Correct Parameter Settings Research Articles

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

Analisis Pengaruh Parameter Terhadap MRR Pada Benda Kerja di Mesin CNC Turning dengan Metode Taguchi

A high material removal rate is one of the goals to be achieved when machining parts with CNC turning. However, slow processing affects product processing time and increases production costs. To overcome this problem, it is necessary to set the correct process parameters so that they provide the most influential contribution to the MRR and produce maximum parameter values. This research aims to determine the influence of parameters (spindle speed, feed speed, and feed depth) on MRR and to determine the correct process parameter settings to produce maximum MRR, namely in this experimental design using an orthogonal array L9 (33) with three replications. The material used is ST-60 steel, with the data analyzed using the Taguchi Larger Is a Better method in analysis software. From the results of the mean factor calculation, the percent contribution to feeding was found to be 19.53%, while the depth of feeding was found to be 64.56%. The optimum MRR value is obtained from a combination of parameters in the CNC Turning process on ST-60 steel which can make the response value optimally obtained at the spindle speed 3,184 [rev/min], feeding 0.22 [mm/rev] and depth of cut 2.5 [mm].

Twnbiome: a public database of the healthy Taiwanese gut microbiome

With new advances in next generation sequencing (NGS) technology at reduced costs, research on bacterial genomes in the environment has become affordable. Compared to traditional methods, NGS provides high-throughput sequencing reads and the ability to identify many species in the microbiome that were previously unknown. Numerous bioinformatics tools and algorithms have been developed to conduct such analyses. However, in order to obtain biologically meaningful results, the researcher must select the proper tools and combine them to construct an efficient pipeline. This complex procedure may include tens of tools, each of which require correct parameter settings. Furthermore, an NGS data analysis involves multiple series of command-line tools and requires extensive computational resources, which imposes a high barrier for biologists and clinicians to conduct NGS analysis and even interpret their own data. Therefore, we established a public gut microbiome database, which we call Twnbiome, created using healthy subjects from Taiwan, with the goal of enabling microbiota research for the Taiwanese population. Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference for conducting case-control studies for a variety of diseases. It is an interactive, informative, and user-friendly database. Twnbiome additionally offers an analysis pipeline, where users can upload their data and download analyzed results. Twnbiome offers an online database which non-bioinformatics users such as clinicians and doctors can not only utilize to access a control set of data, but also analyze raw data with a few easy clicks. All results are customizable with ready-made plots and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/.

Pseudo Wigner-Ville distribution for 3D white light scanning interferometric measurement.

White light scanning interferometry is a commonly used optical measurement method for three-dimensional (3D) surface profiles. In the case of large phase errors, accurate height values can generally be obtained indirectly from the interferometric signal envelope information derived using various envelope extraction methods. However, the current envelope extraction algorithms have the disadvantages of low robustness, increasing the half-width of the envelope information, and requiring correct parameter settings in advance. In this study, the pseudo Wigner-Ville distribution is modified and applied to white light scanning interferometric 3D measurement to avoid the above-mentioned drawbacks. Simulations and experiments are performed in a single-frequency mode (only the approximate central wave-number is used to guide both the proposed and wavelet transform methods). The simulation results prove that the proposed method has a 31.7% higher reconstruction accuracy than the wavelet transform method under a 25 dB signal to noise ratio condition. Concurrently, the proposed method is insensitive to the change in the central wavelength with a constant central wave-number parameter and has a good extraction effect for a long coherent length. The experiments measure standard step objects (VLSI standard, 1.761 ± 0.01 µm), and the reconstruction height error of the proposed method is 0.0035 µm. Simulations and experiments show that the proposed method can adaptively provide accurate envelope information after half-width reduction under the condition that only the approximate central wave-number a priori knowledge is used. Simultaneously, the proposed method is shown to be robust and effective.

