Methodology For Optimal Selection Research Articles

Selection of output inverter for microgrid

The paper considers the problem of a microgrid output inverter selecting, when information about operating conditions is uncertain. The following factors are considered as sources of uncertainty in information about operating conditions: the possibility of the load increasing in the area, for which the microgrid is being designed, the prospects for its integration into the local power supply system, based on the microgrid, operating in the given region, as well as possible changes in the element base due to import substitution.
 A system of criteria and a methodology for optimal selection have been developed, based on a combination of multi-vector optimization approaches and the hierarchy analysis method. The quality criterion of generated energy, the criterion of reliable operation in conditions of information uncertainty and the criterion of economic indicators were cho- sen as vector criteria. The total number of scalar criteria that are components of the specified vector criteria is seven. At the first stage of selection, an algorithm is used for expert assessment of the importance of vector criteria and their scalar components, as well as values of these criteria for compared alternatives, using of the hierarchy analysis method. At the second stage of selection, considered inverters are ranked according to a global criterion, calculated based on results of the first stage. The technique is demon- strated by choosing between three well-known schemes of autonomous inverters: a voltage inverter with PWM-shaping of the output curve, a current inverter with a variable struc- ture and a group multilevel inverter based on a universal source of levels, when they are operating in autonomous or integrated into the system of other microgrid modes. The de- scribed selection technique can be used, when other microgrid conversion equipment de- vices are selecting.

Performance improvement of hybrid photovoltaic/thermal systems: A metaheuristic artificial intelligence approach to select the best model using 10E analysis

Photovoltaic/thermal (PV/T) hybrid systems have until now encountered a real problem of sustainability-energy-cost concordance. Faced with this situation, new types of designs are in full expansion aimed at filling the limits of some. This therefore involves a very appropriate decision-making process. The energy, exergy, economic, environmental, energo-environmental, exergo-environmental, enviro-economic, energy-enviro-economic, exergo-enviro-economic and ergonomic analysis is carried out on seven PV/T configurations and therefore the simplified models are presented for a better interpretation of the mechanisms from different perspectives and the integration of a selection algorithm. Thus, an optimal selection methodology using the hybridization of genetic algorithms and multi-objective optimization by particle swarms based on ten performance indicators is proposed. The results obtained with good convergence and precision allow us to observe that the Air PV/T model is better. However, the study shows good viability of PV/T models with a cost of energy and a return on investment time all lower than 0.1$/kWh and 3 years, respectively. Models with phase change materials (PCM) minimize thermal losses better than those with air, nanofluids or thermoelectric generator (TEG). The bifacial model stands out with a good energy-environmental balance compared to the water model which has a better durability index greater than 2.0 and a good ergonomic factor.

Optimal selection methodology for technical diagnostics of armored wheeled vehicles

This article considers the methodology of optimal choice of means of diagnosing armored wheeled vehicles that are currently obsolete and do not provide a high – guality check for the reliabiliti and obiectiviti of assessing the technical condition of machines of its systems of units and assemdlies, and also does not allow localizing failures and predicting their residual resourse as a whole, the occurrence of such serious problems reguires the search for new better approaches and research methods to increase the efficiency of the technical condition of equipment.

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization

High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single- and multiple-source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes (1) the localization error for each source and (2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases in synthetic signals and 63% in real signals, and in more than 88% and 73% of cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.

Novel selection paradigms for endovascular stroke treatment in the extended time window

Background and purposeThe optimal selection methodology for stroke thrombectomy beyond 6 hours remains to be established.MethodsReview of a prospectively collected database of thrombectomy patients with anterior circulation strokes, adequate CT...

