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Injection control algorithm of diesel electronic control system based on neural network technology

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第2期14卷 613-625页

作者： Gao, Bing Zhang, Shaoming Li, Zhaoyang Guangdong Commun Polytech Coll Maritime Guangzhou 510800 Guangdong Peoples R China Guangdong Ship Automat Engn Technol Res Ctr Guangzhou 510800 Guangdong Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China

With the continuous development of modern industrial control technology, neural network controllers have been used in many disciplines and various fields due to their simple structure and convenient adjustment of the parameters of each controller. In order to explore the current situation of the application of neural network technology to diesel electronically controlled injection control, In this paper, comparative experiment, the experimental data were collected using the initial model parameters and the comparative screening method, analyze several aspects of diesel engine improvement, and simplify The algorithm. And create an injection model that can use neural network technology. When re-adjusting the electronic control setting value of the diesel engine, the results show that the diesel engine is in the EC12.1-EC14.1 electronic control period. From the fuel consumption curve, it can be seen that the lowest fuel consumption rate is about 201 g/kWh, which appears in the electronic control function. The segment is at a temperature of 13.9. Conventional injection model was then compared with the improved nerve, conventional controllers set parameters: hq = 0.5, hu = 0.4, hw = 0.2;improved parameters: hq = 0.5, hu = 0.4, hw = 0.2, hx = 1.1. It is concluded that at 15 s, the improved type has too much adjusting power, which leads to the occurrence of overshoot, and it soon stabilizes at 17.1 s. The classic model adjustment has always been quite satisfactory, and there are no surprises. The speed of control is obviously inferior to the improved model. It is basically realized that starting from the convenience, efficiency and accuracy of the model, a neural network model that can control electronically controlled injection more quickly and effectively is designed.

关键词： neural network technology Diesel electronic control system Injection control algorithm Neuron injection model

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Multi sensor moving image fusion analysis algorithm on the basis of neural network technology

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第3期23卷 1209-1224页

作者： Zhan, Keqiang Fujian Polytech Informat Technol Internet Things & Artificial Intelligence Coll Fuzhou 350003 Fujian Peoples R China

Image fusion can extract the useful information of each channel about the same target to the maximum extent, and get high quality image. However, in this process, the image quality may be affected by noise and reduced. To reduce the image noise's influence on the image fusion effect as well as improve the image fusion quality, a multi sensor moving image fusion analysis algorithm on the basis of neural network technology is proposed. This study designed a window adaptive strategy, use the probability density function, and built an impulse noise model, and use this model to divide each pixel in the image into noise points or signal points to obtain image impulse noise detection results, and use bilateral filtering algorithm to achieve image denoising processing;The fruit fly optimization algorithm is adopted to detect the edge of the multi sensor moving image, extract the image's main edge points, and remove the detail edge points and noise points;nonlinear convolutional layer is used to replace most fusion layers to improve the dense network model, and the cross-entropy loss is used as the loss function in training the network, then use guided filters to generate guide maps, and generate final fusion images. According to experimental results, the noise detection method in this paper can also maintain 79.21% non-noise extraction rate under the noise density of 0.7. The highest correlation coefficient between the proposed algorithm and the standard image is 37.41. Its peak signal-to-noise ratio is as low as 0.09 and as high as 0.52. It has a minimum root mean square error of 8.52. The above values are better than other measured methods, and its edge miss rate can be as low as 1%, the image resolution is higher. It can be seen that its image denoising effect is better. Image denoising effect, and low edge missed detection rate, which effectively improves the effect of image fusion.

关键词： neural network technology multi sensor image fusion impulse noise model fruit fly optimization algorithm improved dense network

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Consequences of neural network technology for cervical screening - Increase in diagnostic consistency and positive scores

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CANCER 1996年第1期78卷 112-117页

作者： Kok, MR Boon, ME LEIDEN CYTOL & PATHOL LAB 2301 GB LEIDENNETHERLANDS

BACKGROUND. Screening programs for the early detection of cervical carcinoma are criticized because of the problem of false-negative diagnoses. A successful approach for solving this problem is applying neural network technology (PAP-NET(R)) to assist the cytotechnologist (CT) in finding the (few) abnormal cells in the smear. METHODS. In 3 consecutive years (1992, 1993, and 1994), 25,767 smears were screened conventionally and 65,527 with the aid of PAPNET by 7 CTs. For each CT, the scores for atypias of undetermined significance, squamous or glandular (ASCUS/AGUS according to the Bethesda classification system), indicated by Positive I, for low grade precursor lesions, by Positive II, for high grade lesions and invasive carcinoma, by Positive III, were calculated for both screening methods. The histologic scores were also calculated. RESULTS. The mean positive scores of the seven CTs were higher for PAPNET than for conventional screening, and the coefficients of variability were lower. For Positive III smears, the consistency in screening was significantly higher for PAPNET than for conventional screening. The higher histologically positive scores for carcinoma in situ and invasive carcinoma indicated an increased screening sensitivity. CONCLUSIONS. As demonstrated by the improvement in the performances of all CTs involved, screening efficacy was enhanced by the use of neural network technology. (C) 1996 American Cancer Society.