A Method for Benchmarking of FEM Packages for Multi-Stage Sheet Metal Forming Simulations

Computer simulation plays a crucial role in the designing of sheet metal stamping processes for the prediction of process output, before try-out die sets are manufactured. Different commercial software packages are available on the market for sheet forming simulation, but their accuracy can vary, depending on the selection of the pre-processing parameters and on their formulation. Software benchmarking can be used to select the most appropriate package for a given application. Calibration, i.e. the inverse determination of the correct set of pre-processing parameters, can be used for improving the prediction accuracy. The scientific literature on numerical simulations of sheet metal forming processes presents some examples of software calibration and very few examples of benchmarking. The literature generally neglects a critical and important issue: the inherent variability of real forming processes. In this work, the experimental results of two similar multi-stage deep drawing processes are presented and compared to the simulation output of two popular software packages used in the industry. Statistical methods for benchmarking and calibration are proposed. The paper demonstrates how benchmarking can be misleading if process variability is not considered.

Three-Dimensional Finite Element Modelling of Sheet Metal Forming for the Manufacture of Pipe Components: Symmetry Considerations

The manufacture of parts by metal forming is a widespread technique in sectors such as oil and gas and automotives. It is therefore important to make a research effort to know the correct set of parameters that allow the manufacture of correct parts. This paper presents a process analysis by means of the finite element model. The use case presented in this paper is that of a 3-m diameter pipe component with a thickness of 22 mm. In this type of application, poor selection of process conditions can result in parts that are out of tolerance, both in dimensions and shape. A 3D finite element model is made, and the symmetry of the tube section generated in 2D is analysed. As a novelty, an analysis of the process correction as a function of the symmetrical deformation of the material in this case in the form of a pipe is carried out. The results show a correct fitting of the model and give guidelines for manufacturing.

OPTIMASI MATERIAL REMOVAL RATE (MRR) BAJA ST 42 PADA PROSES CNC TURNING DENGAN MENGGUNAKAN METODE TAGUCHI

Abstract High MRR is a goal to be achieved in the workpiece machining process using CNC turning. But the slow process will affect the processing time of the product, and will increase production costs. To overcome this, it is necessary to set appropriate process parameters to produce the most influential contribution to MRR and also to produce optimum parameter values. The purpose of this study was to determine the effect of parameters (cutting speed, spindle speed and depth of cut) on MRR and to determine the correct process parameter settings in order to produce optimal MRR on ST 42 steel. The study was conducted using ST 42 steel and using carbide chisels on CNC turning process. The process parameters that will be varied are spindle speed, feeding motion, and infeed depth. The experimental design used was orthogonal matrix L9(33). The repetition is done twice. Furthermore, the optimization of multi-response characteristics is carried out using the S / N Ratio method. From the research results, it was found that in order to reduce the variance of the response simultaneously, the infeed depth had the largest percentage contribution value, which was 46.642%, the spindle speed had a percentage contribution value of 14.362%, and then the feeding motion with a percentage contribution value of -7.548 And to obtain the optimal MRR of the workpiece, the spindle speed is set at 1500 rpm, the feeding motion is set at 0.5 mm / put, and the infeed depth is set at 1 mm. Keywords: Carbide, CNC Turning, Taguchi, S/N Ratio, MRR

Developing bug severity prediction models using word2vec

Bug tracking systems use repositories to keep track of the bugs to improve software quality. A manual analysis of each bug and classifying it according to its severity is an unmanageable job. Therefore, it is imperative to correctly classify the severity of a bug, which otherwise might be misclassified by a laymen user. Text mining techniques have the potential to analyze such massive databases of the textual description of bug reports to classify bug severity levels adequately. Word embedding is a state-of-art text mining technique that captures the semantics of the text and group the words according to their relevance in a document. Words are embedded into real-valued vectors through word-embedding models. Word2vec is a word embedding model, proven to be effective in representing word meanings. However, the configuration of hyperparameters and feature selection affects the performance of word2vec. Tuning hyperparameter is a time-consuming process, and it is crucial to identify the correct set of parameters. The paper outlines the effectiveness of word2vec technique on the efficiency of classifiers to predict a bug severity from a bug report and examines the impact of different averaging methods, including the configuration of word embedding parameters on the classifiers’ efficiency. Results show that the bigger window size enhances the performance of classifiers; however, the influence of the minimum word count parameter was found to be mixed and depends on the selected data sets. Further, out of the classifiers used, Random Forest and Xgboost could classify the severity level for classes with few records or a rare occurrence of words specific to each class. Otherwise, Support Vector Machine and Naive Bayes classifiers performed better and worst, respectively.

