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- 摘要
- 关键词
- 实验方案
- 产品
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[Lecture Notes in Networks and Systems] Renewable Energy for Smart and Sustainable Cities Volume 62 (Artificial Intelligence in Renewable Energetic Systems) || Prediction PV Power Based on Artificial Neural Networks
摘要： The goal of this contribution is to estimate the power delivered by a multicrystals solar photovoltaic module based on artificial neural networks. Two structures of ANNs were tested: multiple-layer perceptron and radial basic function. The results obtained gave good coefficients of correlation, the statistical R2-value obtained is about 0.96 to predict this important parameter.
关键词： Artificial neural network (ANNs),Multiple-layer perceptron (MLP),Radial basic function (RBF),Photovoltaic (PV) power
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd Optoelectronics Global Conference (OGC) - Shenzhen (2018.9.4-2018.9.7)] 2018 IEEE 3rd Optoelectronics Global Conference (OGC) - High Speed Novel Hybrid Modulation Technique of Visible Light Communication Based on Artificial Neural Network Equalizer
摘要： Visible light communication (VLC) which realizes data transmission and universal illumination simultaneously has attracted much attention recently. However, the transmission rate of the VLC remains low due to the low bandwidth performance and inter-symbol interference (ISI). Therefore, a hybrid approach using pulse amplitude modulation and pulse width modulation in conjunction with an artificial neural network (ANN) equalizer is proposed, which can theoretically increase the transmission rate by 4 times compared with the traditional way, and provide variable brightness to realize the integration of data transmission and illumination control. In addition, an artificial neural network equalizer is proposed to undo the effects of ISI, considering that the bandwidth of the LED is only 3MHz. Without the ANN equalizer, the maximum transmission rate of the proposed hybrid modulation link only reaches 36 Mbps under the condition of no signal processing; however, with the ANN equalizer, the transmission speed can up to 2.6 Gbps. The proposed system not only achieves a genuine combination of data transmission and control illumination levels, but also realizes a high data rate with less complexity.
关键词： code division multiple access,pulse amplitude modulation,visible light communication,artificial neural network equalizer
更新于2025-09-23 15:23:52
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FPGA-Based Implementation of an Artificial Neural Network for Measurement Acceleration in BOTDA Sensors
摘要： In recent years, using distributed fiber-optic sensors based on Brillouin scattering, for monitoring pipelines, tunnels, and other constructional structures have gained huge popularity. However, these sensors have a low signal-to-noise ratio (SNR), which usually increases their measurement error. To alleviate this issue, ensemble averaging is used which improves the SNR but in return increases the measurement time. Reducing the noise by averaging requires hundreds or thousands of scans of the optical fiber; hence averaging is usually responsible for a large percent of the entire system latency. In this paper, we propose a novel method based on artificial neural network for SNR enhancement and measurement acceleration in distributed fiber-optic sensors based on the Brillouin scattering. Our method takes the noisy Brillouin spectrums and improves their SNR by 20 dB, which reduces the measurement time significantly. It also improves the accuracy of the Brillouin frequency shift estimation process and its latency by more than 50% in comparison with the state-of-the-art software and hardware solutions.
关键词： Artificial neural network (ANN),digital signal processing,optical fibers,curve fitting,field-programmable gate arrays (FPGAs)
更新于2025-09-23 15:23:52
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Imaging analysis of chlorophyll fluorescence induction for monitoring plant water and nitrogen treatments
摘要： The objective of this study was to check whether different water and nitrogen treatments and, even the water-nitrogen coupling effect of plants could be correctly differentiated via chlorophyll a fluorescence image. We developed a classification method using the imaging analysis of chlorophyll a fluorescence induction based on Artificial Neural Network. The measurements were carried out on scheffera octophylla (Lour.) Harms, and the images were recorded at 690 nm with a high-resolution imaging device consisting of LEDs for an excitation at 460 nm and an Electron-Multiplying CCD camera. The effect of three different water and three different nitrogen treatments on the fluorescence parameters were obtained by hundreds of time-resolved fluorescence images. We used a Radial Basis Function neural network to model and test the sample data. The results showed that the different water and nitrogen statuses of plants were identified by the chlorophyll a fluorescence images and showed a high recognition accuracy. Compared with nitrogen, water had more of an influence on chlorophyll a fluorescence and was easier to identify. However, because the water and nitrogen restrict and promote each other, studying the coupling effect of water and nitrogen is necessary. Nine levels of water-nitrogen coupling plants were tested and classified. We discovered that a significant decrease on the classified accuracy was observed for the high nitrogen and low nitrogen treatments, while under a medium N-supply, the recognition rate was high. The method in this paper allowed plants to be classified under different water and nitrogen treatments, and has the potential to monitor the water and nitrogen coupling effect of plants in situ.
