关键词： Pattern recognition  

摘要： This paper proposes a bi-partitioning approach using link density data to identify an optimal restricted zone (RZ) in a congested monocentric city to assist with the implementation of area-wide congestion management strategies. A composite similarity measure is developed for each link in the network as a weighted average of a density similarity measure (ensuring homogeneity) and a distance similarity measure (ensuring connectivity and compactness). The resulting similarity matrix is fed into a graph clustering method termed symmetric nonnegative matrix factorization (SNMF) to bi-partition the network. To determine the optimal weight as a hyperparameter, we propose a hierarchical search algorithm (HSA) based on the concept of "knee" that is used to find the most significant solution from the Pareto front. The proposed approach is demonstrated on the Melbourne network using a simulation-based dynamic traffic assignment model. Results show that the methodology (i) can effectively capture the spatial variations of the congestion pattern in the network;(ii) is robust to moderate parameter changes;(iii) can be extended to the time-dependent case and hence inform when to activate the area control;and (iv) can perform relatively well in the presence of missing data. When varying the distance threshold as a design parameter, we can observe how the optimal RZ evolves in space, a feature that is critical to devising a double- or multi-layered RZ for hierarchical control purposes.

关键词： Artificial neural networks   classifier   pattern recognition   fall detection   rock climbing   acceleration   altitude   wearable device  

摘要： From an athlete's perspective, the identification of falls during rock climbing is of major importance. It constitutes a solid performance indicator, but more importantly, it could be used to trigger an instantaneous alarm to rescue teams, thus reducing the negative health consequences for the climber. In this context, an artificial neural network-based technique for fall detection during rock climbing is presented in this study. The output of this tool could be used for safety and performance monitoring purposes. The proposed method exploits a neural network for binary pattern recognition. This network is fed with a set of features extracted in real time from the acceleration and altitude signals acquired by means of a wearable device. The classifier is trained and validated with experimental datasets recorded during real climbing sessions of eight athletes through different route grades and conditions. This article illustrates the architecture of the proposed algorithm, feature extraction process, and evaluation of its accuracy. In addition, an analysis of the severity level of the detected falls is conducted. The method is able to identify real fall events with a high success rate, while yielding very few false positive indications of a fall.

关键词： Intuitionistic fuzzy set (IFS)   Similarity measure   Bounded variation   Pattern recognition   Clustering analysis  

摘要： The distance and similarity measures are two interrelated depictions of the patterns which signify the categorization between the Atanassov intuitionistic fuzzy sets (AIFSs) by evaluating the degree of belongingness. In this work, we propose a new similarity measure, termed as hybrid similarity measure, by the combination of intuitionistic fuzzy bounded variation (IFBV) and intuitionistic fuzzy metric based measures. The concept of IFBV which is a technique to approximate arc length of an intuitionistic fuzzy-valued function (IFVF) is also introduced here. The IFVF over an AIFS is geometrically evolved through the generalization of the p-summable IFBV, that is, connecting all the elements of AIFS lie on the structure of IFBV corresponding to power p. The proposed measure overcomes the shortcomings of intuitionistic fuzzy metric based similarity measures by incorporating more flexibility into it. The hybrid similarity measure has been successfully implemented and applied on the several real-world applications in the field of pattern recognition as well as clustering. Further, a detailed comparison of results has been shown against the other existing similarity measures to demonstrate the superiority and validity of the proposed hybrid similarity measure. (C) 2019 Elsevier B.V. All rights reserved.

摘要： Smart grids are a modification of traditional electric power grid to achieve a bidirectional, automatic, intelligent and adaptive power system. In smart grids the electricity distribution and power system management is improved by leveraging advanced two-way communications and integrating computing capabilities to achieve better reliability, stability, efficiency, and security of the power system. The smart grid introduces the two-way flow of data between electricity suppliers and customers to transfer real-time information and facilitate the near real-time balance of supply-demand management. In contrast to many other industries who have the capability to store and reserve their products, the electric power industry does not have such a capability to store a massive amount of electricity using today's technologies. Therefore, due to the storage limitations of electricity, one of the crucial tasks of power system operation is to keep a balance between supply and demand at every moment. As a result, forecasting is an essential and important function in the electricity power grid. Recent advances in the energy industry, including smart grid and smart meters, provide new capabilities to electrical utilities for forecasting electricity demand, modelling customers' usage profiles, optimizing unit commitment and preventing outages. These advances also introduce new challenges to the power grid, such as managing and analysing of large volumes of complex, high dimensional data in an efficient manner. So, utilities need to apply advanced data management and analytical models to extract actionable insights from this information. By leveraging better predictive and analytical models and the high volume of data, utility companies are able to produce a wide range of forecasts including: 1. Forecasting the amount of excess energy generation, the appropriate time to sell it, and the feasibility of transmitting it into the grid 2. Forecasting when and where contingencies are most l

