Optimal Selection Method Research Articles

Ensefgram: An optimal demodulation band selection method for the early fault diagnosis of high-speed train bearings

Early fault diagnosis of high-speed train bearings under road spectrum shocks is challenging. As a conventional narrowband demodulation method, the Fast Kurtogram (FK) class of algorithms have some drawbacks: the fixed-band segmentation strategy makes it difficult for the method to find the optimal demodulation frequency band (ODFB); other major limitations are the poor robustness of the statistical index of the blind algorithm and the poor adaptiveness of the target algorithm given the need to the know the bearing operating state in advance. To address these issues, this paper proposes Ensefgram, an early fault detection method for high-speed train bearings with the acoustic emission signal as an example. First, a filter bank is designed by combining the spectral trend with the median dichotomy strategy, which adaptively segments the frequency band and retains the integrity of the sub-band fault components to the maximum extent. In addition, a novel statistical index, termed the maximum weighted spectral energy frequency factor, is proposed, and it is combined with the envelope spectrum negentropy (ES-negentropy) to form a composite index ENSEF, which can select the ODFB blindly. Finally, the proposed method was validated by simulation and applying test signals close to the real working conditions of a high-speed train. Compared with FK, Infogram, and other methods, Ensefgram provides more accurate detection results for the early fault diagnosis of bearings.

Combinations of Feature Selection and Machine Learning Models for Object-Oriented “Staple-Crop-Shifting” Monitoring Based on Gaofen-6 Imagery

This paper combines feature selection with machine learning algorithms to achieve object-oriented classification of crops in Gaofen-6 remote sensing images. The study provides technical support and methodological references for research on regional monitoring of food crops and precision agriculture management. “Staple-food-shifting” refers to the planting of other cash crops on cultivated land that should have been planted with staple crops such as wheat, rice, and maize, resulting in a change in the type of arable land cultivated. An accurate grasp of the spatial and temporal patterns of “staple-food-shifting” on arable land is an important basis for rationalizing land use and protecting food security. In this study, the Shihezi Reclamation Area in Xinjiang is selected as the study area, and Gaofen-6 satellite images are used to study the changes in the cultivated area of staple food crops and their regional distribution. Firstly, the images are segmented at multiple scales and four types of features are extracted, totaling sixty-five feature variables. Secondly, six feature selection algorithms are used to optimize the feature variables, and a total of nine feature combinations are designed. Finally, k-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) are used as the basic models of image classification to explore the best combination of feature selection method and machine learning model suitable for wheat, maize, and cotton classification. The results show that our proposed optimal feature selection method (OFSM) can significantly improve the classification accuracy by up to 15.02% compared to the Random Forest Feature Importance Selection (RF-FI), Random Forest Recursive Feature Elimination (RF-RFE), and XGBoost Feature Importance Selection (XGBoost-FI) methods. Among them, the OF-RF-RFE model constructed based on KNN performs the best, with the overall accuracy, average user accuracy, average producer accuracy, and kappa coefficient reaching 90.68%, 87.86%, 86.68%, and 0.84, respectively.

TDLC: Tensor decomposition‐based direct learning‐compression algorithm for DNN model compression

SummaryAs a deep neural networks (DNNs) model compression method, learning‐compression (LC) algorithm based on pre‐trained models and matrix decomposition increases training time and ignores the structural information of models. In this manuscript, a tensor decomposition‐based direct LC (TDLC) algorithm without pre‐trained models is proposed. In TDLC, the pre‐trained model is eliminated, and tensor decomposition is first applied to LC algorithm to preserve the structural features of the model. There are two key steps in TDLC. An optimal rank selection method is first proposed in compression‐step (C‐step) of TDLC to find global optimal ranks of tensor decomposition. Second, TDLC utilizes cyclical learning rate, which is different from traditional monotonically learning rates schedule, to improve the generalization performance of uncompressed models in learning‐step (L‐step). TDLC obtains the optimal compression model by alternately optimizing L‐step and C‐step. TDLC is compared with 16 state‐of‐the‐art compression methods in experiments part. Extensive experimental results show that TDLC produces high‐accuracy compression models with high compression rate. Comparing with TDLC‐pre‐trained, TDLC notably achieves 30% training time shorten and 11% parameter reduction in Resnet32, while improving accuracy by 0.2%.

