Development of a verification method of estimated indicators for their use as an optimization criterion

Determining an indicator for the estimation of effectiveness of system operations is an important stage for optimization of technological processes of any enterprise. This step predetermines the established mode of functioning of all its system processes. The fact that all technological processes should be optimized using an agreed optimization criterion is an axiom. Such a possibility appears only in one case – if we apply the efficiency formula as an optimization criterion in all functional systems. This approach maximizes financial possibilities of the owner of an enterprise. The problem is to identify a structure that corresponds to the structure of the original efficiency formula among a set of evaluation indicators that are identical in terms of formal characteristics. There are classes of standard models of operations define now for the practical solution of this problem. Each of them has its own functional orientation. The most developed are the classes of reference models of simple operations. In relation to the classes of reference models of operations with distributed parameters, there is a solution of the problem operational processes with the same duration in time, as well as for processes with different duration of resource expense by an output. In the proposed study, we define a limited class of models of operations with distributed parameters of different duration by an input. The creation of such a class of operations is quite a difficult task, since it is necessary to take into account a time factor and to enable possibility of comparison of operational processes of different duration. To solve this problem, at the first stage, we formed global models of simple operations of different duration with predetermined rating efficiency. At the next stage, we formed reference models of operations with distributed parameters by an output of different duration by compositing. The development of the verification method by determining a class of operations with distributed parameters in terms of input of different duration in time improves reliability of verification results of the estimated indicator significantly. It is necessary to perform the verification procedure whenever we intended to use it as an indicator of effectiveness

While there are multiple methods to ventilate protective clothing, there is still room for improvement. In our research, we are using ventilation elements that are positioned at the ventilation holes in the air space between the body and clothing. These ventilation elements allow air to flow freely while preventing sun radiation, rain drops, and insects from directly accessing the body. Therefore, the shape of the ventilation element is crucial. This led us to study the shape optimization of ventilation elements through the utilization of metamodels and numerical approaches. In order to accomplish the objective, it is crucial to thoroughly evaluate and choose suitable criteria for the optimization process. We know from prior research that the toroidal cut-out shape element provides better results. In a previous study, we optimized the shape of this element based on the minimum pressure difference as a criterion. In this study, we are using different criteria for the shape optimization of ventilation elements to determine which are most effective. This study involves a metamodeling strategy that utilizes local and global approximations with different order polynomials, as well as Kriging approximations, for the purpose of optimizing the geometry of ventilation elements. The goal was achieved by a sequential process. (1) Planning the position of control points of Non-Uniform Rational B-Splines (NURBS) in order to generate elements with a smooth shape. (2) Constructing geometric CAD models based on the design of experiments. (3) Compute detailed model solutions using SolidWorks Flow Simulation. (4) Developing metamodels for responses using computer experiments. (5) Optimization of element shape using metamodels. The procedure is repeated for six criteria, and subsequently, the results are compared to determine the most efficient criteria for optimizing the design of the ventilation element.

To transfer and practically implement academic knowledge in wider society is not straightforward, and a range of measures have been used to improve technology transfer and dissemination of research from universities. One example is the technology transfer offices (TTOs), which have been set up at almost every US research university since the Bayh-Dole Act in 1980. Most TTO operations conform to a 'patent agency' model of operation where the focus is on selling patentable inventions to industrial adopters. The success of traditional TTO operations in terms of technology transfer, economic performance, and impact on academic life has, however, been questioned. This article outlines the principles of an alternative model for university technology transfer operations. The 2g model has a wider focus which might lead to increased technology transfer including a wider number of inventions and areas than current TTO practice.

A general stochastic hybrid process (GSHP) is a mathematical formalism that covers most of the requirements posed by the modelling of complex operations, such as time dependencies, multi-dimensional continuous as well as discrete processes, discontinuities, randomness and model uncertainties. In addition, it is possible to study GSHP by using stochastic analysis methodologies, thereby empowering it with powerful mathematical properties. This guarantees unambiguous simulation possibility of the model and allows speeding up this simulation while keeping the model properties intact. However, using GSHP to construct a model of a complex operation is not easy. To support the modelling and the subsequent verification both by mathematical and by multiple operational domain experts, a supporting graphical modelling formalism is desired. Petri nets have shown to be useful for developing models of various complex applications. Typical Petri net features are concurrency and synchronisation mechanism, hierarchical and modular construction, and natural expression of causal dependencies, in combination with graphical and analytical representations. The aim of this thesis is to combine the strengths of Petri net modelling formalisms and those of GSHP. First, dynamically coloured Petri nets (DCPN) are developed, and proof of equivalence is provided with piecewise deterministic Markov processes, which is a particular class of GSHP. Next, DCPN are extended to stochastically and dynamically coloured Petri nets (SDCPN), and proof of equivalence is provided with GSHP. Subsequently, SDCPN are extended to SDCPN with interconnection mapping types (SDCPNimt) and proof of equivalence is provided with both SDCPN and GSHP. It is shown with illustrative air transport examples that these three classes of Petri net are very effective when it comes to the compositional modelling of operations consisting of many distributed components that behave and interact in a dynamic way with many uncertainties. With the equivalence relations between these formalisms, the properties and strengths of the various approaches are combined. The many applications of the approach developed in this thesis, executed at NLR and beyond, show that both the approach and its combined strengths are acknowledged and supported by practice.

