16561888
Learning to predict train wheel failures
This paper describes a successful but challenging application of data mining in the railway industry. The objective is to optimize maintenance and operation of trains through prognostics of wheel failures. In addition to reducing maintenance costs, the proposed technology will help improve railway safety and augment throughput. Building on established techniques from data mining and machine learning, we present a methodology to learn models to predict train wheel failures from readily available operational and maintenance data. This methodology addresses various data mining tasks such as automatic labeling, feature extraction, model building, model fusion, and evaluation. After a detailed description of the methodology, we report results from large-scale experiments. These results clearly show the great potential of this innovative application of data mining in the railway industry.
15205794
Specification of Multi-Agent Systems in the Gamma Language
The suitability of using chemical reaction metaphor (CRM) to model multi-agent systems (MASs) is justified by CRM's capacity in specifying dynamic features of multi-agent systems. This paper presents a case study that demonstrates the applications of CRM in specifying multi-agent systems. The case study is the specification of a navigation training system. Given the dynamic and concurrent nature of multi-agent systems, we find that the chemical reaction metaphor provides a mechanism for describing the overall architecture of the distributed multi-agent systems precisely and concisely, while giving the design of the real system a solid starting point and allowing step-by-step refinement of the system using transformational methods
14357839
Clustering-training for Data Stream Mining
Mining data streams has attracted much attention recently. Labeled samples needed by most current stream classification methods are more difficult and expensive to obtain than unlabeled ones. This paper proposed a semi-supervised learning algorithm - clustering-training to utilize the unlabeled samples. It uses clustering to select confidently unlabeled samples, and uses them to re-train the classifier incrementally. Experiments on synthetic and real data set showed the effectiveness of the proposed algorithm
2566456
Interval constraint networks for tolerance analysis and synthesis
This paper proposes interval constraint networks and interval propagation techniques for automatic tolerance design. To develop an intelligent automatic tolerance design mechanism, the following interrelated problems are addressed: (1) representing the relationships between the tolerances of the entities, attributes, and the functional requirements of a design component by a constraint network, (2) developing tolerance propagation techniques in the network that will satisfy the constraints between the entities, attributes and functional requirement. For the purpose of tolerance design, we propose new definitions for consistency of constraints and consistency of a constraint network. New forward and backward propagation techniques achieve worst-case tolerance analysis and synthesis, respectively. A tank design problem illustrates the use of the procedures for tolerance analysis and synthesis. The discussion and examples compare the new constraint network and propagation techniques with previous works.
43794915
A Parallel Library for Large-Scale Sparse Eigenvalue Calculation on NEC SX-4
36980746
Decision tree construction for data mining on grid computing
Decision tree is one of the frequently used methods in data mining for searching prediction information. Due to its characteristics which are suitable for parallelism, it has been widely adopted in high performance field and developed into various parallel decision tree algorithms to deal with huge data and complex computation. Following the development of other technology fields, grid computing is regarded as the extension of PC cluster and therefore it future research development is highly valued. This new wave of Internet application is the 3rd generation of Internet applications following the traditional Internet and Web application. We have presented a grid-based decision tree architecture, and hope it can be applied on both parallel and sequential algorithms for the decision tree applications. Also, based on the scope and model of data mining applied in grid environment as well as user equivalent perspective, grid roles can be categorized into three types. We are hoping that through these definitions, software developers can define clear system processes and differentiate the application scope for software applications. To fulfil our architecture, we first apply an existing parallel decision tree algorithm-SPRINT algorithm in the grid environment. The performance and differences in many other areas are compared using different sizes of dataset. The experimental results are used for future reference and further development.
8656490
Large Primes in Stream Cipher Cryptography
The present research is motivated by the observation that if the period T of a certain binary sequence is a prime, then its linear complexity will be bounded from below by the order of 2 modulo T, i.e., LC⩾OrdT(2). A class of generators with state periods T(q, n)=q·2n−1 are constructed for q=3, 5, 7, 9 and arbitrary n on the basis of a pair of m-sequence generators with the same number of stages, each controlling the clock of the other (bilateral stop-and-go clock control). A new test is derived to find the primes among the numbers T(q, n) with the cases 3 | q and 3 | q treated in a unified manner. The orders of 2 modulo some of the primes T(q, n) are given and some additional cryptographic and implementational remarks are made.