SEACKgram: a targeted method of optimal demodulation-band selection for compound faults diagnosis of rolling bearing

Rolling bearing plays an important role in carrying and transmitting power in rotating machinery, and the bearing fault is easy to lead to mechanical accidents, resulting in huge losses and casualties. Therefore, the condition monitoring and diagnosis of rolling bearings are very important to improve the safety of equipment. Compound fault is a common fault evolved from the initial defect, which is characterized by randomness, coupling, concealment, and secondary. The existence of these characteristics brings great challenges to the accurate diagnosis of compound faults. In the diagnosis of compound faults, the traditional methods that select the single optimal demodulation frequency band for analysis and identification sometimes cannot completely extract multiple fault components, which are prone to miss diagnosis and misdiagnosis. In order to solve this problem, the SEACKgram method is proposed by constructing a Square Envelope Unbiased Autocorrelation Correlation Kurtosis (SEACK) index. The frequency band of the original signal is divided by the Maximal Overlap Discrete Wavelet Packet Transform, and the SEACK index is used to quantitatively describe the fault signals of different frequency bands. According to the different fault periods, the resonant frequency bands of the maximum SEACK value are selected, then the resonance band signal is analyzed by square envelope spectrum, and the fault type is identified according to the fault characteristic frequency. The simulated and experimental vibration signals of rolling bearings with compound faults are used to verify the feasibility of the proposed method. The results show that the proposed SEACKgram can improve the accuracy of compound faults identification and would be applied in engineering practice to a certain extent.

An optimal signal selection method based on feature neighborhood using for human gait mode recognition

Signal selection is a crucial issue for classifiers. A feature neighborhood based method is proposed to select the optimal signals in this paper. The neighborhood of a feature is first defined to classify each example according to whether its feature value belongs to the neighborhood. A discernibility matrix is established based on all the classification results of the feature. The classification ability of a feature is then calculated according to its discernibility matrix. Furthermore, the classification ability of a signal combination is achieved by calculating the disjunction of the corresponding feature discernibility matrices. The optimal signal combination (O-combination) with the highest classification ability is finally selected. The proposed method is validated by using the data from two public databases of human motion. The results show that 2∼5 signals are selected from over 50 signals to be the O-combinations for human gait mode recognition. The accuracy rates of gait mode recognition (RARs) of these O-combinations are higher than those of other signal combinations with the same dimensionality. The RARs of the 4- and 5- dimensional O-combinations are even higher than that of the combination formed with all signals. Specifically, the RARs of the 4-dimensional O-combinations are 99.37% and 99.86% on the two public databases. In addition, it is found that the signal of clearance between swing foot and stance foot exists in most of the combinations with high RAR, which indicates that this signal cannot be ignored for gait mode recognition.

The LFIgram: A Targeted Method of Optimal Demodulation Band Selection for Compound Faults Diagnosis of Rolling Bearing

The LFIgram: A Targeted Method of Optimal Demodulation Band Selection for Compound Faults Diagnosis of Rolling Bearing

An Optimal Selection Method of Cloud Manufacturing Resource for Guide Roller based on Combination Algorithm

An Optimal Selection Method of Cloud Manufacturing Resource for Guide Roller based on Combination Algorithm

Multi-criteria model for selection of optical linear terminals based on FUZZY TOPSIS method

