Application of Artificial Neural Network in Modeling Paint Waste Water Treated by Coagulation Oxidation Process

An artificial neural network (ANN) model was established based on data of paint waste water treated by coagulation oxidation process, using the improved back propagation algorithm. The model was then used to fit and predict some experimental data. The results indicated that the errors between computed data and experimental data were much small. Furthermore, the ANN model could correctly reflect the mechanism of some factors which affected the efficiency of paint waste water treatment.     

新乡市居民乡村旅游决策行为及影响因素研究

居民乡村旅游决策行为受众多因素影响,筛选影响乡村旅游决策行为的关键因子,将为景区管理部门提升景区吸引力提供重要依据。本研究筛选了12项观测指标,从4个维度分析了新乡市居民的乡村旅游决策行为,构建了基于AMOS结构方程的决策模型。结果表明：居民收入水平、休闲时间和受教育程度是影响乡村旅游决策的重要因素。景区形象对旅游决策的直接影响最大,表明景区自身因素是促使居民做出最终决策的直接因子。旅游攻略主要通过旅游期望对决策产生影响。提高居民收入水平和乡村旅游景区品牌知名度将对城市居民乡村旅游决策的形成产生正向影响。     

Artificial neural network approach for prediction of ammonia emission from field-applied manure and relative significance assessment of ammonia emission factors

This article presents a systematic method for enhancing the estimation accuracy of ammonia emission from field-applied manure and for assessing the relative significance of ammonia emission factors, using the feedforward-backpropagation artificial neural network (ANN) approach.

The multivariate linear regression (MLR) method well describes the ammonia emission tendency with the emission factor variation. However, ammonia emission from manure slurry is too complex to be captured in a linear regression model. This necessitates a model which can describe complex nonlinear effects between the ammonia emission variables such as soil and manure states, climate and agronomic factors. In the present study, a principle component analysis (PCA) based preprocessing and weight partitioning method (WPM) based postprocessing ANN approach (called the PWA approach) is proposed to account for the complex nonlinear effects.

The ammonia emission is predicted with precision by the 11 emission factors, using the nonlinear ANN approach. The relative importance among the 11 emission factors is identified using the elasticity analysis in the MLR method and using the WPM in the ANN approach. The relative significance obtained quantitatively by the PWA approach in the present study gives an excellent explanation of the most important processes controlling NH₃ emission.     

Application of Artificial Neural Network in Elastic Internal Force Analysis of Two Way Slab

In this paper, the Artificial Neural Network (ANN) is applied to the internal force analysis of structure. After introducing the model and algorithm of back-propagation network, a trained BP network of three layers is used to calculate the maximum of elastic moment of middle span of two-way slab. In order to enhance the generalized capability of network, the modified value of weight should be regarded as convergence standard and in order to accelerate the learning process without vibration, the method of adding momentum coefficient is adopted. The analysis program of BP network is carried out with the software of Matlab. The result demonstrates that application of ANN in structure analysis is feasible.     

Vacuum Circuit Breaker''s Condition Simulation Based on Artificial Neural Network and Electrical Endurance Quality Criterion

How to select the factors that have more great affection on the condition based maintenance and how to make sure the relation among them are two key problems. A new method's proposed for vacuum circuit breaker's condition based maintenance, which combines electrical endurance quality criterions and condition recognition arithmetic based on artificial neural network. The paper proposes a model based on electrical-endurance quality criterions by applying ANN in vacuum circuit breaker's condition based maintenance. The simulation indicates that the criterion selected in this way are reasonable and the network with new learning error function has a much better generalization ability.     

基于Adaboost算法的输电线路舞动预警方法

输电线路舞动是目前尚未被全面准确认识的世界性难题,已严重威胁输电系统的安全稳定运行。文章分析影响舞动的外界气象环境因素,并在此基础上提出一种基于Adaboost集成学习算法的输电线舞动预警方法。采用基于Gini指标的决策桩作为弱分类器,通过对多个弱分类器的训练及加权求和,输出舞动预测结果及其置信度,可为电网运维人员提供决策支撑。最后,使用历史数据进行验证性实验,结果证明了所提方法的有效性。     

带锚FRP受剪加固梁非剥离破坏模式BP网络预测

对带可靠锚固FRP受剪加固混凝土梁的非剥离剪切破坏模式做了细化分类,即包括FRP断裂控制的破坏、受压区混凝土（达到极限应力状态）压碎控制的破坏、FRP断裂与混凝土压碎同步发生的界限破坏等3种模式;利用BP神经网络建立了带锚纤维受剪加固梁破坏模式的智能预测模型,与31根非剥离破坏加固梁试验的对比结果显示：模型总体精度达到90%,说明建立的破坏模式网络预测模型适用于带锚纤维受剪加固梁非剥离剪切破坏模式的判别。     

Study on ANN Based Character Recognition

The theory of Artificial Neural Networks and image processing are analyzed. ANN theory is also investigated . A Back Propagation network is used for classification. Based on the ANN and feature extraction, a character recognition and entry system is developed. Its drawbacks are discussed. Some solutions are presented. An odd feature database is constructed to promote the performance for learning. A 3 layer feedforward network has been proved that it has the ability to identify hand written characters.     

