect and the TOD efficiency in the social justice aspect are insufficiently discussed.
A survey of facial expression recognition in the wild
Authors: Duan Chengjun
Published: July 2023

With the continuous development of facial expression technology, especially the development of deep learning, and the establishment of in-the-wild datesets in recent years, field face recognition has become a hot research field in the wild of facial expression recognition. Different from the traditional facial expression recognition(FER), in-the-wild facial facial expression recognition, the problem of recognition accuracy caused by illumination, occlusion or low image resolution. Therefore, in order to solve these problems, new methods have been put forward continuously in recent years. In this paper, we first continue to summarize the widely used datesets, and then summarize the paper methods of facial expression recognition in the field proposed in the past two years.
Variable and model selection method in linear regression for analysis
Authors: Zhang Borui
Published: July 2023

When making decisions, probabilistic reasoning is used to utilize the known information to predict or determine those unobserved factors and variables that are crucial for the outcome. To find the relationship between outcome and one or more predictors, linear regression is commonly used in statistics and machine learning algorithms. In this article, the basic concept of linear regression and hypothesis testing are reviewed. The common modern methods for variable and model selection including Stepwise selection, Akaikes information criterion, Bayesian information criterion, and Mallows C_p are discussed and reviewed. Each method and criterion have its own uniqueness and limitation depending on the dataset and the purpose of analysis. Through discussing this, this paper aims to inform and explain each different method for variable and model selection in linear regression and provide information to help choose the most suitable methods to predict or find the relationship between response variables and the independent variables for analysis.
Beamforming design based on particle swarm algorithm in IRS-assisted communication system
Authors: Guo JunJia
Published: July 2023

5G and the next generation of wireless technologies are designed to meet the needs of vertical industrial applications like Smart Cities, Industry 4.0, and Smart Grid. These requirements necessitate increased network capacity in order to provide a ubiquitous wireless connection for a large number of devices. Many technologies, such as mm-wave (MMW) communications, massive MIMO systems, and ultra-dense networks (UDN), have been developed in recent years to achieve these goals. However, these technologies have high implementation costs or consume a lot of energy. Intelligent Reflective Surface (IRS) is a low-cost, energy-efficient meta-surface made up of several low-cost passive components. When controlled by the controller, the IRS components can reflect an occurrence signal with a predetermined phase shift for beamforming. This not only improves system performance, but it also addresses the millimeter wave penetration performance issue (MMW). In this paper, a mathematical model of a wireless communication system with IRS in the line-of-sight (LoS) case is developed, as well as a channel model between the base station, the smart reflecting surface, and the user, and a millimeter-wave large-scale fading model. It is investigated the beamforming matrix optimization problem on the base station surface and the intelligent reflecting surface.
Research on inspection of machine room by mobile robot
Authors: Yang Shuhao
Published: July 2023

The machine room inspection robot, as a special robot, can locate and navigate in the machine room independently, and carry sensors to detect equipment faults and record instrument data instead of the machine room staff. In this paper, the relevant standards and existing inspection robots at home and abroad were investigated and the overall scheme was determined based on the actual machine room environment. The monitoring background employs B/S architecture, allowing staff to deliver inspection tasks and manage robots from any computer. The motion map is created using the GMapping algorithm, and the topology map for inspection is created based on the inspection task and feasible path. For raster map localization, the adaptive Monte Carlo localization algorithm is used. The functional layer software for the robot is designed based on the inspection function to be performed by the robot and the man-machine interaction with the monitoring background. The findings of this paper's research indicate that the inspection robot system can meet the requirements of computer room autonomous inspection.
Research and analysis of virtual reality in different fields
Authors: Zhao Yi
Published: July 2023

Virtual reality technology is an emerging technology has been used in many fields. Because of its unique multi-perception, interactivity and autonomy, can enable users to obtain an immersive experience, enrich the sense of experience, VR is promoting the development and creation of multiple industrial fields with high speed. This article partly summarizes the application of VR technology in medical, educational and cultural fields. This paper analyzes the application characteristics and status quo of VR in the medical field, and discusses the progress and advantages of VR in education. It also summarizes the related technology of VR in culture. With the successful research and development of 5G technology, VR technology will also be improved, so VR technology will also be more widely used in various fields and promote the development of various fields.
Common techniques and deep learning application prospects for sound event detection
Authors: Ma Yuxuan
Published: July 2023

