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- 摘要
- 关键词
- 实验方案
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过滤筛选
- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- Optoelectronic Information Science and Engineering
- Wuhan University
- Central South University
- Hubei University
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Observation-Based Analog Ensemble Solar Forecast in Coastal California
摘要： Many studies demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low-dimensional deep features of the DNA methylation data. Experimental results show that these features perform best in breast cancer DNA methylation data cluster analysis, compared with some state-of-the-art methods.
关键词： DNA methylation,restricted Boltzmann machine,deep neural network,beat-value
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Laser Stimulation of Retina and Optic Nerve in Children with Anisometropic Amblyopia
摘要： Extreme learning machine (ELM) is emerged as an effective, fast, and simple solution for real-valued classification problems. Various variants of ELM were recently proposed to enhance the performance of ELM. Circular complex-valued extreme learning machine (CC-ELM), a variant of ELM, exploits the capabilities of complex-valued neuron to achieve better performance. Another variant of ELM, weighted ELM (WELM) handles the class imbalance problem by minimizing a weighted least squares error along with regularization. In this paper, a regularized weighted CC-ELM (RWCC-ELM) is proposed, which incorporates the strength of both CC-ELM and WELM. Proposed RWCC-ELM is evaluated using imbalanced data sets taken from Keel repository. RWCC-ELM outperforms CC-ELM and WELM for most of the evaluated data sets.
关键词： extreme learning machine,Real valued classification,complex valued neural network,class imbalance problem,regularization,weighted least squares error
更新于2025-09-23 15:19:57
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Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networksa??
摘要： Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.
关键词： Convolutional neural network,Machine learning,Hyperspectral microscopy,Food safety,Foodborne pathogen,Rapid classification
更新于2025-09-23 15:19:57
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Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection
摘要： In recent years, deep learning-based crack detection methods have been widely explored and applied due to their high versatility and adaptability. In civil engineering applications, recent research on crack detection through deep convolutional neural network (DCNN) includes road pavement crack detection, bridge inspection, defects detection in shield tunnel lining, etc. Despite the increasing popularity of DCNN on crack detection, many challenges have yet to be properly addressed. For crack detection using three-dimensional (3D) range (i.e., elevation) images, disturbances such as surface variation can negatively affect the detection performance. Besides, some typical non-crack patterns such as grooves can be easily misidentified as cracks, i.e., false positives. Another issue lies in the selection of hyperparameters related with the design of a DCNN architecture. For example, the hyperparameters which are related with network structure (e.g., kernel size, network depth and width) and training (e.g., mini-batch size and learning rate) can impact the network performance to a significant extent. Therefore, they need to be properly determined for optimal performance. However, for deep learning-based roadway crack classification using laser-scanned range images, a comprehensive discussion on the hyperparameter selection/tuning has not been thoroughly performed. This study develops a hyperparameter selection process involving a series of experiments on laser-scanned range images with high diversities, investigating the optimal joint hyperparameter configuration on network structure and training for DCNN-based roadway crack classification. In a comparative study, 36 DCNN architectures with varying layouts are developed for crack classification. These architecture candidates differ in kernel sizes (e.g., 3 × 3, 7 × 7, and 11 × 11), network depths (from 5 to 8 weight layers), and widths (from 16 to 96 kernels in each convolutional layer). The 7-layer DCNN with constant 7 × 7 kernels and increasing network widths yields the highest classification performance among the proposed 36 DCNN classifiers, which may be because it can best reflect the complexity of the acquired laser-scanned roadway range images. Once the optimal architecture layout is determined, further discussion on the selection of min-batch sizes, learning rates, dropout factor and leaky rectified linear unit (LReLU) factor is performed. Experimental results show the optimal architecture with associated training configuration can achieve consistent and accurate performance, under the contamination of surface variations and grooved patterns in laser-scanned range images. Discussion on the hyperparameter selection can provide insights for the development of DCNN in similar applications using laser-scanned range images.
关键词： Roadway crack,Groove,Laser-scanned range image,Hyperparameter selection,Deep convolutional neural network
更新于2025-09-23 15:19:57
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Neural network modeling and simulation of the synthesis of CuInS <sub/>2</sub> /ZnS quantum dots
摘要： The development of recipes for synthesis of quantum dots (QDs), a novel semi-conductor material for application in optoelectronic devices, is currently purely based on experiments. Since the quality of QDs represented by quantum yield (QY) and emission peak strongly depends on a number of different parameters (route, precursors, conditions, etc), a large number of experiments is necessary. In this article, we show that data-driven modeling can be used as a supporting tool for optimization and a better understanding of the synthesis process. By using the results collected during the development of CuInS2/ZnS (CIS/ZnS) QDs, a neural network model has been established. The model is able to predict the optical properties (QY and emission peak) of CIS/ZnS QDs as a function of the most important synthesis parameters, such as reaction temperature, time of CIS core formation and ZnS shell growth, feed molar ratio of Cu/In and Zn/Cu, various starting precursors, and types of ligands. Finally, a model analysis under various effects influencing the quality of QDs is performed.
关键词： CuInS2/ZnS,optimization,simulation,neural network,quantum dots
更新于2025-09-23 15:19:57
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Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging
摘要： Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.
关键词： granite soils,artificial neural network,hyperspectral camera,soil water characteristic curve,water content,visible and near-infrared
更新于2025-09-23 15:19:57
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Real-Time Weld Quality Prediction Using a Laser Vision Sensor in a Lap Fillet Joint during Gas Metal Arc Welding
摘要： Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.
关键词： deep neural network,camera calibration,laser vision sensor,gas metal arc welding,weld quality prediction
更新于2025-09-23 15:19:57
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Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm
摘要： The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more e?ective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.
关键词： YOLOv3,real-time,neural network,concealed object detection,deep learning,passive millimeter wave
更新于2025-09-23 15:19:57
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Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems
摘要： The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm.
关键词： water pumping system management,photovoltaic power,empirical models,forecast,artificial neural network,neuro fuzzy inference system
更新于2025-09-23 15:19:57
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The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting
摘要： The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
关键词： NARX neural network,photovoltaic (PV) power forecasting,multisite modelling,short-term forecasting,smart persistence model,classification methods
更新于2025-09-23 15:19:57