An efficient framework for high-accuracy sorting of retired Li-ion batteries of electric vehicles

Passive acoustic monitoring captures spatiotemporal dynamics of urban zoo soundscapes.

Kernel-based multivariate statistical process control (K-MSPC) extends classical MSPC by capturing nonlinear dependence through kernel principal component analysis (K-PCA). In practice, however, K-MSPC performance depends strongly on the choice of kernel and hyperparameters, which are often selected using non-procedural and time-consuming searches (e.g., grid/line search) that do not scale to richer parameterisations. We propose a fully procedural kernel-learning framework for K-MSPC inspired by Kernel Flows, in which kernel parameters are learned using stochastic subsampling and gradient-based optimisation. Because MSPC relies on a truncated K-PCA subspace, we optimise kernel parameters through an intermediate kernel principal component regression (K-PCR) discrimination objective that is structurally aligned with the latent-variable representation used by monitoring statistics. Building on this framework, we demonstrate extensions that become computationally feasible: learning variable-wise (additive) kernel parameters and learning kernel combinations to adapt the kernel family to data. The approach is evaluated on the Tennessee Eastman Process benchmark. Results show improved fault detection, including difficult faults that are poorly detected by standard K-PCA baselines, while providing diagnostic insights via variable contribution and attribution analyses. The proposed optimisation requires access to faulty (or simulated faulty) data and thus represents a supervised/semi-supervised calibration strategy complementary to classical unsupervised MSPC. • Kernel MSPC is performant to detect challenging faults, if optimisation is done right. • Kernel MSPC optimisation can be achieved through optimising Kernel PCR. • A Kernel Flows-inspired procedure is appropriate for optimising individual-parameter kernels. • Fitting individual kernel parameters brings performance improvement and diagnostics.

There are too many variables in the thermal and humidity monitoring of agricultural product storage environments and they are highly correlated, making it difficult for traditional methods to effectively extract key information. There are problems such as insufficient accuracy, data redundancy, and large monitoring errors. In practical applications, the current indicator system cannot accurately capture subtle changes in environmental conditions, which affects warehouse management decisions, resulting in increased monitoring accuracy and production costs, making it difficult to quickly respond to rapid environmental changes. This paper uses Principal Component Analysis (PCA) to optimize monitoring indicators and aims to improve monitoring accuracy and reduce product losses by reducing the dimension of indicators and eliminating redundant data. First, a preliminary monitoring indicator system that includes five dimensions, namely temperature and humidity conditions, gas environment, air flow, storage structure, and energy consumption management, is established, and the corresponding monitoring data are collected. Then, the PCA method is used to reduce the dimension of each indicator, eliminate redundant data, and extract the core principal components that best reflect the changes in heat and humidity in the storage environment. Finally, Kernel Principal Component Analysis (KPCA) is used to optimize the principal component extraction and improve the monitoring accuracy. The results show that the optimized monitoring system significantly improves the monitoring accuracy, and the overall average error improvement is 49.06%. The conclusion shows that using KPCA to optimize monitoring indicators can improve the accuracy of warehouse environment monitoring and provide more scientific decision support for warehouse management.

Reservoir prediction and mixed probability kernel principal component analysis based on Small-Sample Data

Abstract Due to the delay in the laboratory results of the melt flow index (MFI) for polypropylene (PP) batch processes, production personnel are unable to observe MFI changes in a timely manner and guide the production of the next batch. To obtain the MFI values in a timely manner, a prediction method based on time‐series feature matching for MFI is proposed. First, the process mechanism is analyzed to determine the process variables affecting MFI, and the corresponding historical data is collected. Then, in the offline training phase, expert rules based on the process mechanism are constructed to extract historical batch data and integrate batch MFI values. Next, for the extracted long time‐series data, it is transformed into a spatiotemporal matrix, and singular value decomposition (SVD) is used to extract features. In the online prediction phase, considering the peak characteristics of hydrogenation times, a similarity calculation method using a pseudo‐4th‐order central moment (P4CM) is proposed. This is combined with Euclidean distance to compare the similarity between online and historical batches for prediction. Finally, a comparative experiment with Kernel principal component analysis (KPCA) is conducted, demonstrating the feasibility of the proposed prediction method.