Modern techniques for employing deep learning for sound event identification (SED) challenges have improved significantly. In this paper, the author discusses the development of deep learning models for SED tasks in recent years; and the performance advantages and disadvantages shown by using different deep learning methods for the same sound event dataset. This paper also introduces a few techniques effectively increase the precision of sound detection and possible development trends of SED task methods by analyzing the entries in the 2016-2017 Acoustic Scene and Event Detection and Classification (DCASE) Challenge. Through analysis, this paper finds that the accuracy of the deep learning model used for SED to identify target events will continue to improve to be suitable for industrial and life scenarios, so this is still a valuable research.
The impact of security and privacy threat modeling on blockchain-enabled-electronic voting system
Authors: V. Maheshwari , M. Prasanna
Published: July 2023

Blockchain is a decentralized, distributed ledger that records transactions between two parties. A blockchain-based software system is a new and innovative approach to software engineering that uses blockchain technology. This approach has several advantages over traditional software engineering approaches, including improved security and transparency. The most common software engineering approaches are waterfall, agile and hybrid models. Each of these has its strengths and weakness. A blockchain-based system has the advantage of being more secure and transparent than any of these approaches. It also can track changes more accurately, which can improve quality control. Blockchain technologies have incredible potential but also have some problems. One problem is security and privacy issues, which brings into question the resilience of existing security and trust mechanisms. The distributed application (dApp) framework for the proposed electronic voting system is built with the help of blockchain technology in this proposal. As a result, fewer crimes has committed against sensitive data during the electoral process because of immutability, transparency and privacy. Ganache, Metamask and hashing algorithms are used to develop the dApp. The paper's strengths lie in its ability to create and analyze threat models for blockchain-enabled-electronic voting systems and to identify the types of threats using Microsoft STRIDE.
Diffusion-limited aggregation
Authors: Huang Junze , Xu Binyang
Published: July 2023

Models of physical processes from the fields of physics and chemistry may be used to create a variety of beautiful visuals and structures that resemble living things. Diffusion limited aggregation, also known as DLA, is an illustration of this. It is a term used to explain, among other things, how zinc ions diffuse and assemble onto electrodes in an electrolytic solution. "Diffusion" refers to the random movement of the particles that make up the structure before they adhere ("Aggregate") to it. "Diffusion-limited" refers to the fact that the structure grows one particle at a time rather than in chunks of particles because the particles are thought to be in low concentrations, preventing them from coming into touch with one another. Other instances include the development of coral, the route of lightning, the coalescence of dust or smoke particles, and the development of certain crystals.Nature uses a variety of interesting and often simple processes to produce amazing shapes, patterns, and forms that span all scales and never cease to surprise and enlighten the keen observer. From the From the micro to the macro, matter uses a variety of logical, observable processes to arrange, enhance, and transform, often stacking on top of each other in complex ways. In this paper, Mathematicians will discuss a process called diffusion-restricted aggregation or DLA, which uses randomly moving and "sticky" particles to produce fractal branching structures.
Probalistic model of spam filter system
Authors: Xiong Yanzhi , Wei Tianlan
Published: July 2023

Numerous tasks have been carried out using probabilistic reasoning, including picture recognition, computer diagnosis, stock price prediction, movie recommendation, and cyber intrusion detection. But until recently, the breadth of probabilistic programming was constrained (partly because of the low processing power), and the majority of inference methods had to be created manually for every job. Nevertheless, in 2015, 3D representations of human faces were created from 2D photographs of such faces using a 50-line probabilistic computer vision software. The inference approach of the software, which was developed in Julia using the Picture package, was based on inverted graphics. "What used to require thousands of lines of code" might now be accomplished in just 50. Probabilistic computing is a method to create systems that help us make decisions in the face of uncertainty. In this paper we propose a spam-filtering system. The novelty of our spam-filtering system is that we utilize probabilistic programming to improve spam-filtering reasoning systems. We implement several factors from email to establish a model to get the output. Our preliminary results show that we successfully accomplish a spam-filtering system. More problem of classification of spam filter can be described in a probabilistic modelling language in our future work.
Research on Yolo-based target recognition detection
Authors: hao Zhang
Published: July 2023