Enhanced partial frequency variation starting of hydroelectric pumping units: Model based design and experimental validation

The hydroelectric pumped storage is one of the most sustainable solutions to store electrical energy for optimizing the grid operation. However, the pump motor starting may constitute a significant issue for the power plant. Indeed, the motor’s large power and the long power lines (needed for connecting remotely located plants to the grid) may prevent a direct-on-line start. On the other hand, constraints as spaces availability in the powerhouse, and time and cost availability (especially for revamping projects) limit the feasibility of other starting methods. In this paper, a novel starting method is proposed for the revamping of a hydroelectric power plant (HPP), where a synchronous machine is used to drive the pump. On the basis of the power plant characteristics, an enhanced version of the synchronous starting with partial frequency variation is proposed, leading to the creation of a startup sequence capable of being integrated into the existing excitation control systems’ software (of both the generator and the motor installed in the power plant). Such Enhanced Partial Frequency Variation (EPFV) starting method combines the advantages of back-to-back starting method to the ones of asynchronous starting, without requiring additional equipment in respect to a conventional direct-on-line start solution. This is enabled by the model-based design approach here applied, which allows to determine the correct set of parameters and variables’ thresholds required for the machine startup, taking into account the specific characteristics of the HPP in study. The developed starting sequence, implemented into the real HPP by using the capabilities of the existing digital excitation control systems, allowed the correct starting of the motor. This is demonstrated by experimental data obtained during the 5MVA motor starting tests. The results also prove the effectiveness of the model-based design approach in enabling the definition of the pump starting sequence, as well as validating the mathematical model used for the sequence design.

Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation

We introduce a nested optimization procedure using semi-definite relaxation for the fitting step in Hamiltonian-based cluster dynamical mean-field theory (DMFT) methodologies. We show that the proposed method is more efficient and flexible than state-of-the-art fitting schemes, which allows us to treat as large a number of bath sites as the impurity solver at hand allows. We characterize its robustness to initial conditions and symmetry constraints, thus providing conclusive evidence that in the presence of a large bath, our semi-definite relaxation approach can find the correct set of bath parameters without needing to include \emph{a priori} knowledge of the properties that are to be described. We believe this method will be of great use for Hamiltonian-based calculations, simplifying and improving one of the key steps in cluster dynamical mean-field theory calculations.

The Impact of Environmental Factors on the Thermal Characteristic of a Lithium–ion Battery

To draw reliable conclusions about the thermal characteristic of or a preferential cooling strategy for a lithium–ion battery, the correct set of thermal input parameters and a detailed battery layout is crucial. In our previous work, an electrochemical model for a commercially-available, 40 Ah prismatic lithium–ion battery was validated under heuristic temperature dependence. In this work the validated electrochemical model is coupled to a spatially resolved, three dimensional (3D), thermal model of the same battery to evaluate the thermal characteristics, i.e., thermal barriers and preferential heat rejection patterns, within common environment layouts. We discuss to which extent the knowledge of the batteries’ interior layout can be constructively used for the design of an exterior battery thermal management. It is found from the study results that: (1) Increasing the current rate without considering an increased heat removal flux at natural convection at higher temperatures will lead to increased model deviations; (2) Centralized fan air-cooling within a climate chamber in a multi cell test arrangement can lead to significantly different thermal characteristics at each battery cell; (3) Increasing the interfacial surface area, at which preferential battery interior and exterior heat rejection match, can significantly lower the temperature rise and inhomogeneity within the electrode stack and increase the batteries’ lifespan.

