Adaptive Selection Algorithm Research Articles

Rihi Mesh Placement Algorithm In Collocation Methods

Various adaptive mesh selection strategies for solving single higher order two-points boundary value problems (BVPs) by using collocation methods are intensively investigated for along time and they are now well established. In this work we concern with numerical investigations of adaptive mesh selection algorithms using the criterion function rihi for solving first order system of BVPs and developing some algorithms. The algorithms perform quite nicely and appear competitive with De Boor algorithm.

Robust weighted fuzzy margin-based feature selection with three-way decision

Feature selection has shown noticeable benefits to the tasks of machine learning and data mining, and an extensive variety of feature selection methods has been proposed to remove redundant and irrelevant features. However, most of the existing methods aim to find a feature subset to perfectly fit data with the minimum empirical risk, thus causing the problems of overfitting and noise sensitivity. In this study, a robust weighted fuzzy margin-based feature selection is proposed for uncertain data with noise. Concretely, a robust weighted fuzzy margin based on fuzzy rough sets is first introduced to evaluate the significance of different features. Then, a gradient ascent algorithm based on the noise filtering strategy and three-way decision is developed to optimize the sample and feature weights to further enlarge the fuzzy margin. Finally, an adaptive feature selection algorithm based on the robust weighted fuzzy margin is presented to generate an optimal feature subset with a large margin. Extensive experiments on the UCI benchmark datasets show that the proposed method could obtain high-quality feature subsets and outperform other representative methods under different noise rates.

Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.

A curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields

Due to the undulating topography, the world contains numerous irregularly shaped small fields. Autonomous navigation along the curved edges of these fields is crucial for achieving complete unmanned operation in these small-field environments. Based on this, we proposed an accurate and robust curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields. Leveraging the significant height difference between headland and farmland, we employed an efficient RGB-D multimodal semantic segmentation network to achieve high-precision segmentation of headland area. Meanwhile, a slope-based feature points adaptive selection algorithm and a feature points transformation algorithm fusing multimodal depth data were proposed innovatively for curved navigation path extraction. By utilizing the RGB-D segmentation model ESANet, we achieved a mean intersection over union (mIoU) of 96.94 %, surpassing other RGB segmentation models. The mean deviations between the ground truth path and the extracted path were less than 6.43 pixels on an image with a resolution of 640 × 480. The proposed method can finally achieve a processing efficiency of 23.80 FPS on images in 640 × 480 resolution, and the field experiments tested that the mean deviations of the agricultural vehicle navigating along the extracted curved navigation path were less than 0.188 m, which met the real-time performance requirement and accuracy requirement of autonomous navigation.

Predicting the Students Performance Using WUM and Machine Learning Technique

Abstract: This project aims to develop a predictive model for assessing students’ academic performance by integrating web usage mining (WUM) and machine learning (ML) techniques. The model leverages user-provided data such as gender, parental profession, reading score, and writing score, along with behavioural insights from online educational platform interactions. The methodology includes data preprocessing, feature extraction, and adaptive algorithm selection to enhance prediction accuracy and adaptability across various student profiles. The integration of WUM provides a robust framework for educational institutions to tailor interventions and support systems effectively.

RETRACTED: Adaptive prototype selection algorithm for fuzzy monotonic K-nearest neighbor

RETRACTED: Adaptive prototype selection algorithm for fuzzy monotonic K-nearest neighbor

An adaptive evolutionary-reinforcement learning algorithm for hyperspectral band selection

Band selection is one of the most important considerations for hyperspectral imaging (HSI) datasets that involves selecting fewer bands with higher classification accuracy. As a combinatorial optimization problem, evolutionary algorithm has been applied in the field, but the optimization ability and number of selected bands are limited with the control of exploration and exploitation. In this paper, an adaptive evolutionary-reinforcement learning algorithm (ERLA) is proposed for band selection of HSI datasets, only a suitable phase contributes to updating the corresponding agents in each iteration, and the coding length is adaptively lessened with the potential solution space. In addition, reinforcement learning is used to choose a feasible action in each iteration, and the unselected bands are removed from the candidate band combination in the previous state. A simulation study is conducted on four public HSI datasets, where the proposed framework has delivered an overall accuracy of 98% for 12% of bands in Chikusei dataset, 92% for 11% in KSC dataset, 93% against 19% for Xiongan dataset, and 97% in Longkou dataset with 10% bands retained, demonstrating that the proposed framework outperforms other approaches with several targets, and that the number of selected bands is obviously decreased with satisfactory classification accuracy.

