Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.     

时空数据语义理解:技术与应用

随着移动互联网的发展与手持智能终端的普及，海量带有用户时空属性的数据被生成.理解这些数据表达的语义信息对推测用户需求，分析用户偏好，进而提供精准时空推荐和预测服务具有重要作用.因此，近些年来，时空数据语义理解正成为时空数据挖掘领域的研究热点.从技术和应用两个层面，对近些年来国内外研究者在该领域的研究成果进行了系统的归类和总结.技术层面上，依据语义理解的不同任务，提出了时空数据语义理解的研究框架；并依次从地理位置语义理解、用户行为语义理解、热点事件语义理解3个主要任务，归纳了时空数据语义理解所包含的相关研究成果和关键技术.应用层面上，分别总结了时空数据语义理解在时空推荐和时空预测中的应用.最后，从数据质量、算法模型和计算模式3个方面，归纳了时空数据语义理解面临的主要挑战以及未来的研究方向.     

Predicting ENR Construction Cost Index Using Machine-Learning Algorithms

Construction Cost Index (CCI) is calculated monthly and published by Engineering News-Record (ENR). CCI is utilized for capital project budgeting and construction cost estimation, especially in cases where mid- and long-term forecasts are needed. Accurate prediction of CCI helps avoid underestimating and overestimating project costs but the current prevailing time series prediction models do not show promising results, especially in mid- and long-term forecasting. The capability of two machine-learning algorithms, k nearest neighbor (k-NN) and perfect random tree ensembles (PERT), are utilized to enhance CCI forecasting, especially in the mid- and long-term. The proposed machine-learning algorithms are able to significantly enhance the predictability of forecasting CCI in all the scenarios, short-, mid-, and long-term. Data from January 1985 to December 2014 is collected from ENR and bureau of labor statistics to conduct empirical studies and quantitatively measure the performance of the proposed methods. As the outcomes show, the prediction accuracies of both proposed methods are better than those of current prevailing time series models under all the tested scenarios. It is anticipated that cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-precise bids for contractors and developing more-accurate budgets for owners.     

Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics

The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment. The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions. To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span. A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration. The wireless monitoring system collects sensor data on a daily schedule and communicates the data to the Internet where it is stored in a curated data repository. Bridge response data in the repository are autonomously processed to extract truck load events using machine learning, compensate for environmental variations using nonlinear regression and to quantitatively assess anomalous bridge performance using statistical process control.     

Neuroimage‐based clinical prediction using machine learning tools

Classification of structural brain magnetic resonance (MR) images is a crucial task for many neurological phenotypes that machine learning tools are increasingly developed and applied to solve this problem in recent years. In this study binary classification of T1‐weighted structural brain MR images are performed using state‐of‐the‐art machine learning algorithms when there is no information about the clinical context or specifics of neuroimaging. Image derived features and clinical labels that are provided by the International Conference on Medical Image Computing and Computer‐Assisted Intervention 2014 machine learning challenge are used. These morphological summary features are obtained from four different datasets (each N > 70) with clinically relevant phenotypes and automatically extracted from the MR imaging scans using FreeSurfer, a freely distributed brain MR image processing software package. Widely used machine learning tools, namely; back‐propagation neural network, self‐organizing maps, support vector machines and k‐nearest neighbors are used as classifiers. Clinical prediction accuracy is obtained via cross‐validation on the training data (N = 150) and predictions are made on the test data (N = 100). Classification accuracy, the fraction of cases where prediction is accurate and area under the ROC curve are used as the performance metrics. Accuracy and area under curve metrics are used for tuning the training hyperparameters and the evaluation of the performance of the classifiers. Performed experiments revealed that support vector machines show a better success compared to the other methods on clinical predictions using summary morphological features in the absence of any information about the phenotype. Prediction accuracy would increase greatly if contextual information is integrated into the system. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 89–97, 2017     

基于社交关系的微博主题情感挖掘

微博情感分析是社交媒体挖掘中的重要任务之一，在个性化推荐、舆情分析等方面具有重要的理论和应用价值.挖掘性能良好且可同步进行文档主题分析与情感分析的主题情感模型近来在以微博为代表的社交媒体情感分析中备受关注。然而，绝大多数现有主题情感模型都简单地假设不同微博的情感极性是互相独立，这与微博生态的现实状况不相一致的，从而导致这些模型无法对用户的真实情感进行有效建模。基于此，本文综合考虑了微博用户相互关联的事实，提出基于LDA和微博用户关系的主题情感模型SRTSM，该模型在LDA中加入情感层与微博用户关系参数，利用微博用户关系与微博主题学习微博的情感极性。新浪微博真实数据集上的大量实验表明，与代表性算法JST、Sentiment-LDA与DPLDA相比较，SRTSM模型能对用户真实情感与讨论主题进行更加有效的分析建模.     

基于SVM的多项式循环程序秩函数生成

程序终止性问题是自动程序验证领域中的一个研究热点.秩函数探测是进行终止性分析的主要方法.针对单重无条件分支的多项式循环程序，将其秩函数计算问题归结为二分类问题，从而可利用支持向量机（SVM）算法来计算程序的秩函数.与基于量词消去技术的秩函数计算方法不同，该方法能在可接受的时间范围内探测到更为复杂的秩函数.     

3D computer vision based on machine learning with deep neural networks: A review

Recent advances in the field of computer vision can be attributed to the emergence of deep learning techniques, in particular convolutional neural networks. Neural networks, partially inspired by the brain's visual cortex, enable a computer to “learn” the most important features of the images it is shown in relation to a specific, specified task. Given sufficient data and time, (deep) convolutional neural networks offer more easily designed, more generalizable, and significantly more accurate end‐to‐end systems than is possible with previously employed computer vision techniques. This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data. Through a backdrop of the mammalian visual processing system, we hope to also provide inspiration for future advances in automated visual processing.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.