Operational cloud classification for the Iberian Peninsula using Meteosat Second Generation and AQUA-AIRS image fusion

The aim of this work was the adaptation and improvement of a previous cloud detection and classification algorithm that was developed for the Meteosat-7 satellite. The functions of this satellite have now been taken on by the new series of Meteosat Second Generation (MSG) satellites, which are not just replicas but new, much improved versions of their predecessor. The formerly used Advanced/Tiros-N Operational Vertical Sounder (A/TOVS) probe has also been superseded technologically by new sensors with better spatial resolution, capable of carrying out more accurate measurements at a greater number of wavelengths. This is the case of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA and AQUA satellites and of the Atmospheric Infrared Sounder (AIRS) probe. In this context, new potential improvements are analysed for this algorithm by using these new platforms and sensors and the results are compared to those obtained in the first classification.     

Physical and statistical approaches for cloud identification using Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Data

In this paper a cloud detection algorithm applied to the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) data is described. In order to obtain a good performance in cloud detection, physical, statistical and temporal approaches have been used. In the statistical algorithm, the spectral and textural features of the MSG-SEVIRI images have been used as input, while, in the physical tests, a set of dynamic thresholds has been used. The physical algorithm does not use real time ancillary data— such as sea surface temperature map and NWP temperature and humidity profiles. A further test is applied to that pixels having low confidence to be clear or cloudy. This test takes advantage of the best MSG-SEVIRI temporal resolution and it applies the K-Nearest Neighbour classifier to the spectral and textural features calculated in “temporal” boxes 3 × 3 pixels, defined “temporal” because their elements belong to three subsequent MSG-SEVIRI images. The MACSP (cloud MAsk Coupling of Statistical and Physical methods) algorithm has been validated against the MODIS cloud mask and compared with CPR (Cloud Profiling Radar) and SAFNWC cloud masks. The outcomes show that the MACSP detects 91.8% of the total number of the pixels used for validation against MODIS cloud mask correctly, while the SAFNWC cloud mask detects 89.2% of them correctly. In particular, the MACSP classifies as cloudy 8.8% of the pixels classified by the MODIS cloud mask as clear, while the SAFNWC cloud mask classifies as cloudy 12.1% of them. The MACSP detects 91.2% of the cloudy CPR pixels and 90.8% of the cloud-free CPR pixels, considered for comparison, correctly. On the other hand, the SAFNWC and CPR cloud masks agree in the detection of 90.7% of the cloudy pixels and of 90.2% of the cloud-free pixels.     

Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting

In this article, a novel technique based on artificial neural networks (NN) is proposed for cloud coverage short-term forecasting (nowcasting). In particular, the capabilities of multi-layer perceptron NN and time series analysis with nonlinear autoregressive with exogenous input NN are explored and applied to the European meteorological system ‘Meteosat Second Generation’ with its payload Spinning Enhanced Visible and InfraRed Imager. The general neural architecture consists of a first stage addressing the prediction of the radiance images at six bands (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm). In a second stage a cloud masking algorithm, always based on NN, is applied to the predicted images for the cloud coverage nowcasting. The scheme was compared with the most basic forecast algorithm for the prediction: the persistent model. Two test areas characterized by different climatology have been considered for the performance analysis. The results show that about 85% of the changes occurring in the time window were recognized by the proposed technique.     

Generation of a diagnosis model for hybrid-electric vehicles using machine learning

Online fault-diagnosis on system level for complex mechatronic systems takes multiple sensor measurements of the various components into account and contributes to a significantly increased system reliability by tracking down faults in the system at run time, enabling fault-specific recovery actions, such as reconfigurations. Ongoing efforts in the technological development of automobiles, especially in the field of driver assistance systems, yield more and more safety-critical systems, e.g., breaking control systems, and thus generate a high demand for reliable online diagnosis systems. In order to perform fault-diagnosis on system level, the interrelations between all measurements must be determined, which is a challenging and often demanding task done by human system experts. In this paper we present a systematic approach based on machine learning to establish online diagnosis for a hybrid-electric vehicle model in the context of the DAKODIS research project. With this paper we publish the Matlab/Simulink HEV research platform including a fault injection framework and data processing algorithms for active fault-diagnosis and recovery evaluations.     

