Order Selection Research Articles

Prediction of flame transfer function and combustion instability on a partially premixed swirling combustor by the system identification and CFD methods

Combustion instability can lead to high amplitude oscillations of pressure in the combustion chamber, which would seriously affect the normal operation of the combustion system. In order to reduce the cost and time of combustion chamber design and testing, combustion instability of the partially premixed swirl combustor was predicted, by solving the Helmholtz equation coupled the flame transfer unction (FTF). In the experimental study, by changing the equivalence ratio, the pressure in the combustion chamber appeared to oscillate in different forms including combustion noise, intermittent oscillation and limit cycle oscillation, while the frequency of the self-excited oscillations was obtained to be 165 Hz at the equivalence ratio of 0.7. In Computational Fluid Dynamics (CFD) Simulations, by applying Discrete Random Binary Signal (DRBS) excitation at the inlet, the FTF has been calculated by system identification method, where the excitation results of perturbation signals with different magnitudes were studied. And the result was basically consistent with the FTF calculated by the single-frequency excitation method, which means that the system identification method is suitable for calculating the FTF of the partially premixed swirl combustor. The pulse excitation method has been proposed to determine the selection of the filtering order, and the accuracy of the method has been validated through comparing the FTF results obtained by using different filter orders for discrimination. Moreover, coupling the FTF into the Helmholtz equation, the oscillation frequency of the system was predicted as 159.7 Hz, compared with the experimentally measured oscillation frequency of 165 Hz, which verified that the model could be effectively used for the prediction of oscillation frequency in the partially premixed combustion systems.

A Qualitative Study of the Psychological Effects of Motivational Quotes

Motivational quotes wield and obtain a sense of power towards inspiring individuals to strive towards their goals, harnessing both internal and external motivations. The absence of motivational quotes tends to serve as catalysts, igniting the drive to propel one through challenges and pursue excellence. Externally, motivational quotes often encapsulate universal truths or wisdom, offering perspective and guidance from varied sources and figures. This paper explores the effectiveness of motivational quotes in medical and educational contexts, with a focus on their impact on individuals' behavior. While numerous studies have reported a generally positive attitude toward the use of motivational quotes, specific concerns have emerged. This paper has observed that motivational quotes, delivered through various media, have successfully motivated individuals to take positive actions, such as adhering to medication regimens and quitting smoking. However, some studies have highlighted potential limitations, including overexposure leading to reduced effectiveness, the contextual relevance of motivational quotes, and the distinction between external and internal motivation. This paper presents an overview of the existing research, offering insights into the mixed opinions on the use of motivational quotes for motivation. It calls for future studies to consider contextual factors, audience mindset, media selection, and quote suitability in order to enhance their efficacy.

Logic-based Benders decomposition for order acceptance and scheduling on heterogeneous factories with carbon caps

We study an integrated order acceptance and scheduling problem for heterogeneous factories with carbon caps, order-dependent processing speeds, and setup times. Three joint decisions including order selection, order assignment and order sequencing are made to maximize profits, i.e., total revenue minus total production and tardiness cost. Firstly, we develop a mixed integer programming model to simultaneously optimize the three decisions and propose several dominance rules to enhance the model. Secondly, due to the particular structure of the problem, we propose a logic-based Benders decomposition (LBBD) method that decomposes the complicated original problem into a master problem and a number of subproblems. The master problem aims to determine order acceptance and order assignment, and each subproblem is for determining order sequencing in each factory. A branch and bound algorithm is then designed to quickly solve the subproblems. The branch and check framework is implemented to accelerate the solving process of the LBBD. Finally, numerical experiments verify that the proposed dominance rules play a promising role in enhancing the model, and the results of the algorithm comparison experiments show the significant advantages of proposed LBBD algorithm combined with the branch and check framework. Sensitivity experiments reveal that carbon cap serves as a major constraint on order acceptance.

ARX-Naïve Bayes based structural damage identification

ABSTRACT The nature of damage identification is close to a pattern recognition process that classifies different damage patterns. The naïve Bayes classifier (NBC) can effectively handle multiple-classification problems by choosing patterns with high probabilities. Therefore, by absorbing the autoregressive model with exogenous inputs (the ARX model), an ARX-Naïve Bayes damage identification strategy has been proposed in which the autoregressive coefficients of the ARX model are taken as the sensitive damage feature. The classification training and test sample datasets are then built on these coefficients corresponding to various damage scenarios. The model order of an AR model is first determined for the subsequent order selection of the ARX model, whose autoregressive coefficients are further used to construct the NBC. This procedure can enhance the pattern recognition robustness to uncertainties such as measurement noises. Different damage patterns are determined by calculating the sum of logarithmic likelihoods of testing samples. The effectiveness of the proposed method has been verified against a bridge benchmark model having different damage scenarios under the noise pollution. In addition, an experimental five-story shear frame structure was adopted for validation, showing that compared with the SVM algorithm suitable for handling binary classification problems, the proposed method excels in multi-classification of damage patterns.

Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study

PurposeThe purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.Design/methodology/approachThe mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.FindingsThe developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.Originality/valueThis study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

Model Selection for Exponential Power Mixture Regression Models.

Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions for FMLR models via an exponential power error distribution, which includes normal distributions and Laplace distributions as special cases. Under some regularity conditions, the consistency of order selection and the consistency of variable selection are established, and the asymptotic normality for the estimators of non-zero parameters is investigated. In addition, an efficient modified expectation-maximization (EM) algorithm and a majorization-maximization (MM) algorithm are proposed to implement the proposed optimization problem. Furthermore, we use the numerical simulations to demonstrate the finite sample performance of the proposed methodology. Finally, we apply the proposed approach to analyze a baseball salary data set. Results indicate that our proposed method obtains a smaller BIC value than the existing method.

Orbit Determination of High Orbit Spacecraft based on BDS-3 using Extended Kalman Filter

The positioning of highly elliptical orbit (HEO) spacecraft is very important in deep space exploration and other missions. The Beidou Navigation Satellite System (BDS-3), which was completed in 2020, has put forward new ideas for using GNSS technology to make up for the lack of earth-based TT&C systems. At the same time, the selection of the gravity field order and the perturbation forces must be considered when using dynamical methods for HEO spacecraft positioning. In this paper, simulation experiments are carried out based on the Two-Line Orbital Element (TLE) data of BDS. Two HEO satellites with different semimajor-axis (67509.6 km, 212857.3 km) are selected as the target spacecraft orbits. The single point positioning (SPP) method and Extended Kalman Filter (EKF) method are used for HEO spacecrafts positioning respectively. The experiments show that a 20×20 order gravity field model can be chosen for the orbital integral of HEO spacecraft orbits, and the integration error caused by solar gravity and lunar gravity should be taken into account with the change of orbital altitude. Adding Geostationary Orbit (GEO) and Inclined GeoSynchronous Orbit (IGSO) navigation satellites improved the visibility of these two orbits by 19.45% and 16.64%, respectively, and improved the GDOP value by 14.1% and 3.8%, respectively, compared to observing MEO satellites only. The use of the EKF method can effectively improve the positioning accuracy of the HEO spacecraft compared to the SPP method. For the spacecraft in HEO-1, the RMS value of the positioning errors of the EKF method was 67.82 m, and the accuracy of each direction was improved by 44.28%, 38.85%, and 38.62%, respectively. For the spacecraft in HEO-2, the positioning errors in the x direction of EKF method was within 12 km and within 2 km in the y and z directions. The accuracy of these three directions was improved by 84.91%, 79.24%, and 58.64%, respectively.

Model Order Selection and Meta Analysis-Based Cooperative Wideband Spectrum Sensing

Model Order Selection and Meta Analysis-Based Cooperative Wideband Spectrum Sensing

A novel grey forecasting model with generalised fractal derivative and its optimisation

PurposeThe purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.Design/methodology/approachThe paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.FindingsThe study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.Research limitations/implicationsThe research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.Originality/valueThe study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

Species selection in unfamiliar terrain: participants’ preferences and practices around Mount Elgon, Uganda

Efforts to restore the world forests and trees are obviously significant and still increasing. Selection of tree species in this context is based on prevailing challenges impacting on livelihood needs. A study was conducted around Mount Elgon between January 2018 and July 2020. The objectives were to; i) document the socio-economic factors of participants in tree multiplication and planting, ii) identify the farmers practices and choice of species used under farming contexts iii) assess the relationship between the participants socio-economic factors, practices and choices of tree species. A multi-stage sampling approach was followed to select a total of 150 participants. The participants were engaged through semi-structured and key informant interviews. Quantitative data were analyzed in MINITAB 19. Results show that tree multiplication and planting activities were dominated (84%) by a vibrant group of males between the ages of 15 and 40 years old. Up to 60% of this group were illiterate or inexperienced in tree planting and multiplication. Exotic trees (containing Eucalyptus grandis, Grevillea robusta and Neolamarckia cadamba) were highly valued for firewood, timber and small stems used to support food crops including Musa spp., Phaseolus vulgaris and Solanum lycopersicum. The indigenous species (mainly Cordia africana, Maesopsis eminii, Albizia spp. and Ficus spp.) were on the other hand treasured for shade in the coffee-banana farming systems as well as serving social-cultural benefits, counting medicine and rituals. The participants gender significantly influenced the choice of tree species adopted (P<0.001). For example, the men were more interested in timber and carbon related tree species while the women and the youths were generally involved in apiculture and fruit tree growing. With all the anticipated benefits and publicity about the exotic trees in this region, the participants have no choice but to follow the advice from the various tree planting campaigns. We recommend co-operation of the stakeholders especially during tree germplasm selection in order to meet performance expectations. The expectations include developing individual species breeding protocols based on site conditions for tree seed collection, seedling multiplication and planting.

