Telecommunication service description using state transition rules

This study estimated availability of an air compression system and a nitrogen generation system in liquefied natural gas—floating production storage and offloading unit (LNG-FPSO) with different design stages to investigate the gap between the availability at the early design stage and that at the late design stage. Although availability estimation in the early design stage is more important than the late design stage, it is difficult to estimate the availability accurately in the early design stage. The design stage was divided into three depending on the design progress. Monte Carlo simulation technique was employed for the availability estimation. The results of the availability estimation showed that there was 0.434% difference between the early and late design stages. This meant that the availability in the early design stage was underestimated due to limited information. A sensitivity analysis was performed to investigate critical factors affecting the results. The investigated factors were failure rate, repair time, redundant equipment, and modified preventive maintenance schedule. The most critical factor was redundant equipment. It increased 0.486% availability.

Understanding the dynamics of a system is crucial in various scientific and engineering domains. Machine learning techniques have been employed to learn state transition rules from observed time-series data. However, these data often contain sequences of noisy and ambiguous continuous variables, while we typically seek simplified dynamics rules that capture essential variables. In this work, we propose a method to extract a small number of essential hidden variables from high-dimensional time-series data and learn state transition rules between hidden variables. Our approach is based on the Restricted Boltzmann Machine (RBM), which models observable data in the visible layer and latent features in the hidden layer. However, real-world data, such as video and audio, consist of both discrete and continuous variables with temporal relationships. To address this, we introduce the Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machine (RTGB-RBM), which combines the Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) to handle continuous visible variables and the Recurrent Temporal Restricted Boltzmann Machine (RT-RBM) to capture time dependencies among discrete hidden variables. Additionally, we propose a rule-based method to extract essential information as hidden variables and represent state transition rules in an interpretable form. We evaluate our proposed method on the Bouncing Ball, Moving MNIST, and dSprite datasets. Experimental results demonstrate that our approach effectively learns the dynamics of these physical systems by extracting state transition rules between hidden variables. Moreover, our method can predict unobserved future states based on observed state transitions.

A state transition rule is viewed as a cause and effect relationship. An example is selected from the common sense world. Six different instances of this relationship are enumerated. A temporal logic based on observation is proposed. This logic is used to model the enumerated instances. Assuming some necessary properties of a transition rule only two relevant models are selected. One of them ensures that the precondition is necessary where the other guarantees its sufficiency. Noting these two characteristics the first one is opted for proving the safety property where the second is used to prove progress. We have considered Peterson's two process mutual exclusion algorithm as an illustrative example

Effective design collaboration during the early design stages in architecture is a condition for effective overall design and construction. This can be facilitated, supported and promoted through visual technological means. Collaborative design protocols implemented within the engineering fields (electrical and mechanical) can be adapted to the built environment design with emerging augmented reality technologies being the enablers for that shift. Architectural design requires a strongly visual approach to communication and means through which multiple disciples can collaborate effectively. Therefore, the aim of the research is to investigate principles and protocols to assist early design stage collaborative design among multidisciplinary teams, through visual and tactile user interfaces. This paper explores the current paradigm in AEC (architecture, engineering and construction), design collaboration and multidisciplinary group dynamics during the early design and conceptual stages in architecture. Afterwards, a design protocol is suggested, literature review regarding collaborative design and design protocols is summarized and further research is proposed on optimization and standardization of early design collaborative design in AEC.