Automated diagnosis of sewer pipe defects based on machine learning approaches

In sewage rehabilitation planning, closed circuit television (CCTV) systems are the widely used inspection tools in assessing sewage structural conditions for non man entry pipes. Currently, the assessment of sewage structural conditions by manually interpretation on CCTV images seems inefficient, especially for several thousands of frames in one inspection plan. Also, the assessment work significantly involves engineers’ eye sight and professional experience. With a purpose of assisting general staffs in diagnosing pipe defects on CCTV inspection images, a diagnostic system by applying machine learning approaches is proposed in this paper. This research was first to use image process techniques, including wavelet transform and computation of co-occurrence matrices, for describing the textures of the pipe defects. Then, three neural network approaches, back-propagation neural network (BPN), radial basis network (RBN), and support vector machine (SVM), were adopted to classify pipe defect patterns, and their performances were compared and discussed. The diagnostic system of pipe defects was applied to a sewer system in the 9th district, Taichung City which is the largest city in middle Taiwan. The result shows that the diagnosis accuracy of 60% derived by SVM is the best and also better than the diagnosis accuracy of 57.4% derived by a Bayesian classifier.     

Enhancing software model encoding for feature location approaches based on machine learning techniques

Software and Systems Modeling - Feature location is one of the main activities performed during software evolution. In our previous works, we proposed an approach for feature location in models...     

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.     

An efficient approach for damage identification based on improved machine learning using PSO-SVM

Engineering with Computers - Structural health monitoring (SHM) and Non-destructive Damage Identification (NDI) using responses of structures under dynamic excitation have an imperative role in the...     

Prediction of TBM operation parameters using machine learning models based on BPSO

Rational parameters of TBM (Tunnel Boring Machine) are the key to ensuring efficient and safe tunnel construction. Machine learning (ML) has become the main method for predicting operating parameters. Grid Search and optimization algorithms, such as Particle Swarm Optimization (PSO), are often used to find the hyper parameters of ML models but suffer from excessive time and low accuracy. In order to efficiently construct ML models and enhance the accuracy of predicting models, a BPSO (Beetle antennae search Particle Swarm Optimization) algorithm is proposed. Based on the PSO algorithm, the concept of BAS (Beetle Antennae Search) is integrated into the updating process of an individual particle, which improves the random search capability. The convergence of the BPSO algorithm is discussed in terms of inhomogeneous recursive equations and characteristic roots. Then, based on the proposed BPSO prototype, a hybrid ML model BPSO-XGBoost (eXtreme Gradient Boosting) is proposed. We applied the model to the Hangzhou Central Park tunnel project for the prediction of screw conveyer rotational speed. Finally, our model is compared with existing methods. The experimental results show that the BPSO-based model outperforms other traditional ML methods. The BPSO-XGBoost is more accurate than PSO-XGBoost and BPSO-RandomForest for predicting the speed. Also, it is verified that the hyper parameters optimized by the BPSO are better than those optimized by the original PSO. The comprehensive prediction performance ranking of models is as follows: BPSO-XGBoost > PSO-XGBoost > BPSO-RF > PSO-RF. Our models have preferable engineering application value.     

Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms

Multimedia Tools and Applications - This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face...     

An efficient model for the prediction of SMAP sea surface salinity using machine learning approaches in the Persian Gulf

ABSTRACT

Sea Surface Salinity (SSS) is a pre-eminent parameter in oceanology causing extreme climate and weather events such as floods and droughts. Therefore, knowledge discovery of SSS is increasingly becoming a fundamental problem in recent years. However, not only the inadequacy of in-situ SSS data in large ocean basins are hampering conduction of detailed analyses of patterning SSS variations but also conventional data-gathering techniques for SSS estimation are often too expensive and time-consuming to meet the amount of data required in SSS estimation studies. Conversely, the brand-new Soil Moisture Active-Passive (SMAP) mission could provide validated SSS data along with its main objective soil moisture retrieval. As a result, collecting a candidate data set of surface’s parameters as inputs to SSS with the aid of Pearson correlation and Boruta feature selection techniques, this paper aims to study the predictive skills of machine learning approaches to estimate SMAP radiometer SSS in the Persian Gulf region from April 2015 to April 2017. Thus, four machine learning methods including Support Vector Regression (SVR), artificial neural network (ANN), random forest (RF) and gradient boosting machine (GBM) were adopted to model the SSS. Two approaches of GBM and RF provided scarcely equivalent predictions for both the calibration and validation data sets that were distinguishably substantiated by experimental results and simulations, nonetheless, slightly superior results were attained with the GBM model by correlation coefficient (r) = 0.734, root mean squared error (RMSE) = 0.906 and mean absolute error (MAE) = 0.627. The findings demonstrate promising SSS estimation from SMAP, which could provide a baseline to perceive the large-scale changes in SSS.     

A learning architecture based on reinforcement learning for adaptive control of the walking machine LAURON

The learning of complex control behaviour of autonomous mobile robots is one of the actual research topics. In this article an intelligent control architecture is presented which integrates learning methods and available domain knowledge. This control architecture is based on Reinforcement Learning and allows continuous input and output parameters, hierarchical learning, multiple goals, self-organized topology of the used networks and online learning. As a testbed this architecture is applied to the six-legged walking machine LAURON to learn leg control and leg coordination.     