基于Agent技术的玉米生产管理协同决策系统研究

针对传统农业信息系统在解决分布式网络环境下的复杂决策问题的不足,利用多Agent技术智能性、交互性、协作性的优点,应用基于UML的面向Agent的系统分析方法,提出了基于Agent技术的作物生产管理协同决策系统的设计思想和实现方法,用于实现分布式环境下的作物信息共享、软件共享、协同工作和群决策支持;本文以玉米为实验对象,结合玉米生长模拟模型和玉米知识模型,实现预测功能和决策功能的有机耦合与集成。通过开发实践分析,多Agent作物协同决策模型及系统为解决农业系统复杂问题的决策提供了新的技术手段和方法,提高了作物生长预测和栽培决策的精确性。     

热管管外结冰过程研究

通过对热管制冰传热过程的理论分析,得出影响结冰速率的主要因素是热管的温度与半径.建立了管外结冰一维传热模型,分别以半径和时间为步长对结冰速率进行迭代求解,并利用半径微元法得出了不同温度以及不同圆管半径下的制冰速率曲线.与试验结果相比,所得理论模型能够较好地与其吻合.     

Short-Term Electric Load Forecasting Based Trends Combination Model

In short term load forecasting based on ANN,weather is one of the important factors which impacts on load greatly. In order to capture the effect of weather on load, this paper presents a novel thought based on ANN and trends combination short term load forecasting. Decompose the underlying relationships between load and weather variables into three main trends of weekly, daily and hourly. Three separated ANNs capture each trend. Another ANN to arrive at the final forecast combines the forecasts yielded by individual ANNs. The performances of the proposed model and the traditional model are compared on the basis of one week ahead hourly forecasts. Results indicate that the proposed ANN based model can achieve greater forecasting accuracy than the traditional ANN based model.     

Disassembly Process Planning of Products Based on Matrix Operation

Based on fuzzy Petri nets, a fuzzy reasoning Petri nets (FRPN) model is defined. And then, combining fuzzy Petri nets with matrix operation, an algorithm of formalized fuzzy reasoning process is proposed. Based on the fuzziness and uncertainness of the knowledge in the disassembly process of products, a decision making model in disassembly process is established. Furthermore, taking realistic disassembly as an example, an algorithm of decision-making in disassembly sequence is discussed. The result shows that the model of decision-making has strong parallel operation ability in the disassembly process sequence. It also can make intelligent decisions based on the product information originating from each disassembly step.     

利用神经网络提取棉花叶片数字图像氮素含量的初步研究

选取6种输入向量组合，利用线性网络、BP网络以及径向基网络等3种神经网络模型进行比较研究，筛选最适宜网络模型和最佳输入组合，建立叶片数字图像彩色信息和叶片氮含量的关系模型，探索利用神经网络技术获取叶片数字图像信息的方法。结果表明，径向基网络在利用数字图像(B，H，G-R，G/R)指标作为网络输入向量时，能够实现获取棉花叶片数字图像氮含量的目标。径向基网络训练的180组样本的训练精度均达到极显著水平（r = 0.9022**），30组测试样本的预测值与实测值也达到极显著相关（r = 0.8674**），径向基网络和(B，H，G-R，G/R)向量是一种适合本研究的数学模型。对利用神经网络提取棉花叶片数字图像氮含量技术的初步探索，拓展了神经网络和数字图像技术在农业生产中的应用。     

Prediction Model of Exploitation Sink Using Ann and GIS

A new idea is put forward on researches of prediction method of coal mining subsidence supported by GIS, and a new method is given about quantitative prediction of mining subsidence by means of GIS and ANN(Artificial Neural Network).This paper has completed a lot of work concerning choosing factor,data processing, establishing and validating preliminary ANN prediction model. Further more,data processing is carried out by GIS software,and the BP training method is used for modeling the exploitation sink system. Subsequently,the error is qualitatively analyzed with considering the result of verification. Researches show that the ANN prediction model supported by GIS has theoretical feasibility and realistic significance in predicting complex exploitation sink system,and GIS and ANN possess wide application prospects in the prediction of exploitation sink.     