Aiming at Bernoulli serial lines with batching machines and finite buffers, this study develops analytical methods for performance analysis and system-theoretic properties. Batching machines can process several jobs simultaneously as a batch, as long as the number of jobs in a batch does not exceed the machine’s batch capacity. The batch capacities of all machines are not necessarily equal. Batching machines bring about a new characteristic of state transitions depending on batch capacities and machine states. For two-machine lines, the joint impacts of batching machines on state transitions are analyzed. Theoretically, the state transition rules are revealed. Based on the state transition rules, this study proposes a hierarchical state transition diagram, proves the ergodicity condition, and derives analytical formulas to evaluate the performance measures. Then, for multi-machine lines, this study develops a computationally efficient aggregation method with high accuracy. Furthermore, the impacts of system parameters, including machine efficiency pattern, batch capacity pattern, batch capacity mismatch, and system size, on the accuracy are qualitatively analyzed. Finally, this study investigates the reversibility and monotonicity properties. These analytical methods and results help production managers to scientifically evaluate, accurately predict, and continuously improve Bernoulli serial lines with batching machines.

It is thought that the reveal of generalized mechanism of hierarchical morphosis phenomena of living system is the fundamental of various field, such as mass-production of molecular machines of nanotechnology, artificial synthetic of biology, and so on. This study developed the model to simulate the morphogenetic mechanism of cells under the condition of two-dimensional cellular automata. We considered not only the state transition rules but also the concentration diffusion of the field. Thus our model was able to simulate morphogenetic formation in few state transition rules.

Feature selection is an important task which can significantly affect the performance of a pattern recognition system. In this paper, we present a feature selection algorithm based on Ant Colony Optimization (ACO) for human activity recognition. The algorithm incorporates the classification performance into the state transition rule, and uses the pheromone of the ant colony to find the optimal feature set, then selects feature set with small size and high classification accuracy. We perform experiments on human activity dataset using four classifiers, Naive Bayes, Random Forest, K-Nearest Neighbor and Support Vector Machine(NB, RF, KNN and SVM). The results show that the proposed method can improve the recognition accuracy, and it is comparable to other feature selection methods.

The paper examined the problem of modeling and design of software computer systems. The article briefly describes the content aspects of the various stages of the implementation of software complexes and systems. The methodology of research and analysis of systems, which uses the method of computer simulation modeling, is given. The aim of the work is to create a new universal approach to the design and implementation of software systems of various levels of complexity. During the study, the following general scientific methods were used: system analysis, synthesis, formalization, modeling, decomposition, structuring, description, comparison. The materials used in the study were: computers and software development tools. As a result of the study proposed a universal agent-module approach to the design and implementation of software systems based on simulation agent-based modeling. The study presents the processes of modeling and designing software systems, their stages, and the possibility of their integration within the described approach. Examples of formal model and structure of the software system under design based on the agent-module approach are presented. The proposed approach can be used in the process of software development at the initial stages related to modeling and designing of software systems. Integration of modeling and design stages in the proposed approach will significantly reduce software development time and simplify the design process by making it clearer and more formalized. Materials of the article are of scientific and practical value for a wide range of specialists interested in system analysis, modeling, design and implementation of software systems.

Some important parameters of the machine tool layout have to be selected in the early stages of design when a shortage of information makes application of the finite element method impractical. As a computer-aided tool in these stages, the form-shaping function (FSF)-based approach can be used. It allows evaluation of the kinematic and geometric errors and compliance issue static deformations, which contribute significantly to the total machining error as applied to precision machine tools. The FSF-based methodology gives important information allowing layouts’ comparison and structure optimization under uncertainty in requirements and limitations relating to the designed machine under strong time constraints. Six different layouts of a turning center are investigated to compare the effect of geometrical errors and compliance of machine structure on part accuracy in the range of less than five microns. Motion errors of machine tool components were measured by laser equipment during machine tool assembly and inspection. Compliance-induced errors are included in the evaluation to take into consideration cutting forces. Obtained data were used as the input for simulation of machining a sample workpiece—a sphere whose size, position, and form errors are used as criteria for layout comparison. Since the FSF-based results correlate well with the experimental data, the developed approach can be considered as an effective tool for application in the early stages of machine design.