A Metropolized Adaptive Subspace Algorithm for High-Dimensional Bayesian Variable Selection

A simple and efficient adaptive Markov Chain Monte Carlo (MCMC) method, called the Metropolized Adaptive Subspace (MAdaSub) algorithm, is proposed for sampling from high-dimensional posterior model distributions in Bayesian variable selection. The MAdaSub algorithm is based on an independent Metropolis-Hastings sampler, where the individual proposal probabilities of the explanatory variables are updated after each iteration using a form of Bayesian adaptive learning, in a way that they finally converge to the respective covariates’ posterior inclusion probabilities. We prove the ergodicity of the algorithm and present a parallel version of MAdaSub with an adaptation scheme for the proposal probabilities based on the combination of information from multiple chains. The effectiveness of the algorithm is demonstrated via various simulated and real data examples, including a high-dimensional problem with more than 20,000 covariates.

MetaABR: A Meta-Learning Approach on Adaptative Bitrate Selection for Video Streaming

Video streaming is one of the most popular Internet applications that makes up a large amount of Internet traffic. A fundamental mechanism in video streaming is adaptive bitrate (ABR) selection which decides the proper compression level for each chunk of a video to optimize the users' quality of experience (QoE). The existing ABR algorithms require significant tuning and do not generalize to diverse network conditions and personalized QoE objectives. In this paper, we propose a novel framework for meta-learning based ABR design and discuss challenges of deploying learning based ABR mechanism in real-world video streaming systems. We utilize the proposed framework to design MetaABR, a novel adaptive bitrate selection algorithm based on meta-reinforcement learning to maximize users' QoE. By jointly training multiple learning tasks with a shared meta-critic, it can provide transferrable meta-knowledge to supervise bitrate selection across tasks, and can be applied to efficiently learn a new task in unseen environment with only a few trials. We implement MetaABR on an emulation platform which connects to the Linux network protocol stack through virtual network interfaces. Extensive experiments based on real-world traces and wireless testbed show that MetaABR achieves the best comprehensive QoE compared with the state-of-the-art ABR algorithms in a variety of network environments.

An online fault detection and diagnosis method of sensors in district heating substations based on long short-term memory network and adaptive threshold selection algorithm

District heating system is an essential energy infrastructure. Due to the harsh working environment, sensors in district heating substations are prone to faults, which will mislead the control strategies of substations. We propose an online fault detection and diagnosis (FDD) method for sensors in substations. The proposed method establishes a Sequence-to-Sequence Prediction Model based on Long Short-Term Memory network with Spatial-Temporal Attention (abbreviated as STALSTM-seq2seq model) for each target sensor. The model can learn normal and evolving data patterns from historical time series of sensors and predict current sensor observation. Then, an adaptive threshold selection algorithm is used to autonomously determine current anomaly threshold based on the prediction error vector of the model. If the prediction error exceeds the anomaly threshold, the sensor malfunctions. Otherwise, the sensor operates normally. Through a comprehensive case study, we demonstrate that the adaptive threshold selection algorithm can fully utilize the forecasting ability of the STALSTM-seq2seq model, and thereby ensure the accuracy of sensor FDD. Overall, the online sensor FDD method achieves an average F1 score of 0.8473. The research shows that the proposed method provides an effective and robust solution to timely and accurately identify sensor faults for district heating substations.