The load of the elastic tooth drum-type pepper harvester is a key parameter affecting harvesting efficiency and quality. Real-time analysis and prediction of drum load are crucial for stabilizing harvester operation and optimizing performance. Existing research focuses on either machine vision-based image analysis, which is difficult to collect in the field, or parameter-mapping methods, which suffer from time lag. This study proposes a GARCH-KPCA-ATLSTM method for load prediction, combining the generalized autoregressive conditional heteroskedasticity (GARCH) model, kernel principal component analysis (KPCA), and attention-enhanced long short-term memory (ATLSTM). EMD is first applied to denoise and reconstruct the load signal, removing mechanical vibration and other interferences. Conditional heteroskedasticity is confirmed, and the GARCH series (one symmetric and three asymmetric models) is introduced to extract fluctuation features. KPCA reduces dimensionality, removing redundant information and saving 2.91 s in computation while slightly improving accuracy. Additive attention in LSTM emphasizes critical information, enhancing learning of nonlinear relationships and further improving prediction. Comparative experiments demonstrate the model’s reliability. The method achieves RMSE = 0.911, MAE = 0.682, MBE = −0.025, MAPE = 1.147%, R2 = 0.968, with a runtime of 2.023 s, confirming high accuracy and stability. This study provides a theoretical and technical foundation for real-time load prediction of pepper harvesters.

Abstract To address the challenge of low adaptability in distribution network planning caused by significant regional differences in electricity consumption, this paper proposes a distribution network planning method based on a self‐adjusting parameter double deep Q‐network (SAP‐DDQN). First, considering the disparities in electricity consumption across different locations, distribution areas are classified according to load density. For each type of distribution area, appropriate calibration criteria are selected, and indicator models are established covering reliability, economics and flexibility criteria. Subsequently, key indicators under each criterion are extracted using the analytic hierarchy process and kernel principal component analysis. A deep reinforcement learning model for distribution network planning is then developed to achieve rapid optimization of the network configuration. Finally, the effectiveness of the proposed method is validated using a 24‐node distribution network and an actual 84‐node urban distribution network in China. Test results demonstrate that the proposed method can accurately select the optimal network configuration for each distribution area and provide a specific planning scheme.

This study employs a comprehensive electrochemical methodology to evaluate the electrochemical kinetics of Ni-doped CuO nanoparticles synthesized via the solution combustion method. Working electrodes were prepared by coating a composite slurry onto nickel foam and tested in a three-electrode system with a 3 M KOH electrolyte. Cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS) were employed to investigate charge storage mechanisms and interfacial dynamics. A key advancement is the use of the R2-Window Linear Discharge (R2WLD) method to segment GCD curves and extract linear and pseudocapacitive discharge regions with high fidelity. Additionally, nonlinear Kernel Principal Component Analysis (KPCA) was employed to classify electrochemical regimes based on scan rate behavior. To interpret the evolution of discharge symmetry, a 1D Ising model was adapted to model energetic state transitions under increasing perturbation. These findings underscore the critical role of defect and lattice engineering in tuning the functional response of correlated oxide systems.

Abstract The Nelson–Siegel model is widely used in fixed income markets to produce yield curve dynamics. The multiple time-dependent parameter model conveniently addresses the level, slope, and curvature dynamics of the yield curves. In this study, we present a novel state-space functional regression model that incorporates a dynamic Nelson–Siegel (DNS) model and functional regression formulations applied to a multi-economy setting. This framework offers distinct advantages in explaining the relative spreads in yields between a reference economy and a response economy. To address the inherent challenges of model calibration, a kernel principal component analysis is employed to transform the representation of functional regression into a finite-dimensional, tractable estimation problem. A comprehensive empirical analysis is conducted to assess the efficacy of the functional regression approach, including an in-sample performance comparison with the DNS model. We conducted the stress testing analysis of the yield curves’ term structure within a dual economy framework. The bond ladder portfolio was examined through a case study focused on spread modeling using historical data for US Treasury and UK bonds.

