Algorithms for forming the coefficient matrix of a system of differential equations Chapman-Kolmogorov models of non-stationary service systems

The paper considers a new approach to building models of nonstationary service systems based on: the formation of all possible states of a nonstationary service system with a finite number of applications and rules of transition between them; the formation of the coefficient matrix of Chapman-Kolmogorov differential equation system; the numbering procedure for all states. A critical analysis is made of the algorithms for the formation of the coefficient matrix and the numbering procedure for all states: sequential, recursive and recursive with grouping. Its comparison with the recursive algorithm is given, as well as the optimal structure for storing the list of states for the sequential algorithm. Recommendations for the practical application of software implementations of the considered algorithms are discussed. Theoretical foundations for building and calculating models of nonstationary service systems have been developed. It is compared to the recursive algorithm. The optimal structure for storing the list of states for a sequential algorithm is given.

Extensional and Intensional Semantics of Bounded and Unbounded Nondeterminism

We give extensional and intensional characterizations of functional programs with nondeterminism: as structure preserving functions between biorders, and as nondeterministic sequential algorithms on ordered concrete data structures which compute them. A fundamental result establishes that these extensional and intensional representations are equivalent, by showing how to construct the unique sequential algorithm which computes a given monotone and stable function, and describing the conditions on sequential algorithms which correspond to continuity with respect to each order. We illustrate by defining may-testing and must-testing denotational semantics for sequential functional languages with bounded and unbounded choice operators. We prove that these are computationally adequate, despite the non-continuity of the must-testing semantics of unbounded nondeterminism. In the bounded case, we prove that our continuous models are fully abstract with respect to may-testing and must-testing by identifying a simple universal type, which may also form the basis for models of the untyped {\lambda}-calculus. In the unbounded case we observe that our model contains computable functions which are not denoted by terms, by identifying a further "weak continuity" property of the definable elements, and use this to establish that it is not fully abstract.

Adaptive data-driven selection of sequences of biological and cognitive markers in clinical diagnosis of dementia

Combining the right--potentially invasive and expensive, markers at the appropriate time is critical to obtain reliable yet economically sustainable decisions in the preclinical diagnosis of dementia. We propose a data-driven analytical framework to individualize the selection of prognostic biomarkers that balance accuracy, costs of opportunity due to delaying the decision, and cost of acquisition depending to prescribed cost parameters. We compared sequential and non-sequential decision strategies based on a linear mixed-effects classification model that integrates irregular, multi-variate longitudinal data. The framework was applied to separate participants that progress to Alzheimer's disease from the ones that do not within a time interval of three years. As expected, the highest accuracy was obtained by combining all available data from 20.9 measurements per subject on average that were acquired over 4.8 years on average. The proposed sequential algorithm empirically outperformed alternative methods by having lowest costs for a range of tested cost parameters. With the default cost parameters, the sequential algorithm reached an accuracy of 0.84, specificity of 0.86, and sensitivity of 0.82 (0.89, 0.91, and 0.88 with all available data, respectively) while requiring only 2.9 measurements on average (86 percent less observations than all available data) and a time interval of half a year on average (89 percent shorter than all time points). Our sequential algorithms established the decision based on individualized sequences of measurements with reduced process costs compared to non-sequential classification strategies while maintaining competitive accuracy.

USING SEQUENTIAL CLUSTERING TO IDENTIFICATION OF HYBRID TIMED AUTOMATA FROM ANALOG IIOT DATA

In Industrial IoT (IIoT) systems, timed automata provide a highly useful abstraction for diagnosis and control tasks. Applying them requires automaton to be learned in passive online manner using positive samples only. Such kind of learning is supported by Hybrid timed Automata (HTA) and algorithm OTALA, but requireds a sequence of discrete events rather than continuous analog time series typically found in IIoT. Recent attempts to cover this gap, taken by A. von Birgelen, O. Niggemann, and others, involved pre-processing observations with a self-organized map (SOM) and watershed transform, yet resulting models have proven ineffective in some real-world systems. In this paper, incremental model-based clustering (IMCF) is employed to learn timed automaton from analog IIoT data. IMCF is a sequential algorithm that processes observed time-series online and splits them into a sequence of discrete states with either crisp or fuzzy transitions between them. Such transitions are then treated as events required for HTA identification with OTALA. Obtained models are evaluated in a case of IIoT system that has proved to be challenging for existing modelling techniques. Experimental results show 24,9–76,8% increase in model’s performance and suggest that discretizing obtained with IMCF has higher informativeness for HTA identification. Finally, wider perspectives of applying HTA in IIoT are discussed, and remaining principal limitations are identified as discrete nature of state transitions, and lack of long-term memory for transitions.