With the development of science and technology, the hotness of target recognition detection has increased year by year in recent years, and the subject has its wide application value in various aspects such as medical, daily life and military. This design on the subject, based on the popular target detection algorithm in recent years - Yolo algorithm, based on the latest Yolov5 algorithm model, the improvement and optimization of the algorithm, combined with the hot topics of current affairs, real-time monitoring of whether the target is wearing a mask, using the dataset provided by Aliyun Tianchi, to conduct experiments. The experimental results are: with Yolov5s base model as the prototype, after optimization and improvement, the fusion rate of target features is improved on the basis of maintaining the original training speed, which effectively improves the accuracy and precision of target recognition.
A hybrid parallelization approach based on workers grouping algorithm
Authors: Lu Ruoyu
Published: July 2023

As the volume of model data increases, traditional machine learning is not able to train models efficiently, so distributed machine learning is gradually used in large-scale data training. Currently, commonly used distributed machine learning algorithms are based on data parallelism, and often use an overall synchronous parallel strategy when passing data, but using this strategy makes the overall training speed limited by the computation speed of the slower workers in the cluster. While the asynchronous parallel strategy maximizes the computational speed of the cluster, there is a delay in updating the parameters of the global model, which may lead to excessive computational errors or non-convergence of the model. In this paper, the author combines these two data delivery methods by grouping workers together and using synchronous parallelism for the workers in the group and asynchronous parallelism for the components for training. The experiment shows that the hybrid parallelism strategy can reduce the training time with guaranteed correctness.
Comparing the effect of CNN and linear regression on facial expression recognition
Authors: Fang Bohan , Gan Tongrui
Published: July 2023

In Modern computer vision technology, facial expression recognition plays an important role. Many cases leverages this technology. This paper studies the Convolutional Neural Networks(CNN) in recognising the facial expression. We first show our research method to learn the model. Then we use the dataset to illustrate the efficiency CNN and Linear Regression. By showing the result of the two methods, we can clearly see the advantages of the CNN. We hope our research can help researchers have a better understanding about the CNN model.
Approaches to complete coverage path planning using neural network and grid division
Authors: Zhang Yiming
Published: July 2023

Since the goal of using complete coverage path planning is to generate a continuous and uninterrupted path that covers an area of interest while avoiding obstacles, its use could be extremely vital in today's field of robotics. Not only could it help our daily lives like lawnmowers, window cleaners, and painter robots, but it could also solve some dangerous or complex but vital problems for human beings; For example, mine detection, vacuum cleaning, and photogrammetry. This paper proposes two paths of successful approaches to Complete Coverage Path Planning: Neural Network and Grid division. After detailed data comparisons, both proved to have been efficient and successful, respectively. In addition, both plans' field applications would be placed at the end.
The research on phased array radar technology and its applications
Authors: Xie Jiangjian
Published: July 2023

Radar technology first appeared in 1842, proposed by the Austrian physicist Doppler. Over time, radar has had different applications in many fields. Among them, the use and advanced degree of radar in the military field is the highest. It was used to detect enemies to attack or defense. In recent decades, a more advanced phased array radar can replace the traditional radar in the military. In this report, it is focused on the principle and function of phase array radar and the applications and development of it are also discussed. Research on current phased array radar will help to develop higher performance and lower power consumption radar in the future. Combining the advantages and disadvantages of phased array radar helps to have a clearer view on the future direction of radar research.
The application of wireless sensor technology in gymnasium temperature and humidity control system
Authors: Gao Ruihang
Published: July 2023

The main objective of this study on temperature and humidity control in indoor sports halls is to provide an environment that is suitable for the people exercising in the halls and a way to minimize unnecessary sports injuries. The research methods used in this paper are both field monitoring of the environment and online data collection. The paper concludes with a temperature and humidity monitoring network based on a wireless sensor network to detect environmental temperature and humidity and thus achieve a system that can regulate it.
Comparative analysis of Reed-Solomon code and liberation code for two-disk failure recovery in RAID-6
Authors: Cao Zichao
Published: July 2023