Functioning of an adaptive automated system of operational management of the locomotive park should be based on an accurate operational forecast of the rail direction train operation. For this purpose it is necessary to create a forecast model of the rail direction train operation, the main task of which is the calculation of the readiness moments of trains’ various categories for departure at the technical stations, as well as the calculation of the readiness moments of locomotives and crews for departure with trains. The forecast model of the rail direction train operation includes block forming input information (forming information about the train, locomotive, crew), the trains’ arrival forecasting module (calculation of the forecast time of trains’ arrival), the modules forecasting work of the locomotives (calculation of the forecast time of locomotives’ readiness) and the crews (calculation of the forecast time of locomotive crews’ readiness) and the station functional model (calculation of the forecast time of trains’ readiness for departure) and the locomotive depot model (calculation of the forecast time of locomotives’ and crews’ readiness for departure with trains). The development of the work plan of the locomotives and crews using adaptive model can be considered as an optimization problem solution of binding locomotives and crews to the trains. As an optimality criterion is most advisable to choose the total operational costs associated with downtime of wagons (wagon-hours), locomotives (locomotive-hours) and locomotive crews (crew-hours) at the technical stations while expecting the departure. The optimal variant of the operational work plan of the locomotive park at the railway direction is determined by the minimum of the operational costs associated with unproductive downtime at the technical stations.

Objective.Establishing control and specification limits for volumetric modulated arc therapy (VMAT) pre-treatment quality assurance (PTQA) is essential for streamlining PTQA workflows and optimizing plan complexity. This study aimed to develop and implement new methods and tools across treatment sites of varying complexity using multiple global and local gamma index criteria.Approach.350 VMAT plans comprising brain, prostate, pelvis and head and neck treatments were retrospectively compiled. For each site, control limits were obtained using statistical process control (SPC) along with heuristic methods (scaled weighted variance (SWV), weighted standard deviation (WSD), skewness correction (SC)). Specification limits were derived employing a new formalism aligned with the heuristic approaches. Calculations were performed under various global and local gamma index criteria using custom-built software (freely available athttps://github.com/AEvgeneia/SPC_GUI_Scientific_Tool.git).Main results.WSD and SC control and specification limits were comparable, while SWV deviated with increasing complexity and stricter gamma index criteria. Conventional criteria (e.g. global 3%/2 mm) lacked sensitivity to detect subtle errors. Global 2%/1 mm and 1%/2 mm, and local criteria stricter than 3%/1 mm, met sensitivity requirements for low-complexity plans while maintaining clear separation between control and specification limits to identify plans with suboptimal delivery accuracy. High-complexity plans showed that global criteria stricter than 3%/1 mm and all evaluated local criteria are optimal, provided specification limits for the most stringent criteria remain clinically acceptable.Significance.A nuanced framework is provided for determining control and specification limits for gamma index passing rates, as well as corresponding thresholds for the mean gamma index, allowing for site-specific detection of suboptimal treatment plans. The open-source software tool developed can operationalize the proposed methodology facilitating the clinical adoption of advanced statistical methods. Site-specific thresholds could serve as inputs for machine learning and deep learning algorithms aimed at automating error detection and PTQA classification for plan complexity management.

Over the past several decades, soil vapor extraction (SVE) systems have successfully reduced the mass of volatile organic compounds in contaminated soils at many sites. However, predicting when operation of these systems should end once mass removal rates decline and become asymptotic to some low value is still a difficult task. To help make this decision, a simple decision model has been derived from a more rigorous cost‐risk‐based objective function that requires extensive Monte Carlo analyses. Use of the simple model to make a decision on continued operation does not require the same extensive modeling effort. Yet, use of the model does require the contaminant removal rate to be asymptotic to some low value indicating that the removal process has become dominated by diffusion from relatively low permeable soil. At this stage of the removal process, the SVE system should be replaced with an alternative approach if the cost of this alternative approach is less than the capital cost of continued SVE system operation. Applicability of the simple model is discussed through a comparison of the results predicted by the simple model for an operating SVE system to the rigorous results determined with the objective function. Results from this study show that under most conditions the simple model can be used to make a decision of whether a SVE system should continue to operate.

