Search Results (184)

Fourier transform infrared (FTIR) spectroscopy-based gas remote sensing has been widely applied for long-range atmospheric composition analysis. However, when deployed for longwave infrared methane detection, spectral features of methane are significantly interfered by water vapor variations at the edge of atmospheric window, which compromises detection performance. To address the spectral fitting degradation caused by relative changes between methane and water vapor signals, this study incorporates temperature, relative humidity, and sensing distance into the cost function, establishing a continuous optimization space with concentration path lengths (CLs) as variables, which are the product of the concentration and path length. A hybrid differential evolution and Levenberg–Marquardt (D-LM) algorithm is developed to enhance parameter estimation accuracy. Combined with a three-layer atmospheric model for real-time reference spectrum generation, the algorithm identifies the optimal spectral combination that provides the best match to the measured data. Algorithm performance is validated through two experimental configurations: Firstly, adaptive detection using synthetic spectra covering various humidity–methane concentration combinations is conducted; simulation results demonstrate that the proposed method significantly reduces the mean squared error (MSE) of fitting residuals by 95.8% compared to the traditional LASSO method, effectively enhancing methane spectral feature extraction under high-water-vapor conditions. Then, a continuous monitoring of controlled methane releases over a 500 m open path under high-outdoor-humidity conditions is carried out to validate outdoor performance of the proposed algorithm; field measurement analysis further confirms the method’s robustness, achieving a reduction in fitting residuals of approximately 57% and improving spectral structure fitting. The proposed approach provides a reliable technical pathway for adaptive gas cloud detection under complex atmospheric conditions. Full article

Magnetic localization technology plays a significant role in medical device navigation and human–computer interaction. However, existing localization methods based on local optimization suffer from poor initial solutions and slow convergence. To address the aforementioned challenges, this paper presents a hybrid localization approach, referred to as the Improved Artificial Lemming Algorithm (IALA) Integrated with Levenberg–Marquardt (LM) Optimization. Building upon the Artificial Lemming Algorithm (ALA), the proposed method incorporates an adaptive Gaussian–Lévy hybrid mutation strategy designed to enhance search performance through improved exploration–exploitation dynamics, as quantitatively demonstrated by the diversity-based analysis where IALA maintains higher exploration percentages on multimodal functions while achieving superior optimization results on high-dimensional problems. By introducing a competitive foraging mechanism inspired by the aggressive behavior of the Tasmanian Devil Optimization (TDO) algorithm, it enhances population diversity and search initiative. Furthermore, a time-varying tracking and escape strategy is adopted to improve dynamic optimization performance in complex solution spaces. The proposed method leverages IALA to generate high-quality initial solutions, significantly accelerating the convergence speed and stability of the LM algorithm, thereby improving the overall performance of the permanent magnet localization system. The experimental results show that, using a horizontal test platform of 60 mm × 60 mm with 41 uniformly distributed test points, and acquiring data at vertical heights ranging from 15 mm to 65 mm in 5 mm increments for two distinct orientations of the permanent magnet, the IALA-LM algorithm achieves an average localization success rate of 96.9% over 902 trials, with a mean position error of 1.1 mm and a mean orientation error of 0.17°. Compared with the standard LM algorithm, the proposed IALA-LM algorithm reduces the position error by approximately 66.7% (from 3.3 mm to 1.1 mm) and the orientation error by approximately 94.3% (from 3.0° to 0.17°). Consequently, the proposed method enables high-precision, high-stability, and high-efficiency localization of permanent magnets. It can provide reliable spatial pose estimation support for demanding applications such as miniature implantable or ingestible medical devices (e.g., capsule endoscopy, intramedullary nail fixation, and tumor localization), human–computer interaction, and industrial inspection. Full article

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization. Full article

A phase-modulated ellipsometer enables non-contact, high-precision determination of thin-film optical parameters and thickness through polarized light modulation analysis. However, conventional ellipsometers suffer from limited measurement accuracy due to systemic calibration drift and environmental interference. This research presents a novel metrological approach integrating backpropagation neural networks (BP) with a hybrid Particle Swarm Optimization–Levenberg–Marquardt (PSO-LM) algorithm for thin-film thickness quantification. The proposed framework simultaneously determines system parameters and ellipsometry coefficients (ψ, Δ) via multi-objective optimization, achieving calibration-free thickness characterization with sub-nanometer precision. Experimental validation was performed on SiO₂/Si samples with thicknesses ranging from 20 nm to 500 nm. Results demonstrate that the proposed method achieves a root mean square error (RMSE) of <0.006 across the entire thickness range, outperforming the traditional calibration-based method (RMSE ~ 0.008). In addition, the adaptability and stability of the algorithm to complex optical systems are also verified, providing a new method for industrial film thickness monitoring. Full article

