Eigenclassifiers for combining correlated classifiers

In practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ula?, M. Semerci, O.T. Y?ld?z, E. Alpayd?n, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298–1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets. Simulations using 14 classifiers on 38 data sets indicate that hyperparameters and overlapping training sets have higher effect on positive correlation than features and algorithms. Second, we propose postprocessing before fusing using principal component analysis (PCA) to form uncorrelated eigenclassifiers from a set of correlated experts. Combining the information from all classifiers may be better than subset selection where some base classifiers are pruned before combination, because using all allows redundancy.     

Node trust evaluation in mobile ad hoc networks based on multi-dimensional fuzzy and Markov SCGM(1, 1) model

Due to the nature of distribution and self-organization, mobile ad hoc networks rely on cooperation between nodes to transfer information. One of the key factors to ensure high communication quality is an efficient assessment scheme for risks and trust of choosing next potential cooperative nodes. Trust model, an abstract psychological cognitive process, is one of the most complex concepts in social relationships, involving factors such as assumptions, expectations and behaviors. All of the above make it difficult to quantify and forecast trust accurately. In this paper, based on the theories of fuzzy recognition with feedback, SCGM(1, 1) model and Markov chain, we present a pattern of prediction making. The analysis and experimental computation show that this scheme is efficient in trust prediction for ad hoc networks.     

Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization

Modeling NO_x emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NO_x combustion. This paper presents an efficient NO_x emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN) and generalized regression (GRNN) neural networks. A large number of NO_x emissions data from an actual power plant, was employed to train and validate the SVR model as well as two neural networks models. Moreover, an ant colony optimization (ACO) based technique was proposed to select the generalization parameter C and Gaussian kernel parameter γ. The focus is on the predictive accuracy and time response characteristics of the SVR model. Results show that ACO optimization algorithm can automatically obtain the optimal parameters, C and γ, of the SVR model with very high predictive accuracy. The predicted NO_x emissions from the SVR model, by comparing with the BPNN model, were in good agreement with those measured, and were comparable to those estimated from the GRNN model. Time response of establishing the optimum SVR model was in scale of minutes, which is suitable for on-line and real-time modeling NO_x emissions from coal-fired utility boilers.     

Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance

The advantage of using cerebellar model articulation control (CMAC) network has been well documented in many applications. However, the structure of a CMAC network which will influence the learning performance is difficult to select. This paper proposes a dynamic structure CMAC network (DSCN) which the network structure can grow or prune systematically and their parameters can be adjusted automatically. Then, an adaptive dynamic CMAC neural control (ADCNC) system which is composed of a computation controller and a robust compensator is proposed via second-order sliding-mode approach. The computation controller containing a DSCN identifier is the principal controller and the robust compensator is designed to achieve L₂ tracking performance with a desired attenuation level. Moreover, a proportional–integral (PI)-type adaptation learning algorithm is derived to speed up the convergence of the tracking error in the sense of Lyapunov function and Barbalat’s lemma, thus the system stability can be guaranteed. Finally, the proposed ADCNC system is applied to control a chaotic system. The simulation results are demonstrated that the proposed ADCNC scheme can achieve a favorable control performance even under the variations of system parameters and initial point.     

An optimum design selection approach for product customization development

Consumer preferences and information on product choice behavior can be of significant value in the development processes of innovative products. In this paper, product customization evaluation and selection model is introduced to support imprecision inherent of qualitative inputs from customers and designers in the decision making process. Focusing on customer utility generation, an optimum design selection approach based on fuzzy set decision-making is proposed, where design attributes priority is identified from customer preferences using an analytical hierarchy process. A multi-attribute analysis diagram is developed to visualize the preference of each attribute from the expert’s group decision. Conjoint analysis is used in the product customization to focus on customer utility generation in terms of multiple criteria. The use of the decision-making method is illustrated with a case example that highlights the utility of the proposed method.     

RS-485总线以太网接口转换装置研究

传统的串口网口转换装置采用纯硬件实现,虽然具有数据传输速度快的优点,但功能单一、灵活性差。为此,提出一种基于ARM9平台的嵌入式系统来实现接口转换。硬件采用MOXA UC-7101,应用程序在μCLinux下开发,通信遵守modbus协议,实现在UC-7101与上位机网络正常时数据的SD卡检索和正确及时传输。测试结果表明,在网络中断时,可自动查询下位机设备,并实现数据SD卡存储。     

鄂尔多斯盆地东南部长7油层组沉积相研究

为了明确鄂尔多斯盆地东南部长7油层组沉积体系类型和沉积相展布特征,总结其沉积演化规律,以沉积学的基本原理和方法为指导,综合运用地球化学、盆地分析等理论和方法,通过野外露头、钻井、测井等资料的综合分析,认为鄂尔多斯盆地东南部长7油层组主要发育三角洲和湖泊沉积体系,可进一步划分为3类亚相,其中以三角洲前缘和深湖2种亚相为主．     

太阳能飞机工作条件对太阳能电池性能的影响

太阳能飞机能源系统的核心部件是太阳能电池,性能受外界环境影响动态变化,准确预测其性能是飞机方案设计的重要基础工作。文章以光伏发电模型为基础,通过模拟太阳辐射和太阳能电池温度的逐时变化,预测了太阳能电池在飞行时间、速度和高度等工作条件变化时的性能。研究结果表明,受一天内太阳辐射和环境温度的变化影响,飞行时间是太阳能电池性能最大的影响因素,性能指标以太阳时12点为中心左右基本对称变化。飞行高度的改变伴随太阳辐射和环境温度变化,每升高1 km,开路电压提高1.52%,短路电流提高1.49%,最大功率提高3.65%。飞行速度的改变使得太阳能电池温度变化,每加速10 km/h,开路电压提高3.90%,短路电流降低0.31%,最大功率提高5.32%。以上研究,预测了太阳能电池在不同工作条件下的变化情况,为正确选择和改进能源系统方案奠定了理论基础。     

机器学习在河流流量参数估计中的应用

针对经典水位流量关系模型在刻画河流动态变化特性时存在的局限性,提出采用局部加权回归算法估计河流流量;为了提高参数估计精度,提出一种聚类局部加权回归方法。首先对训练样本进行聚类,然后使用k-最近邻方法将新的水位样本划分进最恰当的聚类中,最后估计河流日流量。该方法在估计过程中,避免了不相关信息的干扰,从而提高了日流量数据估计的效率和精度。利用某水文站的实测数据对方法进行测试,仿真结果表明该方法估计精度较高,为水位流量关系模型参数估计提供了新的有效方法。     

汽车辅助泊车引导系统研究

根据国内外辅助泊车系统发展及应用现状相关文献资料,利用Simulink中的CAN模块,建立仿真模型,输出方向盘转角和泊车位信息,之前系统已经可以调用VC＋＋库函数实现停车位以及车辆在一定方向盘转角下未来行走路线的实时刷新显示功能,在这基础上根据获取的泊车位信息利用路径规划算法规划出一条泊车入位路径,然后将这条路径显示在显示屏上,与车辆行走趋势路线共同指导驾驶员完成泊车动作.通过实车实验,此方法可以快速安全指引驾驶员完成泊车入位操作.