- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China
摘要： Day of the year-based (DYB) models can achieve great accuracy in daily global solar radiation estimation without specific meteorological elements. Many empirical models (EMs) and machine learning (ML) methods have been proposed for DYB models. However, the number of their comparative studies based on diverse climates is limited. In this study, a grand total of 14 DYB models are established to estimate daily global solar radiation based on measured data from 1994 to 2015 at 35 meteorological stations in six climate zones of China. Detailed tasks are as follows: (1) Seven EMs and seven ML models are trained for solar radiation estimation. (2) A new EM and two novel ML models are proposed, i.e. hybrid 3th order polynomial and sine wave model, adaptive neuro-fuzzy inference system (ANFIS) optimized by chaotic firefly algorithm (CFA) and ANFIS optimized by whale optimization algorithm with simulated annealing and roulette wheel selection (WOASAR). (3) Four statistical indicators are utilized to compare those models, and the best model for each station is decided. (4) We discuss the model parameters and climate variances of six specific stations in different climate zones. The comparison results demonstrate superb estimation precision and climate adaptability of the newly proposed models.
关键词： Day of the year,Empirical models,Global solar radiation estimation,Machine learning
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Histogram-Based Image Pre-processing for Machine Learning
摘要： This paper proposes to use some image processing methods as a data normalization method for machine learning. Conventionally, z-score normalization is widely used for pre-processing of data. In the proposed approach, in addition to z-score normalization, a number of histogram-based image processing methods such as histogram equalization are applied to training data and test data as a pre-processing method for machine learning. We evaluate the effectiveness of the proposed approach by using a support vector machine algorithm and a random forest one. In experiments, the proposed scheme is applied to a face-based authentication algorithm with SVM/random forest classifiers to confirm the effectiveness. For SVM classifiers, both z-score normalization and image enhancement work well as a pre-processing method for improving the accuracy. In contrast, for random forest classifiers, a number of image enhancement methods work well, although z-score normalization is unuseful for improving the accuracy.
关键词： Support Vector Machines,Pre-processing,Contrast Enhancement,Random Forest,Machine Learning
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Machine Learning-based Stereo Vision Algorithm for Surround View Fisheye Cameras
摘要： Recently, automated emergency brake systems for pedestrian have been commercialized. However, they cannot detect crossing pedestrians when turning at intersections because the field of view is not wide enough. Thus, we propose to utilize a surround view camera system becoming popular by making it into stereo vision which is robust for the pedestrian recognition. However, conventional stereo camera technologies cannot be applied due to fisheye cameras and uncalibrated camera poses. Thus we have created the new method to absorb difference of the pedestrian appearance between cameras by machine learning for the stereo vision. The method of stereo matching between image patches in each camera image was designed by combining D-Brief and NCC with SVM. Good generalization performance was achieved by it compared with individual conventional algorithms. Furthermore, feature amounts of the point cloud reconstructed by the stereo pairs are utilized with Random Forest to discriminate pedestrians. The algorithm was evaluated for the actual camera images of crossing pedestrians at various intersections, and 96.0% of pedestrian tracking rate with high position detection accuracy was achieved. They were compared with Faster R-CNN as the best pattern recognition technique, and our proposed method indicated better detection performance.
关键词： NCC,automated emergency brake systems,machine learning,SVM,Faster R-CNN,stereo vision,pedestrian detection,D-Brief,Random Forest,surround view camera system
更新于2025-09-04 15:30:14
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Machine Learning for 100Gb/s/λ Passive Optical Network
摘要： To respond the growing bandwidth demand by emerging applications such as fixed-mobile convergence for 5G and beyond 5G, 100Gb/s/λ access network becomes the next research focus of passive optical network (PON) roadmap. Intensity modulation and direct detection (IMDD) technology is still considered as a promising candidate for 100Gb/s/λ PON attributed to its low cost, low power consumption and small footprint. In this paper, we achieve 100Gb/s/λ IMDD PON by using 20G-class optical and electrical devices due to its commercial linear and nonlinear availability. To mitigate the system distortions, neural network (NN) based equalizer is used and the performance is compared with feedforward equalizer (FFE) and Volterra nonlinear equalizer (VNE). We introduce the rules to train and test the data when using NN-based equalizer to guarantee a fair comparison with FFE and VNE. Random data has to be used for training, but for test, both random data and psudo-random bit sequence (PRBS) are applicable. We found NN-based equalizer has the same performance with FFE and VNE in the case of linear distortion only, but outperforms them in strong nonlinearity case. In the experiment, to improve the loss budget, we increase the launch power to 18 dBm, achieving a 30-dB loss budget for 33Gbaud/s PAM8 signal at the system frequency response of 16.2 GHz, attributed to the strong nonlinear equalization capability of NN.
