Fault diagnosis in power transformers using multi-class logical analysis of data

This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of condition based maintenance (CBM), historical data containing condition indices and the state of the machine are the inputs to LAD. After training and testing phases, LAD generates patterns that characterize the faulty states according to the type of fault, and differentiate between these states and the normal state. These patterns are found by solving a mixed 0–1 integer linear programming problem. They are then used to detect and to identify a future unknown state of equipment. The diagnostic technique has already been tested on several known machine learning datasets. The results proved that the performance of this technique is comparable to other conventional approaches, such as neural network and support vector machine, with the added advantage of the clear interpretability of the generated patterns, which are rules characterizing the faults’ types. To demonstrate its merit in fault diagnosis, the technique is used in the detection and identification of faults in power transformers using dissolved gas analysis data. The paper reaches the conclusion that the multi-class LAD based fault detection and identification is a promising diagnostic approach in CBM.     

An implementation of logical analysis of data

Describes a new, logic-based methodology for analyzing observations. The key features of this “logical analysis of data” (LAD) methodology are the discovery of minimal sets of features that are necessary for explaining all observations and the detection of hidden patterns in the data that are capable of distinguishing observations describing “positive” outcome events from “negative” outcome events. Combinations of such patterns are used for developing general classification procedures. An implementation of this methodology is described in this paper, along with the results of numerical experiments demonstrating the classification performance of LAD in comparison with the reported results of other procedures. In the final section, we describe three pilot studies on applications of LAD to oil exploration, psychometric testing and the analysis of developments in the Chinese transitional economy. These pilot studies demonstrate not only the classification power of LAD but also its flexibility and capability to provide solutions to various case-dependent problems     

Development of data governance components using DEMATEL and content analysis

The Journal of Supercomputing - Organizations gain insight and derive value from data that drive their direction, and so data governance, which is a method of managing competitive data, is becoming...     

基于模糊与自校正技术的超声电机伺服控制

通过电机的工作原理和速度特性分析,选定驱动频率为其控制变量;为实现电机快速而高精度位置控制,引入模糊和自校正复合控制技术,其中,模糊逻辑控制技术意在实现控制的快速性,自校正技术用以提高控制精度;组建了控制系统,完成了相应的控制实验;结果表明:在超声电机上采用这种复合控制技术上可以得到较好的控制效果。     

Increasing the efficiency of combinatorial logical data analysis in recognition and classification problems

Problems of increasing the efficiency of combinatorial logical data analysis in recognition problems are examined. A technique for correct conversion of initial information for reduction of its dimensionality is proposed. Results of testing this technique for problems of real medical prognoses are given. Djukova Elena V. Born 1945. Graduated from Moscow State University in 1967. Candidate’s degree in Physics and Mathematics in 1979. Doctoral degree in Physics and Mathematics in 1997. Dorodnicyn Computing Center, Russian Academy of Sciences, leading researcher. Moscow State University, lecturer. Moscow Pedagogical University, lecturer. Scientific interests: discrete mathematics and mathematical method of pattern recognition. Author of 70 papers. Peskov Nikolai V. Born 1978. Graduated from Moscow State University in 2000. Candidate’s degree in 2004. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of ten papers. Inyakin Andrey S. Born 1978. Graduated from Moscow State University in 2000. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of ten papers. Sakharov Aleksei A. Born 1980. Graduated from Moscow State University in 2003. Moscow Pedagogical University, graduate student. Scientific interests: discrete mathematics and mathematical method of pattern recognition. Author of three papers.     

Position estimation using principal components of range data

This paper describes a new approach to mobile robot position estimation, based on principal component analysis of laser range data. An eigenspace is constructed from the principal components of a large number of range data sets. The structure of an environment, as seen by a range sensor, is represented as a family of surfaces in this space. Subsequent range data sets from the environment project as a point in this space. Associating this point to the family of surfaces gives a set of candidate positions and orientations (poses) for the sensor. These candidate poses correspond to positions and orientations in the environment which have similar range profiles. A Kalman filter can be used to select the most likely candidate pose based on coherence with small movements.

The first part of this paper describes how a relatively small number of depth profiles of an environment can be used to generate a complete eigenspace. This space is used to build a representation of the range scan profiles obtained from a regular grid of positions and orientations (poses). This representation has the form of a family of surface (a manifold). This representation converts the problem of associating a range profile to possible positions and orientations into a table lookup. As a side benefit, the method provides a simple means to detect obstacles in a range profile. The final section of the paper reviews the use of estimation theory to determine the correct pose hypothesis by tracking.     

Genetic algorithms for problems of logical data analysis in discrete optimization and image recognition

Genetic algorithms for the search for minimal covering of a Boolean matrix are developed and studied. This problem arises in image recognition if methods of combinatorial (logical) analysis of information are used to synthesize recognizing procedures.     