关键词： neural network technology diagnostic consistency interobserver variability cervical screening

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Design of Computer network Security Management System based on neural network technology 2

Design of Computer Network Security Management System based ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Liu, Jie Zibo Normal College Department of Information Zibo China

ISBN: (纸本)9798350317558

The internet has gradually penetrated into people's daily lives. However, with the popularization of the Internet, its negative impact has also followed. With the emergence of the Internet, behaviors such as hackers, industrial spies, and malicious intrusions have led to the emergence of network security issues. This article started from the perspective of network risk assessment, constructed a computer network security management system, and evaluated and predicted its security situation. The artificial neural network model using the GABP (Genetic Algorithm Back Propagation) algorithm was used for network security evaluation to improve the accuracy of the system. In the training samples, the accuracy of GABP was 95%, and the accuracy of LDA (Linear Discriminant Analysis) was 92%. In the predicted samples, the accuracy of GABP was 99%, and the accuracy of LDA was 88%. The GABP algorithm model had high theoretical significance and broad application prospects in network security assessment and verification. © 2024 IEEE.

关键词： Computer network Security Management System GABP Algorithm neural network technology Security Situation System Accuracy

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Exploration on Text Detection Optimization Algorithm Based on neural network technology 1

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13th International Conference on Frontier Computing, FC 2023

作者： Liang, Junxia Qi, Yongjun Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China School of Information and Communication Technology Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

ISBN: (数字)9789819992997

ISBN: (纸本)9789819992980

In this rapidly changing era of information technology, images have always been the main tool for human communication of information. In daily life, a large number of natural scene images, such as road signs, advertising slogans, etc., often contain important textual information, which is of great help to people’s understanding of image content. Text is a type of linguistic information that exists in various aspects of people’s lives. The textual information in it can help people understand, describe, and analyze natural scenes, and has important social value. neural network technology is of great help for optimizing text detection. This paper discussed the optimization performance of neural network algorithm for text detection, and verified the progressiveness of the algorithm through experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Algorithm Optimization Experimental Study neural network technology Text Detection

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Prediction Model of Weekly Retail Price for Eggs Based on Chaotic neural network

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Journal of Integrative Agriculture 2013年第12期12卷 2292-2299页

作者： LI Zhe-min CUI Li-guo XU Shi-wei WENG Ling-yun DONG Xiao-xia LI Gan-qiong YU Hai-peng Agricultural Information Institute Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-Information Service TechnologyMinistry of Agriculture

This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in *** the process of determining the structure of the chaotic neural network,the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension,and then the number of hidden layer nodes is estimated by trial and ***,this model is applied to predict the retail prices of eggs and compared with *** result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of *** empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.

关键词： chaos theory chaotic neural network neural network technology short-term prediction weekly retail price of eggs

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Coal mine electrical safety management and accident prevention based on neural network and signal processing

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2022年第6期22卷 2257-2266页

作者： Miao, Leigang Niu, Yuanyuan Jiangsu Vocat Inst Architectural Technol Coll Construct Management Xuzhou 221116 Jiangsu Peoples R China Jiangsu Prov Xuzhou Technician Inst Coll Rail Transit Xuzhou Jiangsu Peoples R China

Based on the upgrading of electronic components, communication and computer technology have made rapid progress. neural network technology which mainly relies on the simulation of animal nerves, realize large-scale node information processing functions. Signal processing can complete the processing of various types of signals to obtain key data. With the further shortage of global energy, countries have begun to pay attention to the development of energy. As one of the traditional energy supply methods, coal mines have received a lot of financial support from the government. However, coal mine safety accidents have always been the focus of many enterprises and governments. In the process of coal mine development, there are many electrical equipment, and there are many changes in the underground environment. The problems of flooding, subsidence, and rodents biting off the line cause the paralysis of the electrical system and eventually lead to safety accidents in coal mine development. The neural network technology, can add all the electrical equipment in the mine operation, and to the network for analysis, and the signal processing technology can centrally process the signals of a variety of electrical equipment, and analyze the abnormal signals sent by the electrical equipment, and finally realize the prediction of electrical accidents. This paper firstly introduces the neural network and signal processing technology used in the text. Using the examples of hidden safety hazards in mining areas discovered during the team's inspection as a basis, and combining with the actual situation in our country, these cases are classified. Next, using literature analysis and comparative research methods to analyze the problems existing in the current coal mine electrical safety management and accident prevention. A programme of safety management and accident prevention. The 0-Net monitoring system constructed by the institute has its own unique advantage that it has more inputs

关键词： Coal mine safety neural network technology signal processing technology

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Less medical intervention after sharp demarcation of grade 1-2 cervical intraepithelial neoplasia smears by neural network screening

