ANN在发酵过程中的应用

人工神经网络具有非线性的自适应的信息处理能力 ,已广泛地用于化工、通信等领域。文中综述了ANN在发酵过程的仿真与预测、错误诊断、自动控制和配方优化等方面的应用。在此基础上 ,对ANN在发酵工业中的应用前景进行了展望。     

木材科学与工程专业课程教学改革探讨

木材科学与工程专业包含木材学、人造板工艺学、胶黏剂与涂料等主要课程。但是目前已有的教学内容、教学方法和模式无法与当前产业的发展需求相适应。因此,文章分析了传统教学中的现存问题,并从木材科学与工程专业课程的教学内容、教学形式,以及实践教学等方面提出教学改革方案,通过不断发展与更新教学内容、增加学生的课堂参与,提高自主科研实验在教学过程中的比重等方式进行课程教学改革,目的是提高学生的学习积极性、创新性和实践能力,适应木材工业的发展需求,为木材科学与工程行业的发展培养高水平的专业科研人才。     

基于人工神经网络算法的电子鼻系统在食品无损检测中的应用

电子鼻技术是一门新兴的气体分析技术,因其具有响应速度快、检测时间短等优点而被广泛应用于医疗诊断、环境监测、农副产品与食品的检测中。文章首先介绍了几种常用神经网络算法的,并比较了不同算法的优缺点,然后重点介绍了基于人工神经网络算法的电子鼻系统在水果检测鉴别、肉制品检测、茶叶品质鉴定、乳制品、酒类等食品检测方面的应用,最后对电子鼻技术、ANN算法目前存在问题及发展趋势进行了阐述。     

卓越农林人才教育计划下生物质材料加工技术新开课程建设探索

鉴于农林生物质资源加工利用产业的蓬勃发展,北京林业大学木材科学与工程专业在2015版人才培养方案及教学计划中增设了《生物质材料加工技术》课程。本文结合新时期国家卓越人才农林教育计划的培养要求以及木材科学与工程的学科特色和专业定位,就新开课程的教学背景、教学目标、教学内容和教学方法等进行了讨论和分析。     

浅谈基于机器学习的木材干燥建模方法

介绍了目前国内外对于木材干燥建模的研究现状.针对木材干燥过程的强耦合、非线性等特点,探讨了应用目前常用的两种基于机器学习的非线性系统建模方法-神经网络和支持向量机建立木材干燥模型的可行性,给出了基于神经网络和支持向量机的木材干燥模型,并分析了这两种木材干燥模型的结构和优缺点.     

纳米压痕测试技术在木材改性研究中的应用

纳米压痕作为一项测量材料微观力学性能的新技术发展迅速,不仅被应用于木材力学性能的研究,还延伸到功能化处理和改性木材细胞壁强度变化等方面的研究。本文概述了近年纳米压痕技术在木质材料改性、细胞壁功能化处理中的应用,分析了应用中应注意的问题,为应用纳米压痕表征改性木材微观力学性能研究以有益提示。     

木材细胞微观构造数字图像处理研究的进展

木材细胞的解剖构造是木材科学重要的基础研究内容之一,本文概括了国内外数字图像处理技术在木材细胞解剖构造中的研究进展和应用现状。随着科技水平的进步,图像处理技术在木材细胞科学的研究及应用前景将更加广阔。     

大学生专业素质提升途径探索——以西南林业大学木材科学与工程专业为例

专业素质作为大学生综合素质提升的核心要素,是教育现代化与创新人才培养必不可少的重要环节。为培养具有过硬本领的木材科学与工程专业技术人才,主动融入国家经济发展需求,本文对木材科学与工程专业学生专业素质现状进行分析,并提出相应的改革措施及思路,以期为培养高素质创新型人才提供支持。     

顺势而生家具专业添新军-西南林业大学首批家具工程本科新生顺利入学

值西南林业大学家具工程本科专业方向成功开办之际,分析了该专业的办学背景、办学特点与培养方案,论述了该专业的办学特色、依托的木材科学与技术学科发展平台以及发展愿景.     

中国林业科学研究院制浆造纸研究开发中心简介

本实验室是国家林业局直属的唯一专业从事“林纸一体化技术”研究的专门机构。在速生木材制浆适应性评估、速生木材高效清洁制浆和废水弃物处理和资源化利用工程等技术领域具有鲜明的特色，是我国速生木材高得率制浆及相关新技术研发的知名科研机构。     

人工神经网络在食品工业中的应用

人工神经网络对非线性系统有很强的处理能力，适合对食品加工过程的仿真。综述了人工神经网络在加工：建模、工艺优化、过程控制与预测及在食品分析中的应用，旨在为人工神经网络在食品工业中的更广泛应用提供一定的理论基础和依据。     

甘草酸提取工艺的优化研究

以甘草中主要生理活性物质甘草酸的超声提取工艺为研究对象,在传统的正交试验方法的基础上,用人工神经网络方法进行优化,获得了超声提取甘草酸的优化工艺条件,即乙醇浓度40%、超声次数4次、提取温度20℃、提取时间20min、超声频率13kHz、固液比为1:10。该优化工艺条件操作简便、能耗低、提取率高,是合理可行的。     

Predicting Milk Shelf-life Based on Artificial Neural Networks and Headspace Gas Chromatographic Data

The usefulness of artificial neural networks (ANN) for milk shelf-life prediction by multivariate interpretation of gas chromatographic profiles and flavor-related shelf-life was evaluated and compared to principal components regression (PCR). The training set consisted of dynamic headspace gas chromatographic data collected during storage of pasteurized milk (input information for the neural network used to make a decision) and its corresponding shelflife (prediction or response). ANN had better predictability than PCR. A standard error of the estimate of 2 days in shelf-life resulting from regression analysis of experimental vs predicted values indicated a high predictability of ANN.     

Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks

Massive datasets such as gene expression profiles are accumulating along with the development of DNA microarray technologies. In this paper, we focus on mining biological relevant information such as typical expression patterns and the interconnections of gene networks from massive datasets. At first, the algorithm of a self-organizing map (SOM) was used to cluster gene expression data. Then, for the typical patterns extracted by the SOM, a three-layer artificial neural network (ANN) model was used to extract the relationships between the expression patterns. In order to evaluate the clustering analysis based on the SOM, biological and statistical indices were introduced. To validate the efficiency of the scheme proposed for extracting the relationships between the expression patterns with the ANN, a test dataset was created and used for the test. Finally, the interconnections of a typical pattern of early G1, late G1, S, G2, and M phases in a yeast cell cycle were extracted and visualized.     

Artificial neural network predictive model for allergic disease using single nucleotide polymorphisms data

The purpose of this study was to develop a novel diagnostic prediction method for allergic diseases from the data of single nucleotide polymorphisms (SNPs) using an artificial neural network (ANN). We applied the prediction method to four allergic diseases, such as atopic dermatitis (AD), allergic conjunctivitis (AC), allergic rhinitis (AR) and bronchial asthma (BA), and verified its predictive ability. Almost all the learning data were precisely predicted. Regarding the evaluation data, the learned ANN model could correctly predict a diagnosis with more than 78% accuracy. We also analyzed the SNP data using multiple regression analysis (MRA). Using the MRA model, less than 10% of patients with the above allergic diseases were correctly diagnosed, while this figure was more than 75% for persons without allergic diseases. From these results, it was shown that the ANN model was superior to the MRA model with respect to predictive ability of allergic diseases. Moreover, we used two different methods to convert the genetic polymorphism data into numerical data. Using both methods, diagnostic predictions were quite precise and almost the same predictive abilities were observed. This is the first study showing the application and usefulness of an ANN for the prediction of allergic diseases based on SNP data.     

Prediction of yarn crimp in PES multifilament woven barrier fabrics using artificial neural network

This research was aimed to develop artificial neural network (ANN) models to predict yarn crimp in woven barrier fabrics. For ANN training, 52 polyester (PES) multifilament barrier fabrics were produced by varying weft yarn and filament fineness, yarn type, weft density, weave type, and loom parameters. The supervised training of neural network was performed using Matlab® ANN toolbox function ‘trainbr’ which is the incorporation of Levenberg-Marquardt (LM) optimization and automated Bayesian regularization into backpropagation. From modeling outcomes, it was observed that both warp and weft yarn crimp models have generalized well with excellent coefficient of determination and trivial mean absolute error when tested on novel data. Moreover, input rank analysis of optimized network provided important information about model stability with respect to input variables, and trend analysis elucidated the input-crimp behavior using different input levels.     

A new approach to discriminate varieties of tobacco using vis/near infrared spectra

Vis/near infrared reflectance spectroscopy appears to be a rapid and convenient non-destructive technique that can measure the quality and compositional attributes of many substances. Principal component analysis (PCA), which offered a qualitative analysis of tobacco samples, was used to analyze the clustering of tobacco samples. A new method combined wavelet transform (WT) with Artificial Neural Network (ANN) was presented to establish a discrimination model. The model regarded the compressed spectra data as the input of ANN, and 80 samples were selected randomly as calibration collection whereas the remaining 20 were being prediction collection. High correlation coefficient (r=0.999) was achieved, which was better than PCA-SRA-ANN and PLS-ANN. It indicated that WT combined with ANN is an available method for variety discrimination based on the Vis/NIR spectroscopy technology. Some sensitive wave bands were also analyzed to develop tobacco varieties discrimination apparatus through PLS models.     

基于人工神经网络优化抄纸浆料配比

提出了应用人工神经网络技术进行抄纸浆料配比优化的方法,介绍了优化原理和过程.以卷烟纸为例,建立了多种浆料的配比与纸张主要物理性能指标之间的人工神经网络模型.该模型比传统回归模型有着更高的预测精度.以此模型为基础,通过扫描仿真,获得了针叶木浆、麻浆及填料配抄生产卷烟纸的各组分的配比范围,并从中优选出最佳配比.     

Application of data mining technique in predicting worsted spun yarn quality

Today’s industry gives first priority to information technology. Since understanding the structures and relationships dominated of data can help industrial managers to attend in competitive market successfully, a special mechanism must be developed to process data stored in a system. Hence, the focus on widespread use of data mining gains increasing attention. The purpose of this paper is using data-mining technique in textile industry. More than 150,000 data includes testing of raw materials, manufacturing process parameters and yarn quality parameters, during one year in worsted spinning factory were collected. Next, yarn quality was predicted by using data-mining methods containing clustering and artificial neural network (ANN). In order to evaluate the proposed method, the results obtained were compared with conventional methods based on ANN. The results showed that the performance of data-mining technique is more accurate than that of ANN.