Reduction In Mean Square Error Research Articles

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified “gold standard” edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on “gold standard” regulatory edges from previous experimental work with non-linear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals.

A Short-Term Forecasting Method for High-Frequency Broadcast MUF Based on LSTM

This paper proposes a short-term forecasting method for high-frequency broadcast Maximum Usable Frequency (MUF) based on Long Short-Term Memory (LSTM) to meet the demand for refined and precise high-frequency broadcast coverage. Based on the existing infrastructure of broadcast and television stations, we established an experimental verification system to collect data for approximately three years. Two links were selected based on data quality: Urumqi to Lhasa and Lanzhou to Lhasa. A short-term forecast of MUF was conducted using the data from these two links. Comparison and analysis were conducted between the forecasting results of our model and those of the REC533 model. Our proposed method outperforms the REC533 forecasting results overall, with a reduction in root mean square error (RMSE) of 0.66 MHz and an improvement in forecast accuracy of 14.77%. The comparison result demonstrates the feasibility and accuracy of our model.

Nonlinear Model Predictive Control—Cross-Coupling Control with Deep Neural Network Feedforward for Multi-Hydraulic System Synchronization Control

This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm. Furthermore, a novel disturbance compensator based on the system's inverse model is introduced to effectively handle disturbances, encompassing unmodeled errors and noise. The proposed innovative controller, known as nonlinear model predictive control—cross-coupling control with deep neural network feedforward (NMPC-CCC-DNNF), is designed to minimize synchronization errors and counteract the impact of disturbances. The stability of the control system is rigorously demonstrated. Finally, simulation results underscore the efficacy of the NMPC-CCC-DNNF controller, showcasing a remarkable 60.8% reduction in synchronization root mean square error (RMSE) compared to other controllers, reaching up to 91.1% in various simulations. These results affirm the superior control performance achieved by the NMPC-CCC-DNNF controller.

An Improved LKF Based SOC Estimation and a Power Management Strategy to Enhance the Cycle Life of BES in a Microgrid

Nowadays, utility grid experiences significant challenges due to increased adoption of renewable energy resources (RES). The intermittency of RES is ridden by using battery energy storage (BES). The system control often aims at maximizing self-consumption, neglecting its effect on effective life of BES. A power management strategy (PMS) is proposed in this work that helps to increase the extraction of maximum energy without compromising on operational robustness of BES for grid applications. Maximum depth of discharge (DOD) is limited to a certain mean state of charge (SOC) to improve the cycle life of BES. A lifecycle model is derived for BES to compute a compensating term for estimating the DOD limit after each cycle. An enhancement to existing Linear Kalman filtering (LKF) technique is also presented in this work for SOC estimation of BES. A significant reduction in root mean square error (RMSE) is achieved using improved LKF-based SOC estimation technique. A second-order generalized integrator with a pre-filter based frequency locked loop (SOGI-WPF-FLL) controls microgrid under abnormal utility grid and load conditions. Resynchronization of microgrid with utility grid is demonstrated using SOGI-WPF-FLL filter without causing any maloperations. System is simulated under various operating conditions in MATLAB/Simulink environment and validated on a real-time OP5700-based test-bench.

A Deep-Learning-Based Error-Correction Method for Atmospheric Motion Vectors

Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions.

Forecast of coal consumption in salt lake enterprises based on temporal gated recurrent unit network with squeeze-and-excitation attention

Accurate coal consumption forecasting is key to improving energy supply efficiency and strategic planning. However, current forecasting methods mainly emphasize temporal variations, ignoring key influencing factors such as seasonality and environmental changes. In addition, the complex structure of neural networks complicates the selection of ideal hyperparameters. To address these issues, this study proposes a novel squeezing-and-excitation temporal gated recurrent unit (SE-TGRU) method. By incorporating squeezing-and-excitation attention modules, this method autonomously assesses the relative significance of diverse historical time points, unveiling their correlations to capture essential information about coal consumption. Notably, a novel reverse learning-based dwarf mongoose optimization (RL-DMO) algorithm is devised to comprehensively analyze data within a specified time window. This augmentation enhances the ability of the model to discern data anomalies with heightened sensitivity, enabling corresponding adjustments during the training process. Finally, the efficacy of the proposed method in coal consumption forecasting is verified through extensive experimentation using actual data sourced from the procurement department of a salt lake enterprise. The experimental results demonstrate that the proposed method exhibits an 11 % improvement in the R-squared (R2) indicator and an 18 % reduction in the root mean square error (RMSE) compared to the baseline method.