A Comprehensive Algorithm for Estimating Lithium-Ion Battery Parameters From Measurements

The use of equivalent circuit models for simulating the operating behavior of lithium-ion batteries is well established in the automotive and the renewable energy sector. However, finding the correct parameter set for these models is still a challenging task. This manuscript proposes a comprehensive methodology for estimating the required, temperature dependent simulation parameters from battery measurements. Based on a specific load current and prior system knowledge, an algorithm first analyses the correlation between current steps and the measured terminal voltage. Second, a combination of particle swarm optimization and Gauss–Newton algorithm fits the initially estimated parameters from the first algorithm to the measurement data. Finally, the dependency of each simulation parameter on both the state of charge and the battery temperature is determined. As this contribution aims at modeling reversible effects of lithium-ion batteries, ageing effects are neglected. The validation against measurement data proves that the generated parameter set enables the user to accurately reproduce and investigate the operating behavior of the chosen battery. Applied to a lithium-iron-phosphate cell, the comparison between measurements and simulations in standardized real-life automotive driving cycles (Artemis, FTP75 and WLTC) shows a terminal voltage error of less than 1.09% within the typical operational window between state of charge 0.15 and 0.95.

Optimal online selection of type 1 diabetes-glucose metabolism models

We address an optimal experimental design (OED) procedure for the online selection of type-1-diabetes (T1D) mellitus glucose metabolistic models. A fully observable reduced-order nonlinear dynamic model is presented and subsequently parameterised for Göttingen Minipigs and patients, that were both subject to an automatic insulin delivery. A bank of continuous–discrete unscented Kalman filters (CDUKF) is designed and parameterised for Göttingen Minipigs and patients. Based on this filter bank of CDUKF, a novel online OED design procedure is developed, that is used to identify the correct parameter set out of several available sets for measured blood glucose concentrations. The procedure utilises forward model simulations to calculate optimal system inputs. This leads to the identification of the correct parameter set under arbitrary conditions. Results are presented for both subgroups.

Investigation of the Transient Characteristics for Laser Cladding Beads Using 420 Stainless Steel Powder

To understand the different aspects of the laser cladding (LC) process, process models can be of aid. Presently, the correct parameter settings for different manufacturing processes, such as machining and casting, are based on simulation tools that can evaluate the influence of the process parameters for different conditions. However, there are no comprehensive, focused simulation process planning tools available for the LC process. In the past, most of the research has focused on the experimentally based optimization strategies for a process configuration, typically for a single track bead in steady-state conditions. However, an understanding of realistic transient conditions needs to be explored for effective process planning simulation tools and build strategies to be developed. A set of cladding experiments have been performed for single and multiple bead scenarios, and the effects of the transient conditions on the bead geometry for these scenarios have been investigated. It is found that the lead-in and lead-out conditions differ, corner geometry influences the bead height, and when changing the input power levels, the geometry values oscillate differently than the input pulses. Changes in the bead geometry are inherent when depositing material; consequently, real-time adjustments for the process setting are essential. The dynamic, time varying heating and solidification, for multiple layer scenarios, leads to challenging process planning and real-time control strategies.

Camera Calibration for 3D Leaf-Image Reconstruction using Singular Value Decomposition

Features of leaves can be more precisely captured using 3D imaging. A 3D leaf image is reconstructed using two 2D images taken using stereo cameras. Reconstructing 3D from 2D images is not straightforward. One of the important steps to improve accuracy is to perform camera calibration correctly. By calibrating camera precisely, it is possible to project measurement of distances in real world to the image plane. To maintain the accuracy of the reconstruction, the camera must also use correct parameter settings. This paper aims at designing a method to calibrate a camera to obtain its parameters and then using the method in the reconstruction of 3D images. Camera calibration is performed using region-based correlation methods. There are several steps necessary to follow. First, the world coordinate and the 2D image coordinate are measured. Extraction of intrinsic and extrinsic camera parameters are then performed using singular value decomposition. Using the available disparity image and the parameters obtained through camera calibration, 3D leafimage reconstruction can finally be performed. Furthermore, the results of the experimental depth-map reconstruction using the intrinsic parameters of the camera show a rough surface, so that a smoothing process is necessary to improve the depth map.