Optical networks are an integral part of modern telecommunication systems. Huge traffic and ever-increasing requirements for data transmission capacity and quality encourage the wider use of modern optical technologies in telecommunications. The rational choice of equipment for the design of optical networks is an urgent task at present. The subject of this study is the process of selecting optical line terminals, which is associated with the evaluation of possible options by a set of indicators. The OLT (optical line terminal) in PON technology implements the function of organizing subscriber lines and is also a node equipment - an L4 switch that combines the functions of routing (IP), traffic fragmentation (VLAN), switching (MAC), quality of service (QoS), and some necessary network service functions. The optimal structuring of PON access networks demands a judicious selection of software and hardware, guided by their tactical and technical characteristics, in view of the substantial information load they entail. One of the effective approaches to solving such problems is the use of MCDM (Multiple Criteria Decision Making) methods. Purpose: to construct a TOPSIS model of the optimal choice of optical linear terminals in the conditions of unclear information for different cases of aggregation of evaluations of decision makers. Task: to formalize the process of selecting optical linear terminals; develop a multi-criteria mathematical model for the effective selection of optical line terminals. Methods used in the study: The fuzzy TOPSIS method, four methods of aggregating the opinions of decision makers. The following results were obtained. Alternatives and criteria for their evaluation are defined. On the basis of interviews with decision makers, evaluations of the degree of importance of the criteria and alternatives to the criteria were determined. Linguistic changes were used to describe decision-makers’ evaluations, which were interpreted as triangular fuzzy numbers. During this study, four methods of aggregating the evaluations of decision makers were used. A fuzzy TOPSIS model for selecting an optical line terminal is constructed. The ranking of the selected optical line terminals is obtained and the best alternative is determined. The results of modeling with different methods of aggregating assessments of decision makers are compared. Conclusions. The use of the Fuzzy TOPSIS method for optimal selection of optical linear terminals is proposed. The influence of the methods of aggregation of evaluations of decision-makers is analyzed.

EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms

Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users’ preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor’s preferences has proved to be very effective in the portfolio’s composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation.

Performance Analysis and Simulation of IRS-Aided Wireless Networks Communication

This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus.

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

Accuracy improvement of the load forecasting in the district heating system by the informer-based framework with the optimal step size selection

Accurate load forecasting is crucial for effectively regulating regional heat network systems. However, existing forecasting methods often rely on subjective experience to determine the forecasting step, which is limited by the presence of thermal inertia, leading to suboptimal accuracy. To address this limitation, an optimal step size selection method based on the Informer-based framework is proposed to enhance load forecasting accuracy in heat exchange stations. This method leverages the Attention mechanism within the Informer model, enabling the capture of global information in a single step. To verify the effectiveness of the proposed method, real operational data from a typical thermal power plant in North China is utilized to analyze and test the impact of data distribution and prediction step size on the model's prediction capability. The performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Comparative analysis against Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) models demonstrates that the Informer algorithm with optimal prediction step size achieves the highest prediction accuracy. Notably, the proposed method achieved a minimum reduction of 62.7 %, 46.5 %, and 42.9 % in MSE, MAE, and MAPE, respectively, significantly surpassing the performance of alternative prediction methods.

Optimal scale selection approach for classification based on generalized multi-scale formal context

The classification of multi-scale data is an important research topic in granular computing. Its research goal is to determine the most appropriate scale and achieve better classification performance. However, determining the optimal scale is often a difficult problem due to lacking better metrics and optimization methods. In order to solve this problem, this paper proposes the optimal scale selection criteria for generalized multi-scale formal contexts. That is, the optimal scale uses the coarsest conditional attributes and finest decision attributes to optimize the combination of granularities of attributes. We combine these criteria with multi-objective optimization methods for developing an algorithm to fast compute the optimal scale. Experiments show that for the selected 14 data sets and 11 comparative classification methods, there are 9 classification methods with higher classification accuracies on more than 9 data sets. Therefore, the optimal scale selection method proposed in this paper is feasible and can effectively improve the performance of the classification method.

Gaussian mutation–orca predation algorithm–deep residual shrinkage network (DRSN)–temporal convolutional network (TCN)–random forest model: an advanced machine learning model for predicting monthly rainfall and filtering irrelevant data