To address the challenges of difficult feature extraction and suboptimal parameter configuration for cyclotron ion source fault diagnosis in complex environments, this study proposes an intelligent diagnostic framework integrating Kernel Principal Component Analysis (KPCA), an Improved Sparrow Search Algorithm (ISSA), and a Support Vector Machine (SVM). The KPCA algorithm is employed for dimensionality reduction to handle the highly nonlinear nature of fault data. Regarding algorithmic evolution, the basic SSA is enhanced by integrating dynamic weights, opposition-based learning, and Cauchy mutation strategies, which effectively overcome the diagnostic bottlenecks inherent in cyclotron scenarios. Furthermore, the ISSA facilitates the global adaptive optimization of key SVM parameters, eliminating the stochasticity of empirical tuning and fundamentally enhancing the model's robustness. Experimental results across 30 independent tests demonstrate that the KPCA-ISSA-SVM model achieves an average accuracy of 97.6% in multi-class fault detection. Compared with other classic diagnostic models, the proposed framework exhibits superior precision and stability, providing an effective technical approach with significant engineering value for the precise monitoring of ion source statuses.

Personalized itinerary planning for large-scale passengers under resource constraints is a critical challenge in enhancing the operational efficiency and service quality of cruise tourism. Traditional clustering methods, which primarily rely on geometric similarity, often fail to address the intricate coupling between passenger preferences and finite venue capacities, lacking predictive capability for the ultimate planning quality. To overcome these limitations, this study proposes a novel bio-inspired data-driven hybrid optimization framework for the cruise itinerary planning task unit partition. The framework innovatively integrates a Genetic Balanced Clustering Algorithm (GBCA) for multi-objective passenger grouping, Kernel Principal Component Analysis (KPCA) for feature extraction from preference data, an improved Adaptive Spiral Flying Sparrow Search Algorithm (ASFSSA) for hyperparameter optimization, and a Kernel Extreme Learning Machine (KELM) for data-driven prediction of itinerary planning quality. This synergy enables the framework to dynamically allocate venue capacities based on group preferences and optimize partitioning towards maximizing overall benefits, ensuring load balance and fairness. Extensive experiments on simulated cruise scenarios demonstrate that the proposed framework significantly outperforms conventional methods, improving segmentation quality by at least 40% while exhibiting superior convergence speed and stability. This work provides a scalable, intelligent solution for complex resource-constrained scheduling problems, showcasing the effective application of bio-inspired data-driven methodologies in engineering optimization.

Abstract Accurate prediction of industrial equipment remaining useful life requires reliable degradation labels and models capable of capturing multi-scale temporal dynamics and interactions among heterogeneous sensors. In practical environments with variable operating conditions, strong noise, and heterogeneous degradation behaviours, widely used single-threshold labeling schemes induce label shift and phase misalignment across units, while conventional fusion architectures operate at fixed temporal scales and treat sensor channels largely independently, limiting robustness and prediction accuracy. To address these issues, this paper proposes an adaptive RUL prediction framework, AD-TCN-MSCDIM, which couples an unsupervised dynamic label recalibration strategy with a multi-scale temporal--sensor modeling backbone. First, the proposed MsTD-FIR-Rec method performs parallel multi-scale time-delay embedding and zero-phase finite impulse response filtering, combined with kernel principal component analysis based slope calibration, to identify unit-wise failure turning points and reconstruct phase-consistent RUL labels. Building on these refined targets, a Gated Multi-Scale U-Net architecture with a Multi-Scale Channel-Aware U-module adaptively fuses global temporal context and channel-wise information, while a dual-branch adaptive dilated causal temporal convolutional decoder jointly captures long-term degradation trends and local fluctuations. Experiments on the C-MAPSS benchmark and the more realistic, noise-contaminated N-CMAPSS dataset show that the proposed framework achieves overall better performance to representative baseline methods in terms of RMSE and the Score, and ablation studies confirm the effectiveness and reusability of MsTD-FIR-Rec and MSCA-U under multi-condition, noisy industrial scenarios.