A truncated Gaussian distribution based multi-scale segment-wise fusion transformer model for multi-step commodity price forecasting

Accurately forecasting commodity price trends is crucial for producers, market participants, and related enterprises to make informed decisions regarding production planning and scheduling. However, achieving high accuracy in multi-step forecasting poses significant challenges due to the unique financial characteristics inherent in commodities. Thus, this paper proposes a novel truncated Gaussian distribution based multi-scale segment-wise fusion Transformer for multi-step commodity price forecasting. First, a multi-scale segment-wise fusion module, which capture the time dependencies from different time granularity, is designed to describe the time-varying trend characteristics of commodity prices. Second, considering the characteristics of price range fluctuation and truncation, a truncated Gaussian distribution is introduced to describe price uncertainty. Last, to evaluate the proposed method’s effectiveness, extensive experiments are conducted using real data on energy chemical product prices. The experimental results demonstrate that the proposed method accurately captures price change trends and effectively estimates price uncertainty. Compared to the widely adopted Autoformer, our approach achieves approximately 30% reductions in both root mean square error (RMSE) and mean absolute error (MAE) metrics. Additionally, it exhibits certain advantages over the current state-of-the-art (SOTA). In the 20-step and 60-step multi-step prediction tasks, the proposed method achieves RMSE values of 91.18 and 142.94, respectively, surpassing the current SOTA. The introduced research framework provides valuable insights for decision-makers engaged in analyzing and forecasting commodity markets. The code is available on https://github.com/dean-ob/TGD-MSSF.

[Low-dose CT reconstruction based on high-dimensional partial differential equation projection recovery].

We propose a low-dose CT reconstruction method using partial differential equation (PDE) denoising under high-dimensional constraints. The projection data were mapped into a high-dimensional space to construct a high-dimensional representation of the data, which were updated by moving the points in the high-dimensional space. The data were denoised using partial differential equations and the CT image was reconstructed using the FBP algorithm. Compared with those by FBP, PWLS-QM and TGV-WLS methods, the relative root mean square error of the Shepp-Logan image reconstructed by the proposed method were reduced by 68.87%, 50.15% and 27.36%, the structural similarity values were increased by 23.50%, 8.83% and 1.62%, and the feature similarity values were increased by 17.30%, 2.71% and 2.82%, respectively. For clinical image reconstruction, the proposed method, as compared with FBP, PWLS-QM and TGV-WLS methods, resulted in reduction of the relative root mean square error by 42.09%, 31.04% and 21.93%, increased the structural similarity values by 18.33%, 13.45% and 4.63%, and increased the feature similarity values by 3.13%, 1.46% and 1.10%, respectively. The new method can effectively reduce the streak artifacts and noises while maintaining the spatial resolution in reconstructed low-dose CT images.

Improved general unit hydrograph model for dam-break flood hydrograph

Dam breaks pose significant risks, necessitating simple and effective predictive models for disaster management. This study aims to enhance the understanding of dam-break flood hydrographs, an aspect less emphasized in traditional research focusing mainly on peak discharge and flow depth values. Employing a series of experiments, an improved general unit hydrograph model was used to analyze both discharge and flow depth hydrographs. This model is then critically compared with the standard gamma and log-normal asymmetric distribution models. The results reveal that the improved unit hydrograph model significantly outperforms these traditional models, particularly in predicting the rising stage of discharge hydrographs, evidenced by a 65 % reduction in root mean square error. Comparison of this model with real dam failures in natural river valleys shows its potential to be applied to changes in river slope and different dam-break rates. These advancements present a more precise methodology for predicting dam-break flood hydrographs, offering benefits for disaster management strategies.

