- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Short Term Day-Ahead Solar Radiation Prediction Using Machine Learning Techniques
摘要： The task of solar power forecasting becomes vital to ensure grid constancy and to enable an optimal unit commitment and cost-effective dispatch. Each year latest techniques and approaches appear to increase the exactitude of models with the important goal of reducing uncertainty in the predictions. The aim of the paper is to compile a big part of the knowledge about solar power forcing, to focus on the most recent advancements and future trends. Firstly, the inspiration to achieve an accurate forecast is presented with the analysis of the economic implications it may have. To address the problem superlative prediction models are rummaged by us using machine learning techniques. We make a comparison between multiple regression techniques for creating prediction models, along with linear least squares and support vector machines using multiple kernel functions. Predictions are analyzed by us in our experiments for the day ahead solar radiation data and it is shown that a machine learning approach yields feasible results for short-term solar prediction. The proposed model achieves a root mean square error improvement of around 29% compared to others proposed model except one.
关键词： Forecasting,SVR,Renewable energy,Short-term,Machine learning
更新于2025-09-23 15:22:29
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Prediction of excitation wavelength of phosphors by using machine learning model
摘要： Luminescent materials are the integral part of green revolution helping us in saving the energy. Much effort been made to design and discover the novel phosphors for solid-state lighting. The current paper focuses on the development of machine learning (ML) model based on simple luminescent materials to predict the excitation to the closest possible accuracy using easily accessible key attributes using least absolute shrinkage and selection operator (LASSO) and artificial neural network (ANN) ML approach. These selected attributes expected to correlate with the excitation of material. The style for studying the material property has the potential to turn down the cost and time involved in an Edisonian approach to the lengthy lab experiment to identify excitation.
关键词： Solid-state lighting,Phosphor,Machine learning,Excitation wavelength,Luminescence
更新于2025-09-23 15:22:29
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Mapping salt marsh soil properties using imaging spectroscopy
摘要： Tidal salt marshes sequester and store blue carbon at both short and long time scales. Marsh soils shape and maintain the ecosystem by supporting complex biogeochemical reactions, deposition of sediment, and accumulation of organic matter. In this study, we examined the potential of imaging spectroscopy techniques to indirectly quantify and map tidal marsh soil properties at a National Estuarine Research Reserve in Georgia, USA. A framework was developed to combine modern digital image processing techniques for marsh soil mapping, including object-based image analysis (OBIA), machine learning modeling, and ensemble analysis. We also evaluated the efficacy of airborne hyperspectral sensors in estimating marsh soil properties compared to spaceborne multispectral sensors, WorldView-2 and QuickBird. The pros and cons of object-based modeling and mapping were assessed and compared with traditional pixel-based mapping methods. The results showed that the designed framework was effective in quantifying and mapping three marsh soil properties using the composite reflectance from salt marsh environment: soil salinity, soil water content, and soil organic matter content. Multispectral sensors were successful in quantifying soil salinity and soil water content but failed to model soil organic matter. The study also demonstrated the value of minimum noise fraction transformation and ensemble analysis techniques for marsh soil mapping. The results suggest that imaging spectroscopy based modeling is a promising tool to quantify and map marsh soil properties at a local scale, and is a potential alternative to traditional soil data acquisition to support carbon cycle research and the conservation and restoration of tidal marshes.
关键词： Salt marsh,Object-based modeling,Soil properties,Imaging spectroscopy,Machine learning
更新于2025-09-23 15:22:29
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[IEEE 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Singapore, Singapore (2018.11.18-2018.11.21)] 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Camera Based Decision Making at Roundabouts for Autonomous Vehicles
摘要： Being able to join roundabouts correctly is crucial for an autonomous vehicle to maintain not only its own safety but also a normal traffic order for others. In order to know the right time and speed for entering roundabouts, the location, speed and direction of the approaching vehicles need to be taken into consideration. This study investigated the feasibility of leveraging computer vision and machine learning to help autonomous vehicles decide to wait or to enter when reaching roundabouts. A grid-based image processing approach with a single camera at normal roundabouts (GBIPA-SC-NR) is proposed in this paper to characterize traffic situations that can be used for machine learning algorithms to learn the roundabout joining criteria. Video road clips recorded when human drivers reach and then join various roundabouts at different locations were utilised for this learning process, with a selection of four supervised classification algorithms (i.e. the Support Vector Machines, Random Forests, K-Nearest Neighbours, and Decision Tree). The trained classifiers using the proposed approach were evaluated on 507 test videos captured at roundabouts, where the SVM showed the best performance with a 90.28% classification accuracy. This result suggests that the proposed grid-based image processing method can be applied to effectively help autonomous vehicles made the right decision when reaching a roundabout.