Order selection for heterogeneous semiparametric hidden Markov models.

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness.

Progress of Construction of Gene-Modified Soybean Based on the CRISPR/Cas9 System

From the first discovery by Japanese scholar Ishino in 1987 to being named and classified in 2002, CRISPR technology was proven by the Nobel Prize for Chemistry in 2020. In 2010, the type II CRISPR/Cas system (CRISPR/Cas9) was developed as an RNA-targeted genome editing system, which, as a novel and emerging genome editing technology in recent years, has a promising application prospect because of its high efficiency, simplicity and low cost. There is a serious mismatch between the plant area and yield of soybeans in China with market demand. Soybean is an important source of oil and biofuel. It is closely related to national food security. The large demand has led to a high dependence on imports of soybeans in China. Therefore, there is an urgent need to breed Chinese soybean varietal strains with outstanding forms in soybean selection and breed in order to breeding good strains and realize the goal of basically satisfying independent production. This review summarizes the research of the CRISPR/Cas9 system in constructing biological models of specific genes or genomic deletions in soybeans, introduces the latest development of this technology, and discusses for the advantages of CRISPR/Cas9 system in breeding target soybeans with good resistance and high economic production value.

A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast

Accurate electricity consumption forecast plays an important role in managing and operating a country’s power system and is a challenging task due to the data exhibiting the mixture characteristics of trend, periodicity and randomness. To accurately describe the dynamic changes of seasonal fluctuations of electricity consumption, a novel discrete time-varying grey Fourier model with fractional order terms is proposed by coupling Fourier functions with fractional order time-varying terms, as the grey action, to fit the amplitude variations hidden in seasonal pattern and improve the fitting and forecasting accuracy. Moreover, the mechanism and properties of the proposed model are discussed. Then the optimal fractional order is determined by the Dingo Optimization Algorithm (DOA) and the optimal Fourier order is selected by the hold-out method to enhance the robustness of the model. Subsequently, the results of designed experiments based on Monte-Carlo method testify the validity of the Fourier order selection method and the proposed model. Finally, this model is applied to predict the monthly residential electricity consumption of China from 2015 to 2022. The results indicate that the proposed model captures the dynamic amplitude variations of the time series and has better prediction performances than other benchmark grey prediction models and non-grey prediction models.

An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series

As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive moving average (VARMA) model can capture much richer temporal patterns than the widely used finite-order VAR model. However, its practicality has long been hindered by its non-identifiability, computational intractability, and difficulty of interpretation, especially for high-dimensional time series. This article proposes a novel sparse infinite-order VAR model for high-dimensional time series, which avoids all above drawbacks while inheriting essential temporal patterns of the VARMA model. As another attractive feature, the temporal and cross-sectional structures of the VARMA-type dynamics captured by this model can be interpreted separately, since they are characterized by different sets of parameters. This separation naturally motivates the sparsity assumption on the parameters determining the cross-sectional dependence. As a result, greater statistical efficiency and interpretability can be achieved with little loss of temporal information. We introduce two l 1 -regularized estimation methods for the proposed model, which can be efficiently implemented via block coordinate descent algorithms, and derive the corresponding nonasymptotic error bounds. A consistent model order selection method based on the Bayesian information criteria is also developed. The merit of the proposed approach is supported by simulation studies and a real-world macroeconomic data analysis. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots

AbstractClosed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

VOLATILITY ANALYSIS OF CRUDE OIL PRICES IN NIGERIA

This study investigates the symmetric and asymmetric characteristics as well as the persistence of shocks in the Nigerian crude oil returns, utilizing monthly and daily crude oil prices spanning from January 2006 to September 2022 and November 3, 2009, to November 4, 2022, respectively. Descriptive statistics, normality measures, time plots, and the Dickey-Fuller Generalized Least Squares unit root test were employed to analyze the series properties. Symmetric ARMA (1,1)-GARCH (2,1) and asymmetric ARMA (1,1)-TARCH (2,1) models for monthly and daily returns, with varying innovation densities, were utilized, alongside symmetric GARCH (1,1) and asymmetric TARCH (1,1) models. Model selection criteria including AIC, SIC, HQC, and log likelihood guided the order and error distribution selection. Results revealed non-normal distributions for both monthly and daily prices and returns, non-stationarity in prices, and weak stationarity in log returns with ARCH effects detected in both returns. Symmetric models exhibited volatility clustering, high shocks persistence, mean-reverting behaviour, and predictability in both returns. Asymmetric models identified asymmetry with leverage effects in both returns, indicating that negative shocks induce greater volatility than positive shocks of the same magnitude. Mean reversion and volatility half-life findings suggested that crude oil prices tend to revert to their long-run averages. The study recommended promoting market information flow and aggressive trading to enhance market depth and mitigate the volatile nature of the Nigerian crude oil market.