A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process

Welding frames with differing geometries is one of the most crucial stages in the production of high-end bicycles. This paper proposes a parallel algorithm and a mixed integer linear programming formulation for scheduling a two-machine robotic welding station. The time complexity of the introduced parallel method is O(log2n) on an n3-processor Exclusive Read Exclusive Write Parallel Random-Access Machine (EREW PRAM), where n is the problem size. The algorithm is designed to take advantage of modern graphics cards to significantly accelerate the computations. To present the benefits of the parallelization, the algorithm is compared to the state of art sequential method and a solver-based approach. Experimental results show an impressive speedup for larger problem instances—up to 314 on a single Graphics Processing Unit (GPU), compared to a single-threaded CPU execution of the sequential algorithm.

Parallelism exploration in sequential algorithms via animation tool

Different high performance techniques, such as profiling, tracing, and instrumentation, have been used to tune and enhance the performance of parallel applications. However, these techniques do not show how to explore the potential of parallelism in a given application. Animating and visualizing the execution process of a sequential algorithm provide a thorough understanding of its usage and functionality. In this work, an interactive web-based educational animation tool was developed to assist users in analyzing sequential algorithms to detect parallel regions regardless of the used parallel programming model. The tool simplifies algorithms’ learning, and helps students to analyze programs efficiently. Our statistical t-test study on a sample of students showed a significant improvement in their perception of the mechanism and parallelism of applications and an increase in their willingness to learn algorithms and parallel programming.

An improved calibration and uncertainty analysis approach using a multicriteria sequential algorithm for hydrological modeling

Hydrological models are widely used as simplified, conceptual, mathematical representatives for water resource management. The performance of hydrological modeling is usually challenged by model calibration and uncertainty analysis during modeling exercises. In this study, a multicriteria sequential calibration and uncertainty analysis (MS-CUA) method was proposed to improve the efficiency and performance of hydrological modeling with high reliability. To evaluate the performance and feasibility of the proposed method, two case studies were conducted in comparison with two other methods, sequential uncertainty fitting algorithm (SUFI-2) and generalized likelihood uncertainty estimation (GLUE). The results indicated that the MS-CUA method could quickly locate the highest posterior density regions to improve computational efficiency. The developed method also provided better-calibrated results (e.g., the higher NSE value of 0.91, 0.97, and 0.74) and more balanced uncertainty analysis results (e.g., the largest P/R ratio values of 1.23, 2.15, and 1.00) comparing with other traditional methods for both case studies.

Research on Fish Slicing Method Based on Simulated Annealing Algorithm

Multiobjective optimization is a common problem in the field of industrial cutting. In actual production settings, it is necessary to rely on the experience of skilled workers to achieve multiobjective collaborative optimization. The process of industrial intelligence is to perceive the parameters of a cut object through sensors and use machines instead of manual decision making. However, the traditional sequential algorithm cannot satisfy multiobjective optimization problems. This paper studies the multiobjective optimization problem of irregular objects in the field of aquatic product processing and uses the information guidance strategy to develop a simulated annealing algorithm to solve the problem according to the characteristics of the object itself. By optimizing the mutation strategy, the ability of the simulated annealing algorithm to jump out of the local optimal solution is improved. The project team developed an experimental prototype to verify the algorithm. The experimental results show that compared with the traditional sequential algorithm method, the simulated degradation algorithm designed in this paper effectively improves the quality of the target solution and greatly enhances the economic value of the product by addressing the multiobjective optimization problem of squid cutting. At the end of the article, the cutting error is analyzed.

Parallel Strategy to Factorize Fermat Numbers with Implementation in Maple Software

In accordance with the traits of parallel computing, the paper proposes a parallel algorithm to factorize the Fermat numbers through parallelization of a sequential algorithm. The kernel work to parallelize a sequential algorithm is presented by subdividing the computing interval into subintervals that are assigned to the parallel processes to perform the parallel computing. Maple experiments show that the parallelization increases the computational efficiency of factoring the Fermat numbers, especially to the Fermat number with big divisors.