关键词： Autonomous Vehicle,Grid-based Image Processing,Machine Learning,Roundabout
更新于2025-09-23 15:22:29
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The Optical Barcode Detection and Recognition Method Based on Visible Light Communication Using Machine Learning
摘要： Visible light communication (VLC) has developed rapidly in recent years. VLC has the advantages of high confidentiality, low cost, etc. It could be an effective way to connect online to offline (O2O). In this paper, an RGB-LED-ID detection and recognition method based on VLC using machine learning is proposed. Different from traditional encoding and decoding VLC, we develop a new VLC system with a form of modulation and recognition. We create different features for different LEDs to make it an Optical Barcode (OBC) based on a Complementary Metal-Oxide-Semiconductor (CMOS) senor and a pulse-width modulation (PWM) method. The features are extracted using image processing and then support vector machine (SVM) and artificial neural networks (ANN) are introduced into the scheme, which are employed as a classifier. The experimental results show that the proposed method can provide a huge number of unique LED-IDs with a high LED-ID recognition rate and its performance in dark and distant conditions is significantly better than traditional Quick Response (QR) codes. This is the first time the VLC is used in the field of Internet of Things (IoT) and it is an innovative application of RGB-LED to create features. Furthermore, with the development of camera technology, the number of unique LED-IDs and the maximum identifiable distance would increase. Therefore, this scheme can be used as an effective complement to QR codes in the future.
关键词： CMOS image sensor,machine learning,image processing,RGB-LED,visible light communication (VLC)
更新于2025-09-23 15:22:29
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Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning
摘要： Formability and stability issues are of core importance and difficulty in current research and applications of perovskites. Nevertheless, over the past century, determination of the formability and stability of perovskites has relied on semi empirical models derived from physics intuition, such as the commonly used Goldschmidt tolerance factor, t. Here, through high-throughput density functional theory (DFT) calculations, a database containing the decomposition energies, considered to be closely related to the thermodynamic stability of 354 halide perovskite candidates, is established. To map the underlying relationship between the structure and chemistry features and the decomposition energies, a well-functioned machine learning (ML) model is trained over this theory-based database and further validated by experimental observations of perovskite formability (F1 score, 95.9%) of 246 A2B(I)B(III)X6 compounds that are not present in the training database; the model performs a lot better than empirical descriptors such as tolerance factor t (F1 score, 77.5%). This work demonstrates that the experimental engineering of stable perovskites by ML could solely rely on training data derived from high-throughput DFT computing, which is much more economical and efficient than experimental attempts at materials synthesis.
关键词： halide double perovskite,stability,machine learning,high-throughput
更新于2025-09-23 15:22:29
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Soft Failure Localization in Elastic Optical Networks
摘要： Soft failure localization to early detect service level agreement violations is of paramount importance in elastic optical networks (EONs), while it allows anticipating possible hard failure events. Nowadays, effective and automated solutions for soft failure localization during lightpaths’ commissioning testing and operation are still missing. In this paper, we focus on presenting soft failure localization algorithms based on two different active monitoring techniques. First, the Testing optIcal Switching at connection SetUp timE (TISSUE) algorithm is proposed to localize soft failures during commissioning testing phase by elaborating the estimated bit-error rate (BER) values provided by low-cost optical testing channel (OTC) modules. Second, the FailurE causE Localization for optIcal NetworkinG (FEELING) algorithm is proposed to localize failures during lightpath operation using cost-effective optical spectrum analyzers (OSAs) widely deployed in network nodes. Results are presented to validate both algorithms in the event of several soft failures affecting lasers and filters.
关键词： machine learning algorithms,soft failure localization,monitoring and data analytics
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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
摘要： In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The key contributions are as follows. We present a novel dataset based on Sentinel-2 satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Networks (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards various Earth observation applications. We demonstrate how the classification system can assist in improving geographical maps.
关键词： Deep Learning,Land Use Classification,Earth Observation,Convolutional Neural Network,Machine Learning,Dataset,Land Cover Classification
更新于2025-09-23 15:21:21
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Fault Classification in Electrofusion Polyethylene Joints by Combined Machine Learning, Thermal Pulsing and IR Thermography Methods - A Comparative Study
摘要： The capability of conveniently classifying the fault types in the electrofusion joints can certainly increase the security of polyethylene gas pipelines. Therefore in the current study, we use machine learning to push the horizons of our recent thermal pulsing and IR thermography method, to identify ovality versus unalignment faults. To do so, we extend our experimental IR-thermography data bank and then apply k-means, Random Forests and GLMNet algorithms in a two stage approach. The overall classification accuracy for k-means and Random Forests were 70.37% and 84.21% respectively; GLMNet could successfully outperform the others with a classification accuracy of 93.75%.
关键词： Machine Learning,Electrofusion Polyethylene Joint,IR Thermography,Fault Classification,Thermal Pulsing NDT
更新于2025-09-23 15:21:21
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding
摘要： In this paper, we propose a two-pass encoding framework to handle the problem of sequence-level rate control. We consider the sequence-level encoding parameter constant rate factor (CRF) as the factor to be adjusted. The proposed framework mainly has two key contributions. First, we provide a second order model to characterize the relationship between the bitrate and CRF. The proposed second order model outperforms the traditional linear model significantly. Second, we adopt a shallow neural network to train the relationship between the content-dependent features with the second-order model parameters. The proposed neural network is quite simple but able to estimate the model parameters accurately. We implement the proposed algorithm under tensorflow. Experimental results show that our proposed method obviously outperforms the state-of-the-art method.
关键词： sequence-level,constant rate factor,video coding,Rate control,Machine learning,second order model
更新于2025-09-23 15:21:21