Maintaining desirable texture, color, and flavor during hot-air drying is crucial for improving the commercial value of dried shrimp. This study aims to address the limitations of previous research on hot-air drying of shrimp, which focused solely on the meat. The objective is to simultaneously investigate the dual effects of hot-air drying conditions on the textural and physicochemical properties of both the shrimp shell and meat. This provides a theoretical foundation for preserving the optimal texture, color, and flavor of dried shrimp snack products. After drying and separation, the textural and physicochemical properties of the two components were comprehensively evaluated, including hardness, crispness, chewiness, springiness, color (L*, a*, b*), rehydration rate, sensory attributes, and odor characteristics. Furthermore, to elucidate the complex interrelationships among these variables, two predictive models were established: a Partial Least Squares Regression (PLSR) model and an Artificial Neural Network (ANN) model optimized using the Levenberg–Marquardt algorithm. The PLSR model achieved a calibration accuracy of R² = 0.38 and a validation accuracy of R² = 0.32, whereas the optimized LM-ANN model exhibited markedly superior predictive capability (R²_Training = 0.99, R²_Validation = 0.98), effectively capturing nonlinear associations between drying parameters and quality attributes of both meat and shell. Finally, a user-oriented prediction module was established based on the optimized ANN model, allowing flexible input of variables and prediction of quality outcomes. This integrated framework may provide a novel approach for modeling and optimizing the hot-air drying process of shrimp, offering practical guidance for quality control and texture customization of dried shrimp products. Full article

The information and communication technology revolution has provided researchers with new opportunities to enhance traffic prediction methods. Accurate long-term traffic forecasts are essential for sustainable infrastructure planning, supporting proactive maintenance and efficient resource allocation. They also enable environmental impact assessments and help reduce carbon footprints through optimized traffic flow, minimized idling, and better planning for low-emission infrastructure. Most traffic prediction studies focus on short-term urban traffic, but there remains a gap in methods for long-term planning of rural highways, which pose significant challenges for intelligent transportation systems. This paper assesses and compares six prediction models for long-term daily traffic volume prediction, including two traditional time series methods (ARIMA and SARIMA) and four artificial neural networks (ANNs): three feedforward networks trained with Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), and Levenberg–Marquardt (LM), along with a nonlinear autoregressive (NAR) network. Applying mean absolute percentage error (MAPE) as the performance metric, the results showed that all models effectively captured the data’s nonlinearity, though their accuracy varied significantly. The NAR model proved to be the most accurate, with a minimum average MAPE of 2%. The Bayesian Regularization (BR) algorithm achieved superior performance (average MAPE: 4.50%) among the feedforward ANNs. Notably, the ARIMA, SARIMA, and ANN-LM models exhibited similar performance. Accordingly, the NAR model is recommended as the optimal choice for long-term traffic prediction. Implementing these models with optimal design will enhance long-term traffic volume forecasting, supporting sustainable transportation and improving intelligent highway operation systems. Full article

Background: Recent studies have shown that digital microscopy (DM) combined with a convolutional neural network (CNN) model is a valid approach for detecting intestinal protozoa and helminth ova or larvae in both trichrome-stained and wet-mount stool preparations. This study evaluated the diagnostic performance of a DM/CNN workflow for routine detection of intestinal parasites in a clinical microbiology laboratory. Methods: A clinical validation was conducted using the Grundium Ocus 40 scanner combined with the Techcyte Human Fecal Wet Mount (HFW) algorithm. The system was evaluated on (a) 135 reference samples and (b) 208 routine clinical samples submitted for intestinal parasite testing. Analytical sensitivity, precision, and limit of detection (LOD) were assessed. Results: For reference samples, the DM/CNN workflow achieved a positive slide-level agreement of 97.6% (95% CI: 94.4–100%), following a confidence threshold adjustment for Schistosoma mansoni, and a negative agreement of 96.0% (95% CI: 86.6–98.9%) compared with light microscopy (LM). Dilution series with reference samples revealed slightly lower analytical sensitivity of the DM/CNN at higher dilutions. Both intra- and inter-run precision studies demonstrated high reproducibility and stability. In prospective testing on 208 routine samples, overall agreement between DM/CNN and LM was 98.1% (95% CI: 95.2–99.2%) with a Cohen’s Kappa coefficient of κ = 0.915. Minor discrepancies involved Blastocystis spp., with DM/CNN showing slightly higher sensitivity. Conclusions: For the first time, we show that the combination of the Grundium Ocus 40 scanner and the Techcyte HFW algorithm provides a reliable, low-throughput screening solution that can effectively assist diagnostic technicians by pre-classifying putative parasitic structures for targeted expert review. Despite its lower throughput, the system substantially reduces the manual review process and simplifies the parasitological workflow. Implementation in a clinical microbiology laboratory requires extensive site-specific validation to account for differences in sample processing and imaging conditions. Moreover, optimization of confidence thresholds for specific classifiers is essential to ensure consistent analytical performance across different laboratory settings. Full article

