Accurate Results Research Articles

FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation

In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. In this paper, a fuzzy learning module is designed on the skip path of U-Net network because of the widespread boundary uncertainty and region blurring of medical image segmentation. The module sets several fuzzy membership functions for the input encoded features to describe the similarity degree between the feature points, and applies fuzzy rules to the fuzzy membership functions, thus enhancing the modeling ability of the model for uncertain boundaries and fuzzy regions. In addition, in order to improve the accuracy and robustness of the model segmentation results, we introduced an iterative attention feature fusion method in the test phase, which added local context information to the global context information in the attention module to fuse the prediction results of each denoising time step. Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.

Comparison of BMI and % BF determined by bioimpedance analysis for obesity screening in young women.

The body mass index (BMI) is used as the most common and simplest measure to predict obesity. The aim of the study was to compare the BMI method and % BF (body fat) determined by bioimpedance analysis for obesity screening and to evaluate the correlation between these methods. 200 women aged 18-25 years were included in the study. BMI ≥ 25/ ≥ 30 kg/m2 was the criterion for assessment of overweight/obesity. Body fat (BF) in % and kg was determined by bioimpedance analysis. For assessment of obesity by % BF, the criterion was ≥ 30% fat. Based on BMI, 4.5% of women were obese, 14.5% were overweight. According to % BF, up to 30% of women were obese. The largest differences between BMI and % BF categorization were found in the underweight and overweight groups. According to the BMI 43% of women in the underweight category and 34.5% in the overweight category had a % BF in the normal range. By correlation analysis, we found a strong positive statistically significant correlation in women between BMI and BF (%) (rs = 0.782, p < 0.01) and also between BMI and BF (kg) (rs = 0.880, p < 0.01). Both of these methods assess body composition in their own specific way, complement each other, and other combining them, we achieve more accurate results for determining the overall health status of an individual.

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-Hoc Interpretable Fault Diagnosis With Limited Fault Samples.

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.

Regional Evaluation Study of VFTO Interference to Secondary Side Cables Based on Cloud Model and MARCOS.

With the advent of the data era, most power secondary side equipment tends to be digitized. The power system needs more accurate numerical results to further improve its operating efficiency. Therefore, it is important to study the electromagnetic interferences of very fast transient overvoltage (VFTO) generated by gas-insulated switchgear (GIS). To protect the secondary side cable from interferences, the secondary side cable is wrapped with an outer shield and the shield is grounded. When the interference of VFTO comes, it will couple the interference current and interference voltage on the shield of the cable. By grounding, the interference is greatly discharged. However, due to the grounding resistance, there will be a potential difference between the grounding points at the two ends of the shield of the cable. This causes a corresponding interference current to flow through the shield, which will affect the transmission of signals inside the cable. In the actual substation, the resistivity of the soil, the ambient temperature and humidity of the area, and so on will have impacts on the grounding resistance. In addition, the irregularity of the cable arrangement and the time of the use of the cable will have impacts on the signal transmission of the cable. Based on the abovementioned issues, this article proposed a comprehensive assessment method based on the combination of the cloud model and measurement of alternatives and ranking according to compromise solution (MARCOS). The method brings the cloud model into MARCOS by the algorithm of the contribution of the cloud droplets. It overcomes the difficulty of cloud model quantification. By comparing the results of the proposed method with the actual conditions at the substation and the results of the common MARCOS assessment method, the validity of the method is verified, and a reference scheme is provided for substation optimization.

Fractionation in young cores: Direct determinations of nitrogen and carbon fractionation in HCN