Behavioral event data and their analysis

Social science is broadly defined as the analysis of human behavior whether it be at an individual or a group level. In this work, we explore the analysis of human behavior encoded as a trail of their events over time and space, which we refer to as behavioral event data. We show that such data offers challenges to data mining algorithm designers as the data to analyze is naturally multi-way, involves complex patterns that form/reform over time, and has complex interactions between groups in the population. Though the data naturally lends itself to be represented as graphs and tensors we show how existing techniques are limited in their usefulness and outline our own algorithms to overcome these challenges. In this paper, using the adversarial event behavior of blue and red forces, we show three core problems and solutions in event behavior analysis: (1) Decomposing behavior to identify areas of intense activity, (2) Predicting what groups of events are likely to occur, and (3) Analysis to identify interacting behavior given a known template.     

The logical control of an elevator

This paper presents a detailed example of the design of a logical feedback controller for finite state machines. In this approach, the control objectives and associated control actions are formulated as a set of axioms each of the form X implies Y, where X asserts that (i) the current state satisfies a set of conditions and (ii) the control action y will steer the current state towards a given target state; Y asserts that the next control input will take the value y. An automatic theorem prover establishes which of the assertions X is true, and then the corresponding control y is applied. The main advantages of this system are its flexibility (changing the control law is accomplished through changing only the axioms) and the fact that, by the design of the system, control actions will provably achieve the control objectives. The illustrative design problem presented in this paper is that of the logical specification and logical feedback control of an elevator     

Abstract data types for the logical modeling of complex data

In this paper we propose a logical data model for complex data. Our proposal extends the relational model by using abstract data types for domains specification and an extended relational algebra is also introduced. The introduction of the parameterized type Geometry(S), where S is a ground set of elements, allows the representation of complex aggregated data. As an example, we discuss how our model supports the definition of geographical DBMSs. Moreover, to show the generality of our approach, we sketch how the model can be used in the framework of statistical applications.     

SOFA: An extensible logical optimizer for UDF-heavy data flows

Recent years have seen an increased interest in large-scale analytical data flows on non-relational data. These data flows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant building blocks of such data flows are user-defined predicates or functions (Udfs). However, the heavy use of Udfs is not well taken into account for data flow optimization in current systems.Sofa is a novel and extensible optimizer for Udf-heavy data flows. It builds on a concise set of properties for describing the semantics of Map/Reduce-style Udfs and a small set of rewrite rules, which use these properties to find a much larger number of semantically equivalent plan rewrites than possible with traditional techniques. A salient feature of our approach is extensibility: we arrange user-defined operators and their properties into a subsumption hierarchy, which considerably eases integration and optimization of new operators. We evaluate Sofa on a selection of Udf-heavy data flows from different domains and compare its performance to three other algorithms for data flow optimization. Our experiments reveal that Sofa finds efficient plans, outperforming the best plans found by its competitors by a factor of up to six.     

利用连通分支对基因表示数据的聚类算法

在生命科学中,需要对物种及基因进行分类,以获得对种群固有结构的认识。利用数据聚类方法,有效地辨别／识别基因表示数据的模式,对它们进行分类。将特征相似性大的归为一类,特征相异性大的归为不同类。这对于研究基因的结构、功能、以及不同种类基因之间的关系都具有重要意义。利用图论的方法对分子生物学中基因表示数据进行初始聚类,然后再结合别的算法,如K－近邻自学习聚类算法或基于中心点的自学习聚类算法,对其进一步求精。对于某种聚类判别准则,能够产生全局最优簇。最后对算法进行了分析和讨论,并用模拟数据进行了实验验证。     

VisuaLeague: Player performance analysis using spatial-temporal data

In recent years, the phenomenon of eSports has been a growing trend and consequently, in addition to players, other groups of users, including coaches and analysts, took an interest in online video games and the data extracted from them. Among many types of video games, one of the most widely played is the MOBA (Multiplayer Online Battle Arena) League of Legend (LoL) game. Similary to traditional sports, players and coaches/analysts analyse all game events, such as, players’ movements, to understand how they play to define new strategies and improve their performance. Our main goal is to get a better understanding of which visualizations techniques are more adequate to handle this type of spatio-temporal information data, associated to player performance analysis in video games. To address this goal, we inquired players to identify the analytical questions they need to support for performance analysis and designed the VisuaLeague prototype for the visualization of in-game player trajectories, using animated maps, and events during a LoL match. This paper presents a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.

     

Life-cycle data management of engineered-to-order components using radio frequency identification

Management of engineered-to-order (ETO) components and their related information is a challenging task due to the complexity of information and its flow. Different information items are generated, accessed and exchanged between different organizations and they must continually flow through design, production, construction, and operations and maintenance. Current manual and labor-intensive methods are inefficient; as a result, information is frequently incomplete, inaccurate or unavailable during the life-cycle of a facility. This paper provides a vision of intelligent components, which know their identities, locations and history, and communicate this information to their environments. It proposes streamlining information flow through supply chains by utilizing radio frequency identification (RFID) technology. To explore the technical feasibility of intelligent components, component-related information flow patterns in ETO supply chains were identified and analyzed. Requirements analysis and corresponding technology deployment and testing were performed for three types of ETO components through different life-cycle phases. These experiments demonstrated that it is technically feasible to have intelligent components in construction supply chains by using RFID technology; that status information can be collected automatically; and that maintenance information can be stored and retrieved during the service life of a facility.