基于模糊谱聚类的不确定蛋白质相互作用网络功能模块挖掘

针对谱聚类融合模糊C-means（FCM）聚类的蛋白质相互作用（PPI）网络功能模块挖掘方法准确率不高、执行效率较低和易受假阳性影响的问题，提出一种基于模糊谱聚类的不确定PPI网络功能模块挖掘（FSC-FM）方法。首先，构建一个不确定PPI网络模型，使用边聚集系数给每一条蛋白质交互作用赋予一个存在概率测度，克服假阳性对实验结果的影响；第二，利用基于边聚集系数流行距离（FEC）策略改进谱聚类中的相似度计算，解决谱聚类算法对尺度参数敏感的问题，进而利用谱聚类算法对不确定PPI网络数据进行预处理，降低数据的维数，提高聚类的准确率；第三，设计基于密度的概率中心选取策略（DPCS）解决模糊C-means算法对初始聚类中心和聚类数目敏感的问题，并对预处理后的PPI数据进行FCM聚类，提高聚类的执行效率以及灵敏度；最后，采用改进的边期望稠密度（EED）对挖掘出的蛋白质功能模块进行过滤。在酵母菌DIP数据集上运行各个算法可知，FSC-FM与基于不确定图模型的检测蛋白质复合物（DCU）算法相比，F-measure值提高了27.92%，执行效率提高了27.92%；与在动态蛋白质相互作用网络中识别复合物的方法（CDUN）、演化算法（EA）、医学基因或蛋白质预测算法（MGPPA）相比也有更高的F-measure值和执行效率。实验结果表明，在不确定PPI网络中，FSC-FM适合用于功能模块的挖掘。     

Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

Performance improvement of conventional and inverted polymer solar cells with hydrophobic fluoropolymer as nonvolatile processing additive

The morphology of the photoactive layer critically affects the performance of the bulk heterojunction polymer solar cells (PSCs). To control the morphology, we introduced a hydrophobic fluoropolymer polyvinylidene fluoride (PVDF) as nonvolatile additive into the P3HT:PCBM active layer. The effect of PVDF on the surface and the bulk morphology were investigated by atomic force microscope and transmission electron microscopy, respectively. Through the repulsive interactions between the hydrophilic PCBM and the hydrophobic PVDF, much more uniform phase separation with good P3HT crystallinity is formed within the active layer, resulting enhanced light harvesting and improved photovoltaic performance in conventional devices. The PCE of the conventional device can improve from 2.40% to 3.07% with PVDF additive. The PVDF distribution within the active layer was investigated by secondary ion mass spectroscopy, confirming a bottom distribution of PVDF. Therefore, inverted device structure was designed, and the PCE can improve from 2.81% to 3.45% with PVDF additive. Our findings suggest that PVDF is a promising nonvolatile processing additive for high performance polymer solar cells.     

ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.     

钛合金表面多磁极耦合旋转磁场光整加工特性

面向激光增材制造钛合金表面的光整加工需求，设计出一种多磁极耦合旋转磁场光整加工装置来研究光整加工特性。基于ANSYS Maxwell仿真软件分析了光整加工装置的磁场强度分布。搭建了实验光整平台，分析了主轴转速、C轴转速和加工间隙对表面质量的影响。结果表明，在主轴转速500 r/min、C轴转速160 r/min和加工间隙0.7 mm的加工条件下，表面粗糙度Ra由5.991 μm下降至0793 μm。扫描电子显微镜(SEM)观测表明，光整后的钛合金表面沉积层消失，表面质量得到显著改善。     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.     

添加剂对工业齿轮油性能影响的研究

研究了硫化烯烃、磷酸酯胺盐、硫磷酸酯极压抗磨剂,含氮杂环衍生物多功能添加剂和防锈剂等,对工业齿轮油抗乳化性能、极压抗磨性能、防锈性能的影响;以及硫化烯烃、磷酸酯盐极压抗磨剂与含氮杂环衍生物多功能添加剂之间的协和效应。试验结果表明:1.2%~1.6%复合添加剂调制的CKD220工业齿轮油中,磷酸脂盐的正确选用可以提高齿轮油的抗乳化性能、防锈性能和减少防锈剂用量。硫化烯烃与磷酸脂盐、含氮杂环衍生物多功能添加剂的合理组合,可以提高齿轮油的极压抗磨性能和防锈性能,减少添加剂总加剂量。     

Prediction Model for BTA Deep-Hole Machining using Fuzzy Clustering Approach: Experimental Study

In this article, a subtractive clustering-based fuzzy identification method and a Sugeno-type fuzzy inference system are used for modeling in metal cutting. This approach is considered with its application on the experimental study of Boring and Trepanning Association (BTA) deep-hole drilling. The model for the surface roughness is identified by using the cutting speed and feed as input data and roughness as the output data. Using subtractive clustering in both input and output spaces performs the model-building process. Minimum error model is obtained through enumerative search of clustering parameters. The fuzzy model obtained is capable of predicting the surface roughness for a given set of inputs (speed and feed). Therefore, the operator can predict the quality of the surface for a given set of working parameters and will then be able to set the machining parameters to achieve a certain surface quality. The fuzzy model is verified experimentally by further experimentation using different sets of inputs. The tool life is also investigated using the same approach. The fuzzy inference system obtained is capable of predicting the tool life for a given set of cutting parameters. Therefore, the operator will be able to predict how many minutes the cutting tool is going to last and will set the time for the next tool change.     

高温酸化助排剂HC2-1的研究

为了满足高温油藏和深井酸化作业残酸返排的需要，通过表面活性剂的筛选和复配研究，得到了高温酸化助排剂HC2—1。其性能评价结果表明，HC2—1具有使用浓度低、表面活性高的特点，在20％的HCI溶液中，当其浓度为50mg／L时，表面张力为20．7mN／m；具有良好的耐温性能，在180℃下恒温48h，HC2—1仍保持较高的表面活性；具有良好的耐盐能力，加有HC2—1的20％HCl溶液和CaCO3反应至HCl完全消耗，整个反应过程体系无新相生成且表面张力基本不变；可增大酸液体系的润湿角，进一步降低毛细管阻力，使酸液返排率由46％提高到97％，在促进酸液返排的同时与原油不发生乳化反应。