Pitfalls of the Fourier Transform Method in Affine Models, and Remedies

We study sources of potentially serious errors of popular numerical realizations of the Fourier method in affine models, and explain that, in many cases, a calibration procedure based on such a realization will be able to find a correct parameter set only in a rather small region of the parameter space, with a blind spot: an interval of strikes depending on the model and time to maturity, where accurate calculations are extremely time consuming. We explain how to construct more accurate and faster pricing and calibration procedures. Efficient realizations require analytic continuation of the characteristic function not only into a strip around the real axis (the problem of finding the maximal strip of analyticity has been extensively studied in the literature under the name moment explosion) but into much wider regions of the complex plane, and an appropriate conformal deformation of the contour of integration. In the paper, we study analytic continuation of the characteristic functions in wide classes of affine models, models with jumps including. An important ingredient of our method is the study of the associated system of generalized Riccati equations. As a byproduct, we show that the straightforward application of the Runge-Kutta method may lead to sizable errors, and suggest certain remedies. The methodology of the paper can be applied to other models (e.g. quadratic term structure models, Wishart dynamics, 3/2-model), where European options and other options can be priced using the Fourier transform techniques.

Pitfalls of the Fourier Transform Method in Affine Models, and Remedies

ABSTRACTWe study sources of potentially serious errors of popular numerical realizations of the Fourier method in affine models and explain that, in many cases, a calibration procedure based on such a realization will be able to find a “correct parameter set” only in a rather small region of the parameter space, with a blind spot: an interval of strikes depending on the model and time to maturity, where accurate calculations are extremely time-consuming. We explain how to construct more accurate and faster pricing and calibration procedures. An important ingredient of our method is the study of the analytic continuation of the solution of the associated system of generalized Riccati equations, and contour deformation techniques. As a byproduct, we show that the straightforward application of the Runge–Kutta method may lead to sizable errors, and suggest certain remedies. In the paper, the method is applied to a wide class of stochastic volatility models with stochastic interest rate and interest rate models of An(n) class. The methodology of the paper can be applied to other models (e.g., quadratic term structure models, Wishart dynamics, 3/2-model).

Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: I. Determination of Parameters

The parameterization of a physico-chemical model constitutes a critical part in model development. Conclusions about the internal state of a battery can only be drawn if a correct set of material parameters is provided for the material combination under consideration. In this work, parameters to fully parameterize a physico-chemical model for a 7.5 Ah cell produced by Kokam are determined and are compared with existing literature values. The paper presents parameter values and procedures to determine the parameters. Cells were opened under argon atmosphere and the geometrical data were measured. Hg-porosimetry was conducted to determine porosity, particle radius as well as tortuosity of the electrodes and the separator. Conductivity and diffusion constants of the electrolyte as well as the electronic conductivity of the active material were measured detecting the voltage response to a dc current. Finally, electrochemical measurements were conducted on laboratory-made coin cells in order to determine open circuit voltage curves, diffusion coefficients and charge transfer kinetics of the electrodes as well as their balancing. In part II of this paper, a physico-chemical model is introduced and a validation of the measured parameter set is given by comparing model results with experiments.

Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: II. Model Validation

To draw reliable conclusions about the internal state of a lithium-ion battery or about ageing processes using physico-chemical models, the determination of the correct set of input parameters is crucial. In the first part of this publication, the complete set of material parameters for model parameterization has been determined by experiments for a 7.5 Ah cell produced by Kokam. In this part of the publication, the measured set of parameters is incorporated into a physico-chemical model. Model results are compared to validation test results conducted on the Kokam cell. The influence of current rate and temperature is considered as well as a comparison with pulse tests is shown. It is discussed to which extent material parameters obtained by experimental investigation of laboratory coin cells can be transferred to commercial cells of the same material. The validity of physico-chemical models to describe cells is shown.

Numerical modeling and optimization of machining duplex stainless steels

The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM) introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also the optimum set of process input variables for turning duplex stainless steels is defined.