Systematic analyses of AISNPs screening and classification algorithms based on genome-wide data for forensic biogeographic ancestry inference

Identifying the biogeographic ancestral origin of biological sample left at a crime scene can provide important evidence for judicial case, as well as clue for narrowing down suspect. Ancestry informative single nucleotide polymorphism (AISNP) has become one of the most important genetic markers in recent years for screening ancestry information loci and analyzing the population genetic background and structure due to their high number and wide distributions in the human genome. In this study, based on data from 26 populations in the 1000 Genomes Project Phase 3, a Random Forest classification model was constructed with one-vs-rest classification strategy for embedded feature selection in order to obtain a panel with a small number of efficient AISNPs. The research aim was to clarify differentiations of population genetic structures among continents and subregions of East Asia. ADMIXTURE results showed that based on the 58 AISNPs selected by the machine learning algorithm, the 26 populations involved in the study could be categorized into six intercontinental ancestry components: North East Asia, South East Asia, Africa, Europe, South Asia, and America. The 24 continental-specific AISNPs and 34 East Asian-specific AISNPs were finally obtained, and used to construct the ancestry prediction model using XGBoost algorithm, resulting in the Matthews correlation coefficients of 0.94 and 0.89, and accuracies of 0.94 and 0.92, respectively. The machine learning models that we constructed using population-specific AISNPs were able to accurately predict the ancestral origins of continental and intra-East Asian populations. To summarize, screening a set of high-perform AISNPs to infer biogeographical ancestral information using embedded feature selection has potential application in creating a layered inference system that accurately differentiates from intercontinental populations to local subpopulations.

A note on estimating multivariate Gaussian mixtures with unknown number of components

. In this article, a new penalized likelihood method is proposed for finite multivariate Gaussian mixture models to conduct order selection and model estimation. The method is proved to achieve order selection consistency and have root-n convergence rate. Asymptotic normality is established for the proposed estimator. A modified EM algorithm is developed for computation. Extensive simulations and a real data analysis are conducted to illustrate the performance of our method.

Online Auction System with AI

An online auction is type of an auction that takes place over the Internet. It is a popular method for buying and selling products and services. Such systems give the best price to the seller as well as the buyers. The application proposed in this paper was developed with the objective of making the system reliable, easier and fast. Through this application, anyone can sell anything on the website by sitting at home. It’s as easy as browsing the app website. Even non-technical persons can easily interact with the process of buying and selling on the application. An online auction system permits a customer to submit online orders for items and/or services from a store that serves both walk-in customers and online customers. The online auction system presents an online display of an order cut off time and an associated delivery window for items selected by the customer of any 2nd hand products. The online auction system does not settle with a credit supplier of the customer until the item selected by the customer is picked from inventory. Therefore, customers can go online to modify their order. In addition, the service window is presented to the customer as a function of the order and service type selected by the customer, and also the choice is made according to the person's preference, where the order selection is correct. When ordering goods, a virtual shopping cart is also provided by many shopping systems for holding items selected for purchase. Until a customer completes their shopping trip, successive items selected for purchase are placed into the virtual shopping cart. Virtual shopping carts may be examined at any time, and their contents can be deleted and edited at the customer’s end.

ATwo‐StageMethod for Order Selection inModel‐FreePredictive Control

One of the primary advantages of model‐free predictive control over conventional methods is that it does not use any mathematical models and relies only utilizes measured input/output data from the storage. The capability of model‐free predictive control has been already demonstrated in nonlinear systems using linear and polynomial regression for data storage. However, identifying the appropriate order that aligns with the actual system order remains a primary challenge, selecting an incorrect order may result in increasing redundant terms, ultimately leading to instability issues. In this study, we employed the Singular Value Decomposition (SVD) order selection technique, combined with the Bayesian Information Criterion (BIC), to identify the appropriate input and output orders of the system as well as the optimal horizon order in predictive control. This combined technique was subsequently applied to determine the appropriate order for model‐free predictive control. Our findings confirmed the effectiveness of the proposed method using numerical simulations in both linear and nonlinear systems. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.