Context. Nitrogen fractionation is a powerful tracer of the chemical evolution during star and planet formation. It requires robust determinations of the nitrogen fractionation across different evolutionary stages. Aims. We aim to determine the 14N/15N and 12C/13C ratios for HCN in six starless and prestellar cores and to compare the results between the direct method using radiative transfer modeling and the indirect double isotope method, assuming a fixed 12C/13C ratio. Methods. We present IRAM observations of the HCN 1–0, HCN 3–2, HC15N 1–0 and H13CN 1–0 transitions toward six embedded cores. The 14N/15N ratio was derived using both the indirect double isotope method and directly through non-local thermodynamic equilibrium (NLTE) 1D radiative transfer modeling of the HCN emission. The latter also provides the 12C/13C ratio, which we compared to the local interstellar value. Results. The derived 14N/15N ratios using the indirect method are generally in the range of 300-550. This result could suggest an evolutionary trend in the nitrogen fractionation of HCN between starless cores and later stages of the star formation process. However, the direct method reveals lower fractionation ratios of around ~250, mainly resulting from a lower 12C/13C ratio in the range ~20–40, as compared to the local interstellar medium value of 68. Conclusions. This study reveals a significant difference between the nitrogen fractionation ratio in HCN derived using direct and indirect methods. This can influence the interpretation of the chemical evolution and reveal the pitfalls of the indirect double isotope method for fractionation studies. However, the direct method is challenging, as it requires well-constrained source models to produce accurate results. No trend in the nitrogen fractionation of HCN between earlier and later stages of the star formation process is evident when the results of the direct method are considered.

A new dıagnostic tool for assessment of pelvıc dysfunction ın MR defecography.

Pelvic floor dysfunction is characterized by incomplete fecal defecation, affecting quality of life in a negative way. Magnetic resonance defecography (MRD) is a useful examination that is ionizing radiation-free and easily reproducible, and which provides anatomical and functional details that are obtainable through multiplanar and dynamic examinations. We aimed to detect pathology using MRD in patients with suspected pelvic floor dysfunction and determine its cause. MRD was performed in 79 individuals. Dynamic images were obtained at rest, straining, and during defecation. Pelvic hiatus mediolateral diameters were compared between groups. Defecation phase provided significantly more accurate results than the straining phase for determining the existence and severity of pathology. Conclusıons: The defecation phase is the most accurate phase for identifying the existence and severity of pathology. As the pelvic hiatus mediolateral diameter is thought to be an important factor in triggering pelvic dysfunction.

Identification of the Aeromechanical Parameters of a Compressor Rotor from Blade Tip Timing Data

Abstract This article presents a maximum likelihood approach to identifying the mistuning, aerodynamic forcing functions, and aerodynamic influence coefficients of compressor rotor blades from noisy experimental measurements of blade deflection. The maximum likelihood estimation approach leads naturally to a nonlinear least squares optimization problem that can be solved using numerical optimization techniques such as quasi-Newton methods. An eigenmode decomposition approach is presented that permits the optimization objective function and gradient to be efficiently calculated so that the optimization is computationally feasible, even for experiments involving very large data sets. Simulation results show that the method has the potential to accurately identify the mistuning and aerodynamic nodal diameter forcing functions, but obtaining accurate results for influence coefficients may not be possible if there is significant disk flexibility. We demonstrate the method using both simulated and experimental blade vibration data from rotor 2 of the Purdue University three-stage axial compressor research facility collected using a light probe tip timing system as the rotor is slowly accelerated or decelerated through a Campbell diagram crossing.

Few-Shot Synthetic Online Transfer Learning for Cross-Site Neurological Disease Diagnosis

Cross-site datasets expand the data size and could improve the disease diagnosis capabilities of machine learning models. However, differences in data distribution between different sites can lead to poor model generalizability. Although transfer learning is a mainstream method often used to tackle the issue, most transfer learning studies assume that all the target samples are given in the training procedure, which is not available in clinical applications where the target samples arrive sequentially. Online transfer learning (OTL) aims to accomplish clinical diagnostic tasks by adaptively updating the ensemble model containing source and target classifiers. However, OTL is limited by zero initialization and requires numbers of samples to iterate. This results in the underperformance of OTL in clinical applications where few samples can be obtained. In this article, we propose a new framework named few-shot synthetic OTL (FSOTL) to address this issue. FSOTL uses synthetic data to warm up the model in an online fashion. It not only alleviates the problem of scarcity of samples in the target domain but also enables the model to gain more knowledge. Our experiments show that FSOTL performs more stably and achieves more accurate results with few target samples, thereby offering a promising cross-site online computer-aided diagnosis system for large-scale applications.

Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.

Measurement-device-independent quantum random number generation over 23 Mbps with imperfect single-photon sources

Quantum randomness relies heavily on the accurate characterization of the generator implementation, where the device imperfection or inaccurate characterization can lead to incorrect entropy estimation and practical bias, significantly affecting the reliability of the generated randomness. Measurement-device-independent (MDI) quantum random number generation (QRNG) endeavors to produce certified randomness, utilizing uncharacterized and untrusted measurement devices that are vulnerable to numerous attack schemes targeting measurement loopholes. However, existing implementations have shown insufficient performance thus far. Here, we propose a high-speed MDI-QRNG scheme based on a robust measurement tomography approach against the imperfection of single-photon sources. Compared with the conventional approach, the decoy-state method is introduced to obtain more accurate tomography results and a tighter lower bound of randomness. Finally, by using a high-speed time-bin encoding system, we experimentally demonstrated the scheme and obtained a reliable min-entropy lower bound of 7.37×10−2 bits per pulse, corresponding to a generation rate over 23 Mbps, which substantially outperforms the existing realizations and makes a record in discrete-variable semi-device-independent QRNGs.

Symmetry and Graph Bi-Regularized Non-Negative Matrix Factorization for Precise Community Detection

Community is a fundamental and highly desired pattern in a Large-scale Undirected Network (LUN). Community detection is a vital issue when LUN representation learning is performed. Owing to its good scalability and interpretability, a Symmetric and Non-negative Matrix Factorization model is frequently utilized to tackle this issue. It adopts a unique Latent Factor (LF) matrix for precisely representing LUN’s symmetry, which, unfortunately, leads to a reduced LF space that decreases its representation learning ability to a target LUN. Motivated by this discovery, this study proposes a Symmetry and Graph Bi-regularized Non-negative Matrix Factorization (B-NMF) method that: a) leverages multiple LF matrices when representing LUN, thereby boosting the representation learning ability; b) constructs a symmetry regularization term that implies the equality constraint among its multiple LF matrices, thereby illustrating LUN’s intrinsic symmetry; and c) incorporates graph regularization into its learning objective, thereby illustrating LUN’s local geometry. A theoretical proof is given to theoretically validate B-NMF’s convergence ability. The regularization hyperparameters are selected by validating model modularity, thereby guaranteeing B-NMF’s practicability in addressing real application issues. Extensive experimental results on ten LUNs from real applications demonstrate that the proposed B-NMF-based community detector significantly outperforms several baseline and state-of-the-art models in achieving highly-accurate community detection results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —LUNs are very-commonly seen in real applications like a social network system. Communities in LUNs are vital for various knowledge discovery-related applications. For accurately detecting them, a detector should guarantee its high representation learning ability to a target LUN. To do so, this paper presents a B-NMF model that is able to perform precise representation learning to LUNs, thereby achieving accurate community detection results. In comparison with conventional Symmetric and Non-negative Matrix Factorization-based community detectors, a B-NMF-based community detector enjoys its enlarged latent feature space, which ensures its higher representation ability to a target LUN. It depends on two regularization hyperparameters, which can be selected by performing grid-search on the target LUN via its modularity evaluation. This paper gives the empirical values of B-NMF’s regularization hyperparameters based on the parametersensitivity tests on the involved experimental datasets. The proposed B-NMF model is shown to be highly suitable for addressing community detection and clustering tasks on LUNs from real applications.

Image dermoscopy skin lesion classification using deep learning method: systematic literature review

Classifying skin lesions poses a significant challenge due to the distinctive characteristics and diverse shapes they can exhibit, particularly in identifying early-stage melanoma. To address the shortcomings of the prior method, a neural network-driven strategy was introduced to differentiate between two types of skin lesions based on dermoscopic images. This new approach comprises four key stages: i) initial image processing, ii) skin lesion segmentation, iii) feature extraction, and iv) classification using deep neural networks (DNNs). Computers can also provide more accurate diagnosis results. In the review process, the articles are analyzed and summarized to contribute to developing methods or application development in skin lesion diagnosis. The stages include defining the relevant theory, input data, methods used (architecture and modules), training process, and model evaluation. This review also explores information based on trends and users, emphasizing the skin lesion segmentation process, skin lesion classification process, and minimal datasets as recommendations for future research.