Predictive Model of Performance of Metal-packed Evaporative Air Conditioning System

Based on characters of Artificial Neural Network(ANN) such as non-liner mapping,self-learning,the ANN model was set up to predict the relationship between effective factors and performance of metal-packed evaporative air conditioning system.In order to reflect the performance of the predictive model,experimental data were used to test and verify the model.The outcome of the ANN model is in good agreement with experimental data and relative difference is within 6 percent.So it is feasible to predict the performance of metal-packed evaporative air conditioning system.     

Application of soft sensor in the on line measurement of theiron ore’s surface temperature

It is difficult to measure the surface temperature of iron ore directly. A method is put forward to handle this problem by using soft sensing technique. This on line measurement method is used to replace the Lagrange interpolation off line method to estimate surface temperature. The method used L M optimum algorithm to build up ANN soft sensor model combined with off line learning neural network to establish the correlation between input variables and target variables, to achieve the surface temperature on line detection. The results of simulation and experimentation indicate that the method is reasonable and feasible.     

基于DANP法识别影响养殖户病死猪处理行为的关键因素

为了规范养殖户病死猪处理行为,保障猪肉安全与公共健康,以及保护生态环境。在借鉴国内外现有研究成果的基础上,归纳总结了可能影响生猪养殖户病死猪处理行为的四个维度和17个主要因素,运用决策实验分析法(Decision Making Trial and Evaluation Laboratory,DEMATEL)和网络层次分析法(Analytic Network Process)相结合的DANP法(DEMATEL-based ANP),定量分析影响生猪养殖户病死猪处理行为维度及因素间的相互关系,识别关键因素。研究结果表明,生产经营特征是最重要的维度,相关法律法规认知、生猪疫情与防疫认知、成本与收益、养殖规模和养殖模式是五个关键因素。证实了DANP的方法能有效识别影响养殖户病死猪处理行为的关键因素,且结论可为政府规范生猪养殖户病死猪处理行为提供了有益的决策参考。     

Fuzzy decision for wastewater treatment process selectionbased on gray theory and its application

Decision making of sewage treatment process directly affects the accuracy and efficiency of the process selection. An optimized fuzzy decision method is proposed based on fuzzy decision and contrast weighting method. Moreover, a web based decision making support system for the selection of sewage treatment process is developed with the Apache+PHP+MySQL development environment. The system had realized the online decision making for sewage treatment process, and enhanced accuracy and reduced the decision making time.     

Fisher discriminant analysis model for selecting the retaining structure type of deep foundation pit

Aiming at the problem of traditional evaluation methods of deep foundation pit for selecting the retaining structure type, based on the statistical theory and following the principle of security, economic and reasonable, a Fisher discriminant analysis(FDA) model for selecting the retaining structure type for deep foundation pit is established. 10 selected indicators which influence selection of deep excavation program are taken into account as discriminant factors, and the supporting schemes for deep foundation pit are classified into 5 groups, viz. gravity of the cement-soil type, soil nailing wall, pile anchors, pile supports and underground continuous wall. After training and testing 64 sets of measured data, the discriminant functions of FDA are solved, the re-substitution method is introduced to verify the stability of FDA model and the ratio of mis-discrimination is 14.1%. Another 10 groups of measured data are tested as forecast samples by the proposed model, and the correct rate is equal to 100%. Therefore, the feasibility of the proposed model is validated. Moreover, the proposed model is adopted for the New World Center Project in China, and the prediction results are in line with the artificial neural network(ANN) and the actual situation. The result shows that the deep foundation pit supporting structure lectotype decision of FDA model has excellent discriminant performance and the resubstitution error rate is low. It is easy and efficient to make discriminant analysis using this model and it provides efficient method to select deep excavation retaining structure and a practical new approach to choose the structural type of deep foundation pit optimization.     

A Decision-making Framework Model of Networked Cooperative Product Development

The objective of decision-making of Networked Cooperative Product Development is analyzed. The objective system of decision-making is proposed, which is constituted of six decision-making factors including T (time), Q(quality), C(cost), R(resources), S(security and secrecy) and K(knowledge). Basing this objective system, the TQCRSK decision-making model is constructed. Finally, a case study in which a practical decision-making problem in an application of Networked Cooperative Product Development is analyzed and solved successfully with the model shows the TQCRSK model is practical.