Constructability is a concept with relative and not absolute value to increase optimization capacity of resources such as workforce, time, cost, quality, and working environment conditions. Given the growing complexity of projects and the increased number of failed and abandoned projects, the necessity to implement constructability in projects has become more tangible. Although the effects of lack of quantitative definition of constructability role in the traditional construction approaches are evident and have led to lack of coordination in performance of construction projects, so far no comprehensive quantitative approach has been considered to analyze the obstacles to constructability implementation. This study aims to identify and categorize the obstacles to facilitating the presence of contractors in the early stages of planning and design to implement constructability. In this study a comprehensive list of obstacles to constructability implementation is developed as a questionnaire. This questionnaire was presented to the experts, active in the field of construction. Finally, its results were analyzed using exploratory factor analysis method. Totally, 63 obstacles were questioned, then they were categorized by some of the experts of this industry into five categories of macro factors, including contractual, environmental, managerial, technical, and organizational. The significance of this study is due to this fact that identifying and categorizing the key obstacles to constructability implementation provides a useful reference for managers and owners of the construction industry to identify and develop solutions to resolve them. Identifying the obstacles to the presence of contractors in the planning and design stage and having a quantitative view toward this issue affects project implementability. In this regard, one can present more effective solutions to facilitate the presence of contractors in the early stages of design and also improves the effectiveness of constructability.

Introduction (problem statement and relevance). The need to increase the operating speeds and input torque of automobile transmissions because of their vibration is becoming a more and more urgent problem.The purpose of the study was to substantiate the applicability of harmonic analysis, which makes it possible to determine the actual values of vibration values during a steady oscillatory process, for the initial assessment of the gearbox vibration activity.Methodology and research methods. The calculation of the gearbox vibration activity was carried out by modal and harmonic analyses, implemented by the finite element method, and followed by the experimental study of gearbox vibration on a test bench.Scientific novelty and results. The presented experimental and calculated results of the research show that the difference between the experimental and calculated values is no more than 4.2%. This proves the possibility of applying modal and harmonic analysis to predict the gearbox vibration state at an early stage of product design at the stand.Practical significance. The given calculation algorithm makes it possible to predict, with a sufficient degree of accuracy, the vibration of the gearbox before the manufacture of a prototype in experimental production.

A performance-based generative design framework based on a design grammar for high-rise office towers during early design stage

Supplier involvement in automotive component design: are there really large US Japan differences?

This work describes the production of an application Error Propagation Metrics from XMI which can extract process and display software design metrics from XMI files. The tool archives these design metrics in a standard XML format defined by a metric document type definition.;XMI is a flavour of XML allowing the description of UML models. As such, the XMI representation of a software design will include information from which a variety of software design metrics can be extracted. These metrics are potentially useful in improving the software design process, either throughout the early stages of design if a suitable XMI-enabled modelling tool is deployed, or to enable the comparison of completed software projects, by extracting design metrics from UML models reverse engineered from the implemented source code.;The tool is able to derive the error propagation of metrics from test XMI files created from UML sequence and state diagrams and from reverse engineered Java source code. However, variation was observed between the XMI representations generated by different software design tools, limiting the ability of the tool to process XMI from all sources. Furthermore, it was noted that subtle differences between UML design representations might have a marked effect on the quality of metrics derived.;In conclusion in order to validate the usefulness of these metrics that can be extracted from XMI files it would be useful to follow well-documented design projects throughout the total design and implementation process. Alternatively, the tool might be used to compare metrics from well-matched design implementations. In either case design metrics will only be of true value to software engineers if they can be associated empirically with a validated measure of system quality.

When the ant colony algorithm is used to solve the global path planning of the unmanned boat, it is prone to problems such as local optimization and too slow convergence speed. This paper combines the problems encountered in the actual application of the unmanned boat and proposes an improved ant colony algorithm. Based on the traditional ant colony algorithm, the state transition rules are optimized and the pheromone volatilization coefficient is dynamically adjusted. The simulation results show that the improved ant colony algorithm compared with the traditional ant colony algorithm, the shortest path and path finding time of the USV to the destination are optimized, and the smoothness of the USV's search path is also improved.