Non-deterministic Technique Research Articles

Analytical solution for single-pixel ptychography through linear modeling

Amplitude-modulated single-pixel ptychography (SPP) enables non-interferometric complex-field imaging of objects. However, the conventional iterative and nondeterministic reconstruction methods, based on the ptychography algorithm, pose challenges in fully understanding the role of critical optical parameters. In response, this paper introduces an innovative analytical approach that establishes a theoretical foundation for the uniqueness of SPP reconstruction results. The proposed method conceptualizes SPP as a system of linear equations in the frequency domain, involving both object and modulated illumination. Solving this equation system reveals a determined solution for the complex object, providing an alternative to iterative and nondeterministic techniques. Through a series of simulations, this approach not only validates the uniqueness of SPP reconstruction, but also explores key properties influencing accuracy.

A technique for supporting decision process of global software project monitoring and rescheduling based on risk analysis

ABSTRACT We present a novel, non-deterministic technique for global software project monitoring and re-scheduling that can be used by IT project managers to react on dynamically changing project conditions as the project knowledge grows and available resources are changed. The technique is adaptive, allows controlling overall risk level in each control step and makes advantage of the full PM Iron Triangle. We also shortly discuss the proposed technique in application to projects that are spread globally in different countries. The technique is verified on sample project data and its quality is assessed. What is more, we present the benefits and drawbacks of the proposed technique and consider suggestions for future work.

Robust uncertainty quantification in structural dynamics under scarse experimental modal data: A Bayesian-interval approach

The accurate prediction of the dynamic behaviour of a complex component or system is often difficult due to uncertainty or scatter on the physical parameters in the underlying numerical models. Over the past years, several non-deterministic techniques have been developed to account for these model inaccuracies, supporting an objective assessment of the effect of these uncertainties on the dynamic behaviour. Still, also these methods require a realistic quantification of the scatter in the uncertain model properties in order to have any predictive value. In practice, this information is typically inferred from experiments. This uncertainty quantification is especially challenging in case only fragmentative or scarce experimental data are available, as is often the case when using modal data sets. This work therefore studies the application of these limited data sets for this purpose, and focuses more specifically on the quantification of interval uncertainty based on limited information on experimentally obtained eigenfrequencies. The interval approach, which is deemed to be the most robust against data insufficiency, typically starts from bounding the data using the extreme values in the limited data set. This intuitive approach, while of course representing the experiments, in general yields highly unconservative interval estimates, as the extreme realisations are typically not present in the limited data set. This work introduces a completely new approach for quantifying the bounds on the dynamic properties under scarce modal data. It is based on considering a complete set of parametrized probability density functions to determine likelihood functions, which can then be used in a Bayesian framework. To illustrate the practical applicability of the proposed techniques, the methodology is applied to the well-known DLR AIRMOD test case where in a first step, the bounds on the experimental eigenfrequencies are estimated. Then, based on a calibrated finite element model of the structure, bounds on the frequency response functions are estimated. It is illustrated that the method allows for a largely objective estimation of conservative interval bounds under scarce data.

FSB-EA: Fuzzy search bias guided constraint handling technique for evolutionary algorithm

Constraint handling technique (CHT) is a critical task for the evolutionary algorithms (EAs) on solving the constrained optimization problems (COPs). In the CHTs development, the definition of the search bias in the search process of an EA is one of the important issues. Based on the different search bias definitions, this study divides the CHTs into two categories: deterministic and non-deterministic techniques. The deterministic techniques define the search bias with an artificial rule. These techniques generally perform well for several scenarios. However, they may have poor performance for some others. This is mainly due to the difficulty to establish an accurate mapping between the complex search scenarios and the uncertain search biases. In contrast with the deterministic techniques, the non-deterministic techniques apply stochastic based methods to escape this mapping establishment which will improve the robustness of the algorithm. However, the probability-based blind random selection can affect negatively on the search efficiency of the algorithm. To compensate the deficiencies of these two categories, this paper proposes a fuzzy search bias guided CHT, namely FSB. The search bias in the FSB is guided by a membership function. It is used to meet the fuzzy knowledge that a solution with large constraint violation biases the search toward solutions with smaller constraint violation and a solution with small constraint violation biases the search toward solutions with better objective function value. The experimental results show that FSB is more robust and more efficient than the other compared techniques.

Evaluating random set technique for reliability analysis of deep urban excavation using Monte Carlo simulation

Over the last few years, the importance of non-deterministic techniques in Geotechnical engineering is highlighted in literature. Random set (RS) theory is recently being utilized for reliability analysis of geotechnical problems, however, its application in deep urban excavation is not yet investigated for a real case study. Hence, in this paper, an attempt is made to investigate the feasibility of the RS method in determining the reliability of deep excavations. For this reason, a 27-meter-deep urban excavation in Tehran, Iran is considered as a case study. The excavation is numerically modelled. Subsequently the reliability analysis is performed. Findings indicate that the probability of collapse of excavation is as low as 10−7 which suggests that the project is safe against collapse. The aforementioned conclusion is confirmed when RS results were compared with site observation. Additionally, RS results were evaluated using Monte Carlo (MC) technique. Although further studies for more real case studies are recommended, good agreement between the results of MC and RS methods suggests the feasibility of RS technique for estimating the reliability of deep urban excavations.