Monitoring water resources requires accurate predictions of rainfall data. Our study introduces a novel deep learning model named the deep residual shrinkage network (DRSN)—temporal convolutional network (TCN) to remove redundant features and extract temporal features from rainfall data. The TCN model extracts temporal features, and the DRSN enhances the quality of the extracted features. Then, the DRSN–TCN is coupled with a random forest (RF) model to model rainfall data. Since the RF model may be unable to classify and predict complex patterns and data, our study develops the RF model to model outputs with high accuracy. Since the DRSN–TCN model uses advanced operators to extract temporal features and remove irrelevant features, it can improve the performance of the RF model for predicting rainfall. We use a new optimizer named the Gaussian mutation (GM)–orca predation algorithm (OPA) to set the DRSN–TCN–RF (DTR) parameters and determine the best input scenario. This paper introduces a new machine learning model for rainfall prediction, improves the accuracy of the original TCN, and develops a new optimization method for input selection. The models used the lagged rainfall data to predict monthly data. GM–OPA improved the accuracy of the orca predation algorithm (OPA) for feature selection. The GM–OPA reduced the root mean square error (RMSE) values of OPA and particle swarm optimization (PSO) by 1.4%–3.4% and 6.14–9.54%, respectively. The GM–OPA can simplify the modeling process because it can determine the most important input parameters. Moreover, the GM–OPA can automatically determine the optimal input scenario. The DTR reduced the testing mean absolute error values of the TCN–RAF, DRSN–TCN, TCN, and RAF models by 5.3%, 21%, 40%, and 46%, respectively. Our study indicates that the proposed model is a reliable model for rainfall prediction.

Optimal action selection to estimate the aperture of bag by force-torque sensor

ABSTRACT In this study, we applied Particle Filter to the state estimation of deformable object based on force-torque sensor observations, and proposed information gain-based optimal action selection method. The objects that exist on the production sites, such as packed circuit boards and bags containing objects, are translucent and deformable. For these objects, it is difficult to apply the state estimation methods based on image sensor widely used in production sites. In addition, it is not possible to uniquely determine the shape of the object. In this study, Particle Filter based on observation by force-torque sensor is applied to represent the object state probabilistically. On the other hand, observation by force-torque sensor requires the robot to move actually, which takes more time than observation by image sensor, and the number of times the robot has to move should be reduced as much as possible. Therefore, in this research, we evaluate the information gain of particle distribution, and the action that can most narrow down the state of the object is the contact action with the largest information gain. The proposed method was applied to the estimation of the aperture of the packed circuit board and evaluated.

DTMSgram: a novel optimal demodulation frequency band selection method for wheelset bearings fault diagnosis under wheel-rail excitation

Due to the influence of the wheel-rail excitation and complex transmission path, the fault signature of wheelset bearings is often obscured by complex background noise, which brings great challenges to the adaptive determination of the informative frequency band (IFB) in envelope analysis. In this paper, the vibration response characteristics of the axle box under wheel–rail excitation are revealed through full-scale bench tests. The experimental results show that tread damage will provoke periodic transient impacts and it has an obvious sparsity in the frequency domain. Inspired by this feature, a DTMSgram method is proposed to enhance the fault feature components of vibration signals through time-domain and frequency-domain noise reduction technology, and improve the accuracy of demodulation frequency band selection. Firstly, the amplitude spectrum of different weight coefficients is used to preprocess the vibration signal and adjust the full band component of the vibration signal. Then, the autocorrelation process is performed on each layer’s narrowband filtered signal envelope in kurtogram to further reduce noise interference from a time-domain perspective. Moreover, a two-dimensional color map is constructed showing the normalized squared envelope spectrum of the IFBs determined simultaneously from the modified signal. Finally, the effectiveness of the proposed method is verified by simulated signal and experimental data of three sets of full-size wheelset bearing systems. The analysis results indicate this proposed method can effectively overcome the influence of complex wheel–rail excitation interference and can diagnose multi-source faults simultaneously.

Simulation and evaluation study of atmospheric aerosol nonsphericity as a function of particle size

