A Lie group formulation of Kane’s equations for multibody systems

We propose a Lie group approach to formulate the Kane’s equations of motion for multibody systems. This approach regards the set of rigid body transformations as the special Euclidean group SE(3). By expressing rigid body displacements as exponential maps generated from the Lie algebra se(3), it subsequently manipulates rigid body kinematics as convenient matrix operations. With this approach, all the individual quantities involved in Kane’s equations can be computed explicitly in an intrinsic manner, and the motion equations can be obtained systematically and efficiently. An example is presented to illustrate its use and effectiveness.     

Formalization of algorithmic knowledge of object domains in terms of the algebra of algorithmics

Clones of algebras of n-relations are examined. These clones include algebras equipotent to Codd’s algebra. The results obtained earlier for semigroup (grammatical and algorithmic) clones are extended to clones of algebras of functional n-relations. One-dimensional and multidimensional computing structures and related object domains are outlined. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 3–17, November–December 2007.     

An algorithmic approach for fuzzy inference

To apply fuzzy logic, two major tasks need to be performed: the derivation of production rules and the determination of membership functions. These tasks are often difficult and time consuming. This paper presents an algorithmic method for generating membership functions and fuzzy production rules; the method includes an entropy minimization for screening analog values. Membership functions are derived by partitioning the variables into the desired number of fuzzy terms and production rules are obtained from minimum entropy clustering decisions. In the rule derivation process, rule weights are also calculated. This algorithmic approach alleviates many problems in the application of fuzzy logic to binary classification     

PBAC: Provision-based access control model

Over the years a wide variety of access control models and policies have been proposed, and almost all the models have assumed “grant the access request or deny it.” They do not provide any mechanism that enables us to bind authorization rules with required operations such as logging and encryption. We propose the notion of a “provisional action” that tells the user that his request will be authorized provided he (and/or the system) takes certain actions. The major advantage of our approach is that arbitrary actions such as cryptographic operations can all coexist in the access control policy rules. We define a fundamental authorization mechanism and then formalize a provision-based access control model. We also present algorithms and describe their algorithmic complexity. Finally, we illustrate how provisional access control policy rules can be specified effectively in practical usage scenarios. Published online: 22 January 2002     

Algorithmic solution of Abel’s equation

The solution scheme for Abel’s equation proposed in this article avoids to a large extent thead hoc methods that have been discovered in the last two centuries since Abel introduced the equation named after him. On the one hand, it describes an algorithmic method for obtaining almost all closed form solutions known in the literature. It is based on Lie’s symmetry analysis. Secondly, for equations without a symmetry, a new method is proposed that allows to generate solutions of all equations within an equivalence class if a single representative has been solved before. It is based on functional decomposition of the absolute invariant of the equation at hand for which computer algebra algorithms have become available recently.     

Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.     

Fuzzy expert system for solving lost circulation problem

Lost circulation is the most common problem encountered while drilling oil wells. This paper describes a distributed fuzzy expert system, called Smart-Drill, aimed in helping petroleum engineers to diagnose and solve lost circulation problems. To represent and manipulate perception-based evaluations of uncertainties of facts and rules, the expert system uses an uncertainty model with qualitative scales of plausibility values and multi-set-based fuzzy algebra of strict monotonic operations. Its realization in inference procedures permits taking into account the change of plausibility of premises in expert systems rules. Original tools like CAPNET Expert System Shell, Knowledge Acquisition Tool and WITSML Converter implementing the proposed model were used for the development of the Smart-Drill. Overall, the system architecture is discussed and implementation details are provided. Both desktop and Web-based implementations permit petroleum engineers benefit from the system working out in the field. The system is currently at field testing phase in PEMEX, Mexican Oil Company.     

Program-substitution and admissibility of rules in algorithmic logic

Summary The purpose of this work is to show a point of view upon the notions of program-substitution and admissibility of rules which are the tools for proving properties of programs of algorithmic logic and the so-called extended algorithmic logic with quantifiers and with non-deterministic programs. We prove that the set of theses of algorithmic logic is closed under each program-substitution. This substitution rule allows us to formulate a problem of algorithmic structural completeness as a question about derivability of all structural, finitary and admissible rules. We prove the incompleteness of algorithmic logic strengthened by the substitution rule and its algorithmically structural completeness. This work was supported by the Polish Academy of Sciences CPBP 08–15.     

An elementary and unified approach to program correctness

We present through the algorithmic language DHL (Dijkstra-Hehner language), a practical approach to a simple first order theory based on calculational logic, unifying Hoare and Dijkstra’s iterative style of programming with Hehner’s recursive predicative programming theory, getting the “best of the two worlds” and without having to recur in any way to higher-order approaches such as predicate transformers, Hoare logic, fixed-point or relational theory.     

Rule-based document structure understanding with a fuzzy combination of layout and textual features

Document image processing is a crucial process in office automation and begins at the ‘OCR’ phase with difficulties in document ‘analysis’ and ‘understanding’. This paper presents a hybrid and comprehensive approach to document structure analysis. Hybrid in the sense that it makes use of layout (geometrical) as well as textual features of a given document. These features are the base for potential conditions which in turn are used to express fuzzy matched rules of an underlying rule base. Rules can be formulated based on features which might be observed within one specific layout object. However, rules can also express dependencies between different layout objects. In addition to its rule driven analysis, which allows an easy adaptation to specific domains with their specific logical objects, the system contains domain-independent markup algorithms for common objects (e.g., lists). Received June 19, 2000 / Revised November 8, 2000     

Is Evolution Algorithmic?

