油茶籽饼多酚的乙醇提取工艺优化研究

研究了油茶籽饼多酚的乙醇提取优化工艺,将正交试验设计方法和BP人工神经网络分析方法相结合,充分挖掘正交试验数据信息,通过仿真模拟和优化,获得油茶籽饼多酚的乙醇较优提取工艺条件:乙醇浓度为60%,提取时间为3.5 h,提取温度为70℃,料液比为1∶15。     

IWSF-FA模袋混凝土抗压强度、孔隙结构和物质组成

为优化模袋混凝土衬砌性能,合理利用当地粉煤灰和废弃硅粉代替部分水泥制备模袋混凝土衬砌.通过抗压强度试验、固体紫外试验、核磁共振和热重试验研究双掺粉煤灰-工业废弃硅粉替代适量水泥后对模袋混凝土抗压特性、内部孔隙结构和水化物质组成的影响.结果表明：合理双掺工业废弃硅粉和粉煤灰(如FA15S4)可以提高模袋混凝土的抗压强度;紫外吸收光谱表明其水化产物C-S-H和CH组成优于其他组别;核磁共振T₂谱分布呈现“三峰结构”,左峰信号幅度最高,双掺工业废弃硅粉-粉煤灰有利于改善其内部孔隙结构.并且双掺工业废弃硅粉和粉煤灰后,热学性质良好.建立了基于BP神经网络理论的模袋混凝土早期抗压强度预测模型,预测结果与试验测试值的最大相对误差为2.7%.研究结果可为工业废弃硅粉和粉煤灰混凝土在水工衬砌工程中的应用提供参考依据.     

BP Neural Networks and Its Application in Lung Cancer Intelligent Diagnosis System

The learning algorithm of networks is discussed. The programming example of 3 layer BP networks is given with Visual C++6.0 program langue. Based on this model, a lung cancer intelligent diagnosis system is successfully implemented. Furthermore, the paper introduces network's structure design, preferencesand the source of stylebookdatum in factual applications. The ameliorative arithmetic is applied to the study of networks and BP dynamic evolving process is designed. The experiments indicatecell images are recognized and classified by the trained neural network. The study illustrates the system has feasibility and clinical value in lung cancer diagnosis.     

Craft Orbit Selection by Knowledge Based Neural Network

There are more than one measure devices during the craft launching proceeding, reliable craft orbit measurement selection is important for launching monitor and safety control. This paper proposes a novel orbit selection method by knowledge based neural network, which combines the human knowledge and orbit samples based training together. The false spur orbit data are deleted according the data continuity rule at first, then the difference between orbit data is computed, and at last a neural network is trained to test reliability of each data and select the orbit with high precision among all orbit data. Simulations and experiments show the efficiency and reliability of our method.     

A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

Maize haploid breeding technology is able to identify haploid seeds non‐destructively, rapidly and at low cost with the help of Near‐infrared (NIR) spectral analysis. However, due to the hybridization of numerous parents and the low production rate of haploid, the haploid data collection becomes a burden for engineering this technology. Biologically, there are considerable similarities between the progeny of the same female parent and different male parents. Based on this advantage, similar spectral data can be transferred when the NIR technology is employed. A revised method of Transfer adaptive boost (TrAdaBoost) is proposed to improve identifying for the backpropagation neural network (BPNN) classifier. To avoid the negative transfer, a screening thresh is used to select out similar data, and the amount of these data are limited to join current training. The results show that the identification performances are improved significantly when the data amount is small. This method shows a high ability to make the seed identification more convenient for engineering maize haploid breeding.     

The optimal energy efficiency of wireless networks nodes based on solar cells

To improve the energy efficiency of solar cells in wireless sensor networks nodes, the energy relationship between solar cells and the wireless sensor networks nodes is studied. An adaptive algorithm is adopted to ensure the wireless sensor networks work normally at different sunlight intensities. An energy model is designed that reveals the energy relationship between the output power of the solar cells and the power of nodes working normally at a standard sunlight intensity. The experimental results show that the model represents the adaptive energy relationship between the solar cells and the nodes, ensures that the nodes work stably in a long term, and extends the life cycle of the sensor networks as long as possible. The model has important guiding significance for wireless sensor networks designation.     

Analysis of shift schedule for the automatic transmission vehicle based on fuzzy neural network

The advantages and disadvantages of present methods for shift schedule analysis of automatic transmission vehicle are analyzed. An optimum shift schedule method based on fuzzy neural network is proposed. The structure and algorithm with Takagi-Sugeno mode is studied. Fuzzy logic rules with two parameters and membership functions for shift schedule are established according to the skilled driver’s experience and expert’s knowledge. The membership functions and fuzzy logic rules are modified through train mechanism of artificial neural network based on experiment sample. The fuzzy neural network is trained and simulated. The simulation results indicate that this shift schedule method based on fuzzy neural network of Takagi-Sugeno model is feasible and correct.     

Fast algorithm for apparent resistivity calculation based on back propagation neural networks

To avoid the complex numerical calculation for the electromagnetic field and determine underground abnormality, a neural network based method is proposed. In consideration of turn off transmitter current, the effect of a linear ramp turn off current on transmitter is corrected. The characteristics of transient expression and the traditional calculation algorithm for apparent resistivity are analyzed, and a predigest structure of network is obtained based on the kernel expression. The three layer back propagation(BP) neural network is trained by using sample data in homogeneous half space, and its number in hidden layer was determined. The method proposed is compared with two traditional calculation methods with simulation experiments. The result demonstrates that BP neural network has a high speed of processing data and is useful in explanation of the transient electromagnetic method.     

Routing protocol of the wireless sensor networks for bridge health monitoring

By analyzing the reason that why the existing routing protocols cannot be applied to the wireless sensor networks for bridge health monitoring, a new routing protocol is proposed. Since the locations of the collecting modules are fixed, the proposed protocol exchanges the routing information between the neighbor nodes by adjusting the exchange cycle according to reliability of the modules. For the low routing security requirement, while to increase the routing efficient, the middle nodes replying the routing requirements with variable thresholds is introduced. In order to make full use of information in route discovering procedure, the backup routes mechanism is adopted. The proposed protocol can efficiently forward data and is suitable for the bridge health monitoring.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.