关键词： 贝叶斯分类算法   决策支持   生源预测  

摘要： 高职教师在教学过程中为了更好地完成教学任务,运用多种教学方法和教学模式。为了更好地实现科学教学,符合当前互联网与大数据高速发展的时代背景,雨课堂出现并逐步融入到高职教育的课堂中。本文从雨课堂的基本功能入手,提出教师在使用雨课堂进行教学过程中所面临的挑战以及应对的具体措施。旨在为使用雨课堂授课的教师带来一些意见和建议,让使用雨课堂进行教学的教师在享受便利的同时也能够看清挑战,进而提高自身教育教学能力、采取最合适有效的策略,创造出更加精彩的课堂。

关键词： 5G networks   Coverage maps   Machine learning   Radial basis networks   Multi-layer perceptron  

摘要： Accurate assessment of the wireless coverage of a station is considered a key feature in 5G networks. Determining the reception coverage of transmitters becomes a complicated problem when there are interfering transmitters, and it becomes increasingly more complicated when the transmission powers of those transmitters are not uniform. In this paper, we compare different Machine Learning techniques that can be used to predict the wireless coverage maps. We consider the following Machine Learning methods: (1) Radial Basis Network;a type of Artificial Neural Network which typically uses Gaussian kernels, (2) an Artificial Neural Network which uses a sigmoid function as an activator,(3) A Multi-Layer Perceptron with two hidden layers, and (4) the K-Nearest-Neighbors technique. We show how it is possible to train the Neural Networks to generate coverage maps based on samples and we check the accuracy level of the learning process on a test set, using these four different learning techniques. The conclusion of our experiments is that if the sample points are randomly located, the Radial Basis Network and the Multi-Layer Perceptron perform better than the other methods. Thus, these models can be considered promising candidates for learning coverage maps, and can be used for efficient spectrum management within the framework of 5G cellular networks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： high-frequency trading   limit order book   mid-price   machine learning   ridge regression   single hidden feedforward neural network  

摘要： Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of similar to 4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large-scale dataset can serve as a testbed for devising novel solutions of expert systems for high-frequency limit order book data analysis.

关键词： Massive MIMO   localization   machine learning (ML)   Gaussian process (GP) regression   Cramer-Rao bounds  

摘要： We propose a supervised machine learning (ML) approach based on Gaussian process (GP) regression to position users in a distributed massive multiple-input multiple-output (DM-MIMO) system from their uplink received signal strength (RSS). The proposed approach serves as a proof-of-concept that we can localize users by training an ML model with noise-free RSS and using the trained model to estimate the test user locations from their noisy RSS. We consider two GP methods for localization, namely, the conventional GP (CGP) and the numerical approximation GP (NaGP). We find that the CGP provides unrealistically small 2 sigma error-bars on the location estimates. Therefore, we derive the true predictive distribution and employ NaGP to obtain realistic 2 sigma error-bars on the location estimates. Next, we derive a Bayesian Cramer-Rao lower hound (BCRLB) on the root-mean-squared-error (RMSE) performance of the two GP methods. Numerical studies reveal that: 1) the NaGP indeed provides realistic 2 sigma error-bars on the estimated locations;2) both the CGP and NaGP achieve RMSEs that are close to the BCRLBs;3) the presence of correlated shadowing improves the RMSE performance;and 4) extrapolation to the zero input noise scenario can significantly improve the RMSE achieved by the NaGP.

关键词： Alzheimer's disease   MRI   AD-PS   Machine learning   High-dimensional  

摘要： Introduction: The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a well-characterized imaging database of AD patients and cognitively normal subjects. We called this index AD Pattern Similarity (AD-PS) scores. To demonstrate the construct validity of the scores, we investigated their associations with several AD risk factors. The ADNI and WHIMS imaging databases were collected with different goals, populations and data acquisition protocols: it is important to demonstrate that the approach to estimating AD-PS scores can bridge these differences. Methods: MRI data from both studies were processed using high-dimensional warping methods. High-dimensional classifiers were then estimated using the ADNI MRI data. Next, the classifiers were applied to baseline and followup WHIMS-MRI GM data to generate the GM AD-PS scores. To study the validity of the scores we investigated associations between GM AD-PS scores at baseline (Scan 1) and their longitudinal changes (Scan 2 - Scan 1) with: 1) age, cognitive scores, white matter small vessel ischemic disease (WM SVID) volume at baseline and 2) age, cognitive scores, WM SVID volume longitudinal changes respectively. In addition, we investigated their associations with time until classification of independently adjudicated status in WHIMS-MRI. Results: Higher GM AD-PS scores from WHIMS-MRI baseline data were associated with older age, lower cognitive scores, and higher WM SVID volume. Longitudinal changes in GM AD-PS scores (Scan 2 - Scan 1) were also associated with age and changes in WM SVID volumes and cognitive test scores. Increases in the GM AD-PS scores predicted decreases in cognitive scores and increases in WM SVID volume. GM AD-PS scores and their longitudinal changes

