关键词： RSW joint of multiple sheets   Ultrasonic test   Image processing   Static strength   Fatigue behavior   Artificial neural network   Genetic algorithm  

摘要： Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions is unknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate

关键词： Data mining methods   Antihypertensive drugs   Drug-related attributes   Blood pressure control   Machine learning  

摘要： Globally, methods of controlling blood pressure in hypertension patients remain inefficient. The difficulty of prescribing appropriate drugs specific to a patient's clinical features serves as one of the most important factors. Characterizing the critical drug-related features, just like that of the antibacterial spectrum (where each item is sensitive to the targeted drug's effectiveness or a specified indication), may help a doctor easily prescribe appropriate drugs by matching a patient's attributes with drug-related features, and effectiveness of the selected drugs would also be ascertained. In this study, we aimed to apply data mining methods to obtain the clinical characteristics spectrum or important clinical features of five frequently used drugs (Irbesartan, Metoprolol, Felodipine, Amlodipine, and Levamlodipine) for hypertension control by comparing successful and unsuccessful cases. Spectrum analysis based on a statistical method and five algorithms based on machine learning were used to extract the critical clinical features. A visualized relative weight matrix was then achieved by combining the results from the characteristic spectrum and machine learning-based methods. Our results indicated that the five targeted antihypertension agents had different importance orders of the 15 relative clinical features. Clinical analysis showed that the extracted important clinical attributes of the five drugs were both reasonable and meaningful in the selection of hypertension treatment. Therefore, our study provided a data-driven reference for the personalization of clinical antihypertensive drugs.

关键词： Big data   User-generated content   LDA   Sentiment analysis   Machine learning  

摘要： Marketing scholars are interested in the big data of user-generated content (UGC) from social media platforms. However, the majority of current UGC studies have been conducted in the business-to-consumer (B2C) context. To fill the knowledge gap in business-to-business (B2B) research, we investigate whether UGC has differential impacts on stock performance for B2B and B2C firms by using big data. We collect a large dataset of 84 million tweets from 20.3 million Twitter accounts and 8 years of stock data for 407 companies from the S&P500 index. The results from machine learning methods are transformed into a monthly panel data. We conduct fixed effects model on the panel data. We find that UGC has a significant impact on firms' stock performance and that its impact on stock performance is much stronger among B2C firms than among B2B firms. While consumers' positive sentiment does not play a significant role in stock performance, consumers' negative sentiment and WOM significantly impact stock prices.

关键词： Analytics   Online returns   Predictive model   Variable selection   LASSO   Machine learning  

摘要： In 2015, U.S. consumers returned goods worth $261 billion and the return rates for online sales sometimes exceeded 30%. Manufacturers and retailers have an interest in predicting return volume to address operational challenges in managing product returns. In this paper, we develop and test data-driven models for predicting return volume at the retailer, product type and period levels using a rich data set comprised of detailed operations on each product, and retailer information. The goal is to achieve a good prediction accuracy out of sample. We consider main effects and detailed interaction effects models using various machine learning methods. We find that Least Absolute Shrinkage and Selection Operator (LASSO) yields a predictive model achieving the best prediction accuracy for future return volume due to its ability to select informative interaction terms out of more than one thousand possible combinations. The LASSO model also turns in consistent performance based on several robustness tests and is easy to implement in practice. Our work provides a general predictive model framework for manufacturers to track product returns. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Waste heat recovery   Organic Rankine Cycle   Dynamic Programming   Transient optimization   Real-time implementation   Machine learning  

摘要： This study aims to present a method for real-time realization of Dynamic Programming algorithm for power optimization in an organic Rankine Cycle waste heat recovery system. Different from existing studies, for the first time machine learning algorithms are utilized to extract the rules from offline Dynamic Programming results for optimal power generation. In addition, for the first time a single state Proper Orthogonal Decomposition and Galerkin Projection based reduced order model is combined with Dynamic Programming for its high accuracy and low computation cost. For a transient driving cycle, Dynamic Programming algorithm is utilized to generate the optimal working fluid pump speed. A total of eleven state-of-art machine learning algorithms are screened to predict this pump speed. Random Forest algorithm is then selected for its best pump speed prediction accuracy. A rule-based method is added to the Random Forest model to improve energy recovery. As one of the main discoveries in this study, in the rule extraction process, the Random Forest model reveals that the time delayed exhaust gas mass flow rate and exhaust temperature improve the rule extraction accuracy. This observation points out the difference between steady state and transient optimization and that the steady state optimization results do not necessarily hold true in transient conditions. Another key observation is that Random Forest - rule-based method retrieves 97.2% of the energy recovered by offline Dynamic Programming in a validation driving cycle. In addition, the inclusion of rule-based method significantly increases the Random Forest model's energy recovery from 66.5% to 97.2%. This high accuracy means that the machine learning models can be used to extract Dynamic Programming rules for real-time application.

关键词： Downscaling   Precipitation   Representative Concentration Pathway   Drainage basin   Model output statistics  

摘要： Downscaling considerably alleviates the drawbacks of regional climate simulation by general circulation models (GCMs). However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. This study developed multiple machine learning (ML) downscaling models, based on a Bayesian model average (BMA), to downscale the precipitation simulation of 8 Coupled Model Intercomparison Project Phase 5 (CMIP5) models using model output statistics (MOS) for the years 1961-2005 in the upper Han River basin. A series of statistical metrics, including Pearson's correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. Moreover, the BMA and the best ML downscaling model were used to downscale precipitation in the 21st century under Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios. The results show the following: (1) The performance of the BMA ensemble simulation is clearly better than that of the individual models and the simple mean model ensemble (MME). The PCC reaches 0.74, and the RMSE is reduced by 28%-60% for all the GCMs and 33% compared to the MME. (2) The downscaled models greatly improved station simulation performance. Support vector machine for regression (SVR) was superior to multilayer perceptron (MLP) and random forest (RF). The downscaling results based on the BMA ensemble simulation and SVR models were regarded as the best performing overall (PCC, RMSE, and Rbias were 0.82, 35.07, mm and -5.45%, respectively). (3) Based on BMA and SVR models, the projected precipitations show a weak increasing trend on the whole under RCP4.5 and RCP8.5. Specifically, the average rainfall during the mid- (2040-2069) and late (2070-2099) 21st century increased by 3.23% and 1.02%, respectively, compared to the base year (1971-2000) under RCP4.5, while they increased by 4.25% and 8.30% under RCP8.5. Additionally,

关键词： 血管造影图像分割   模型驱动方法   数据驱动方法   机器学习方法   深度学习方法  

摘要： 血管分割能够为计算机临床辅助与可视化诊疗提供关键的结构和病灶信息。由于血管造影图像涉及多种数据模态以及个体化差异和复杂病理,诸多挑战性的问题仍待解决。我们对血管分割的主流方法进行了回顾和分析,并且讨论了血管分割的研究格局和发展趋势,旨在为该领域的进一步研究提供参考。