Background/Objectives: Medical image segmentation is a crucial task for diagnosis, treatment planning, and monitoring of cancer; however, it remains one of the toughest nuts to crack for Artificial Intelligence (AI)-based clinical applications. Deep-learning models have shown near-perfect results for narrow tasks such as single-organ Computed Tomography (CT) segmentation. Still, they fail to deliver under practicality, in which cross-modality robustness and multi-organ delineation are essential (e.g., liver Dice dropping to 0.88 ± 0.15 in combined CT-MR scenarios). That fragility exposes two structural gaps: (i) rigid task-specific architectures, which are not flexible enough to adapt to various clinical instructions, and (ii) the assumption that a universal loss function is best in all cancer imaging applications. Methods: A novel multimodal segmentation framework is proposed that combines natural language prompts and high-fidelity imaging features through Feature-wise Linear Modulation (FiLM) and Conditional Batch Normalization, enabling a single model to adapt dynamically across modalities, organs, and pathologies. Unlike preceding systems, the proposed approach is prompt-driven, context-aware, and end-to-end trainable to ensure alignment between computational adaptability and clinical decision-making. Results: Extensive evaluation on the Brain Tumor Dataset (cancer-relevant neuroimaging) and the CHAOS multi-organ challenge demonstrates two key insights: (1) while Dice loss remains optimal for single-organ tasks, (2) Jaccard (IoU) loss outperforms when multi-organ, cross-modality divides cancer segmentation boundaries. Empirical evidence has thus been offered that optimality of a loss function is task- and context-dependent and not universal. Conclusions: The design framework’s principles directly address what is documented in workflow requirements and display capabilities that may connect algorithmic innovation with clinical utility once validated through prospective clinical trials. Full article

Accurate kinematic parameter identification is essential for space manipulators to attain millimeter-level positioning accuracy and robust motion control. This study develops a universal strategy for comprehensive parameter identification by establishing a generalized geometric error model using Denavit–Hartenberg (DH) parameterization. For robotic calibration, the Fibonacci spiral sampling technique optimizes pose selection, ensuring end-effector poses fully cover the manipulator’s workspace to enhance identification convergence. By combining the local convergence capability of the Levenberg–Marquardt (LM) algorithm with the global search characteristics of Particle Swarm Optimization (PSO), we propose a novel hybrid PSO-LM optimization algorithm, achieving synergistic enhancement of global exploration and local refinement. An experimental platform using a laser tracker as the metrology reference was constructed, with a 6-degree-of-freedom (6-DOF) space manipulator selected as a validation case. Experimental results demonstrate that the proposed method significantly reduces the average positioning error from 10.87 mm to 0.47 mm, achieving a 95.7% improvement in relative accuracy. These findings validate that the parameter identification approach can precisely determine the actual geometric parameters of space manipulators, providing critical technical support for high-precision on-orbit operations. Full article

Artificial neural networks have been evaluated and compared for modeling extreme wave forces exerted on coastal bridges during hurricanes. Long Short-Term Memory (LSTM) is selected for deep learning neural networks. A feedforward neural network (FFNN) is employed to represent the shallow learning network for comparison purposes. The two case studies consist of an emerged bridge deck destroyed by Hurricane Ivan and a submerged bridge deck impaired in Hurricane Katrina. Datasets for model training and verifications consist of wave elevation and force time series resulting from previous validated numerical wave load modeling studies. Results indicate that both deep LSTM and shallow FFNNs are able to provide very good predictions of wave forces with correlation coefficients above 0.98 by comparing model simulations and data. Effects of training algorithms on network performance have been investigated. Among several training algorithms, the adaptive moment estimation (Adam) training optimizer leads to the best LSTM performance, while Levenberg–Marquardt (LM) optimized backpropagation is among the most effective training algorithms for FFNNs. In general, a shallow FFNN-LM network results in slightly higher correlation coefficients and lower error than those from an LSTM-Adam network. For sharp variation in nonlinear wave forces in the emerged bridge case study during Hurricane Ivan, FFNN-LM predictions of wave forces show better matching with the quick variations in nonlinear wave forces. FFNN-LM’s speed is approximately 4 times faster in model training but is about twice as slow in model verification and application than the LSTM-Adam network. Neural network simulations have shown substantially faster than CFD wave load modeling in our case studies. Full article

