PhotoGeo: a photo digital library with spatial-temporal support and self-annotation

The recent popularity of digital cameras has posed a new problem: how to efficiently store and retrieve the very large number of digital photos captured and chaotically stored in multiple locations without any annotation. This paper proposes an infrastructure, called PhotoGeo, which aims at helping users with the people photo annotation, event photo annotation, storage and retrieval of personal digital photos. To achieve the desired objective, PhotoGeo uses new algorithms that make it possible to annotate photos with the key metadata to facilitate their retrieval, such as: the people who were shown in the photo (who); where it was captured (where); the date and time of capture (when); and the event that was captured. The paper concludes with a detailed evaluation of these algorithms.     

Abnormal behavior detection using dominant sets

Smart surveillance systems are increasingly being used to detect potentially dangerous situations. To do so, the common and easier way is to model normal human behaviors and consider as abnormal any new strange behavior in the scene. In this article, Dominant Sets is adapted to model most frequent behaviors and to detect any unknown event to trigger an alarm. It is proved that after an unsupervised training, Dominant Sets can robustly detect abnormal behaviors. The method is tested in several different cases and compared to other usual clusterization methods such as KNN, mixture of Gaussians or Fuzzy \(K\) -Means to confirm its robustness and performance. The overall performance of abnormal behavior detection based on Dominant Sets is better, being the error ratio at least \(1.5\) points lower than the others.     

Pattern recognition for characterization of pharmaceutical powders

A large part of pharmaceutical manufacturing involves the use of particulate materials. It is well known that both particle size and shape affect the physical characteristics of tablets. An image processing and analysis algorithm based on the invariant image moment technique was developed in this work to determine the particle shape by comparing features (moments) extracted from templates to those extracted from each of the objects in the image. First it determines the particle shape (rectangle, circle, etc.) and then calculates its specific dimensions (diameter, aspect ratio).The statistical validation of the vision system obtained a repeatability of 0.0012 in and 0.5% relative standard deviation and accuracy within 0.1 to 0.9% of the average value considered as true value. Also the pattern recognition technique indicated high precision and accuracy for images containing particles with some level of contact between them. The shape recognition of microcrystalline cellulose (MCC) indicated that particles of equant and acicular shape as defined by USP are predominant. The results suggest that image processing and analysis would be a suitable tool for pharmaceutical process analytical technologies (PAT) and process optimization.     

Multirate control with incomplete information over Profibus-DP network

When a process field bus-decentralized peripherals (Profibus-DP) network is used in an industrial environment, a deterministic behaviour is usually claimed. However, due to some concerns such as bandwidth limitations, lack of synchronisation among different clocks and existence of time-varying delays, a more complex problem must be faced. This problem implies the transmission of irregular and, even, random sequences of incomplete information. The main consequence of this issue is the appearance of different sampling periods at different network devices. In this paper, this aspect is checked by means of a detailed Profibus-DP timescale study. In addition, in order to deal with the different periods, a delay-dependent dual-rate proportional-integral-derivative control is introduced. Stability for the proposed control system is analysed in terms of linear matrix inequalities.     

Synthesis of UHMW-PE by a Highly Active Homogeneous System: η5-(C5H5)2Ti(η1-OC(O)C6H5)2/MAO

Summary The complex ⁵-(C₅H₅)₂Ti (¹-OC(O)C₆H₅)₂ was synthesized and activated with MA0 for ethylene polymerization. The resultant polymer is UHMW-PE with a viscosimetric molecular weight of Mv = 6.8 x 10⁶ to 0.7 x 10⁶ g/mol dependent on the temperature of the reaction. The effects of the Al/Ti molar ratio and temperature of the reaction on the catalytic activity and polymer properties were studied.     