Identification and Positioning of Abnormal Maritime Targets Based on AIS and Remote-Sensing Image Fusion.

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements.

A transformer-based method for correcting significant wave height numerical forecasting errors

Accurate significant wave height (SWH) forecasting is essential for various marine activities. While traditional numerical and mathematical-statistical methods have made progress, there is still room for improvement. This study introduces a novel transformer-based approach called the 2D-Geoformer to enhance SWH forecasting accuracy. The 2D-Geoformer combines the spatial distribution capturing capabilities of SWH numerical models with the ability of mathematical-statistical methods to identify intrinsic relationships among datasets. Using a comprehensive long time series of SWH numerical hindcast datasets as the numerical forecasting database and ERA5 reanalysis SWH datasets as the observational proxies database, with a focus on a 72-hour forecasting window, the 2D-Geoformer is designed. By training the potential connections between SWH numerical forecasting fields and forecasting errors, we can retrieve SWH forecasting errors for each numerical forecasting case. The corrected forecasting results can be obtained by subtracting the retrieved SWH forecasting errors from the original numerical forecasting fields. During long-term validation periods, this method consistently and effectively corrects numerical forecasting errors for almost every case, resulting in a significant reduction in root mean square error compared to the original numerical forecasting fields. Further analysis reveals that this method is particularly effective for numerical forecasting fields with higher errors compared to those with relatively smaller errors. This integrated approach represents a substantial advancement in SWH forecasting, with the potential to improve the accuracy of operational SWH forecasts. The 2D-Geoformer combines the strengths of numerical models and mathematical-statistical methods, enabling better capture of spatial distributions and intrinsic relationships in the data. The method's effectiveness in correcting numerical forecasting errors, particularly for cases with higher errors, highlights its potential for enhancing SWH forecasting accuracy in operational settings.

Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning

As autonomous vehicles (AVs) become more common on public roads, their interaction with human-driven vehicles (HVs) in mixed traffic is inevitable. This requires new control strategies for AVs to handle the unpredictable nature of HVs. This study focused on safe control in mixed-vehicle platoons consisting of both AVs and HVs, particularly during longitudinal car-following scenarios. We introduce a novel model that combines a conventional first-principles model with a Gaussian process (GP) machine learning-based model to better predict HV behavior. Our results showed a significant improvement in predicting HV speed, with a 35.64% reduction in the root mean square error compared with the use of the first-principles model alone. We developed a new control strategy called GP-MPC, which uses the proposed HV model for safer distance management between vehicles in the mixed platoon. The GP-MPC strategy effectively utilizes the capacity of the GP model to assess uncertainties, thereby significantly enhancing safety in challenging traffic scenarios, such as emergency braking scenarios. In simulations, the GP-MPC strategy outperformed the baseline MPC method, offering better safety and more efficient vehicle movement in mixed traffic.

A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation

Wind power prediction holds significant importance for the stable operation of power systems and the enhancement of energy utilization efficiency. Interval prediction, capable of providing more uncertain information, has garnered widespread attention. In this study we have designed a hybrid prediction framework for both deterministic and interval prediction. Initially, the original wind power data undergo cleaning using the quartile method and the fuzzy C-means (FCM) clustering algorithm. This approach not only eliminates scattered outliers but also identifies concentrated outliers more effectively. Subsequently, five distinct single prediction models were employed for point predictions. The critic weight method was applied to yield point forecasting results with higher prediction accuracy. Nonparametric kernel density estimation (KDE) and normal distribution (ND) were incorporated to calculate the prediction interval (PI) under varying confidence levels. Finally, an improved northern goshawk optimization (INGO) algorithm was proposed, integrating three enhancement methods: levy flight, sinusoidal mapping, and a reverse learning strategy. This optimization aims to fine-tune the weight coefficients of the aforementioned two interval prediction methods. The resulting PI overcomes the limitations of a single-interval prediction model. To effectively demonstrate the forecasting performance of the proposed prediction framework, two datasets from a wind turbine in northwest China were selected for simulation verification. In deterministic prediction, the designed hybrid forecast model demonstrates a reduction in root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) by 8.5%, 16.98%, and 11.49%, respectively, compared to other single prediction models. In interval prediction, the prediction interval coverage probability (PICP) is enhanced, while the prediction interval normalized average width (PINAW) is effectively reduced. The designed interval prediction model can reduce the comprehensive evaluation index coverage width-based criterion (CWC) by 10.51%, 9.62%, and 12.95% on average at 95%, 90%, and 80% confidence levels, respectively. Simulation results verified the validity and feasibility of the proposed prediction framework.

