Optical Memory and Neural Networks最新文献_第7页

Lessen Pressure Drop and Forecasting Thermal Performance in U-Tube Heat Exchanger Using Chimp Optimization and Deep Belief Neural Network 利用 "黑猩猩优化 "和 "深度信念神经网络 "降低 U 型管式热交换器的压降并预测热性能

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Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/s1060992x23040033

Shailandra Kumar Prasad, Mrityunjay Kumar Sinha

{"title":"Lessen Pressure Drop and Forecasting Thermal Performance in U-Tube Heat Exchanger Using Chimp Optimization and Deep Belief Neural Network","authors":"Shailandra Kumar Prasad, Mrityunjay Kumar Sinha","doi":"10.3103/s1060992x23040033","DOIUrl":"https://doi.org/10.3103/s1060992x23040033","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In the chemical, pharmaceutical, and petroleum industries, Shell and U-Tube Heat Exchangers (STHX) were extensively utilized. Baffles must be positioned at the right distance and angle to increase the heat exchangers' capacity to convey heat and, as a result, lower pressure in the shell. The rate of heat transfer in an STHX has been improved, and pressure drop has been reduced using a variety of models. But those methods are not provided satisfactory pressure drop reduction. In the proposed model, an optimal Unilateral Ladder-Type Helical Baffles (ULHB) design and intelligent performance prediction system based U-tube heat exchanger was designed to reduce the pressure drop as well as predict the heat exchanger performance. The shell and tubes were made up of steel and copper material, respectively. A baffle was placed above tubes to barrier the flow of cold water. The design of the baffle was accomplished by using Chimp Optimization Algorithm (ChOA) and is motivated by the hunting behaviour of chimpanzees. After designing the exchanger, its fluid analysis was verified, and the parameter values of the heat exchanger were collected to create a dataset. Based on that data, the intelligent performance prediction-system was designed. The controlling system analysed the given data to predict the performance of the heat exchanger. The suggested model has a pressure drop of 55 Pa, a heat transfer coefficient of 411 <i>U,</i> and 86% accuracy for the thermal performance prediction process. The proposed model provides better performance by improving heat transfer efficiency and significantly reduces pressure drop.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review on Pest Detection and Classification in Agricultural Environments Using Image-Based Deep Learning Models and Its Challenges 基于图像的深度学习模型在农业环境中的害虫检测和分类及其挑战综述

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Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/s1060992x23040112

P. Venkatasaichandrakanth, M. Iyapparaja

{"title":"Review on Pest Detection and Classification in Agricultural Environments Using Image-Based Deep Learning Models and Its Challenges","authors":"P. Venkatasaichandrakanth, M. Iyapparaja","doi":"10.3103/s1060992x23040112","DOIUrl":"https://doi.org/10.3103/s1060992x23040112","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Agronomic pests cause agriculture to incur financial losses because they diminish production, which lowers revenue. Pest control, essential to lowering these losses, involves identifying and eliminating this risk. Since it enables management to take place, identification is the fundamental component of control. Utilizing the pest’s traits, visual identification is done. These characteristics differ between animals and are intrinsic. Since identification is so difficult, specialists in the field handle most of the work, which concentrates the information. Researchers have developed various techniques for predicting crop diseases using images of infected leaves. While progress has been made in identifying plant diseases using different models and methods, new advancements and discussions still offer room for improvement. Technology can significantly improve global crop production, and large datasets can be used to train models and approaches that uncover new and improved methods for detecting plant diseases and addressing low-yield issues. The effectiveness of machine learning and deep learning for identifying and categorizing pests has been confirmed by prior research. This paper thoroughly examines and critically evaluates the many strategies and methodologies used to classify and detect pests or insects using deep learning. The paper examines the benefits and drawbacks of various methodologies and considers potential problems with insect detection via image processing. The paper concludes by providing an analysis and outlook on the future direction of pest detection and classification using deep learning on plants like peanuts.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lessen Pressure Drop and Forecasting Thermal Performance in U-Tube Heat Exchanger Using Chimp Optimization and Deep Belief Neural Network 利用 "黑猩猩优化 "和 "深度信念神经网络 "降低 U 型管式热交换器的压降并预测热性能