A new spectral distance based on adaptive selection algorithm for non-stationary time series

We propose a distance metric to quantify the dissimilarity between time series from the perspective of the frequency domain, especially non-stationary time series, called the Wasserstein Hilbert marginal spectral distance (WHMS). It calculates the Wasserstein distance between the Hilbert marginal spectral (HMS) of the time series, where HMS is the integral of Hilbert spectrum. Hilbert spectrum is obtained by the Hilbert transform performed on the intrinsic mode functions (IMFs) generated after the Empirical Mode Decomposition (EMD) operation. On this basis, we propose an IMF adaptive selection algorithm to improve the feature extraction accuracy of the HMS, free from a large number of prior experiments to set the threshold. We demonstrate that WHMS distance can be used as a general measure of time series from both theoretical and practical aspects. Combined with multidimensional scaling (MDS), we comprehensively evaluate the proposed method through simulation experiments and empirical data. The results confirm the good applicability of the WHMS distance combined with the IMF adaptive selection algorithm, which can distinguish different types of complex systems, is robust to noise, and is superior to Wasserstein–Fourier (WF) distance, Wasserstein–Fourier distance with short time Fourier transform (STFT), Euclidean distance and Chebyshev distance.

Target selection in shrinkage estimation of covariance matrix: A structural similarity approach

The shrinkage estimator of a high-dimensional covariance matrix relies on a preassigned target matrix during data processing. This paper provides an adaptive approach for selecting the optimal Toeplitz target matrix. We discover a sufficient and necessary condition for characterizing the two kinds of target matrices with the Toeplitz structure, and we propose an adaptive selection algorithm by measuring the similarity between the data and the Toeplitz structure. Numerical simulations and an empirical study on monetary funds verify the effectiveness of the selection approach.

Parameter selection of suppressed relative entropy fuzzy c-means clustering algorithm

The relative entropy fuzzy c-means (REFCM) clustering algorithm improves the robustness of the fuzzy c-means (FCM) algorithm against noise. However, its increased complexity results in slower convergence. To address this issue, we have proposed a suppressed REFCM (SREFCM) algorithm, in which a constant suppression rate, α, is selected. However, in cases where external factors, such as changes in the data structure, are present, relying on a fixed α value may result in a decline in algorithm performance, which is clearly unsuitable. Therefore, the adaptive selection of parameters is a critical step. Based on the data structure itself, this paper proposes an algorithm for adaptive parameter selection utilizing partition entropy coefficient and alternating modified partition coefficient, and compares it to six parameter selection algorithms based on generalized rules: θ′ type, ρ type, β type, τ type, σ type and ξ type. Empirical findings indicate that adapting parameters can enhance the partitioning capability of the algorithm while ensuring a rapid convergence rate.

Application of IoT technology in cyber security prevention system

Abstract In the process of gradually expanding the scale of computer networks and the design of network systems becoming more and more complex, people pay more and more attention to the construction of network security protection systems. Starting from the blockchain encryption technology, the article establishes the authentication and access management key based on the elliptic curve encryption algorithm and combines the maximum entropy model with the hidden Markov model to construct the MEMM for intrusion detection of network security. Based on the effective signal-to-noise ratio model of the network channel, an adaptive channel selection strategy based on the UCB algorithm is proposed. The IoT security prevention system is built based on IoT technology, and each functional module of the system is designed. The system’s authentication security, network intrusion detection, adaptive channel selection, and concurrency performance were tested after the design was completed. The encryption operation time of the ECC algorithm was improved by 41.53% compared to the RSA algorithm, the average time of the MEMM network intrusion detection was 41.54ms, and the false alarm rate of the intrusion detection was kept below 16.5%. The average packet collection rate of the nodes in the adaptive channel selection algorithm is 90.98%. The maximum system throughput is up to 62.19MB, and the extreme difference in data volume between different nodes is only 38 entries. Constructing a network security prevention system based on IoT technology and combining multiple encryption techniques can ensure the secure transmission of network data.

An Adaptive Feature Selection Algorithm for Student Performance Prediction

An Adaptive Feature Selection Algorithm for Student Performance Prediction

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

A method based on improved ant colony algorithm feature selection combined with GWO-SVR model for predicting chlorophyll-a concentration in Wuliangsu Lake.