摘要： Although central tolerance mechanisms purge self-reactive B cells during development based on BCR signal strength, mechanisms that block the differentiation of autoreactive effector and memory B cells from mature pools remain poorly understood. Prior observations implicate nucleic acid sensing TLRs in autoimmunity, and more recent findings show that TLR9 is also involved in maintaining peripheral tolerance. Studies of the immunological changes that occur during aging revealed a subset of B cells denoted Age-associated B cells which expands in settings of aging and in autoimmunity. Further studies demonstrated that TLR9 signals poise activated B cells to adopt an Age-associated B cell phenotype, but BCR-delivered TLR9 signals cause programmed cell death that, if circumvented by costimulation, allows continued differentiation to the ABC fate. Together, these observations suggest molecular pattern recognition, rather than BCR epitope specificity per se, is a fundamental mediator of tolerogenic outcomes in the peripheral B cell activation.

关键词： Asymptotic normality   high dimension low sample size   martingale difference sequence   naive canonical correlation coefficient   naive Hotelling T2 statistic  

摘要： This paper examines the high dimensional asymptotics of the naive Hotelling T-2 statistic. Naive Bayes has been utilized in high dimensional pattern recognition as a method to avoid singularities in the estimated covariance matrix. The naive Hotelling T-2 statistic, which is equivalent to the estimator of the naive canonical correlation, is a statistically important quantity in naive Bayes and its high dimensional behavior has been studied under several conditions. In this paper, asymptotic normality of the naive Hotelling T-2 statistic under a high dimension low sample size setting is developed using the central limit theorem of a martingale difference sequence.

关键词： Clustering algorithms   Demand response   Load patterns   Smart meters   Symbolic aggregate approximation (SAX)  

摘要： The availability of smart meter data allows defining innovative applications such as demand response (DR) programs for households. However, the dimensionality of data imposes challenges for the data mining of load patterns. In addition, the inherent variability of residential consumption patterns is a major problem for deciding on the characteristic consumption patterns and implementing proper DR settlements. In this regard, this paper utilizes a data size reduction and clustering methodology to analyze residential consumption behavior. Firstly, the distinctive time periods of household activity during the day are identified. Then, using these time periods, a modified symbolic aggregate approximation (SAX) technique is utilized to transform the load patterns into symbolic representations. In the next step, by applying a clustering method, the major consumption patterns are extracted and analyzed. Finally, the customers are ranked based on their stability over time. The proposed approach is applied on a large dataset of residential customers smart meter data and can achieve three main goals: 1) it reduces the dimensionality of data by utilizing the data size reduction, 2) it alleviates the problems associated with the clustering of residential customers, 3) its results are in accordance with the needs of systems operators or demand response aggregators and can be used for demand response targeting. The paper also provides a thorough analysis of different aspects of residential electricity consumption and various approaches to the clustering of households which can inform industry and research activity to optimize smart meter operational use. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Chemical profile   cachaca   chemometrics   quality control  

摘要： Sugarcane spirits and cachaca are distilled beverages derived from the fermentation of sugarcane juice. The production of these spirits has significant influence on the economy of several regions in Brazil, being the third most consumed distilled beverage in the world. To ensure the safety for human consumption and also to add value to these products, it is imperative to apply quality control techniques. The complexity of food matrices along with the fact that the currently used instrumental techniques have several disadvantages, such as being expensive, laborious and time-consuming, have turned research attention to multivariate analysis techniques. In consequence, chemometric techniques have been applied in laboratories around the world aiming at data reduction, pattern recognition, cluster analysis, classification and quantification of data. This article provides an overview of the application of analytical techniques with nonsupervised and supervised pattern recognition methods for the analysis of sugarcane spirits samples. Assessments discussed include promising results for the discrimination among samples, verification of adulteration, tendencies in sensorial characteristics as well as relationships between chemical information and the geographical origins.

关键词： 神经网络   BP算法   模式识别  

摘要： 传统的BP神经网络在模式识别中存在识别速度慢和识别准确率低等若干不足,针对这些问题对BP神经网络改进。通过将改进的BP算法与传统的BP算法比较,发现改进后的BP算法在收敛速率和局部极值点上有了较大的改善且提高了准确率。将改良后的BP算法应用于对无线信道的分类识别中,输出结果表明,优化后的BP算法是一种速度快误差小的神经网络分类算法。

关键词： 模式识别   视频分析   联合实验室   人工智能   视频大数据   国家重点实验室   期刊杂志   审稿人  

摘要： 王金桥,中国科学院自动化研究所研究员、中科视语(北京)科技有限公司创始人、博士研究生导师,智能媒体计算联合实验室、视频大数据云识别联合实验室、视频深度识别联合实验室主任,中国科学院大学人工智能学院教授,是从事研究图像与视频分析领域的专家.同时兼任TPAMI、IJCV、TIP、TSCVT、TNNLS等国际期刊杂志以及CVPR、ICCV、IJCAI、AAAI、ICME、ICIP、ICPR、ACCV、MMM、PCM、ACM Multimedia等国际会议的审稿人.