Optimized Parameter Settings Research Articles

Well-posedness and stability in set optimization with applications

This paper is about the well-posedness and stability in set optimization with applications to vector-valued games involving uncertainty. Some characterizations for several types of well-posedness with their relations in set optimization are given under mild conditions. Some sufficient and necessary conditions for these types of well-posedness in set optimization are also given by using the local C-Lipschitz continuity. Moreover, the upper semi-continuity, lower semi-continuity and compactness of minimal solution mappings are studied for parametric set optimization involving the cone Lipschitz continuous set-valued mapping. Finally, some obtained results are applied to derive the well-posedness for vector-valued games with uncertainty.

Calibration and validation for a real-time membrane bioreactor: A sliding window approach

The paper presents a novel model calibration and validation strategy of membrane bioreactor (MBR) for wastewater treatment. The approach is based on a dynamic model of the activated sludge process and it consists simultaneously on estimating the model’s parameters and computing the dissolved oxygen control input. Activated sludge model No. 1 (ASM1) has been widely used to describe the biological process of activated sludge processes. However, most system states and parameters within ASM1 are not easily obtained, hence not applicable for model calibration and validation. In this work, a reduced-order model presented herein serves as a tool for predicting the dynamic behavior of the MBR plant. The model contains only 4 measurable states, where 13 parameters need to be identified. To reduce the complexity of the calibration, the static sensitivity analysis is performed to select the sensitive parameters. The selected parameters are identified based on directly measurable real-time data obtained from the plant. In addition, the dissolved oxygen is also maintained at a certain level to mimic the real-time control behavior. Model calibration is achieved based on a sliding window optimization problem, which searches for the optimal parameters set and control variables during each identification cycle. Different datasets sampled for the same MBR plant have been used for model validation. Both calibration and validation results are evaluated by several performance indexes, which indicates an acceptable correspondence with the experimental data. The developed model can be employed for process state estimation and control purpose as well as design issues for MBR systems.

Parameter Settings Optimization in Map Reduce Big Data processing using the MOPSO Algorithm

Big data is a commodity that is highly valued in the entire globe. It is not just regarded as data but in the world of experts, we can derive intelligence from it. Because of its characteristics which are Variety, Value, Volume, Velocity, and the growing need of how it can be handled, Organizations are facing difficulties in ensuring optimal as well as affordable processing and storage of large datasets. One of the already existing models used for rapid processing together with storage in big data is known as Hadoop MapReduce. MapReduce is used for large-scale data processing in a parallel and distributed computing environment, while Hadoop is used for running applications and storing data in clusters of commodity hardware Furthermore, the Hadoop MapReduce framework needs to tune more than 190 configuration parameters which are mostly done manually. Due to complex interactions and large spaces between parameters, manual tuning is not effective. Even worse, these parameters must be tuned every time Hadoop MapReduce applications are run. The main goal of this research is to create an algorithm that will improve efficiency by automatically optimizing parameter settings when MapReduce jobs are running. The algorithm employs the Multi-Objective Particle Swarm Optimization (MOPSO) technique, which uses two objective functions to look for a Pareto optimal solution while optimizing the parameters. The results of the experiments have shown that the algorithm has remarkably improved MapReduce job performance in comparison to the use of default settings.

Selective Laser Melting of Patient Individualized Osteosynthesis Plates-Digital to Physical Process Chain.

We report on the exemplified realization of a digital to physical process chain for a patient individualized osteosynthesis plate for the tarsal bone area. Anonymized patient-specific data of the right feet were captured by computer tomography, which were then digitally processed to generate a surface file format (standard tessellation language, STL) ready for additive manufacturing. Physical realization by selective laser melting in titanium using optimized parameter settings and post-processing by stress relief annealing results in a customized osteosynthesis plate with superior properties fulfilling medical demands. High fitting accuracy was demonstrated by applying the osteosynthesis plate to an equally good 3D printed bone model, which likewise was generated using the patient-specific computer tomography (CT) data employing selective laser sintering and polyamid 12. Proper fixation has been achieved without any further manipulation of the plate using standard screws, proving that based on CT data, individualized implants well adapted to the anatomical conditions can be accomplished without the need for additional steps, such as bending, cutting and shape trimming of precast bone plates during the surgical intervention. Beyond parameter optimization for selective laser melting, this exemplified digital to physical process chain highlights the potential of additive manufacturing for individualized osteosynthesis.