Optimal Selection of Basis Functions for Robust Tracking Control of Uncertain Linear Systems—With Application to Three-Dimensional Printing

Abstract There is growing interest in the use of the filtered basis functions (FBF) approach to track linear systems, especially nonminimum phase (NMP) plants, because of its distinct advantages compared to other tracking control methods in the literature. The FBF approach expresses the control input to the plant as a linear combination of basis functions with unknown coefficients. The basis functions are forward filtered through the plant dynamics, and the coefficients are selected such that tracking error is minimized. Similar to other feedforward control methods, the tracking accuracy of the FBF approach deteriorates in the presence of uncertainties. However, unlike other methods, the FBF approach presents flexibility in terms of the choice of the basis functions, which can be used to improve its accuracy. This paper analyzes the effect of the choice of the basis functions on the tracking accuracy of FBF, in the presence of uncertainties, using the Frobenius norm of the lifted system representation (LSR) of FBF's error dynamics. Based on the analysis, a methodology for optimal selection of basis functions to maximize robustness is proposed, together with an approach to avoid large control effort when it is applied to NMP systems. The basis functions resulting from this process are called robust basis functions. Applied experimentally to a desktop three-dimensional (3D) printer with uncertain NMP dynamics, up to 48% improvement in tracking accuracy is achieved using the proposed robust basis functions compared to B-splines, while utilizing much less control effort.

A knowledge-based approach for representing jobholder profile toward optimal human–machine collaboration in cyber physical production systems

Increasing number of AI-enhanced approaches provide helpful “know-how” for reproducing and imitating skills and finally substituting human jobs with algorithms and intelligent machines. However, complementarity of human and machine, especially in hybrid man-machine settings is still not sufficiently explored. The main objective of this paper is to establish a twofold qualitative and quantitative methodology for optimal selection of a competent jobholder(s) to perform a certain task by semantic modeling and analysis of jobholder (human and machine) profiles corresponding to the task characteristics and learning requirements including knowledge, skills and competences (KSCs). The proposed knowledge-based approach comprises semantic modeling and quantitative methods focusing on measuring and correlating the level of human competences and machine autonomy, and identifying the extent of human–machine complementarity in performing an assigned task. The Vector of Competence and Autonomy (VCA) is built for identifying the extent of human–machine collaboration. The quantitative analysis involves several human factors in association to various combinations of technological components of Digital Assistance Systems with different automation degrees, under certain aspects of complexity of products and workplaces. Applying a set of rules and considering jobholder profiles, VCA values are interpreted and the current state of complementarity is inferred. Furthermore, feasible radical or incremental managerial transition pathways are identified as an initial step to reach the optimal (desired) level of human–machine collaboration in the example of TU Wien Pilot Factory Industry 4.0.

Statistical learning for recommending (robust) nonlinear regression methods

AbstractWe are interested in comparing the performance of various nonlinear estimators of parameters of the standard nonlinear regression model. While the standard nonlinear least squares estimator is vulnerable to the presence of outlying measurements in the data, there exist several robust alternatives. However, it is not clear which estimator should be used for a given dataset and this question remains extremely difficult (or perhaps infeasible) to be answered theoretically. Metalearning represents a computationally intensive methodology for optimal selection of algorithms (or methods) and is used here to predict the most suitable nonlinear estimator for a particular dataset. The classification rule is learned over a training database of 24 publicly available datasets. The results of the primary learning give an interesting argument in favor of the nonlinear least weighted squares estimator, which turns out to be the most suitable one for the majority of datasets. The subsequent metalearning reveals that tests of normality and heteroscedasticity play a crucial role in finding the most suitable nonlinear estimator.

Optimal selection of feed electric drives for boring machines

In this paper are analyzed and formulated the main requirements for the drive systems of a class of modernized boring machines with digital program control. On this basis, a methodology for optimal selection of feed drives is offered. The respective algorithm takes into account the specific features of the technological process, the treated material, the tools used and their wear, the mechanical gears types and the guides. Concrete examples with DC and AC electric drives are presented, illustrating the practical application of the methodology. The research held as well as the results obtained can be used in the development of such electric drives for the studied class of machine tools with digital program control.

An unmanned aerial vehicle optimal selection methodology for object tracking

In this article, an optimal method of unmanned aerial vehicle selection for following a moving ground target is presented. The most suitable unmanned aerial vehicle from an aircraft operating fleet is selected for tracking task. The aircraft choice is done taking regarding the unmanned aerial vehicle distribution in the mission zone, individual performance of fleet platforms, and maneuverability of a ground platform. The unmanned aerial vehicle fleet members’ flying qualities are various, so an optimization process is used to assign an aircraft to follow the ground target. A target trajectory prediction is embedded in tracking algorithm. The simulation results demonstrated the effectiveness of algorithms developed, and in-flight demonstration proved the possibility of realization of computed trajectories by the unmanned aerial vehicle.