Ant Colony Optimization Inspired Algorithm for 3D Object Segmentation into its Constituent Parts

This work focuses on the use of an Ant colony optimization (ACO) based approach to the problem of 3D object segmentation. The ACO metaheuristic uses a set of agents (artificial ants) to explore a search space. This kind of metaheuristic can be classified as a Natural computing non-deterministic technique, which is frequently used when the size of the search space makes the use of analytic mathematical tools unaffordable. The exploration is influenced by heuristic information, determined by each particular problem. Agents communicate with each other through the pheromone trails, which act as the common memory for the colony. In the approach presented, the agents start their exploration at the outer contour of an object. The final result is given after a certain number of generations, when the particular solutions of the agents converge to create the global paths followed by the colony. These paths coherently connect the object's high curvature areas, facilitating the segmentation process. The advantage of this convergence mechanism is that it avoids the problem of over-segmentation by detecting regions based on the global structure of the object and not just on local information.

Obtaining a spiral path for machining STL surfaces using non-deterministic techniques and spherical tool

For milling an arbitrary surface, several models exist in which the tool must be moved close and away from the workpiece with its consequential problems caused by the contact between the tool and the mechanized material. One of these problems is the undesirable mechanizing marks on the surface, which can be avoided with the use of tangential tool-paths, causing another disadvantage: the additional time-consumption. Spiral tool-paths eliminate these problems and allow for continuous machining of the part without approach or withdrawal.Deterministic models for obtaining the tool positions referred to the part need to apply complex mathematical operations. For this reason, the idea of using non-deterministic techniques, with a controlled tolerance margin, allows to avoid those mathematical and geometrical operations. It also makes possible to reduce the processing times with no loss of dimensional quality and a good superficial finishing, which was confirmed with a coordinate measuring machine and with confocal microscopy, respectively.Finally, the effectiveness of the described method was assessed comparing its results with the performance of three of the most relevant CAM software commercially available. The obtained results are shown at the end of this paper.

Historical Development of Power Use Methods for WDS Design and their Evolution towards Optimization Metaheuristics

This paper describes an extension to the Optimal Power Use Surface (OPUS) methodology, which consists in applying a metaheuristic post-optimization process after a network has been designed through the hydraulically-based OPUS algorithm. Once a set of diameter sizes is obtained by means of this hydraulic-based design methodology, it is used as the system's initial configuration for it to be post-optimized using different nondeterministic techniques. The metaheuristics tested in this study include 3 different types of Genetic Algorithms (GAs), Harmony Search (HS) and Simulated Annealing (SA). The proposed methodology is tested on three benchmark problems (Hanoi, Balerma, Taichung) and a fourth network called R28, which is introduced herein. When compared to the results obtained through other methodologies, this algorithm stands out for allowing designs with constructive costs very close to the lowest found in other investigations. However, the improvements with respect to the OPUS algorithm are very small, while the number of iterations required increases by more than 2 orders of magnitude. Moreover the Resilience Index (RI) increases in most cases as well. This extension to the OPUS methodology proves that following hydraulic principles allows obtaining near-optimal results, whose improvement demands a considerable number of iterations, providing minimum benefit as the reduction in cost is only of 1% at the most.

EECPS-WSN: Energy Efficient Cumulative Protocol Suite for Wireless Sensor Network

With the proliferation of wireless sensor network (WSN), the area of communication application is undergoing random exploration of massive wireless communication tool. However, due to dependency on power and various other resources, reliability and robustness of collection of data in WSN cannot be solved yet to optimal level. This paper presents a non-deterministic technique that address such issues from the micro-level in direction of data aggregator where the problem of collision of equivalent data packet from multiple sensors or redundant packets are always a high probability. Powered by protocol suite, the proposed model has the potential to perform selection of unique incoming data packets and discard the redundant one by deploying dual ranking factors. Finally, compared with most frequently used LEACH, the proposed model ensure Energy Efficient Cumulative Protocol Suite for wireless sensor network (EECPS-WSN).

Fuzzy sets, fuzzy algebra, and fuzzy statistics

The extension of algebraic and analytical concepts to the theory of fuzzy sets appears to play a central role in the investigation of nondeterministic techniques. Since an exact description of any real physical situation is virtually impossbile, it is necessary to develop schemes which deal analytically with decision processes in an imprecise environment. In this paper we are concerned with formulating such a framework and with introducing a bibliography of 570 items, all classified with fuzzy set theory and its applications, which will help the readers to come to grips with the literature explosion on the subjects of fuzzy sets, fuzzy algebra, fuzzy statistics, and closely related applications.