As the quantity of data stored in the cloud system is increasing enormously daily requirement for a high tolerance for erasures is stricter than ever before. There are many encoding methods created by engineers to help recover the erased data, and one of the codes that are constantly used in RAID6 store system is the Reed-Solomon code. Though nowadays Reed Solomon code is applied widely, there are quite a lot of restrictions for it in some specific conditions. Under those scenarios, one more advanced encoding technique, Liberation code is introduced as an alternative coding for Reed-Solomon code. There will be a comparison between the Reed-Solomon code and the Liberation code from two aspects.
Artificial intelligence in the field of driving
Authors: Zhu Ziwei
Published: July 2023

The advent of the electrification of cars has made it possible to use a large number of electronic components in cars, and the intelligent car industry is developing rapidly because of the growing maturity of automotive electronics. Smart driving is safer, more comfortable, and more efficient than traditional driving, and it has great prospects for development in terms of brain-controlled car technology, intelligent internet interaction technology, and deep learning technology. It is undeniable that the development of smart driving technology is accompanied by challenges in terms of technology networks, policies and regulations, and economic conditions.
Design of a college apartment distribution management system based on students personality
Authors: Chen Ming
Published: July 2023

With the continuous development of the Internet, the maturity of computer technology has made this technology applicable to various fields. This paper will build a complete and stable university dormitory management system from the university dormitory management, a weak field of information technology. The system aims to reduce the workload of dormitory management and improve work efficiency. The system uses object-oriented development technology to achieve dormitory distribution, fault reporting and other functions. The system design is divided into the hierarchical design of the student dormitory management system, the functional design of the student dormitory management system, the overall architecture design of the student dormitory management system, and the interface design of the student dormitory management system. This system adopts B/S structure and object-oriented development technology to realize all the functions. Through the use of this system, modern information management can be realized in terms of accommodation arrangement, management and other aspects.
Virtual home for college couple in long-distance romantic relationships
Authors: Chen Ruyan
Published: July 2023

College students form a significant portion of all couples that are engaging in long-distance romantic relationships. The physical separation has made maintaining Long-Distance Romantic Relationships especially difficult for college couples. In order to provide design solutions to help college couples in Long-Distance Romantic Relationships better overcome challenges in their relationships, this paper concludes through surveys that the most pressing issue facing Long-Distance Romantic Relationships is feeling and understanding their significant other's emotions. Based on the unique offerings of Virtual Reality (VR) technologies and the limitations of existing social VR technologies, the author proposes the design solution of a virtual HOME that emphasizes co-presence, empathy promotion, and collaborative activities.
Recognition of handwritten digital neural network construction and improvement
Authors: Shao Zhenqun
Published: July 2023

More and more neural network algorithms are being invented and improved, but the most basic algorithms are still useful in learning or practice areas. This essay is about building a digital recognition neural network from scratch, whose implementation has a completely original core code and details and precisely elaborates the whole process of basic neural network construction through detailed mathematical derivation, combined with the idea of programming. Furthermore, this project deeply researched and analyzed recognition accuracy according to the identified data of the neural network built for the project, then improved the algorithm used by increasing the accuracy from 86.93% to 99.1% and finally successfully explained the role of raw data preprocessing.
Current status and prospects of fuel cell vehicle application
Authors: Chen Yize , Chen Shuo
Published: July 2023

With the continuous development and progress of industrial technology, the fossil fuel reserves of the earth are decreasing day by day, and it is more and more difficult to maintain the current energy demand. All sectors of the world are looking for an effective alternative to traditional energy. Hydrogen, known as the clean fuel of the 21st century, has gradually come into people's view because of its high combustion heat value and no pollution of the products after combustion. This paper discusses construction characteristics and working principle of fuel cell vehicles, to prove that the environment problems can be reduced by them. It first introduces the advantages of fuel cells, it produces almost no pollution, thus alleviating the greenhouse effect. It is of the main reasons why FCVs are becoming more and more popular. Secondly, the author also introduces the structure of fuel cell vehicles by comparing their similarities and differences with ordinary cars. The control systems of fuel cell vehicles have also been introduced.
Price prediction of used cars using ISOMAP and SVM
Authors: Bao Yan
Published: July 2023