This paper presents a novel method for cave reservoir characterization based on the Genetic Algorithm (GA) and Kernel Principal Component Analysis (KPCA), aimed at improving the precision of reservoir characterization through adaptive multi-attribute fusion. Sensitive seismic attributes are first extracted using geophysical algorithms and their correlations are analyzed based on geological interpretation. Initial attribute weights are then determined scientifically, ensuring reliable geological input for the fusion process. KPCA, with its strong nonlinear analysis capabilities, is used for efficient clustering and feature extraction of complex cave data, while GA optimizes KPCA's key bandwidth parameter to enhance search efficiency. The GA-KPCA method was validated using both synthetic cave model data and real carbonate rock field data in Tarim Basin, demonstrating significant advantages over traditional methods. The results indicate that the proposed approach effectively addresses the limitations of existing techniques, improving the reservoir identification success rate by approximately 33%, and offering an innovative and efficient solution for cave reservoir exploration and development. This method not only contributes to the advancement of cave reservoir characterization but also provides valuable theoretical and practical insights for future research in the field.

Among the cancers that pose the greatest threat to life worldwide is lung cancer. According to estimates from the World Cancer Research Fund International, there will be 1.8 million new instances of this disease diagnosed in 2022. When medical personnel diagnose and classify patients' conditions proactively, they may treat them safely and efficiently. The advent of the microarray method has made it possible to examine the connections between genes and various diseases, including lung malignancies. Numerous methods have been developed to forecast gene-based diseases, but they still have problems with high computational cost, time consumption, complex data, and inaccurate prediction. Therefore, create an efficient lung cancer detection system in this research by designing an Improved Convolutional Neural Network with Honey Bee Mating Optimization (ICNN-HBMO). First, the system is trained using Omix data, and the dataset is normalized using min-max normalization. Then Kernel Principal Component Analysis (KPCA) technique is employed for feature reduction. Furthermore, an enhanced CNN is employed to classify lung cancer using HBMO. The HBMO algorithm optimizes the weight and bias parameters of the ICNN to improve prediction performance. The developed method is implemented in the Matlab tool, and the improved performance is compared to other existing methods. The developed technique attains high accuracy and high precision of 99.2% and 99%.

Against the backdrop of rapidly growing global photovoltaic (PV) power generation, its inherent intermittency and volatility challenge power grid stability. This paper presents a comprehensive model for PV power forecasting and optimizing active/reactive support capabilities of distributed PV-storage stations. First, a hybrid EMD-KPCA-LSTM model is developed to address PV output variability. It employs Empirical Mode Decomposition (EMD) for multi-scale feature extraction from meteorological data, Kernel Principal Component Analysis (KPCA) for dimensionality reduction, and Long Short-Term Memory (LSTM) networks for high-precision forecasting. Experimental results show significantly improved accuracy and robustness compared to single LSTM or EMD-LSTM models, with effective quantification of prediction uncertainties. Second, a nonlinear programming model based on robust optimization and Sequential Quadratic Programming (SQP) is proposed to optimize active/reactive support. By refining energy storage charging/discharging strategies, the model maximizes grid support while mitigating PV uncertainty via robust optimization and solving efficiently with SQP. Case studies validate that optimized PV-storage stations effectively dampen PV fluctuations, providing stable and substantial active/reactive support to enhance grid flexibility, stability, and renewable energy integration. This work offers critical theoretical and practical insights for developing smarter, greener, and more reliable modern power systems.

China possesses abundant marine fishery resources, which play a vital role in the national economy. Achieving rapid and high-precision classification of underwater targets in complex aquatic environments is of significant importance for enhancing aquaculture intelligence and operational efficiency. To address the challenges of insufficient feature extraction and inefficient classifier parameter optimization in underwater image classification, this study proposes a classification method integrating local binary patterns (LBP), kernel principal component analysis (KPCA), and an improved sparrow search algorithm (SSA). The method first extracts image texture features using LBP and then applies KPCA for nonlinear dimensionality reduction. Subsequently, three optimization strategies—dynamic weighting, boundary contraction, and adaptive mutation—are introduced to enhance SSA, which is then employed to optimize the core parameters of the Support Vector Machine (SVM). Experiments were conducted on an underwater image dataset containing four types of targets: sea urchins, fish, rocks, and scallops. The results demonstrate that, compared with the traditional KPCA-SVM method, the integration of LBP features and the improved SSA increases classification accuracy from 55% to 94.37%, validating the effectiveness of the proposed approach in extracting underwater image features and optimizing classifier parameters. This provides technical support for improving the feasibility of automatic underwater target recognition in aquaculture applications.