Speed-independent circuits offer a potential solution to the timing problems of VLSI. In this book David Dill develops and implements a theory for practical automatic verification of these control circuits. He describes a formal model of circuit operation, defines the proper relationship between an implementation and its specification, and constructs a computer program that can check this relationship. Asynchronous or speed-independent circuit design has gained renewed interest in the VLSI community because of the possibilities it provides for dealing with problems that arise with the increasing complexity of VLSI circuits. Speed-independent circuits offer a way around such phenomena as clock skew, which can be a serious obstacle in the design of large systems. They can expedite circuit design by reducing design time and simplifying the overall process. A major challenge to the successful utilization of speed-independent circuits is correctness. The verification method described here insures that a design is correct and because it can be automated it is a significant advantage over manual verification. Dill proposes two distinct theories - prefix-closed trace structures, which can model and specify safety properties, and complete trace structures, which can also deal with liveness and fairness properties. Trace Theory for Automatic Hierarchical Verification of Speed Independent Circuits is a 1988 ACM Distinguished Dissertation

Probability models of wastewater treatment plant operation

This paper proposes a two-stage stochastic optimization model for proactive planning and reactive operation of resilient power systems under transmission line disruptions. The model, formulated using Generalized Disjunctive Programming (GDP), minimizes total cost, including investment, operation, and resilience penalties, while ensuring load satisfaction under uncertainty. First-stage decisions include line hardening, line addition, and installation of distributed generators or batteries, whereas second-stage decisions include line switching and generator re-dispatch. To improve computational efficiency, two solution strategies are proposed: (i) critical line identification, which focuses on lines whose failures cause major operational issues, and (ii) a scenario reduction-based decomposition algorithm, which gradually adds disruption scenarios instead of solving all at once. Case studies of systems with different scales show that the proposed decomposition algorithm performs more efficiently than Big-M and Hull reformulations. The positive Value of the Stochastic Solution (VSS) further demonstrates that considering disruption scenarios leads to more resilient and cost-effective planning solutions.

A great number of research articles on the physical and mathematical modelling of tundish operation have been reported in the literature. Mazumdar and Guthrie1) summarized a large portion of these in a review article in ISIJ published in 1999. Because of the growing importance of tundish metallurgy and stringent demands regarding steel quality, a lot of further work has been done on tundish technology from1999 till date. This review summarizes the basics of physical modelling, mathematical modelling and the work of different researchers around the globe in the last decade i.e. 1999 to 2009. Current trends in research have been reviewed and critiqued.

In a dedicated, mixed-machine, heterogeneous computing (HC) system, an application program may be decomposed into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC Is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithm-based approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics.

In Part I of this two-part study, a new theory for optimal linear filtering of covariances sampled from an ensemble of forecasts was detailed. This method, especially designed for data assimilation (DA) schemes in numerical weather prediction (NWP) systems, has the advantage of using optimality criteria that involve sample estimated quantities and filter output only. In this second part, the theory is tested with real background error covariances computed using a large ensemble data assimilation (EDA) at the convective scale coupled with a large EDA at the global scale, based respectively on the Applications of Research to Operations at Mesoscale (AROME) and ARPEGE operational NWP systems. Background error variances estimated with a subset of this ensemble are filtered and evaluated against values obtained with the remaining members, which are considered as an independent reference. Algorithms presented in Part I show relevant results, with the homogeneous filtering being quasi optimal. Heterogeneous filtering is also successfully tested with different local criteria, yet at a higher computational cost, showing the full generality of the method. As a second application, horizontal and vertical localization functions are diagnosed from the ensemble, providing pertinent localization length scales that consistently depend on the number of members, on the meteorological variables, and on the vertical levels.

The design of advanced controllers for many industrial processes is heavily dependent on the availability of a model for the process. Construction of appropriate models is often not possible due to the complexity and nonlinearity of the process. Fuzzy logic can be used for different tasks within intelligent control systems because they represent general, nonlinear relationships that can be initialized using expert knowledge. In adaptive control the key problem is system identification and fuzzy logic is used as process identification and acts as an on-line adaptive part of the control system. The presented work in this paper devoted to the development and the implementation of a smooth and robust fuzzy logic based PID controller targeting a real process represented by a DC servomotor. The aim is to overcome the need for an on-line tuning problem associated with the parameters of classical PID approach and enhances the performance. The adopted self-tuning strategy improves the performance automatically until it converges to a predetermined optimal global criterion. The experimental results and the performance of a developed fuzzy logic controller is compared with other methods such as, model reference adaptive controller and other fuzzy logic based controllers using the same process in terms of steady state error, settling time, and response time. The developed controller shows better performance.

In wind energy systems, it is necessary to have a good knowledge of the used generator and its parameters. Despite the motor manufacturer’s specifications, accurately estimating the machine parameter reference values is critical for the perfect control system. In this context, off-line identification tests of a 3 kW permanent magnet synchronous machine were conducted to determine the mechanical and electrical parameters. In addition, this research exploits a meta-heuristic optimization technic dubbed grey wolf optimizer in the on-line parameter’s identification field. The estimate method is focused on the parameter’s estimation linked to a reference model. The rule is based on the cancelation of the current error among the measured and the associated estimated current. For each measurement, the grey wolf estimator adapts the reference model parameters until the model response and the accurate response be similar. All were implemented using the real-time interface of the DSpace DS1104 Controller card while using the ControlDesk graphical user interface and the Simulink/MATLAB environments. Results from hardware simulation confirm the efficacy and benefits of the suggested strategy.