Forecasting price in a new hybrid neural network model with machine learning

A key aspect of asset investment and risk management is the study of forecasting stock prices. We investigate the machine learning stock price prediction in a new hybrid neural network model and put forth a forecasting method based on machine learning, composite data preprocessing method and the proposed new neural network model. To address the challenge of predicting stock prices in the face of market complexity and noise, we use the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and the Savitzky–Golay (SG) filter to de-noise and enhance the data, and employ a neural network with three convolutional layers and a long short-term memory (LSTM) layer that enables it to capture complex temporal patterns in the data. We propose a new hybrid neural network prediction model (CEEMDAN-S-C-LSTM) and adopt a machine learning approach to compare it with the benchmark model using CSI 300 index data. The empirical results validate the effectiveness of the frequency decomposition algorithm and the convolutional layer, and demonstrate that our proposed model outperforms the benchmark model. Compared to the best benchmark model, the CEEMDAN-S-C-LSTM model proposed in this study demonstrates a significant improvement in performance. Specifically, it shows a 45.33% reduction in mean absolute error (MAE), 43.44% reduction in root mean square error (RMSE), 45.01% reduction in mean absolute percentage error (MAPE), and 3.90% improvement in coefficient of determination (R2). The study also explores the effect of different numbers of convolution layers and SG filters on the hybrid model. Our research expands on the use of neural networks and machine learning, offering a novel technical approach to making investment decisions and managing risks in financial systems.

Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Blast furnace high-proportion pellet smelting stands as a pivotal pathway towards achieving low-carbon and environmentally friendly ironmaking. However, challenges in pellet ore production, particularly through the rotary kiln method, have surfaced due to the scarcity of iron ore resources, leading to issues like “ringing” that hamper production efficiency and energy conservation. Effectively predicting and optimally controlling the temperature field within rotary kilns emerges as a crucial strategy to address this pressing challenge. In a pioneering effort, this paper introduces a data-driven machine learning methodology designed to expedite the prediction of temperature field distributions within rotary kilns. Through a detailed analysis of field data from the factory and the insights of experts, the paper has discussed on the key factors influencing the temperature field dynamics in rotary kilns. Leveraging Computational Fluid Dynamics (CFD), a numerical simulation model was established to replicate rotary kiln oxy-coal combustion scenarios. A comprehensive dataset was generated using high-throughput computing technology, encompassing a variety of conditions such as coal injection rates, return air temperature, return airflow volume, fixed carbon content of pulverized coal, and volatile fraction of pulverized coal. This dataset included a total of 625 distinct cases. Subsequently, employing machine learning techniques including random forest (RF) and genetic algorithm-optimized Random forest (GA-RF), predictive models were developed for rotary kiln temperature field dynamics. A rigorous comparative analysis was conducted to explore the impact of different machine learning dataset sizes on the predictive accuracy of the models. The results of this analysis demonstrate that, enhanced by the genetic algorithm of RF, it surpasses the performance of the standard RF approach. This enhancement resulted in a significant improvement in prediction accuracy, as evidenced by the reduction of the Root Mean Square Error (RMSE) from 25.0879 to 17.5330 and the Mean Absolute Error (MAE) from an initial value of 6.5600 to 4.7661. Furthermore, the novel soft-measurement method introduced in this paper significantly reduces simulation time by several orders of magnitude. It has diminished the response time from the traditional industrial methods, which took two to three weeks, to approximately 10 s, as compared to conventional CFD approaches. Remarkably, this approach successfully preserves a high level of accuracy. The implications of these research findings are far-reaching,providing novel insights into the acceleration of CFD simulation calculations and the design of soft measurement sensors customized for black-box reactors in industrial processes.