关键词： Artificial Neural Network,Classification,Recognition,Chlorophyll a Fluorescence
更新于2025-09-23 15:23:52
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Visualizing Interactions of Circulating Tumor Cell and Dendritic Cell in the Blood Circulation Using In Vivo Imaging Flow Cytometry
摘要： Objective: Visualizing cell interactions in blood circulation is of great importance in studies of anticancer immunotherapy or drugs. However, the lack of a suitable imaging system hampers progress in this field. Methods: In this work, we built a dual-channel in vivo imaging flow cytometer to visualize the interactions of circulating tumor cells (CTCs) and dendritic cells (DCs) simultaneously in the bloodstream. Two artificial neural networks were trained to identify blood vessels and cells in the acquired images. Results and Conclusion: Using this technique, single CTCs and CTC clusters were readily distinguished by their morphology. Interactions of CTCs and DCs were identified, while their moving velocities were analyzed. The CTC-DC clusters moved at a slower velocity than that of single CTCs or DCs. This may provide new insights into tumor metastasis and blood rheology. Significance: This in vivo imaging flow cytometry system holds great potential for assessing the efficiency of targeting CTCs with anticancer immune cells or drugs.
关键词： Cell Interaction,Circulating Tumor Cell,In Vivo Imaging Flow Cytometry,Artificial Neural Network,Dendritic Cell
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Integration of Worldview-2 and Lidar Data to MAP a Subtropical Forest Area: Comparison of Machine Learning Algorithms
摘要： This work is committed to explore the integration of airborne LiDAR data and WorldView-2 (WV-2) images to classify land cover and land use in a rural area with the presence of a subtropical forest. Different methods were used for this purpose: two artificial neural networks (ANN) and three decision trees forests. The results demonstrated that the inclusion of LiDAR data significantly improved the classifications in all methods. Excluding the Convolutional Neural Network, the classification algorithms had a nearly similar performance, and none of them achieved the best accuracy for all adopted classes. Forest by Penalizing Attributes (FPA) attained the best general result, with a Kappa index of 0.92, while Rotation Forest obtained the best result in the classification of the two vegetation classes.
关键词： Artificial Neural Network,Data fusion,Forest succession stages,Decision Forest
更新于2025-09-23 15:22:29
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Detection of Circulating Tumor Cells in Fluorescence Microscopy Images Based on ANN Classifier
摘要： Circulating tumor cells (CTCs) is a clinical biomarker for cancer metastasis. CTCs are cells circulating in the body of patients by being separated from primary cancer and entering into blood vessel. CTCs spread every positions in the body, and this is one of the cause of cancer metastasis. To analyze them, pathologists get information about metastasis without invasive test. CTCs test is conducted by analyzing the blood sample from patient. The fluorescence microscope generates a large number of images per each sample, and images contain a lot of cells. There are only a few CTCs in images and cells often have blurry boundaries. So CTCs identification is not an easy work for pathologists. In this paper, we develop an automatic CTCs identification method in fluorescence microscopy images. This proposed method has three section. In the first approach, we conduct the cell segmentation in images by using filtering methods. Next, we compute feature values from each CTC candidate region. Finally, we identify CTCs using artificial neural network algorithm. We apply the proposed method to 5895 microscopy images (7 samplesas), and evaluate the effectiveness of our proposed method by using leave-one-out cross validation. We achieve the result of performance tests, a true positive rate is 92.57% and false positive rate is 9.156%.