Thin-walled components used in aerospace manufacturing are highly susceptible to machining-induced deformation due to their low structural stiffness and dynamic cutting instability. Although signal-based modeling approaches have been reported for machining process monitoring and performance evaluation, deformation prediction of thin-walled structures requires explicit consideration of structural flexibility. To address this challenge, a deformation error prediction framework integrating multi-source dynamic machining signals with static structural flexibility characteristics is proposed, enabling simultaneous representation of process dynamics and structural response. Kernel principal component analysis (KPCA) is employed to reduce the feature dimensionality, and the extracted low-dimensional features are subsequently used as inputs for a kernel-based support vector regression (KSVR) model to establish the prediction framework. The proposed method was validated through 25 milling experiments conducted on Al7075-T6 thin-walled workpieces, where deformation error was measured at predefined monitoring points under varying process conditions. The results indicate that the proposed model achieves high predictive accuracy for machining-induced deformation, with RMSE values below 13 μm and R2 exceeding 0.89 on both validation and testing datasets, demonstrating strong agreement between predicted and experimental results. In addition, machining vibration amplitude exhibits a consistent correlation with deformation error, confirming that increased energy input and cutting instability significantly exacerbate thin-walled workpiece deformation.

Support Vector Machine (SVM) is a popular kernel-based method for data classification that has demonstrated high efficiency across a wide range of practical applications. However, SVM suffers from several limitations, including the potential failure of the optimization process, especially in high-dimensional spaces; the inherently high computational cost; the lack of a systematic approach to multi-class classification; difficulties in handling imbalanced classes; and the prohibitive cost of real-time or dynamic classification. This paper proposes an alternative method, referred to as Kernel-based Optimal Subspaces (KOS), which belongs to the family of kernel subspace methods. Mathematically similar to Kernel PCA (KPCA), KOS achieves performance comparable to SVM while addressing the aforementioned weaknesses. The method is based on computing the minimum distance to optimal feature subspaces of the mapped data. Because no optimization process is required, KOS is robust, fast, and easy to implement. The optimal subspaces are constructed independently, enabling high parallelizability and making the approach well-suited for dynamic classification and real-time applications. Furthermore, the issue of imbalanced classes is naturally handled by subdividing large classes into smaller sub-classes, thereby creating appropriately sized sub-subspaces within the feature space.

Anomaly detection in operating system (OS) kernels is critical for the stability and security of embedded systems, particularly in power monitoring applications. OS kernel behavior is complicated, and typical anomaly detection algorithms frequently fail to detect smaller anomalies, especially in power-sensitive applications where energy efficiency is critical. The goal of this research is to create an effective anomaly detection framework capable of reliably identifying abnormalities in the Chinese OS kernel's behavior through power monitoring, assuring consistent system performance and security. The framework includes several critical steps: First, gather a dataset of system call sequences and power usage logs from the OS kernel. Data pre-processing is utilized to clean and normalize the dataset, ensuring it is formatted consistently for investigation. Feature data extraction is then carried out via the Kernel Principal Component Analysis (Kernel PCA) method that uses such important kernel interaction characteristics as the frequency of system calls and the power consumption behaviour. A novel technique, Fire Hawk Optimizer Fused Fuzzy Logic-Based Deep Belief Networks (Fire-Fuzzy DBN) is a hybrid approach that combines FHO to optimize system parameters, Fuzzy Logic to handle uncertainty in system behavior, and DBNs to extract complex patterns, resulting in a robust, adaptive, and effective solution for detecting kernel anomalies. The outcomes reveal that the proposed Fire-Fuzzy DBN strategy, which was implemented in Python, significantly improves kernel anomaly detection accuracy by 99% over previous techniques. The research data analytics for energy-cost efficient system operation establishes the efficacy of fuzzy testing technology in detecting anomalies in OS kernel interfaces for power monitoring systems, therefore improving embedded system dependability and security.