预分解窑空气炮运用的节能效果

随着能源成本的增加,水泥行业不断寻求利用石油焦炭等替代燃料,但这些替代燃料的使用增加了物料在预热器、窑、冷却机中的阻塞和堆积。对物料的堵塞和堆积,常用的清除方法会带来许多弊端。利用空气炮防止物料的阻塞和堆积比较有效,可以从熟料质量、熟料产量、耐火材料寿命、能源、安全性等方面达到降低成本的目的。马丁公司在预热器和冷却器的改造方面与水泥行业有非常紧密的合作,墨西哥水泥厂是这种合作关系成功的典范。从1996年至今,墨西哥水泥厂已经安装了大约2900个马丁公司的BIG BLASTER空气炮,每年因为安装空气炮带来的收益达27万美元。基于此,UCC万吨熟料生产线也不例外地选用于马丁公司的空气炮。     

Range Unit‐Root (RUR) Tests: Robust against Nonlinearities,Error Distributions,Structural Breaks and Outliers

Abstract. Since the seminal paper by Dickey and Fuller in 1979, unit‐root tests have conditioned the standard approaches to analysing time series with strong serial dependence in mean behaviour, the focus being placed on the detection of eventual unit roots in an autoregressive model fitted to the series. In this paper, we propose a completely different method to test for the type of long‐wave patterns observed not only in unit‐root time series but also in series following more complex data‐generating mechanisms. To this end, our testing device analyses the unit‐root persistence exhibited by the data while imposing very few constraints on the generating mechanism. We call our device the range unit‐root (RUR) test since it is constructed from the running ranges of the series from which we derive its limit distribution. These nonparametric statistics endow the test with a number of desirable properties, the invariance to monotonic transformations of the series and the robustness to the presence of important parameter shifts. Moreover, the RUR test outperforms the power of standard unit‐root tests on near‐unit‐root stationary time series; it is invariant with respect to the innovations distribution and asymptotically immune to noise. An extension of the RUR test, called the forward–backward range unit‐root (FB‐RUR) improves the check in the presence of additive outliers. Finally, we illustrate the performances of both range tests and their discrepancies with the Dickey–Fuller unit‐root test on exchange rate series.     

EDT Method for Multiple Labelled Objects Subject to Tied Distances

Machine Intelligence Research - The success of new scientific areas can be assessed by their potential for contributing to new theoretical approaches aligned with real-world applications. The...     

Selecting linear algebra kernel composition using response time prediction

Numerical linear algebra libraries provide many kernels that can be composed to perform complex computations. For a given computation, there is typically a large number of functionally equivalent kernel compositions. Some of these compositions achieve better response times than others for particular data and when executed on a particular computer architecture. Previous research provides methods to enumerate (a subset of) these kernel compositions. In this work, we study the problem of determining the composition that yields the lowest response time. Our approach is based on a response time prediction for each candidate combination. While this prediction could in principle be obtained using analytical and/or empirical performance models, developing accurate such models is known to be challenging. Instead, we define a feature space that captures salient properties of kernel combinations and predict response time using supervised machine learning. We experiment with a standard set of machine learning algorithms and identify an effective algorithm for our kernel composition selection problem. Using this algorithm, our approach widely outperforms the strategy that would consist in always using the simplest kernel composition and is often close to the fastest kernel compositions among those evaluated. We quantify the potential benefit of our approach if it were to be implemented as part of an interactive computational tool. We find that although the potential benefit is substantial, a limiting factor is the kernel composition enumeration overhead. Copyright © 2014 John Wiley & Sons, Ltd.     

Online identification of appliances from power consumption data collected by smart meters

The efficient use of resources is a matter of great concern in today’s society, especially in the energy sector. Although the main strategy to decrease energy use has long been focused on supply, over the last few years, there has been a shift to the demand side. Under this new line of action, demand-side management networks have emerged and extended from the household level to larger installations, with the appearance of the concepts of Smart Grids and even Smart Cities. The extended use of Smart Meters for measuring residential electricity consumption facilitates the creation of such intelligent environments. In this context, this article proposes a system which extracts value from the collected consumer information to identify the appliances belonging to that smart environment by means of machine learning techniques. Considering the large amount of information that would be handled when millions of homes were sending data, big data technology has been used. An experiment to evaluate the classification method was carried out with seven devices and three different configurations. The results are also reported, achieving promising results, with recognition rates of 75 % after 1 h of training and 100 % after 4 h.