Optimization of Parameters for the Cutting of Wood-Based Materials by a CO2 Laser

This article deals with the laser cutting of wood and wood composites. The laser cutting of wood and wood composites is widely accepted and used by the wood industry (due to its many advantages compared to, e.g., saw cutting). The goal of this research was to optimize the cutting parameters of spruce wood (Pices abies L.) by a low-power CO2 laser. The influence of three factors was investigated, namely, the effect of the laser power (100 and 150 W), cutting speed (3, 6, and 9 mm·s−1), and number of annual rings (3–11) on the width of the cutting kerf on the top board, on the width of the cutting kerf on the bottom board, on the ratio of the cutting kerf width on the top and bottom of the board, on the width of the heat-affected area on both sides of the cutting kerf (this applies to the top and bottom of the board), and on the degree of charring. Analysis of variance (ANOVA) and correlation and regression analysis were used for developing a linear regression model without interactions and a quadratic regression model with quadratic interactions. Based on the developed models, the optimization of parameter settings of the investigated process was performed in order to achieve the final kerf quality. The improvement in the quality of the part ranged from 3% to more than 30%. The results were compared with other research dealing with the laser cutting of wood and wood composites.

Multi objective optimization in machining of Inconel 718 using taguchi method

Nickel based super alloys find wide application in industries like aerospace engine, nuclear reactors and other high temperature applications etc. Inconel 718 alloy is well suited for high temperature applications with excellent mechanical strength. Due to high strength it is difficult to machine. Many researchers were concentrated on optimization of various machining process for single response criteria. It is very difficult to select an optimal parameter for multi response criteria. To overcome this problem multi objective optimization technique is used to find optimal setting of machining parameter in milling of Inconel 718 alloy. In this paper, the effects of various machining parameters like cutting speed, feed, and depth on tool wear, MRR and surface roughness are studied. Three different coated carbide inserts are used for machining process and to perform multi objective optimization which would provide minimum tool wear and surface roughness and maximum MRR.

Performance evaluation and parameter optimization of sparse Fourier transform

The sparse Fourier transform (SFT) dramatically accelerates spectral analyses by leveraging the inherit sparsity in most natural signals. However, a satisfactory trade-off between the estimation performance and the computational complexity commonly requires sophisticated empirical parameter tuning. In this work, we attempt to further enhance SFT by optimizing the parameter selection mechanism. We first derive closed-form expressions of objective performance metrics. On top of this, a parameter optimization algorithm is designed to minimize the complexity, under the premise that the performance metrics can meet the specified requirements. The proposed scheme, termed as optimized SFT, is shown to be able to automatically determine the optimized parameter settings as per the a priori knowledge and the performance requirements in the numerical simulations. Experimental studies of continuous-wave radar detection are also conducted to demonstrate the potential of the optimized SFT in the practical application scenarios.

Particle Swarm Optimization (PSO) for improving the accuracy of ChemCam LIBS sub-model quantitative method

Laser-induced breakdown spectroscopy (LIBS) is a powerful tool for qualitative analysis of chemical composition on planetary surface. Specifically, the quantitative compositional analysis method is a significant challenge for LIBS instrument onboard the Mars Science Laboratory (MSL) rover Curiosity ChemCam. Partial Least Squares (PLS) sub-model strategy is one of the outstanding multivariate analysis methods for calibration modeling, which is firstly developed by ChemCam science team. However, a troubling key issue is there are many parameters that need to be optimized, which increases the uncertainty of predicting outcomes and is time-consuming. In this study, an automatic parameters selection method based on Particle Swarm Optimization (PSO) tool is introduced. In the process of PSO iteration, RMSE minimization is taken as fitness, and finally the optimal sub-model parameters set is searched. In this way, the authors also get the best PLS latent variables of each sub-model by traversal method. Finally, the PSO PLS sub-model (PSO-PLS-SM) gets significant improvement in accuracy for the expanded Chemcam standards (408). And the RMSE of Si, Al, Ca, Na elements has been reduced by more than 20% relative to the conventional predictions.

Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line

Nowadays, reconfiguration and adaptation by means of optimal re-parameterization in Industrial Cyber–Physical Systems (ICPSs) is one of the bottlenecks for the digital transformation of the manufacturing industry. This article proposes a cloud-to-edge-based ICPS equipped with machine learning techniques. The proposed reasoning module includes a learning procedure based on two reinforcement learning techniques, running in parallel, for updating both the data-conditioning and processing strategy and the prediction model. The presented solution distributes computational resources and analytic engines in multiple layers and independent modules, increasing the smartness and the autonomy for monitoring and control the behavior at the shop floor level. The suitability of the proposed solution, evaluated in a pilot line, is endorsed by fast time response (i.e., 0.01 s at the edge level) and the appropriate setting of optimal operational parameters for guaranteeing the desired quality surface roughness during macro- and micro-milling operations.

Small-signal stability modelling, sensitivity analysis and optimization of droop controlled inverters in LV microgrids

Islanded microgrids can enhance the resilience of power systems. Their stability is subject to ongoing research. This work introduces a unique way of sensitivity analysis, parameter tuning and optimization with respect to small-signal stability of grid-forming and grid-supporting droop controlled inverters in microgrids. A large selection of parameters is incorporated and simultaneously optimized based on a genetic algorithm. The sensitivity of parameters so far not considered in literature, such as the measurement filter time constant, is analysed. Simplifications often accepted as valid, e.g. the neglect of current and voltage controller or the line dynamics, are reviewed using the optimized parameter sets and providing new insights into the accuracy of model reduction techniques. Results indicate that the stability can be enhanced drastically by simultaneously optimizing a wide range of parameters. Model simplifications often seen as valid in literature must be carefully considered, as they can result in conservative stability assessment, especially for grid-forming droop control. The results of this work are relevant for the microgrid small-signal stability analysis in numerous contexts, such as reconfiguration, topology optimization or optimal placement of inverters.

The locality dilemma of Sankoff-like RNA alignments.

MotivationElucidating the functions of non-coding RNAs by homology has been strongly limited due to fundamental computational and modeling issues. While existing simultaneous alignment and folding (SA&F) algorithms successfully align homologous RNAs with precisely known boundaries (global SA&F), the more pressing problem of identifying new classes of homologous RNAs in the genome (local SA&F) is intrinsically more difficult and much less understood. Typically, the length of local alignments is strongly overestimated and alignment boundaries are dramatically mispredicted. We hypothesize that local SA&F approaches are compromised this way due to a score bias, which is caused by the contribution of RNA structure similarity to their overall alignment score.ResultsIn the light of this hypothesis, we study pairwise local SA&F for the first time systematically—based on a novel local RNA alignment benchmark set and quality measure. First, we vary the relative influence of structure similarity compared to sequence similarity. Putting more emphasis on the structure component leads to overestimating the length of local alignments. This clearly shows the bias of current scores and strongly hints at the structure component as its origin. Second, we study the interplay of several important scoring parameters by learning parameters for local and global SA&F. The divergence of these optimized parameter sets underlines the fundamental obstacles for local SA&F. Third, by introducing a position-wise correction term in local SA&F, we constructively solve its principal issues.Availability and implementationThe benchmark data, detailed results and scripts are available at https://github.com/BackofenLab/local_alignment. The RNA alignment tool LocARNA, including the modifications proposed in this work, is available at https://github.com/s-will/LocARNA/releases/tag/v2.0.0RC6.Supplementary information Supplementary data are available at Bioinformatics online.

Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm

The cleaning of rice on a combine harvester is a complex process, which leads to differences in impurity ratio of harvested grain, and the impurity ratio is one of the key criteria for the assessment of performance of a combine harvester. Combine operators usually optimize parameter settings only once for harvesting each crop because of time pressure, and therefore differences in site- and temporal-specific conditions are neglected. In this paper, to offer combine operators the opportunity to make better management decision, a machine vision method for grain impurity monitoring of a rice combine harvester in real time was proposed, and the classification of kernel and impurity particles using decision tree algorithm was presented. To obtain images of high quality during harvesting, the structure of the sampling device depending on the working properties in grain bin was designed, the illumination and installation of the light source were optimized, and finally lateral lighting system was constructed. To monitor and recognize grains and impurities, the morphological features of the particles extracted from the images were acquired. The selected 6 features (A1-A6), including area, perimeter, maximal feret diameter, elongation factor, compactness factor and Heywood circularity factor, were fed to the decision tree algorithm for classification. Output of the algorithm, a visualized tree, was used to classify the particles labeled in the binary image. The decision tree provided a classification accuracy of about 76% for the given training data set extracted from the captured images. From the experimental results, it is suggested that the method of monitoring the impurity ratio of harvested grains based on decision tree algorithm using image processing can be recommended as the basis of parameter optimization of combine harvesters.