IF 1

Optical Memory and Neural Networks Pub Date : 2023-12-22 DOI: 10.3103/S1060992X23040033

Shailandra Kumar Prasad, Mrityunjay Kumar Sinha

{"title":"Lessen Pressure Drop and Forecasting Thermal Performance in U-Tube Heat Exchanger Using Chimp Optimization and Deep Belief Neural Network","authors":"Shailandra Kumar Prasad,  Mrityunjay Kumar Sinha","doi":"10.3103/S1060992X23040033","DOIUrl":"10.3103/S1060992X23040033","url":null,"abstract":"<p>In the chemical, pharmaceutical, and petroleum industries, Shell and U-Tube Heat Exchangers (STHX) were extensively utilized. Baffles must be positioned at the right distance and angle to increase the heat exchangers' capacity to convey heat and, as a result, lower pressure in the shell. The rate of heat transfer in an STHX has been improved, and pressure drop has been reduced using a variety of models. But those methods are not provided satisfactory pressure drop reduction. In the proposed model, an optimal Unilateral Ladder-Type Helical Baffles (ULHB) design and intelligent performance prediction system based U-tube heat exchanger was designed to reduce the pressure drop as well as predict the heat exchanger performance. The shell and tubes were made up of steel and copper material, respectively. A baffle was placed above tubes to barrier the flow of cold water. The design of the baffle was accomplished by using Chimp Optimization Algorithm (ChOA) and is motivated by the hunting behaviour of chimpanzees. After designing the exchanger, its fluid analysis was verified, and the parameter values of the heat exchanger were collected to create a dataset. Based on that data, the intelligent performance prediction-system was designed. The controlling system analysed the given data to predict the performance of the heat exchanger. The suggested model has a pressure drop of 55 Pa, a heat transfer coefficient of 411 <i>U,</i> and 86% accuracy for the thermal performance prediction process. The proposed model provides better performance by improving heat transfer efficiency and significantly reduces pressure drop.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 4","pages":"275 - 294"},"PeriodicalIF":1.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Prediction Models for Vulnerable Road User Accident Severity 开发易受伤害道路使用者事故严重程度预测模型

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Optical Memory and Neural Networks Pub Date : 2023-12-01 DOI: 10.3103/s1060992x23040082

{"title":"Development of Prediction Models for Vulnerable Road User Accident Severity","authors":"","doi":"10.3103/s1060992x23040082","DOIUrl":"https://doi.org/10.3103/s1060992x23040082","url":null,"abstract":"<span> <h3>Abstract</h3> <p>Road traffic accidents are considered a significant problem which ruins the life of many people and also causes major economic losses. So, this issue is considered a hot research topic, and many researchers all over the world are focusing on developing a solution to this most challenging problem. Traditionally the accident spots are detected by means of transportation experts, and following that, some of the statistical models such as linear and nonlinear regression were used for accident severity prediction. However, these traditional approaches do not have the capability to analyze the relationship between the influential factor and accident severity. To address this issue, an Artificial Neural Network (ANN) classifier based vulnerable accident prediction model is proposed in this current research. Initially, the past accident data over the past period of years is collected from a specified area. The acquired data consists of a variable factor related to road infrastructure, weather condition, area of the accident, type of injury and driving characteristics. Then, to standardize the raw input data, min-max normalization is used as a pre-processing technique. The pre-processed is sent for the feature selection process in which essential features are selected by correlating the variable factor with accident severity prediction. Following that, the dimension of the features is reduced using Latent Sematic Index (LSI). Finally, the reduced features are fetched into the ANN classifier for predicting the severity of accidents such as low, medium and high. Simulation analysis of the proposed accident prediction model is carried out by evaluating some of the performance metrics for three datasets. Accuracy, error, specificity, recall and precision attained for the proposed model using dataset 1 is 96.3, 0.03, 98 and 98%. Through this proposed vulnerable accident prediction model, the severity of accidents can be analyzed effectively, and road safety levels can be improved.</p> </span>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"60 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating the Efficiency of Using U-Net, Erf-Net and DeepLabV3 Architectures in Inverse Lithography-based 90-nm Photomask Generation 研究在基于反向光刻技术的 90 纳米光掩膜生成中使用 U-Net、Erf-Net 和 DeepLabV3 架构的效率

