Taximeter verification using imprecise data from GPS

Until recently, local governments in Spain were using machines with rolling cylinders for testing and verification of taximeters. However, the tyres condition can lead to errors in the process and the mechanical construction of the test equipment is not compatible with certain vehicles. Thus, a new measurement device should be designed.In our opinion, the verification of a taximeter will not be reliable unless measurements taken on an actual taxi run are used. Global positioning system (GPS) sensors are intuitively well suited for this process, because they provide the position and the speed with independence from those car devices that are under test. Nevertheless, since GPS measurements are inherently imprecise, GPS-based sensors are difficult to homologate. In this paper we will show how these legal problems can be solved. We propose a method for computing an upper bound of the length of the trajectory, taking into account the vagueness of the GPS data. The uncertainty in the GPS data will be modelled by fuzzy techniques. The upper bound will be computed using a multiobjective evolutionary algorithm. The accuracy of the measurements will be improved further by combining it with restrictions based on the dynamic behavior of the vehicles.     

Reconstruct street network from imprecise excavation data using fuzzy Hough transforms

This paper proposes an approach for handling multivariate data in an archaeological Geographical Information System (GIS), providing a new tool to archaeologists and historians. Our method extracts potential objects of known shapes in a geographical database (GDB) devoted to archaeological excavations. In this work, archaeological information is organized according to three components: location, date and a shape parameter, in a context where data are imprecise and lacunar. To manage these aspects, a three-step methodology was developed using fuzzy sets modeling and adapting the fuzzy Hough transform. This methodology is applied in order to define the appropriate tool for a GDB of Roman street remains in Reims, France. The defined queries return an estimation of the possible presence of streets during a fuzzy time interval given by experts on the Roman period in Reims.     

Volumetric data exploration using interval volume

A new type of geometric model called Interval volume for volumetric data exploration is presented. An interval volume represents a three dimensional subvolume for which the associate scalar values lie within a user specified interval, and provides one of the promising approaches to solid fitting, which is an extended concept of traditional surface fitting. A well known isosurfacing algorithm called Marching Cubes is extended to obtain a solid fitting algorithm, which extracts from a given volumetric data set a high resolution, polyhedral solid data structure of an interval volume. Branch-on-Need Octree is used as an auxiliary data structure to accelerate the extraction process. A variety of interval volume rendering methods and principal related operations, including measurements and focusing, are also presented. The effectiveness of measurement coupled visualization capabilities of the presented approach is demonstrated by application to visualizing a four dimensional simulated data from atomic collision research     

Evaluating aggregate operations over imprecise data

Imprecise data in databases were originally denoted as null values, which represent the meaning of “values unknown at present.” More generally, a partial value corresponds to a finite set of possible values for an attribute in which exactly one of the values is the “true” value. We define a set of extended aggregate operations, namely sum, average, count, maximum, and minimum, which can be applied to an attribute containing partial values. Two types of aggregate operators are considered: scalar aggregates and aggregate functions. We study the properties of the aggregate operations and develop efficient algorithms for count, maximum and minimum. However, for sum and average, we point out that in general it takes exponential time complexity to do the computations     

Refining imprecise data by integrity constraints

Uncertain data in databases were originally denoted as null values, which represent the meaning of ‘values unknown at present.” Null values were generalized into partial values, which correspond to a set of possible values, to provide a more powerful notion. In this paper, we derive some properties to refine partial values into more informative ones. In some cases, they can even be refined into definite values. Such a refinement is possible when there exist range constraint on attribute domains, or referential integrities, functional dependencies, or multivalued dependencies among attributes.

Our work actually eliminates redundant elements in a partial value. By this process, we not only provide a more concise and informative answer to users, but also speedup the computation of queries issued afterward. Besides, it reduces the communication cost when imprecise data are requested to be transmitted from one site to another site in a distributed environment.     

OLAP over uncertain and imprecise data

We extend the OLAP data model to represent data ambiguity, specifically imprecision and uncertainty, and introduce an allocation-based approach to the semantics of aggregation queries over such data. We identify three natural query properties and use them to shed light on alternative query semantics. While there is much work on representing and querying ambiguous data, to our knowledge this is the first paper to handle both imprecision and uncertainty in an OLAP setting.     