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CANCER CYTOPATHOLOGY 2001年第3期93卷 173-178页

作者： Kok, MR Boon, ME Schreiner-Kok, PG Hermans, J Grobbee, DE Kok, LP Leiden Cytol & Pathol Lab NL-2301 GB Leiden Netherlands Leiden Univ Med Ctr Dept Med Stat Leiden Netherlands Univ Utrecht Med Ctr Julius Ctr Gen Practice & Patient Oriented Res Utrecht Netherlands Univ Groningen Dept Biomed Technol Groningen Netherlands

BACKGROUND. neural network technology has been used for the daily screening of cervical smears in The Netherlands since 1992. The authors believe this method might have the potential to demarcate diagnoses of Grade 1-2 cervical intraepithelial neoplasia (CIN 1-2). METHODS. Of 133,196 women who were screened between 1992-1995, there were 2236 CIN 1-2 smears;1128 of which were detected by means of neural network screening (NNS) (n = 83,404 women) and 1108 of which were diagnosed by conventional screening (n = 49,792 women). Cytologic and clinical outcomes (first cytologic or histologic follow-up diagnosis) were retrieved for all the women in the study population (n = 1920). Stratification based on clinical outcome resulted in the cases being grouped as overdiagnosed, concordant, or underdiagnosed. The smears were performed by general practitioners, whereas the biopsies were obtained by gynecologists. RESULTS. The prevalence rate for CIN 1-2 was 1.15% (95% confidence interval [95% CI], 1.08-1.23%) for NNS and 1.92% (95% CI, 1.80-2.04%) for conventional diagnosis (P < 0.001). Concordance with histology was significantly higher for NNS (53.9%;95% CI, 50.7-57.0%) compared with conventional screening (29.2%;95% CI, 26.4-32.2%). In addition, overdiagnosis was significantly lower for cases diagnosed by NNS (39.4%;95% CI, 36.3-42.4%) compared with cases diagnosed by conventional screening (62.4%;95% CI, 59.3-65.5%). CONCLUSIONS. neural network-based screening can lead to fewer women being burdened unnecessarily with a cytologic diagnosis of CIN 1-2 by resulting in a sharp demarcation in these diagnoses and a corresponding reduction in unnecessary medical interventions. [See editorial on pages 171-172, this issue.] Cancer (Cancer Cytopathol) 2001;93:173-178. (C) 2001 American Cancer Society.

关键词： cervical intraepithelial neoplasia overdiagnosis concordance underdiagnosis neural network technology medical intervention

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Predicting short-term orders by an improved grey neural network model

Predicting short-term orders by an improved grey neural netw...

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International Conference on Mechatronics and Industrial Informatics (ICMII 2013)

作者： Yang Shenglong Zhang Shengma Wang Cuihua Ma Junjie Minist Agr Key Lab East China Sea & Ocean Fishery Resources Shanghai 200090 Peoples R China Tongji Univ Sch Law Shanghai Peoples R China Tongji Univ Sch Intellectual Property Shanghai Peoples R China

ISBN: (纸本)9783037856949

According to the validation that the random selection of the gray neural network parameters random selection is similar to initial the space position of the particle in the particle swarm algorithm, the gray neural network based on the modified particle swarm optimization (PSO) algorithm is established to improve the robustness and the precision of the net model with applying a improved PSO algorithm to instead of gradient correction method, updating the network parameter and searching the best individual in this algorithm. There are several methods to forecast the short-term orders, including BP, the gray network, the original PSO algorithm and the improved PSO algorithm. Comparing with these methods, the results demonstrated the grey network based on the improved PSO algorithm has better approximation ability and prediction accuracy.

关键词： grey model PSO neural network technology

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neural network Techniques for Effective Fund Review Evaluation 24

Neural Network Techniques for Effective Fund Review Evaluati...

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1st International Conference on Image Processing Machine Learning and Pattern Recognition

作者： Dai, Peikun Wen, Weipeng Xu, Zhixuan Feng, Ye Lai, Pengyu Shantou Univ Sch Business Shantou Peoples R China Shenzhen Xinhe Yusheng Technol Co Ltd Shenzhen Peoples R China Cent South Univ Sch Business Changsha Peoples R China Zhejiang Wanli Univ Coll Foreign Languages Ningbo Peoples R China Univ Macau Inst Collaborat Innovat Beijing Peoples R China

ISBN: (纸本)9798400707032

Finance plays a crucial role in the development of both country and society. Analyzing financial-related data is also essential for advancing the financial industry. In recent years, there has been an abundance of financial-related data, including fund review data. This paper analyzes fund review data and employs neural network technology to evaluate and extract meaningful information from these reviews. By leveraging neural networks, the model excels at identifying complex patterns in the data, thereby enhancing the accuracy of performance evaluations. Our goal is to improve the accuracy and efficiency of fund review evaluations and uncover significant insights.

关键词： Financial development Fund review neural network technology

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