基于人工神经网络的高聚物流变性能预测系统

针对影响高聚物流变性能因素的复杂性,利用VB与MATLAB混合编程将人工神经网络技术移植于应用软件,实现人工神经网络技术的各项功能,通过对采集的样本数据进行网络训练,开发了高聚物流变性能参数预测系统。经验证,系统可将预测精度误差,控制在5％以内。     

Alcohol consumption detection through behavioural analysis using intelligent systems

We describe in this paper a new methodology for blood alcohol content (BAC) estimation of a subject. Rather than using external devices to determine the BAC value of a subject, we perform a behaviour analysis of this subject using intelligent systems. We monitor the user’s actions in an ordinary task and label those data to various measured BAC values. The obtained data-set is then used to train learning systems to detect alcoholic consumption and perform BAC estimation. We obtain good results on a mono-user base, and lower results with multiple users. We improve the results by combining multiple classifiers and regression algorithms.     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote

This paper developed a new variable selection method for soft sensor applications using the nonnegative garrote (NNG) and artificial neural network (ANN). The proposed method employs the ANN to generate a well-trained network, and then uses the NNG to conduct the accurate shrinkage of input weights of the ANN. This paper took Bayesian information criterion as the model evaluation criterion, and the optimal garrote parameter s was determined by v-fold cross-validation. The performance of the proposed algorithm was compared to existing state-of-art variable selection methods. Two artificial dataset examples and a real industrial application for air separation process were applied to demonstrate the performance of the methods. The experimental results showed that the proposed method presented better model accuracy with fewer variables selected, compared to other state-of-art methods.     

基于云计算的预拌混凝土行业在线质量管理

以实现质量的科学化管理和实时的动态监测为目的,提出了预拌混凝土行业基于云计算的在线质量管理。通过分析预拌混凝土企业包括经营数据和生产实时数据等大量数据的存储、整合、分析、挖掘、利用等方面需求,以云计算为平台,运用人工智能（ AI）相关算法,实现了基于实际生产过程的质量预测,为解决影响混凝土强度及工作性能的多组分因素问题提供了思路和方法。提取云平台上的生产实时数据进行模拟实验,以C35的28天抗压强度为例,用1000组数据对网络进行训练,用100组数据对已训练的网络进行检验,结果证明了预测值的准确性,同时也验证了平台的可行性。     

Biomimetic strain hardening in interpenetrating polymer network hydrogels

In this paper, we present the systematic development of mechanically enhanced interpenetrating polymer network (IPN) hydrogels with Young's moduli rivaling those of natural load-bearing tissues. The IPNs were formed by synthesis of a crosslinked poly(acrylic acid) (PAA) network within an end-linked poly(ethylene glycol) (PEG) macromonomer network. The strain-hardening behavior of these PEG/PAA IPNs was studied through uniaxial tensile testing and swelling measurements. The interaction between the independently crosslinked networks within the IPN was varied by (1) changing the molecular weight of the PEG macromonomer, (2) controlling the degree of PAA ionization by changing pH, and (3) increasing the polymer content in the PAA network. Young's moduli and the maximum stress-at-break of the swollen hydrogels were normalized on the basis of their polymer content. Strain hardening in the IPNs exhibited a strong dependence on the molecular weight of the first network macromonomer, the pH of the swelling buffer, as well as the polymer content of the second network. The results indicate that the mechanical enhancement of these IPNs is mediated by the strain-induced intensity of physical entanglements between the two networks. The strain can be applied either by mechanical deformation or by changing the pH to modulate the swelling of the PAA network. At pHs below the pK_a of PAA (4.7), entanglements between PEG and PAA are reinforced by interpolymer hydrogen bonds, yielding IPNs with high fracture strength. At pHs above 4.7, a “pre-stressed” IPN with dramatically enhanced modulus is formed due to ionization-induced swelling of the PAA network within a static PEG network. The modulus enhancement ranged from two-fold to over 10-fold depending on the synthesis conditions used. Variation of the network parameters and swelling conditions enabled “tuning” of the hydrogels' physical properties, yielding materials with water content between 58% and 90% water, tensile strength between 2.0 MPa and 12.0 MPa, and initial Young's modulus between 1.0 MPa and 19.0 MPa. Under physiologic pH and salt concentration, these materials attain “biomimetic” values for initial Young's modulus in addition to high tensile strength and water content. As such, they are promising new candidates for artificial replacement of natural tissues such as the cornea, cartilage, and other load-bearing structures.     

两级定位-路径问题的变邻域人工蜂群算法

建立了两级定位-路径问题的数学模型,提出了一种求解该问题的人工蜂群算法。针对该算法容易出现早熟现象,将近年来国外出现的一种新颖的轨迹式启发式算法--变邻域搜索融入其中,由此提出三种变邻域搜索策略。基于不同变邻域搜索策略的人工蜂群算法和人工鱼群算法的求解效果进行对比仿真。实验结果表明,变邻域人工蜂群算法能有效求解两级定位-路径问题。     

CO2/R170混合物作为复叠式制冷系统低温环路制冷剂的性能

针对CO2/R170混合物在复叠式制冷系统低温环路使用的工况，建立了固液平衡和汽液平衡的数学模型，决定了保证混合物在较低温度下不结晶的配比。在一定的配比范围内对混合物进行了循环性能和可燃性分析。研究了混合物配比对循环性能参数和可燃性的影响，以及中间温度在高温环路使用R290、低温环路使用CO2/R170混合物时，复叠式制冷系统整体制冷系数（COP）的变化趋势。研究表明，这种混合物适宜作为复叠式制冷系统低温环路的制冷剂。     

基于BP网络的注塑件翘曲变形工艺参数优化研究

利用BP神经网络对注塑工艺参数及其相对应的翘曲变形量样本进行训练，得到了描述工艺参数到翘曲量映射关系的人工神经网络（ANN）模型；验证了此模型的准确性；得出了工艺参数与注塑件翘曲变形量的内在联系，为以后的参数优化以及翘曲量预测起到重要的指导作用。     

Artificial neural network approaches on composition-property relationships of jet fuels based on GC-MS

The relationships of composition-properties of 80 jet fuels concerning chemical compositions and several specification properties including density, flashpoint, freezing point, aniline point and net heat of combustion were studied. The chemical compositions of the jet fuels were determined by GC-MS, and grouped into eight classes of hydrocarbon compounds, including n-paraffins, isoparaffins, monocyclopraffins, dicyclopraffins, alkylbenzens, naphthalenes, tetralins, hydroaromatics. Several quantitative composition-property relationships were developed with three artificial neural network (ANN) approaches, including single-layer feedforward neural network (SLFNN), multiple layer feedforward neural network (MLFNN) and general regressed neural network (GRNN). It was found that SLFNNs are adequate to predict density, freezing point and net heat of combustion, while MLFNNs produce better results as far as the flash point and aniline point prediction are concerned. Comparisons with the multiple linear regression (MLR) correlations reported and the standard ASTM methods showed that ANN approaches of composition-property relationships are significant improvement on MLR correlations, and are comparable to the standard ASTM methods.