Measuring Color Index of Transformer Oil–Enabling Single-Wavelength Spectroscopy With Artificial Neural Network-Fuzzy Logic Model

Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others.

A survey on brain MRI segmentation

Neurological disorders pose a significant challenge in medical diagnostics, requiring accurate and efficient analysis of brain Magnetic Resonance Imaging (MRI) scans. This study introduces a novel approach for enhanced neurological diagnosis through the application of deep learning techniques to automate the segmentation of brain structures in MRI images. The proposed method leverages the power of convolutional neural networks (CNNs) to extract intricate patterns and features from complex neuro imaging data. The research involves the development and training of a deep learning model capable of accurately delineating key anatomical regions, such as the cortex, hippocampus, and ventricles. The model is trained on a large data set of annotated MRI scans, optimizing its performance through rigorous validation processes. The utilization of deep learning enables the algorithm to learn and generalize from diverse imaging data, improving its adaptability to variations in patient demographics and scanner characteristics. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a diverse set of MRI datasets, encompassing various neurological conditions. Quantitative metrics, including Dice coefficient and Hausdorff distance, are employed to assess the segmentation accuracy compared to ground truth annotations. Additionally, the clinical relevance of the automated segmentation is validated through collaboration with neurologists and radiologists. The results demonstrate that the deep learning- enabled segmentation method consistently outperforms traditional image processing techniques, providing more accurate and reliable segmentation results. The proposed approach not only streamlines the diagnostic process but also has the potential to uncover subtle abnormalities that may be overlooked by manual inspection. In conclusion, the integration of deep learning into the segmentation of brain MRI scans presents a promising avenue for enhancing neurological diagnosis. The automated and precise delineation of brain structures contributes to the efficiency and accuracy of diagnostic workflows, ultimately improving patient outcomes and facilitating timely interventions in the realm of neurological disorders.

Determination of the Optimum Test Conditions for Measurement of Glucose Level in Liquids

Diabetes is a disease that affects more than 400 million people worldwide and currently lacks a cure. Monitoring blood sugar levels is crucial in minimizing the effects of this disease and protecting against its complications. Invasive and minimally invasive methods are commonly used traditional approaches for detecting and monitoring blood sugar levels. However, these methods bring along psychological and infectious risks. Currently, efforts are being made to develop a non-invasive method for determining blood sugar levels. Microwaves offer the possibility of non-invasive glucose measurement as they do not cause any harmful effects on human tissue. Furthermore, the complex permeability of blood is sensitive to glucose concentration in the microwave band. In literature, most of the studies are done with vector network analyzers (VNA) to detect blood sugar level noninvasively. In this study, an expensive and bulky VNA is replaced by an affordable microwave source and RMS power detector. The influence of the type and diameter of the test tube material used for non-invasive determination of sugar levels is examined with this setup. Additionally, the effect of the distance between the Vivaldi antennas used during measurements and the test tube is investigated. The results indicate that measurements performed using plastic test tubes yield better results compared to glass test tubes. Moreover, reducing the diameter of the test tube leads to improved outcomes. It has been observed that accurate results cannot be obtained if the antennas and the test tube are too close (4.5cm) from each other.

Forecasting Türkiye's International Tourism Demand

The structure of tourist demand is sensitive since it is very easily affected by the consequences of economic, political, and social crises. Since the limited ability to increase tourism supply, it is crucial to analyze the demand structure and develop suitable strategies. This outcome can only be achieved by an accurate and effective demand prediction. There is no singular approach that ensures success in demand forecasting. Hence, in order to estimate demand accurately, it is advisable to create many models and choose the one with the lowest error rate. This study aimed to develop the best-performing prediction model by using monthly data of international visitors who visited Türkiye from January 2002 to August 2023 and stayed in Tourism Ministry-certified accommodations. Within this framework, the data was first analyzed to identify the trend and seasonal component. Afterwards, various models were employed including Naive III, simple moving average, double moving average, seasonal exponential smoothing, and artificial neural networks. The data generated by these models has been analyzed by comparing it with the actual data from the last 24 months, using MAPE and RMSE results. According to the research findings, it has been determined that artificial neural networks produce the most accurate results.

Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Prostate cancer is one of the most common types of cancer in men. It usually grows slowly and may not show obvious symptoms at first. Prostate cancer can be diagnosed by symptoms or by a doctor performing certain tests during routine health checkups. These tests include physical examination, PSA (Prostate Specific Antigen) Test, biopsy, imaging techniques, and Gleason score. In addition, Fourier transform infrared spectroscopy (FT-IR) is an analysis method used for prostate cancer diagnosis. This study aims to demonstrate FT-IR spectroscopy as an alternative method to other diagnostic methods in the diagnosis of prostate cancer. The FT-IR spectroscopy method is used to examine the molecular structure of samples. For prostate cancer diagnosis, FT-IR spectroscopy can be used to identify molecular changes in prostate tissue and identify characteristics by which cancerous cells differ from healthy cells. FT-IR spectroscopy is based on spectral data obtained by exposing samples to infrared radiation. These spectral data are based on properties associated with the movements of the molecules contained in the samples, such as vibration, rotation and bending. Molecular changes caused by diseases such as prostate cancer may be evident in these spectral data. These changes can provide information about the presence or stage of cancerous cells. Data obtained using FT-IR spectroscopy is processed with statistical analysis methods. These analyses are used to identify molecular differences between cancerous and healthy prostate tissues. In this way, FTIR spectroscopy can help obtain sensitive and accurate results in the diagnosis of prostate cancer.

Dynamic analysis of a drilling string for deepwater riserless drilling with vortex-induced vibration considered

Riserless drilling will become the mainstream technology for deepwater drilling in the future owing to its operational characteristics. At present, researchers only analyze the seawater section or formation section but do not analyze the whole drill string to study the dynamics of riserless drilling. In fact, the ocean load affects not only the vortex-induced vibration characteristics of the drill string in seawater but also the load distribution of the drill string in the formation. Ignoring the formation or ocean load will lead to inaccurate prediction results. On this basis, in addition to the traditional drilling string boundary condition of seawater and formation, a new boundary condition of drilling string at mud line for riserless drilling is proposed. Considering vortex-induced vibration, a full-well drilling string dynamic model without riser drilling is established by the finite element method, and the dynamic characteristics of the full-well drilling string under multiple loads are analyzed. In addition, the effects of WOB, surface flow rate, and drilling fluid density on the dynamics of riserless drilling string in seawater and formation sections are discussed. This study provides theoretical guidance for the safety of riserless drilling in complex marine environments.

Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation

This paper presents data-driven models for photovoltaic module temperature prediction and analyzes the relation and effects of ambient conditions to module temperature. A total of 12 different machine learning and regression algorithms are implemented, with a large experimental dataset of 345,600 × 7. Prior to implementing those algorithms, data preprocessing is performed to prepare the datasets and determine the informative attributes for the models. Using PCA with module temperature as target to predict, the selected features for models' inputs were determined to be ambient temperature, solar radiation, wind speed, and relative humidity, and each algorithm is cross-validated and tuned with optimal performance parameters. Results show that while relative humidity is more likely to introduce less information to the model, other aforementioned features are the important parameters to predict the module temperature. While for linear modeling, LASSO algorithm provided the best performance, the ANN model demonstrated the best overall results as it produced the most accurate predictions with lowest errors. A similar performance is attained by the proposed non-linear model, KRR and Gradient Boosting algorithm, with a slight advantage to the KRR model. Furthermore, in comparison to experimental data, the ANN model and the proposed non-linear model provided an R2 values of 0.986 and 0.981, with a MAE of 0.982 and 1.476, and MSE of 2.181and 3.464, respectively. Moreover, the proposed model supplied accurate results when compared to models from literature in an out-of-sample testing, it also proven to be robust and accurate when used to predict the PV power output.

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.