基于改进朴素贝叶斯的区间不确定性数据分类方法

基于Parzen窗的朴素贝叶斯在区间不确定性数据分类中存在计算复杂度高、空间需求大的不足。针对该问题,提出一种改进的区间不确定性数据分类方法IU-PNBC。首先采用Parzen窗估计区间样本的类条件概率密度函数(CCPDF);然后通过代数插值得到类条件概率密度函数的近似函数;最后利用近似代数插值函数计算样本的后验概率, 并用于预测。通过人工生成的仿真数据和UCI标准数据集验证了算法假设的合理性以及插值点数对IU-PNBC算法分类精度的影响。实验结果表明,当插值点数大于15时,IU-PNBC算法的分类精度趋于稳定,且插值点数越多,算法分类精度越高;该算法可以避免原Parzen窗估计对训练样本的依赖, 并有效降低计算复杂度;同时由于该算法具有远低于基于Parzen窗的朴素贝叶斯的运行时间和空间需求, 因此适合解决数据量较大的区间不确定性数据分类问题。     

A belief classification rule for imprecise data

The classification of imprecise data is a difficult task in general because the different classes can partially overlap. Moreover, the available attributes used for the classification are often insufficient to make a precise discrimination of the objects in the overlapping zones. A credal partition (classification) based on belief functions has already been proposed in the literature for data clustering. It allows the objects to belong (with different masses of belief) not only to the specific classes, but also to the sets of classes called meta-classes which correspond to the disjunction of several specific classes. In this paper, we propose a new belief classification rule (BCR) for the credal classification of uncertain and imprecise data. This new BCR approach reduces the misclassification errors of the objects difficult to classify by the conventional methods thanks to the introduction of the meta-classes. The objects too far from the others are considered as outliers. The basic belief assignment (bba) of an object is computed from the Mahalanobis distance between the object and the center of each specific class. The credal classification of the object is finally obtained by the combination of these bba’s associated with the different classes. This approach offers a relatively low computational burden. Several experiments using both artificial and real data sets are presented at the end of this paper to evaluate and compare the performances of this BCR method with respect to other classification methods.     

Querying imprecise data in moving object environments

In moving object environments, it is infeasible for the database tracking the movement of objects to store the exact locations of objects at all times. Typically, the location of an object is known with certainty only at the time of the update. The uncertainty in its location increases until the next update. In this environment, it is possible for queries to produce incorrect results based upon old data. However, if the degree of uncertainty is controlled, then the error of the answers to queries can be reduced. More generally, query answers can be augmented with probabilistic estimates of the validity of the answer. We study the execution of probabilistic range and nearest-neighbor queries. The imprecision in answers to queries is an inherent property of these applications due to uncertainty in data, unlike the techniques for approximate nearest-neighbor processing that trade accuracy for performance. Algorithms for computing these queries are presented for a generic object movement model and detailed solutions are discussed for two common models of uncertainty in moving object databases. We study the performance of these queries through extensive simulations.     

Classification in the presence of class noise using a probabilistic Kernel Fisher method

In machine learning, class noise occurs frequently and deteriorates the classifier derived from the noisy data set. This paper presents two promising classifiers for this problem based on a probabilistic model proposed by Lawrence and Schölkopf (2001). The proposed algorithms are able to tolerate class noise, and extend the earlier work of Lawrence and Schölkopf in two ways. First, we present a novel incorporation of their probabilistic noise model in the Kernel Fisher discriminant; second, the distribution assumption previously made is relaxed in our work. The methods were investigated on simulated noisy data sets and a real world comparative genomic hybridization (CGH) data set. The results show that the proposed approaches substantially improve standard classifiers in noisy data sets, and achieve larger performance gain in non-Gaussian data sets and small size data sets.     