This paper conducts model training and testing for estimating the price of used cars. In the second-hand car market, the residual value is one of the most important reasons for the second-hand car circulation. Because much of the way used cars are traded is traditional used car appraisers rely on their experience with used cars to determine the value of the car. At present, there is no comprehensive evaluation model based on objective factors of second-hand car characteristics. This study is based on the establishment of an objective evaluation of the effectiveness of the price prediction model and the objective factors affecting the second-hand car. In this research, the author wants to replace traditional evaluation methods with deep learning methods. The code written in this study includes the training of the data preprocessing model, flexibly uses NumPy to perform precision calculations, and uses Sklearn to delete highly relevant features for feature screening. The non-linear isomap algorithm is used for dimensionality reduction to keep the data at the same latitude, and finally the SVM algorithm is used for model training. Very unsatisfactory results have been obtained by training the model. The train accuracy and test accuracy were 22.5% and 14.3%, respectively. Through the analysis of the model, it may be that the dimensionality reduction of the isomap algorithm deleted the required influencing factors, resulting in the low training accuracy of the entire model.
Identifikasi Molekuler Trichoderma sp. Isolat T10 ISRI sebagai Agen Hayati Penyakit Busuk Akar dan Pangkal Batang Xylaria
Authors: Jati Wiwit Wicaksono , Kristini Ari , Mulyatni Agustin Sri , Abadi Abdul Latief
Published: July 2023

Penyakit busuk akar dan pangkal batang pada tebu merupakan penyakit yang memiliki arti penting secara ekonomi.  Salah satu agen hayati yang berpotensi mengendalikan penyakit busuk akar dan pangkal batang Xylaria adalah Trichoderma sp. Isolat T10 ISRI memiliki aktivitas enzim kitinase yang tinggi sehingga dapat digunakan sebagai agen hayati. Identifikasi isolate T10 ISRI bertujuan agar potensi lain dari isolate T10 ISRI dapat diktahui. Tujuan dari penelitian ini adalah untuk mengidentifikasi isolat T10 ISRI sampai dengan tingkat spesies melalui identifikasi secara molekuler. Metode identifikasi dilakukan dengan sequencing DNA melalui BLAST dan pembuatan pohon filogenik. Hasil penelitian menunjukkan bahwa spesies isolat T10 ISRI yaitu Trichoderma asperellum dengan kesamaan 100% dengan strain Trichoderma asperellum T sum 66 dari data NCBI dan kedekatan dalam kelompok 0,00001 dari pada jarak diluar kelompok yaitu 0,3287.
Peningkatan Kualitas Fisik Dan Kimia Lahan Marjinal Tebu Melalui Penggunaan Kompos, Pelembab Sintetis Dan Mikoriza
Authors: Puspitasari Arinta Rury , Winarsih Sri
Published: July 2023

Penanaman tebu di lahan marjinal ataupun lahan kering merupakan salah satu alternatif pengembangan tebu karena terbatasnya lahan berpengairan. Dalam usaha untuk meningkatkan kualitas lahan marjnal, dapat diberikan penambahan bahan organik dan pembenah tanah. Tujuan percobaan adalah pemberian kompos, pembenah tanah pada lahan marjinal Madura untuk dapat memperbaiki struktur fisik dan kimia tanah. Percobaan dilaksanakan di Bangkalan, Madura pada areal lahan kering mulai pada bulan Maret sampai dengan November 2015. Penelitian dirancang menurut rancangan petak terbagi. Petak utama adalah aplikasi bahan organik dan tanpa aplikasi kompos. Anak petak terdiri atas 4 perlakuan yaitu pupuk hayati endomikoriza, pelembab tanah, kombinasi mikoriza dan pelembab tanah serta kontrol (tanpa tambahan mikoriza dan pelembab tanah) dan diulang tiga kali. Hasil penelitian pada perlakuan anak petak (pembenah tanah) menunjukkan aplikasi mikoriza dan pelembab tanah memberikan porositas lebih tinggi dan berbeda nyata dengan pelembab tanah serta kontrol. Aplikasi mikoriza dan kompos dapat menyediakan P dalam tanah lebih tinggi mencapai 315,24% dan berbeda nyata dengan perlakuan lainnya. Aplikasi kompos dapat meningkatkan jumlah spora dari awal sebelum aplikasi sebesar 62,24% dan berbeda nyata dengan tanpa perlakuan kompos. Pada pengamatan kelembaban tanah, kandungan C-organik dan KTK tanah tidak ada beda nyata pada semua perlakuan petak utama maupun anak petak.