Aerosol nonsphericity causes great uncertainty in radiative forcing assessments and climate simulations. Although considerable studies have attempted to quantify this uncertainty, the relationship between aerosol nonsphericity and particle size is usually not considered, thus reducing the accuracy of the results. In this study, a coupled inversion algorithm combining an improved stochastic particle swarm optimization algorithm and angular light scattering is used for the nonparametric estimation of aerosol nonsphericity variation with particle size, and the optimal sample selection method is employed to screen the data. Based on the verification of inversion accuracy, the variation of aerosol aspect ratio with particle size based on the ellipsoidal model in global regions has been obtained from Aerosol Robotic Network (AERONET) data, and the effect of nonsphericity on radiative forcing and dry deposition has been studied. The results show that the aspect ratio increases with particle size in all regions, with the maximum ranging from 1.4 to 1.8 in the desert, reflecting the differences in aerosol composition at different particle sizes. In radiation calculations, considering aerosol nonsphericity makes the aerosol cooling effect weaker and surface radiative fluxes increase, but hardly changes the aerosol absorption, with maximum differences of 9.22% and 22.12% at the bottom and top of the atmosphere, respectively. Meanwhile, the differences in radiative forcing between aspect ratios as a function of particle size and not varying with particle size are not significant, averaging less than 2%. Besides, the aspect ratio not varying with particle size underestimates the deposition velocity of small particles and overestimates that of large particles compared to that as a function of particle size, with maximum differences of 7% and 4%, respectively.

Diabetes prediction model for unbalanced community follow-up data set based on optimal feature selection and scorecard.

Diabetes is a metabolic disease and early detection is crucial to ensuring a healthy life for people with prediabetes. Community care plays an important role in public health, but the association between community follow-up of key life characteristics and diabetes risk remains unclear. Based on the method of optimal feature selection and risk scorecard, follow-up data of diabetes patients are modeled to assess diabetes risk. We conducted a study on the diabetes risk assessment model and risk scorecard using follow-up data from diabetes patients in Haizhu District, Guangzhou, from 2016 to 2023. The raw data underwent preprocessing and imbalance handling. Subsequently, features relevant to diabetes were selected and optimized to determine the optimal subset of features associated with community follow-up and diabetes risk. We established the diabetes risk assessment model. Furthermore, for a comprehensible and interpretable risk expression, the Weight of Evidence transformation method was applied to features. The transformed features were discretized using the quantile binning method to design the risk scorecard, mapping the model's output to five risk levels. In constructing the diabetes risk assessment model, the Random Forest classifier achieved the highest accuracy. The risk scorecard obtained an accuracy of 85.16%, precision of 87.30%, recall of 80.26%, and an F1 score of 83.27% on the unbalanced research dataset. The performance loss compared to the diabetes risk assessment model was minimal, suggesting that the binning method used for constructing the diabetes risk scorecard is reasonable, with very low feature information loss. The methods provided in this article demonstrate effectiveness and reliability in the assessment of diabetes risk. The assessment model and scorecard can be directly applied to community doctors for large-scale risk identification and early warning and can also be used for individual self-examination to reduce risk factor levels.

Application of Virtual Reality Technology in Historical and Cultural Landscape Reproduction

Abstract Virtual reality panoramic technology transcends the constraints of time and space, offering audiences immersive visual and auditory experiences that diminish the costs associated with exploring historical and cultural landscapes and enhance communication effectiveness. This study employs fisheye cameras to capture panoramic images of such landscapes. The collected images undergo correction via spherical projection and reflective planar unfolding mapping. Subsequently, an enhanced method for optimal stitching line selection is applied to ensure seamless transitions between spliced images. The process is completed by integrating image interpolation and projection algorithms to virtually recreate historical landscapes. Analytical assessment of the model’s reproduction effects focuses on point error and accuracy. A practical application involving a real historical and cultural landscape is examined, revealing a maximum control point deviation from the optical scan of 0.02m and a minimum of 0.0014m, which negligibly affects the landscape’s virtual representation. Reproducing an actual landscape image yielded a maximum measurement error of 0.02m, a minimum of 0.01m, and an average of 0.0175m, affirming the capability for precise replication. This paper introduces innovative approaches and methodologies for leveraging virtual reality technology in the accurate reproduction of historical and cultural landscapes, illustrating its practical utility and effectiveness.

Intelligent Particle Swarm Optimization Method for Parameter Selecting in Regularization Method for Integral Equation

We use the Tikhonov method as a regularization technique for solving the integral equation of the first kind with noisy and noise-free data. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Intelligent Piratical Swarm Optimization (IPOS) technique. The effectiveness of combining these two approaches IPOS and Tikhonov is demonstrated to be highly practicable.