Imitate and optimize modern control algorithms for forestry cranes by means of artificial neural networks

Modern hydrostatic function drives for agricultural and forestry machines require complex control algorithms. Electric controls offer significant energy and control advantages over the state of the art, such as reduced tendency to oscillate or implementation of a variable power limitation. Therefore, new algorithms are essential for sustainable optimization of future machines. The paper investigates a method to automatically transfer an existing control algorithm to an artificial neural network (ANN), which will be optimized by the Pattern Search algorithm afterwards. The method was applied to a forestry crane with an electro-hydraulic flow-on-demand control. After 41 generations of optimized parameter sets, the ANN control already shows a behavior comparable to the reference control. With this approach it is possible to transfer deterministic algorithms into stochastic algorithms with comparable transfer functions, which can then be optimized using machine learning methods.

Wet chemical deposition of BN, SiC and Si3N4 interphases on SiC fibers

Fiber coatings based on BN, BN/SiC and BN/Si3N4 were deposited on Hi Nicalon type S SiC fibers. The coating parameters were optimized using a design of experiments study. With optimized parameter sets, the coatings exhibited a high degree of coverage on the fibers and almost no fiber bridging could be observed. The coated fiber bundles are flexible and can be processed further by techniques such as filament winding. In comparison to a non-processed reference sample, the maximum tensile load of the fiber bundles with BN, BN/SiC and BN/Si3N4 coatings was reduced by only 5 %, 13 % and 10 %, respectively. The coated fiber bundles retained their tensile strength after thermal annealing up to 1650 °C in a nitrogen atmosphere for 0.5 h. SiCf/SiC samples with BN/SiC fiber coatings exhibited higher values of bending strength and strain-to-failure as a reference sample without fiber coating indicating the functionality of the fiber coatings.

Method development of statistical modeling for the description of welding fume emissions in gas metal arc welding using transient process characteristics

In the past, most approaches to reduce welding fume emissions were primarily based on welding consumables, whereas in the context of the present work, a new concept with direct reference to the welding process technology is pursued. Many other measures to improve occupational health and safety, such as use of extended personal protective equipment (e.g., forced air ventilated helmets) and extraction systems, relate to the reduction of immissions of welding fumes already emitted. In comparison, the selection of optimized parameter settings in terms of gas metal arc welding (GMAW) provides a process-related possibility to reduce welding fume emissions without the need for additional equipment. In order to illustrate and quantify the functional interdependencies between process parameterization and the resulting fume emission generation, a model relationship is developed which is primarily based on empirical investigations. Via post-process analysis, certain features are extracted from the transient measurement data and correlated with the process-specific emission rates. With the help of the model, individual parameters can be iteratively set on the basis of representative process features. Besides parameter optimization with regard to emission-reduced processes, a forecast of the expected fume emission rate can be derived based on the selected settings. The methodology described and examined is applicable to different process spaces of GMAW or even other fume-emitting welding processes.

Microstructure and mechanical properties of 316L austenitic stainless steel processed by different SLM devices

In this work, we examined the influence of different types of selective laser melting (SLM) devices on the microstructure and the associated material properties of austenitic 316L stainless steel. Specimens were built using powder from the same powder batch on four different SLM machines. For the specimen build-up, optimized parameter sets were used, as provided by the manufacturers for each individual SLM machine. The resulting microstructure was investigated by means of scanning electron microscopy, which revealed that the different samples possess similar microstructures. Differences between the microstructures were found in terms of porosity, which significantly influences the material properties. Additionally, the build-up direction of the specimens was found to have a strong influence on the mechanical properties. Thus, the defect density defines the material’s properties so that the ascertained characteristic values were used to determine a Weibull modulus for the corresponding values in dependence on the build-up direction. Based on these findings, characteristic averages of the mechanical properties were determined for the SLM-manufactured samples, which can subsequently be used as reference parameters for designing industrially manufactured components.