关键词： neural network (NN),machine learning,intensity modulation and direct detection (IMDD),digital signal processing (DSP),Passive optical network (PON)
更新于2025-09-04 15:30:14
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Machine Learning for Organic Cage Property Prediction
摘要： We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4+6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt.
关键词： shape persistence,machine learning,symmetry,cavity size,organic cages
更新于2025-09-04 15:30:14
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A machine learning approach for online automated optimization of super-resolution optical microscopy
摘要： Traditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task. Its potential is highlighted on various imaging tasks, such as live-cell and multicolor imaging and multimodal optimization. This online optimization routine can be integrated to various imaging systems to increase accessibility, optimize performance and improve overall imaging quality.
关键词： machine learning,multicolor imaging,online automated optimization,live-cell imaging,super-resolution optical microscopy,multimodal optimization
更新于2025-09-04 15:30:14
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Empirical modeling of dopability in diamond-like semiconductors
摘要： Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p- and n-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.
关键词： dopability,machine learning,diamond-like semiconductors,thermoelectrics,carrier concentration
更新于2025-09-04 15:30:14
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[EAI/Springer Innovations in Communication and Computing] Computational Intelligence and Sustainable Systems (Intelligence and Sustainable Computing) || Investigation of Non-natural Information from Remote Sensing Images: A Case Study Approach
摘要： Rapid changes in non-natural information, such as infrastructure development, require frequent and rapid updates to maps from remote sensing (RS) images for numerous cartographic operations, including road map improvements for intelligent transportation systems, traffic monitoring, rural area development planning, and navigation. Revolutions in satellite imaging technology have improved the interpretation of non-natural information (e.g., roads, building, bridges, dams) for geographic information system (GIS) updates within short period of time compared with ground surveying.
关键词： Road Extraction,GIS,Image Processing,Remote Sensing,Machine Learning
更新于2025-09-04 15:30:14
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[IEEE 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - Helsinki (2018.6.3-2018.6.5)] 2018 - 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON) - CHANNEL-MISMATCH DETECTION ALGORITHM FOR STEREOSCOPIC VIDEO USING CONVOLUTIONAL NEURAL NETWORK
摘要： Channel mismatch (the result of swapping left and right views) is a 3D-video artifact that can cause major viewer discomfort. This work presents a novel high-accuracy method of channel-mismatch detection. In addition to the features described in our previous work, we introduce a new feature based on a convolutional neural network; it predicts channel-mismatch probability on the basis of the stereoscopic views and corresponding disparity maps. A logistic-regression model trained on the described features makes the ?nal prediction. We tested this model on a set of 900 stereoscopic-video scenes, and it outperformed existing channel-mismatch detection methods that previously served in analyses of full-length stereoscopic movies.
关键词： machine learning,channel mismatch,quality assessment,convolutional neural networks,Stereoscopic video
更新于2025-09-04 15:30:14
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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 - The Potential of Sentinel Satellites for Large Area Aboveground Forest Biomass Mapping
摘要： Estimation of aboveground forest biomass is critical for regional carbon policies and sustainable forest management. Both passive optical remote sensing and active microwave remote sensing can play an important role in the monitoring of forest biomass. In this study, the recently launched Sentinel-2 Multi Spectral Instrument satellite and Sentinel-1 SAR satellite systems were evaluated and integrated to investigate the relative strengths of each sensor for mapping aboveground forest biomass at a regional scale. The Australian state of Victoria, with its wide range of forest vegetation was chosen as the study area to demonstrate the scalability and transferability of the approach. In this study aboveground forest biomass (AGB) was defined as the tons of carbon per hectare for the aboveground components (stem, branches, leaves) of all live large trees greater than 10 cm in diameter at breast height (DBHOB). Sentinel-2 and Sentinel-1 data were fused within a machine learning framework using a boosted regression tree model and high-quality ground survey data. Multi-criteria evaluations showed the use of the two independent and fundamentally different Sentinel satellite systems were able to provide robust estimates (R2 of 0.62, RMSE of 32.2 t.C.ha-1) of aboveground forest biomass, with each sensor compensating for the weakness (cloud perturbations and spectral saturation for Sentinel 2, and sensitivity to ground moisture for Sentinel 1) of each other. As archives for Sentinel-2 and Sentinel-1 continue to grow, mapping aboveground forest biomass and dynamics at moderate resolution over large regions should become increasingly feasible.
关键词： Sentinel-2,machine learning,data fusion,Sentinel-1,Victoria,boosted regression tree model,Australia,biomass estimation
更新于2025-09-04 15:30:14