In this study, we propose a time-series clustering approach that selects optimal training data for the development of predictivemodels. The optimal number of clusters was set based on the variation of within-cluster ...
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In this study, we propose a time-series clustering approach that selects optimal training data for the development of predictivemodels. The optimal number of clusters was set based on the variation of within-cluster sums of squares. A predictivemodel was developed with the selection ratio of training data from each of those clusters. Based on the results, a regression model was developed to predict the performance of the model. The search space was applied to the regression model, and the optimal training data ratio were selected satisfying the objective function and constraints. The effectiveness of the method is demonstrated by addressing a commercial bio 2,3-butanediol distillation process. As a result, the number of data for model training was reduced by 49.20% compared to the base case without clustering. The coefficient of determination (R 2 ) showed the same level of performance, and the root mean-square error was improved up to 14.07%.
data-drivenmodels can estimate the buildings' energy consumption using machine learning algorithms. This approach works based on the correlation between energy consumption and various inputs such as weather data,...
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data-drivenmodels can estimate the buildings' energy consumption using machine learning algorithms. This approach works based on the correlation between energy consumption and various inputs such as weather data, occupancy schedules, heating, air conditioning, and physical properties of buildings. Seasonal changes affect buildings' energy use. Hence, the required data for data-drivenmodels (DDMs) during the heating and cooling days could be different. Selecting the most impactful inputs can help to choose the type and quantity of sensors for deployment that improve the model's accuracy and minimize the costs. This paper performs feature selection for heating, cooling, hot water, and ventilation loads in residential buildings under the mixed-humid climate zone. Filter method, wrapper backward elimination, wrapper recursive feature elimination, Lasso regression, linear regression, and Extreme Gradient Boosting (XGBoost) regression are adopted for heating and cooling days, separately. We use twenty-five outputs from a computer model, and the results show that the key features for a DDM are different for heating and cooling days, and XGBoost provides the most accurate forecast. The findings of this paper are useful for selecting proper models, sensors, and inputs for model-predictive control systems during the heating and cooling seasons. (C) 2020 Elsevier Ltd. All rights reserved.
Additive manufacturing (AM), a disruptive technology of building parts layer-by-layer directly from 3D models, has been considered a tempting cleaner production process compared to other conventional production routes...
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Additive manufacturing (AM), a disruptive technology of building parts layer-by-layer directly from 3D models, has been considered a tempting cleaner production process compared to other conventional production routes. This is because AM has demonstrated impressive green characteristics which contributed to significantly reduced material and energy consumptions, a shorter supply chain, and diversion of the waste stream by reparation, etc. The majority of the current quantitative studies on the environmental assessment of AM are based on utilizing the knowledge-intensive Life Cycle Assessment (LCA) methodology. Further, current studies on assessing the environmental performance of AM are based on a limited selection of design- or process-related parameters. These knowledge barriers may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts. Such challenges are particularly prevalent during the product design and planning stages due to the iterative design-evaluation process. Therefore, there is a need for an automated LCA tool to support AM toward elevated sustainability. As such, this paper provides three main contributions to the research community. Firstly, this is the first study to identify a comprehensive set of influential AM design and process parameters that pose an impact on the environmental performance of AM. Secondly, this review also summarizes the impacts of each of these parameters on the environmental sustainability of AM. Lastly, to the best of the authors' knowledge, no work in the literature has been reported on automating LCA for AM. Thus, this paper promotes research toward a more environmentally benign and innovative AM technology by proposing a new framework to automate the environmental assessment of the process. The proposed framework is anticipated to take advantage of the fruitful integration between Machine Learning (ML) and the product process co-design concept to mitig
BACKGROUND Ischemic heart disease (IHD) caused by the narrowing of coronary arteries is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly, and potentially invasive procedu...
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BACKGROUND Ischemic heart disease (IHD) caused by the narrowing of coronary arteries is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly, and potentially invasive procedures. OBJECTIVE To address this problem, we introduce a novel clinical knowledge-enhanced machine learning (ML) pipeline to assist in timely and cost-effective IHD prediction. METHODS Unlike conventional data-driven "black box" ML approaches, we propose an effective mechanism to engage clinical expertise and gain insight into the "black box" at each stage of model development, including data analysis, preprocessing, selecting the most clinically discriminative features, and model evaluation. One-hot feature encoding is introduced to expose hidden bias and highlight the important elements and features. RESULTS Experimental results on the benchmark Cleveland IHD dataset showed that the proposed clinical knowledge-enhanced ML pipeline overperformed state-of-the-art data-driven ML models, using even fewer features. Our model based on one-hot feature encoding and support vector machine achieved the best accuracy of 94.4% and sensitivity 95% by using only 7 discriminative attributes. CONCLUSION We share insights and discuss the effectiveness of incorporating clinical input in machine learning to improve model performance, as well as addressing some practical issues such as data bias and interpretability. We hope this preliminary study on engaging clinical expertise to explore the "black box" would improve the trustworthiness of AI and its potential wider uptake in the medical field.
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