DIGITAL ELEVATION MODEL CORRECTION IN URBAN AREAS USING EXTREME GRADIENT BOOSTING, LAND COVER AND TERRAIN PARAMETERS

Abstract. The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artefacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30-metre DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The corrections achieved significant accuracy gains which are competitive with other proposed methods. There was a 46–53% reduction in the root mean square error (RMSE) of Copernicus DEM, and a 72–73% reduction in the RMSE of AW3D DEM. These results showcase the potential of gradient-boosted decision trees for enhancing the quality of global DEMs, especially in urban areas.

Efficient very large-scale integration architecture design of proportionate-type least mean square adaptive filters

The effectiveness of adaptive filters are mainly dependent on the design techniques and the algorithm of adaptation. The most common adaptation technique used is least mean square (LMS) due its computational simplicity. The application depends on the adaptive filter configuration used and are well known for system identification and real time applications. In this work, a modified delayed μ-law proportionate normalized least mean square (DMPNLMS) algorithm has been proposed. It is the improvised version of the µ-law proportionate normalized least mean square (MPNLMS) algorithm. The algorithm is realized using Ladner-Fischer type of parallel prefix logarithmic adder to reduce the silicon area. The simulation and implementation of very large-scale integration (VLSI) architecture are done using MATLAB, Vivado suite and complementary metal–oxide– semiconductor (CMOS) 90 nm technology node using Cadence register transfer level (RTL) Genus Compiler respectively. The DMPNLMS method exhibits a reduction in mean square error, a higher rate of convergence, and more stability. The synthesis results demonstrate that it is area and delay effective, making it practical for applications where a faster operating speed is required.

Research on the upgrade and maintenance method of apple soluble solids content models

AbstractIn this paper, 3 years (2020, 2021, and 2022) of Bingtangxin apples were used to study the upgrade and maintenance method of apple soluble solids content (SSC) model using near infrared (NIR) spectroscopy. Modeling by partial least squares (PLS) and one‐dimensional convolutional neural network (1D‐CNN) algorithm. The “base model” is built with apples in 2020 and then upgrade and maintain it. The upgraded model is used to predict the sample in 2021 and 2022. Three methods were used to upgrade and maintenance the model, which are based on updated data, direct correction‐based method, and similar band‐based method. Among the three upgrade methods, the model maintenance method using updated data had the greatest improvement in prediction correlation coefficients (Rp) in both years, and both obtained good predictions. The use of direct correction resulted in the greatest reduction in the root mean square error of prediction (RMSEP) in both years. However, the Rp of the model is not improved much by using the direct correction method. The model maintenance method using similar bands combined with different modeling methods also gives good prediction results. Finally, after comparison, the model maintenance method of selecting similar bands is preferred. In the case of limited improvement in the effect of using the similar band method, the model is maintained using the method of adding updated data. The investigation of model upgrade and maintenance methods can reduce human and material consumption and make apple SSC models more versatile.Practical applicationsNIR technology is the first choice for online fruit quality inspection, but its inspection process requires constant upgrading and maintenance with models. The quality of apples varies from year to year, and the model built with samples from a single year does not work well in predicting samples from other years, resulting in the need to upgrade and maintain the fruit quality detection model. However, sometimes, although a large amount of experimental material is lost, it still results in a generic calibration model that is too cumbersome and leads to poor prediction results. At this time, finding an effective model upgrade and maintenance method can both reduce the sample consumption and make the model prediction accuracy improve, and also reduce the model complexity and make the maintained model more robust.

Optimising barrier placement for intrusion detection and prevention in WSNs.

This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method's effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.