Chlorophyll-a (Chl-a) is an important parameter in water bodies. Due to the complexity of optics in water bodies, it is difficult to accurately predict Chl-a concentrations in water bodies by current traditional methods. In this paper, using Sentinel-2 remote sensing images as the data source combined with measured data, taking Wuliangsu Lake as the study area, a new intelligent algorithm is proposed for prediction of Chl-a concentration, which uses the adaptive ant colony exhaustive optimization algorithm (A-ACEO) for feature selection and the gray wolf optimization algorithm (GWO) to optimize support vector regression (SVR) to achieve Chl-a concentration prediction. The ant colony optimization algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GWO-SVR model is built by optimizing SVR using GWO with the selected feature bands as input and comparing it with the traditional SVR model. The results show that the usage of feature bands selected by the presented A-ACEO algorithm as inputs can effectively reduce complexity and improve the prediction performance of the model, under the condition of the same model, which can provide valuable references for monitoring the Chl-a concentration in Wuliangsu Lake.

Research on Adaptive Transmit Diversity Strategy for Reducing Interference in Underwater Optical Multi-Beam Non-Orthogonal Multiple Access Systems

With the rapid development of the underwater Internet of Things (IoT), the number of underwater communication nodes is rapidly increasing. The access capacity of a traditional multi-antenna communication system is limited by the number of transmitting antennas, and multi-beam communication systems using non-orthogonal multiple access (NOMA) technology can enhance the access capacity of the system. However, this can lead to serious inter-beam and intra-beam interference. To address the severe issues of inter-beam and intra-beam interference in underwater multi-beam NOMA systems, we propose an adaptive transmit diversity strategy. We design an algorithm for adaptive selection and merging beams based on the degree of interference between beams in space, which merges LED beams with high interference. Diversity technology is used to reduce interference between beams, and spatial multiplexing is still performed between LED groups with low interference. Within the same beam, we use an OFDM-NOMA scheme to match and group the users. Signals from different user groups are sent through different subcarriers to improve resource utilization. This enhances access capacity while reducing NOMA inter-user interference. Simulation results show that the bit error rate (BER) of users with the adaptive transmit diversity strategy satisfies the forward error correction (FEC) limits in the presence of high inter-beam interference and has a better reachable rate and BER performance compared to the multi-beam access system without interference management. We also analyze the system BER performance of the proposed strategy in the multi-user case, and the BER of all 32 access nodes are lower than the FEC threshold at a communication distance of 5 m. This demonstrates that the strategy can effectively reduce the interference of the multi-beam NOMA system.

An Adaptive Mode Selection Mechanism for Hybrid Active and Passive Communications

In this paper, we propose an adaptive mode selection algorithm for a hybrid active and passive communication system, where the communication paradigm of the wireless-powered communication network (WPCN) is referred to as the active mode, while ambient backscatter (AB) is applied for the passive mode. Specifically, the proposed algorithm switches between the active and passive modes by simultaneously considering both energy harvesting and signal-to-noise ratio (SNR) conditions. Besides, to further improve the system's reliability, we adopt the rate-adaptive technique in passive mode, allowing devices to choose an appropriate rate for data transfer when performing backscattering. We also introduce analytical models to characterize the performance of the proposed mode selection algorithm. Simulation results validate the analysis and indicate that the proposed mode selection algorithm has the advantage in terms of both success probability and average throughput.

Data Augmentation-Based Manufacturing Quality Prediction Approach in Human Cyber-Physical Systems

Abstract The vigorous development of the human cyber-physical system (HCPS) and the next generation of artificial intelligence provide new ideas for smart manufacturing, where manufacturing quality prediction is an important issue in the manufacturing system. However, the small-scale data from humans in emerging HCPS-enabled manufacturing restrict the development of traditional quality prediction methods. To address this question, a data augmentation-based manufacturing quality prediction approach in human cyber-physical systems is proposed in this paper. Specifically, a Data Augmentation-Gradient Boosting Decision Tree (DA-GBDT) model is developed for quality prediction under the HCPS context. In addition, an adaptive selection algorithm of data augmentation rate is designed to balance the trade-off between the training time of the prediction model and the prediction accuracy. Finally, the experimental results of automobile covering products demonstrate that the proposed method can improve the average prediction error of the model compared with the prevailing quality prediction methods. Moreover, the predicted quality information can provide guidance for product optimization decisions in smart manufacturing systems.