Optimal selection of modular products in reconfigurable manufacturing systems using analytic hierarchy process

Reconfigurable manufacturing systems (RMS) are considered as a major resource of providing variable production capacities and capabilities by different manufacturing companies. To fulfil the market requirement variety of a modular product, manufactured in a reconfigurable manufacturing system, a methodology for optimal selection of modular products in reconfigurable manufacturing systems using analytic hierarchy process (AHP) is proposed. The analytic hierarchy process is a theory of measurement through pair-wise comparisons and relies on the decisions of experts to obtain priority scales. The comparisons are made using a scale of absolute judgments that shows how much better one component is than another with respect to a given attribute. AHP is also helpful in taking decision when both qualitative and quantitative attributes are in a system. A case study is given to understand the applicability of the proposed methodology on a given system.

Multi-objective optimization design and performance evaluation for plug-in hybrid electric vehicle powertrains

This study provides an optimal selection methodology for plug-in hybrid electric vehicle (PHEV) powertrain configuration by means of optimization and comprehensive evaluation of powertrain design schemes. The challenge of this study is to reveal each powertrain configuration performance potential in different situations of object trade-off and solve the control-physical integrated optimization problem of the PHEV powertrain design. To determine performance potential, a configuration-sizing-control strategy integrated multi-objective powertrain optimization design is proposed and applied to series, parallel pre-transmission (P2), output power-split, and multi-mode power-split powertrain configurations. Firstly, considering simultaneous optimization of fuel economy, electric energy consumption, and acceleration capacity, the parameters of the powertrain components and vehicle performance of each configuration are optimized based on global optimal control in different situations of object trade-off. Then, the Pareto optimal selection of powertrain configuration and its corresponding optimal component parameters are obtained by performance comparison and non-domination sorting. The results suggest that the P2 configuration and its optimal sizing can be selected when the goal is to optimize acceleration capacity, the multi-mode power-split configuration and its optimal sizing can be selected when the goal is to optimize electric energy efficiency, and the output power-split configuration and its optimal sizing can be selected when the fuel economy needs to be optimized.

Impact of Switching Harmonics on Capacitor Cells Balancing in Phase-Shifted PWM-Based Cascaded H-Bridge STATCOM

The purpose of this paper is to investigate the impact of switching harmonics on the instantaneous power distribution in the cells of a cascaded H-bridge-based STATCOM when using phase-shifted pulse width modulation. The case of high- and low-switching frequency for the converter cells is investigated and the interaction between voltage and current harmonics is analyzed. It is shown that in both cases, this interaction results in an uneven power distribution among the cells in the same phase leg, leading to drifting of the dc-capacitor voltages and thereby the need for proper stabilization control loops. It is also shown that the selected frequency modulation ratio affects the active power distribution among the cells. In particular, the selection of a noninteger frequency modulation ratio helps in providing a more uniform power distribution among cells of the same phase leg, thus contributing to the capacitors balancing. A methodology for optimal selection of the frequency modulation ratio is given. Theoretical conclusions are validated through simulation and experimental results.

Optimal Sensor Network Upgrade for Fault Detection Using Principal Component Analysis

The efficiency of a fault monitoring system critically depends on the structure of the plant instrumentation system. For processes monitored using principal component analysis, the multivariate statistical technique most used for fault diagnosis in industry, an existing strategy aims at selecting the set of instruments that satisfies the detection of a given set of faults at minimum cost. It is based on the minimum fault magnitude concept. Because that procedure discards lower-cost feasible solutions, in this work, a new optimal selection methodology is proposed whose constraints are straightaway defined in terms of the principal component analysis’s statistics. To solve the optimization problem, a level traversal search with cutting criteria is enhanced taking into account that the fault observability is a necessary condition for fault detection when statistical monitoring techniques are applied. Furthermore, observability and detection degree concepts are defined and considered as constraints of the opt...