A parametric study of 3D printed polymer gears

The selection of printing parameters for 3D printing can dramatically affect the dynamic performance of components such as polymer spur gears. In this paper, the performance of 3D printed gears has been optimised using a machine learning process. A genetic algorithm (GA)–based artificial neural network (ANN) multi-parameter regression model was created. There were four print parameters considered in 3D printing process, i.e. printing temperature, printing speed, printing bed temperature and infill percentage. The parameter setting was generated by the Sobol sequence. Moreover, sensitivity analysis was carried out in this paper, and leave-one cross validation was applied to the genetic algorithm-based ANN which showed a relatively accurate performance in predictions and performance optimisation of 3D printed gears. Wear performance of 3D printed gears increased by 3 times after optimised parameter setting was applied during their manufacture.

Breaking 50 Femtosecond Resolution Barrier in MeV Ultrafast Electron Diffraction with a Double Bend Achromat Compressor.

We propose and demonstrate a novel scheme to produce ultrashort and ultrastable MeV electron beam. In this scheme, the electron beam produced in a photocathode radio frequency (rf) gun first expands under its own Coulomb force with which a positive energy chirp is imprinted in the beam longitudinal phase space. The beam is then sent through a double bend achromat with positive longitudinal dispersion where electrons at the bunch tail with lower energies follow shorter paths and thus catch up with the bunch head, leading to longitudinal bunch compression. We show that with optimized parameter sets, the whole beam path from the electron source to the compression point can be made isochronous such that the time of flight for the electron beam is immune to the fluctuations of rf amplitude. With a laser-driven THz deflector, the bunch length and arrival time jitter for a 20fC beam after bunch compression are measured to be about 29fs (FWHM) and 22fs (FWHM), respectively. Such an ultrashort and ultrastable electron beam allows us to achieve 50 femtosecond (FWHM) resolution in MeV ultrafast electron diffraction where lattice oscillation at 2.6THz corresponding to Bismuth A_{1g} mode is clearly observed without correcting both the short-term timing jitter and long-term timing drift. Furthermore, oscillating weak diffuse scattering signal related to phonon coupling and decay is also clearly resolved thanks to the improved temporal resolution and increased electron flux. We expect that this technique will have a strong impact in emerging ultrashort electron beam based facilities and applications.

Gaining advantage by winning contests

We consider a principal who faces many identical competitors, and who can distribute a prize fund over two consecutive contests. The winner of contest one gains an advantage in contest two where his effort is more productive than all rivals. We identify a tipping point for the productivity parameter, below which it is optimal for an effort-maximizing principal to place the whole prize in the second contest. Above this level, a single symmetric contest is preferred. The institution chosen depends inextricably upon the number of competitors and their valuation of future gains and costs. We identify the optimal setting of the productivity parameter, showing that introducing asymmetry can increase total efforts by as much as one quarter compared to a single symmetric contest.

Optimum Design of Shell and Tube Heat Exchangers using Modified Kinetic Gas Molecule Optimizer for the use of Low Temperature in Organic Rankine Cycles

Shell and Tube Heat Exchangers (STHEs) plays a crucial role in an effective design of Organic Rankine Cycle (ORC) power plants.The main aim of this research work is to design a cost-effective ORC in order to exploit low to medium temperature geothermal fluid or low grade industrial waste heat. In this research work, modified Kinetic Gas Molecule Optimization (KGMO) algorithm was developed forfinding the optimized parameter settings of the power plant. In modified KGMO algorithm, feedback learning stage was included for improving the fitness of individual worst particles. In addition, the proposed optimization algorithm was tested on two dissimilar fluids such asR245fa and R134a in order to show the effectiveness of proposed scheme. The experimental investigation showed that the proposed scheme effectively improved the heat exchanger performance as related to the existing schemes.The enhancement factor of proposed scheme was 2.8063 for R245fa fluid and 1.9346 for R134a fluid, which was better compared to the existing schemes; KGMO and Bell-Delaware method.