基于机器学习的中文微博情感分类实证研究

使用三种机器学习算法、三种特征选取算法以及三种特征项权重计算方法对微博进行了情感分类的实证研究。实验结果表明,针对不同的特征权重计算方法,支持向量机(SVM)和贝叶斯分类算法(Nave Bayes)各有优势,信息增益(IG)特征选取方法相比于其他的方法效果明显要好。综合考虑三种因素,采用SVM和IG,以及TF-IDF(Term Frequency-Inverse Document Frequency)作为特征项权重,三者结合对微博的情感分类效果最好。针对电影领域,比较了微博评论和普通评论之间分类模型的通用性,实验结果表明情感分类性能依赖于评论的风格。     

基于机器学习的CPS系统可靠性在线评估方法

为了对CPS系统可靠性进行有效分析与量化,提出了一种实时的CPS可靠性自动在线评估方法,该方法采用机器学习思想构建了评估框架,设计了在线排队算法,实现了对CPS可靠性的实时在线分析与评估,并能及时采取预防措施,确保系统正常无间断运行,极大地提高了系统可靠性。仿真实验结果验证了评估方法的有效性及广泛的应用前景。     

Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

Journal of Intelligent Manufacturing - Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted...     

Distraction detection of lectures in e-learning using machine learning based on human facial features and postural information

Artificial Life and Robotics - While e-learning lectures allow students to learn at their own pace, it is difficult to manage students’ concentration, which prevents them from receiving...     

集成优化核极限学习机的冠心病无创性诊断

冠心病的早期无创性诊断一直是医疗诊断领域的研究热点,为了提高冠心病诊断的准确率和诊断效率,提出了一种新颖的局部Fisher判别分析(LFDA)特征提取方法和集成核极限学习机(KELM)相结合的冠心病诊断模型(LFDA-EKELM)。首先使用LFDA方法剔除不相关特征和冗余特征,找出对分类结果贡献度较高的特征子集,产生不同的训练集以训练粒子群优化的KELM分类器PSO-KELM,并基于旋转森林(RF)构建集成分类器,实现冠心病的智能诊断。实验结果表明,与基于ELM、SVM和BPNN方法相比,提出方法有效提高了冠心病诊断准确率,提升了诊断效率,且分类结果高于已有方法和相似方法,是一种有效冠心病诊断模型。     

Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis

Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes in landslide displacement and inducing factors. In this paper, a novel neural network technique called ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Grey relational analysis is used to sieve out the more influential inducing factors as the inputs in E-ELM. Trend component displacement and periodic component displacement are forecasted, respectively; then, total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. Performances of our model are evaluated by using real data from Baishuihe landslide in the Three Gorges Reservoir of China, and it provides a good representation of the measured slide displacement behavior.     

State of charge estimation for Li-ion battery based on model from extreme learning machine

Lithium-ion (Li-ion) battery state of charge (SOC) estimation is important for electric vehicles (EVs). The model-based state estimation method using the Kalman filter (KF) variants is studied and improved in this paper. To establish an accurate discrete model for Li-ion battery, the extreme learning machine (ELM) algorithm is proposed to train the model using experimental data. The estimation of SOC is then compared using four algorithms: extended Kalman filter (EKF), unscented Kalman filter (UKF), adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF). The comparison of the experimental results shows that AEKF and AUKF have better convergence rate, and AUKF has the best accuracy. The comparison from the radial basis function neural network (RBF NN) model also verifies that the ELM model has lighter computation load and smaller estimation error in SOC estimation process. In general, the performance of Li-ion battery SOC estimation is improved by the AUKF algorithm applied on the ELM model.     

Data transmission over GSM voice channel using digital modulation technique based on autoregressive modeling of speech production

This paper presents a novel digital data modulation and demodulation algorithm ARDMA based on the principles of autoregressive modeling (AR) of speech production. In the first step a sustained voiced speech signal characteristics are analyzed using autoregressive modeling principle and then the two sets of linear prediction (LPC) coefficients are obtained and converted to linear spectrum frequencies (LSF). The input binary data stream drives the selection mechanism of LSF coefficients which are then applied as filter coefficients of the modulation signal synthesis filter. This filter is excited with specially designed excitation signal which corresponds to the basic characteristics of typical excitation signal of human vocal tract. Finally, a speech-alike modulation signal is produced. This modulation signal is then sent through the voice channel of the GSM system. The demodulator analyzes the incoming modulation signal using autoregressive modeling. The most likely LSF vector which modulated the particular symbol was determined by the demodulation process and converted to the respective string of binary data. The performance of proposed modulation scheme was compared to the regular frequency shift keying method (FSK). The performance improvement of ARDMA against FSK is observed at higher bit-rates in the case of three compared GSM speech coders.