Handwritten Bangla character recognition using a soft computing paradigm embedded in two pass approach

The work presented here mainly involves recognition of handwritten Bangla characters using a soft computing paradigm embedded within the framework of previously developed Two pass approach. Bangla script, which has nearly 400 character symbols, is used by a vast majority of population in India and Bangladesh. Considering the large number of character classes, Two pass approach is chosen here with some important extensions. Typically, Two pass approach coarsely classifies an unknown sample pattern in a group of pattern classes in first-pass. In second pass, it makes finer classification of the sample by determining its membership to a class within the group. Two major aspects of extensions made with Two pass approach here are development of an algorithmic approach for grouping of various pattern classes needed for the second pass and a soft computing methodology for optimal selection of local regions of character images for extraction of features, specific to each grouping of pattern classes. To test the performances of the method, benchmark databases of handwritten Bangla characters are also developed. A methodology for reading filled in forms of handwritten Bangla characters is also presented here. The experimental results show significant improvement of recognition rates on handwritten Bangla characters compared to traditionally followed Single pass approach.

Optimal selection and sizing of distributed energy resources for distributed power systems

Optimal selection and sizing of distributed energy resources is an important research problem in the development of distributed power systems. This paper presents a methodology for optimal selection and sizing of distributed energy resources in integrated microgrids using the evolutionary strategy. Integrated microgrid is an innovative architecture in distributed power systems, wherein several microgrids are interconnected with each other for control and management of the distributed power systems. Right coordination between distributed energy resources and proper harmony between microgrids and the main distribution grid are some of the critical challenges in this research problem. Selection of distributed energy resources and their capacities are optimized such that the integrated microgrid provides reliable energy at a cheap cost. In this research, the problem is formulated as a nonlinear mixed-integer minimization problem, which minimizes the sum of capital and annual operational cost of distributed energy resources subject to various system and unit constraints. A novel methodology based on flexible evolutionary strategy was developed in this research and used to design an integrated microgrid for AStar IEDS project. The design results have shown that the proposed methodology provides excellent convergence and feasible optimum solution.

Comparison of regenerative braking technologies for heavy goods vehicles in urban environments

One way to reduce the carbon dioxide emissions of heavy vehicles is to install regenerative braking systems. These capture the kinetic energy of the vehicle during braking and store it, in order to feed it back into the drivetrain during acceleration. It is not clear, however, which of the many available technologies should be used to implement this regenerative braking. This report explores the different possible energy capture and storage technologies for regenerative braking, including electrical, kinetic, hydraulic and compressed air. The basic systems are plotted on a selection chart, and an optimal selection methodology is used to aid in the selection of the lightest and smallest system for regenerative braking. The results of this comparison and selection show that hydraulic energy storage is likely to be 33% smaller and 20% lighter than the closest electrical counterparts and is therefore a logical selection for regenerative braking on the trailers of heavy goods vehicles.

Selection of optimal set of cutting tools for machining of polygonal pockets with islands

Selection of the optimal set of cutting tools is one of the most important steps in process planning for 2.5-D pocket machining. Conventional CAM software requires considerable input from the user in terms of selection of tool sizes and machining strategy. This trial-and-error procedure to determine the optimal process sequence tends to generate conservative and suboptimal results. This paper presents a methodology for optimal selection of a sequence of tools to minimize the total time required to end mill a non-convex polygonal pocket with or without islands using the staircase milling strategy. The algorithm decomposes the pocket geometry into convex regions and mills each region independently by selecting a sequence of tools based on the accessibility of various tools to the region. Strategies have been developed for machining the main pass and the subsequent leftover areas in order to obtain the final pocket geometry. Subsequently, the machining times for each decomposed area are aggregated while accounting for the need to use multiple passes, non-cutting time, and the tool change time. A dynamic programming approach is used to determine the optimal set of tools which minimizes the total processing time. The effect of varying the non-cutting speed and tool change time on the tool path length and number of tool selection is studied.

An Optimal Design Methodology of an Interleaved Boost Converter for Fuel Cell Applications

In this paper, an optimal selection methodology for the number of phases will be proposed for an interleaved boost converter (IBC). Also, the analysis of the input current ripple according to CCM and DCM is carried out. The proposed design methodology will be theoretically analyzed, and its validity verified by simulation as well as with experimental results. Moreover, a comparison of cost and efficiency based on a 600W laboratory prototype using the Ballard NEXA 1.2kW PEMFC system is demonstrated.