High-precision identification of dynamic parameters is crucial for the on-orbit performance of space manipulators. This paper investigates dynamic modeling and parameter identification under special environmental conditions such as microgravity and vacuum. First, a dynamic model of the manipulator incorporating a nonlinear friction term is established using the Newton-Euler method, and an improved Stribeck friction model is proposed to better characterize high-speed conditions and space environmental effects. On this basis, a hybrid parameter identification method combining Particle Swarm Optimization (PSO) and Levenberg–Marquardt (LM) algorithms is proposed to balance global search capability and local convergence accuracy. To enhance identification performance, Fourier series are used to design excitation trajectories, and their harmonic components are optimized to improve the condition number of the observation matrix. Experiments conducted on a ground test platform with a six-degree-of-freedom (6-DOF) manipulator show that the proposed method effectively identifies 108 dynamic parameters. The correlation coefficients between predicted and measured joint torques all exceed 0.97, with root mean square errors below 5.1 N·m, demonstrating the high accuracy and robustness of the method under limited data samples. The results provide a reliable model foundation for high-precision control of space manipulators. Full article

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. Full article

Structured Light LiDAR is susceptible to lens scattering and temperature fluctuations, resulting in some level of distortion in the captured point cloud image. To address this problem, this paper proposes a high-performance 3D point cloud Least Mean Square filter based on Decision Tree, which is called the D−LMS filter for short. The D−LMS filter is an adaptive filtering compensation algorithm based on decision tree, which can effectively distinguish the signal region from the distorted region, thus optimizing the distortion of the point cloud image and improving the accuracy of the point cloud image. The experimental results clearly demonstrate that our proposed D−LMS filtering algorithm significantly improves accuracy by optimizing distorted areas. Compared with the 3D point cloud least mean square filter based on SVM, the accuracy of the proposed D−LMS filtering algorithm is improved from 86.17% to 92.38%, the training time is reduced by 1317 times and the testing time is reduced by 1208 times. Full article

This paper presents three simple and efficient advanced optimization algorithms, namely the best–worst–random (BWR), best–mean–random (BMR), and best–mean–worst–random (BMWR) algorithms designed to address unconstrained and constrained single- and multi-objective optimization tasks of the metal casting processes. The effectiveness of the algorithms is demonstrated through real case studies, including (i) optimization of a lost foam casting process for producing a fifth wheel coupling shell from EN-GJS-400-18 ductile iron, (ii) optimization of process parameters of die casting of A360 Al-alloy, (iii) optimization of wear rate in AA7178 alloy reinforced with nano-SiC particles fabricated via the stir-casting process, (iv) two-objectives optimization of a low-pressure casting process using a sand mold for producing A356 engine block, and (v) four-objectives optimization of a squeeze casting process for LM20 material. Results demonstrate that the proposed algorithms consistently achieve faster convergence, superior solution quality, and reduced function evaluations compared to simulation software (ProCAST, CAE, and FEA) and established metaheuristics (ABC, Rao-1, PSO, NSGA-II, and GA). For single-objective problems, BWR, BMR, and BMWR yield nearly identical solutions, whereas in multi-objective tasks, their behaviors diverge, offering well-distributed Pareto fronts and improved convergence. These findings establish BWR, BMR, and BMWR as efficient and robust optimizers, positioning them as promising decision support tools for industrial metal casting. Full article

Electrochemical impedance spectroscopy (EIS) is a technique used to analyze the kinetics and interfacial processes of electrochemical systems. The selection of an appropriate equivalent circuit model for EIS interpretation was traditionally reliant on expert experience, rendering the process subjective and prone to error. To address these limitations, an automated framework for both model selection and parameter estimation was proposed. The methodology was structured such that initial model screening was performed by a global heuristic search algorithm, adaptive optimization was guided by an integrated XGBoost-based error feedback mechanism, and precise parameter estimation was achieved using a Differential Evolution–Levenberg–Marquardt (DE-LM) algorithm. When evaluated on a purpose-built dataset comprising 4.8 × 10⁵ spectra across diverse circuit and biofilm scenarios, a model classification accuracy of 96.32% was achieved, and a 72.3% reduction in parameter estimation error was recorded. The practical utility of the method was validated through the quantitative analysis of bovine serum albumin–Clenbuterol hydrochloride (BSA-CLB), wherein an accuracy of 95.2% was demonstrated and a strong linear correlation with target concentration (R² = 0.999) was found. Through this approach, the limitations of traditional black-box models were mitigated by resolving the physical meaning of parameters. Consequently, the automated and quantitative monitoring of processes such as biofilm formation was facilitated, enabling the efficient evaluation of antimicrobial drugs or anti-fouling coatings. Full article