关键词： Turning   Roughness   Cutting force   Tool life ANN   SVR  

摘要： Support vector machines are arguably one of the most successful methods for data classification, but when using them in regression problems, literature suggests that their performance is no longer state-of-the-art. This paper compares performances of three machine learning methods for the prediction of independent output cutting parameters in a high speed turning process. Observed parameters were the surface roughness (Ra), cutting force (Fc), and tool lifetime (T). For the modelling, support vector regression (SVR), polynomial (quadratic) regression, and artificial neural network (ANN) were used. In this research, polynomial regression has outperformed SVR and ANN in the case of Fc and Ra prediction, while ANN had the best performance in the case of T, but also the worst performance in the case of Fc and Ra. The study has also shown that in SVR, the polynomial kernel has outperformed linear kernel and RBF kernel. In addition, there was no significant difference in performance between SVR and polynomial regression for prediction of all three output machining parameters.

关键词： Intellectual property analytics   Patent analytics   Machine learning   Deep learning   Artificial intelligence  

摘要： Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics.

关键词： Deep neural networks (DNNs)   eye diagram   high-speed channel   machine learning   signal integrity   support vector regression (SVR)  

摘要： In this work, machine learning methods are applied to high-speed channel modeling for signal integrity analysis. Linear, support vector, and deep neural network (DNN) regressions are adopted to predict the eye-diagram metrics, taking advantage of the massive amounts of simulation data gathered from prior designs. The regression models, once successfully trained, can he used to predict the performance of high-speed channels based on various design parameters. The proposed learning-based approach saves complex circuit simulations and substantial domain knowledge altogether, in contrast to alternatives that exploit novel numerical techniques or advanced hardware to speed up traditional simulations for signal integrity analysis. Our numerical examples suggest that both support vector and DNN regressions are able to capture the nonlinearities imposed by transmitter and receiver models in high-speed channels. Overall, DNN regression is superior to support vector regression in predicting the eye-diagram metrics. Moreover, we also investigate the impact of various tunable parameters, optimization methods, and data preprocessing on both the learning speed and the prediction accuracy for the support vector and DNN regressions.

关键词： Composite material   Effective thermal conductivity   Support vector regression   Gaussian process regression   Convolution neural network  

摘要： Composite materials have a wide range of engineering applications, and their effective thermal conductivities are important thermo-physical properties for real applications. The traditional methods to study effective thermal conductivities of composite materials, such as the effective medium theory, the direct solution of heat diffusion equation, or the Boltzmann transport equation, are all based on developing good physical understanding of heat transfer mechanisms in those composite materials. In this work, we take a completely different approach to predict the effective thermal conductivities of composite materials using machine learning methods. With a set of trustable data, the support vector regression (SVR), Gaussian process regression (GPR) and convolution neural network (CNN) are employed to train models that can predict the effective thermal conductivities of composite materials. We find that the models obtained from SVR, GPR, and CNN all have a better performance than the Maxwell-Eucken model and the Bruggeman model in terms of predicting accuracy. Our work demonstrates that machine learning methods are useful tools to fast predict the effective thermal conductivities of composite materials and porous media if the training data set is available. The machine learning approach also has the potential to be generalized and applied to study other physical properties of composite materials. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Decision tree   Logistic regression   Random forest   Support vector machine   Hasse diagram technique   Aedes japonicus japonicus  

摘要： We tested four machine learning methods for their performance in the classification of mosquito species occurrence related to weather variables: support vector machine, random forest, logistic regression and decision tree. The objective was to find a method which showed the most accurate model for the prediction of the potential geographical distribution of Aedes japonicus japonicus, an invasive mosquito species in Germany. The evaluation of the model trainings was conducted using derivations of a confusion matrix. Furthermore, we introduced two quality indices, 'selectivity' and 'exactness', for the evaluation of the spatial simulation, visualised through the Hasse diagram technique. From the evaluation results we can conclude that a specific combination of two to three models performs better in predicting the potential distribution of the mosquito species than a single model or the random combination of models.