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Optical Memory and Neural Networks Pub Date : 2023-12-01 DOI: 10.3103/s1060992x23040094

I. M. Karandashev, G. S. Teplov, A. A. Karmanov, V. Keremet, A. Kuzovkov

引用次数: 0

Far Resonance Kapitza-Dirac Diffraction: from Raman-Nath to Bragg and Multiple Beam Atomic Interferometer 远共振卡皮查-迪拉克衍射：从拉曼-纳特到布拉格和多光束原子干涉仪

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Optical Memory and Neural Networks Pub Date : 2023-12-01 DOI: 10.3103/s1060992x23070159

引用次数: 0

Anti-Site Defects and Trigonal Center of Holmium in Y3Al5O12:Ho3+ Crystal According to the Results of Wideband EPR Spectroscopy 宽带 EPR 光谱结果显示 Y3Al5O12:Ho3+ 晶体中的反位缺陷和钬的三正交中心

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Optical Memory and Neural Networks Pub Date : 2023-12-01 DOI: 10.3103/s1060992x23070044

引用次数: 0

Enhancement of Knowledge Distillation via Non-Linear Feature Alignment 通过非线性特征对齐加强知识提炼

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Optical Memory and Neural Networks Pub Date : 2023-12-01 DOI: 10.3103/s1060992x23040136

Jiangxiao Zhang, Feng Gao, Lina Huo, Hongliang Wang, Ying Dang

引用次数: 0

Information Added U-Net with Sharp Block for Nucleus Segmentation of Histopathology Images 用于组织病理学图像细胞核分割的带有锐块的信息添加 U-Net

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Optical Memory and Neural Networks Pub Date : 2023-12-01 DOI: 10.3103/s1060992x23040070

Anusua Basu, Mainak Deb, Arunita Das, K. G. Dhal

引用次数: 0

Application of the Variational Principle to Create a Measurable Assessment of the Relevance of Objects Included in Training Databases 应用变分原理对训练数据库中包含的对象的相关性进行可测量评估

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Optical Memory and Neural Networks Pub Date : 2023-11-28 DOI: 10.3103/S1060992X23060024

V. A. Antonets, M. A. Antonets

{"title":"Application of the Variational Principle to Create a Measurable Assessment of the Relevance of Objects Included in Training Databases","authors":"V. A. Antonets, M. A. Antonets","doi":"10.3103/S1060992X23060024","DOIUrl":"10.3103/S1060992X23060024","url":null,"abstract":"<p>We consider the problem of obtaining a measurable assessment of the quality of empirical training data selected by experts. This problem can be solved in those cases where the data can be displayed in the form of histograms. This class includes any diagrams of frequency of occurrence of linguistic objects in samples, for example, lemmas in a text. It also includes discretized temporal signals from different branches of science, technology, and medicine. The proposed method, as well as other known methods, is based on the use of weight functions. With its help, the weight of each histogram is defined as the sum over all its columns of the products of column height by the value of weight function for the corresponding column. However, in contrast to the well-known approaches, the weight function in the proposed approach is not found empirically, but on the basis of the following variation principle. The weight function is considered optimal if the weight of the lightest histogram found with its help is greater than or equal to the weight of the lightest histogram determined by any other weight function. The application of the developed approach to the task of thematic classification of ad texts on electronic trading floors showed that for the selected topics approximately 90% of the lemmas (words) encountered in the training corpus had the weight equal to zero, and almost all words with nonzero weight were semantically related to the topic.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 2","pages":"S265 - S269"},"PeriodicalIF":0.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138449134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0