核函数FISHER鉴别在性别鉴别中的应用

该文在线性FISHER鉴别的基础上,探讨了核函数FISHER鉴别原理;然后将核函数FISHER鉴别用于性别鉴别中,并提出了相应算法;对ORL人脸图像数据库进行实验;详细分析了算法在不同样本分布和不同参数条件下的识别结果;表明了在两类模式识别中核函数FISHER鉴别理论具有良好的识别效果及极佳的推广能力。     

Constructing composite indicators with imprecise data: A proposal

This paper focuses on the construction of a composite indicator for the knowledge based economy, using imprecise data. Specifically, for some indicators we only have information on the interval within which the true value is believed to lie. The proposed approach is based on the Data Envelopment Analysis literature. Given the setting of evaluating countries, this paper discerns a ‘strong country in weak environment’ and ‘weak country in strong environment’ scenario resulting in respectively an upper and lower bound on countries’ performance. Accordingly, we derive a classification of ‘benchmark countries’, ‘potential benchmark countries’, and ‘countries open to improvement’. We illustrate our method with data on the knowledge economy.     

Classification of remotely-sensed image data using artificial neural networks

Abstract

Abstract. Artificial neural networks have been used recently for speech and character recognition. Their application for the classification of remotely-sensed images is reported in this Letter. Remotely sensed image data are usually large in size and spectral overlaps among classes of ground objects are common. This results in low convergence performance of the Back-Propagation Algorithm in a neural network classifier. A Blocked Back-Propagation (BB-P) algorithm was proposed arid described in this Letter. It improved convergence performance and classification accuracy.     

Classification and function prediction of proteins using data compression

Protein has a complicated spatial structure, and has chemical and physical functions which originate from this structure. It is important to predict the structure and function of proteins from a DNA sequence or amino acid sequence from the viewpoint of biology, medical science, protein engineering, etc. However, to data there is no way to predict them accurately from these sequences. Instead, some approaches attempt to estimate the functions based on an approximate similarity in the retrieval of sequences. We propose a new method for the similarity retrieval of an amino acid sequence based on the concept of homology retrieval using data compression. The introduction of compression by a dictionary technique enables us to describe the text data as ann-dimensional vector usingn dictionaries, which is generated by compressingn typical texts, and enables us to classify the proteins based on their similarity. We examined the effectiveness of our proposal using real genome data. This work was presented in part at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, January 15–17, 2001     

Evaluation of probabilistic queries over imprecise data in constantly-evolving environments

Sensors are often employed to monitor continuously changing entities like locations of moving objects and temperature. The sensor readings are reported to a database system, and are subsequently used to answer queries. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), the database may not be able to keep track of the actual values of the entities. Queries that use these old values may produce incorrect answers. However, if the degree of uncertainty between the actual data value and the database value is limited, one can place more confidence in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers, and provide efficient indexing and numeric solutions. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies.     

Fisher鉴别特征的最近邻凸包分类

基于Fisher准则的特征提取方法是模式识别技术的重要分支,其中,Foley-Sammon变换和具有统计不相关性的最佳鉴别变换是这一技术典型代表,本文将它们与一种新型分类器一最近邻凸包分类器相结合,从而实现Fisher鉴别特征的有效分类。最近邻凸包分类器是一类以测试样本点到各类训练集生成类别凸包的距离为分类判别依据的模式分类新方法,具有非线性性,无参性,多类别适用性等特点。实验证实了本文方法的有效性。     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

Designing real-time systems using imprecise discrete-event system specifications

Real-time (RT) systems include hardware and software components interacting in a tight fashion. Although formal methods for RT systems development have advanced, they are sometimes difficult to apply in practical applications, and scalability is compromised as the complexity of the system scales up. Instead, using modeling and simulation (M&S) methods and tools has showed to be useful for verification of practical aspects of RT systems (and having the advantage to be able to including models of the physical environment they interact with). Although several efforts exist in M&S of RT systems, none of them has considered problems of transient overloading in the RT systems specifications. Here, we introduce a new theoretical framework called I-DEVS (imprecise discrete event systems specification) with the goal of guaranteeing responses to inputs within specified time constraints under such transient overloading conditions. The solution presented here has the advantages of a formal specification and the practicality of an M&S-based approach. We also discuss how to define hierarchical models running in RT, and we present a set of tools that can be applied to develop RT-embedded applications, and RT simulations.