In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational.     

Parallelizing fuzzy rule generation using GPGPU

This article proposes a method to parallelize the process of generating fuzzy if-then rules for pattern classification problems in order to reduce the computational time. The proposed method makes use of general purpose computation on graphics processing units (GPGPUs)’ parallel implementation with compute unified device architecture (CUDA), a development environment. CUDA contains a library to perform matrix operations in parallel. In the proposed method, published source codes of matrix multiplication are modified so that the membership values of given training patterns with antecedent fuzzy sets are calculated. In a series of computational experiments, it is shown that the computational time is reduced for those problems that require high computational effort.     

An Algorithmic Toolbox for Network Calculus

Network calculus offers powerful tools to analyze the performances in communication networks, in particular to obtain deterministic bounds. This theory is based on a strong mathematical ground, notably by the use of (min,+) algebra. However, the algorithmic aspects of this theory have not been much addressed yet. This paper is an attempt to provide some efficient algorithms implementing network calculus operations for some classical functions. Some functions which are often used are the piecewise affine functions which ultimately have a constant growth. As a first step towards algorithmic design, we present a class containing these functions and closed under the main network calculus operations (min, max, +, −, convolution, subadditive closure, deconvolution): the piecewise affine functions which are ultimately pseudo-periodic. They can be finitely described, which enables us to propose some algorithms for each of the network calculus operations. We finally analyze their computational complexity.

Making drilling operations visible: the role of articulation work for organisational safety

In the petroleum industry, new technologies and work processes are currently being developed as an innovation strategy for better, faster and safer drilling. In this article, some features of today’s work processes that contribute to successful operations are presented and discussed. The articulation work involved in handling the transient complexity of operations involves making black-boxed and invisible work processes visible and transparent. It is argued that this articulation work contributes to the organisation’s understanding and knowledge of the drilling processes and the dependencies that exist between different actors. In addition to contributing to ongoing problem solving, the articulation work also contributes to the awareness of possible future events. Following this insight, it is argued that efforts to improve operational efficiency and safety by introducing new tools and work processes should focus not only on the capability of new tools to support decisions and actions by instrumentation and automation, but attention should also be paid to the existing articulation work and its role in the accomplishment of work. In that way, the contributions of today’s articulation work can be strengthened instead of lost, and the outcome of the change processes can be even better than anticipated.     

Combining rough decisions for intelligent text mining using Dempster’s rule

An important issue in text mining is how to make use of multiple pieces knowledge discovered to improve future decisions. In this paper, we propose a new approach to combining multiple sets of rules for text categorization using Dempster’s rule of combination. We develop a boosting-like technique for generating multiple sets of rules based on rough set theory and model classification decisions from multiple sets of rules as pieces of evidence which can be combined by Dempster’s rule of combination. We apply these methods to 10 of the 20-newsgroups—a benchmark data collection (Baker and McCallum 1998), individually and in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on the 10 groups of the benchmark data is statistically significant and better than that of the best single set of rules. The comparative analysis between the Dempster–Shafer and the majority voting (MV) methods along with an overfitting study confirm the advantage and the robustness of our approach.     

Adaptive diagnostic system based on fuzzy relations

Causes (diagnoses) are retrieved and identified using observed effects (symptoms) based on fuzzy relations and Zadeh’s compositional rule of inference. An approach to designing adaptive fuzzy diagnostic systems is proposed. It allows solving fuzzy logic equations and designing and adjusting fuzzy relations using expert and experimental information. Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 135–150, July–August 2009.     

Jordan triple disystems

We take an algorithmic and computational approach to a basic problem in abstract algebra: determining the correct generalization to dialgebras of a given variety of nonassociative algebras. We give a simplified statement of the KP algorithm introduced by Kolesnikov and Pozhidaev for extending polynomial identities for algebras to corresponding identities for dialgebras. We apply the KP algorithm to the defining identities for Jordan triple systems to obtain a new variety of nonassociative triple systems, called Jordan triple disystems. We give a generalized statement of the BSO algorithm introduced by Bremner and Sánchez-Ortega for extending multilinear operations in an associative algebra to corresponding operations in an associative dialgebra. We apply the BSO algorithm to the Jordan triple product and use computer algebra to verify that the polynomial identities satisfied by the resulting operations coincide with the results of the KP algorithm; this provides a large class of examples of Jordan triple disystems. We formulate a general conjecture expressed by a commutative diagram relating the output of the KP and BSO algorithms. We conclude by generalizing the Jordan triple product in a Jordan algebra to operations in a Jordan dialgebra; we use computer algebra to verify that resulting structures provide further examples of Jordan triple disystems. For this last result, we also provide an independent theoretical proof using Jordan structure theory.     

Space robotics supporting exploration missions: vision, force control and coordination strategy for crew assistants

Many research activities are being carried out about space exploration missions, in order to strongly improve man’s working conditions in planetary environments. Along this line, this work presents some results obtained within the development of an effective robotic crew assistant. This robot can execute a number of operations, both in a completely autonomous manner (i.e. without requiring human supervision) and in a strict cooperation with the astronauts; thus supporting and helping them in executing operations otherwise very difficult or unfeasible to be accomplished. The developed and then adopted functional and algorithmic control architecture for the considered robot assistant, also including vision and force feedback, is described with some details in the present paper.