关键词： Fluorescence microscopy image,Artificial neural network,Feature analysis,Computer aided diagnosis,Circulating tumor cells
更新于2025-09-23 15:22:29
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Hyperspectral signature analysis using neural network for grade estimation of copper ore
摘要： The ever-increasing demand for the different metal and mineral resources from the earth’s subsurface has brought tremendous pressure on the geochemical laboratory for the growing countries. The success of any mining industry relies on the estimated values of ore grade in the mineral deposit. Hence, rapid assessment of ore grade is critical in daily schedule in mines operations. Commonly the assay value is determined by chemical analysis or X-Ray Fluorescence (XRF), which is one of the constrained by real-time grade estimation, duration of sample preparation and processing. Several researches carried out in exploration and revealed that hyperspectral technique is a promising tool for mineral identification and mapping. The goal of the present study is to determine the effectiveness of narrow band spectroscopy in Cu grade estimation. To achieve this, a multilayer feed-forward neural network model has been developed to establish a functional link between hyperspectral signature derived features with the copper grade. Altogether eight different types of features including absorption depth, band depth center, the area under the absorption curve, full width at half maxima were extracted from continuum removed spectra along with derivative reflectance features, e.g. band depth ratio, 1st and 2nd slopes from the hyperspectral profile. The dimensionality was reduced by applying Principal Component Analysis onto the extracted features. The first seven PCAs are then used as input vector of the ANN model. A five-fold cross-validation exercise is carried out for model performance. The high degree of correlation reveals that the PCA generated feature from hyperspectral data coupled with ANN model could be an alternative approach to predict the copper grade for the copper mine.
关键词： copper grade,ore grade estimation,spectral feature,K-Fold cross validation,principal component analysis,artificial neural network
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE Power & Energy Society General Meeting (PESGM) - Portland, OR, USA (2018.8.5-2018.8.10)] 2018 IEEE Power & Energy Society General Meeting (PESGM) - Study of Impact of Cloud Distribution on Multiple Interconnected Solar PV Plants Generation and System Strength
摘要： Dependence of solar power generation on solar irradiance results in sudden and dramatic variations in power generation following significant changes in cloud distribution over a solar PV plant. Currently, this phenomenon is being one of the most challenging issues in resource planning and maintaining the reliability of modern power grids with high penetration of solar power. The dramatic variation of solar power generation has a direct impact on system strength at the Points of Interconnection (POIs). Hence, the power quality of the system is compromised, especially because solar PV plants are usually interconnected to distribution systems and near load zones. In this paper, an Artificial Neural Network (ANN) based approach is developed to forecast the clouds distribution for the estimation of sudden and dramatic variations in the solar irradiance. This estimate is used to evaluate the system strength in terms of voltage stability at each POI. We apply newly developed methodology to measure the system strength known as Site-Dependent Short Circuit Ratio (SDSCR), which provides more accurate results of system strength evaluation. The validity and effectiveness of the developed approach is confirmed through comparing its results versus the cloud distribution data provided by weather satellites.
关键词： Artificial neural network,renewable energy,system strength,voltage stability,short circuit ratio
更新于2025-09-23 15:22:29
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Sky Image-Based Solar Irradiance Prediction Methodologies Using Artificial Neural Networks
摘要： In order to decelerate global warming, it is important to promote renewable energy technologies. Solar energy, which is one of the most promising renewable energy sources, can be converted into electricity by using photovoltaic power generation systems. Whether the photovoltaic power generation systems are connected to an electrical grid or not, predicting near-future global solar radiation is useful to balance electricity supply and demand. In this work, two methodologies utilizing artificial neural networks (ANNs) to predict global horizontal irradiance in 1 to 5 minutes in advance from sky images are proposed. These methodologies do not require cloud detection techniques. Sky photo image data have been used to detect the clouds in the existing techniques, while color information at limited number of sampling points in the images are used in the proposed methodologies. The proposed methodologies are able to capture the trends of fluctuating solar irradiance with minor discrepancies. The minimum root mean square errors of 143 W/m2, which are comparable with the existing prediction techniques, are achieved for both of the methodologies. At the same time, the proposed methodologies require much less image data to be handled compared to the existing techniques.
关键词： Artificial Neural Network,Photovoltaic Power Generation,Solar Energy,Global Horizontal Irradiance Prediction,Sky Image
更新于2025-09-23 15:21:21