Research on dynamic robust planning method for active distribution network considering correlation

The universality of load subjects in distribution network brings challenges to the reliability of distribution network planning results. In this paper, a two-stage dynamic robust distribution network planning method considering correlation is proposed. The method evaluates the correlation between random variables using the Spearman rank correlation coefficient, and converts the correlated random variables into mutually independent random variables by Cholesky decomposition and independent transformation; expresses the source-load uncertainty by a bounded interval without distribution, and describes the active distribution network planning as a dynamic zero-sum game problem by combining with the two-phase dynamic robust planning; use the Benders decomposition approach to tackle the issue; mathematical simulation is used to confirm the accuracy and efficacy of the method. The results show that the dynamic robustness planning method of active distribution network taking into account the correlation can accurately simulate the operation of active distribution network with uncertain boundaries, which enhances the reliability and economy of the active distribution network planning results

SIMILARITY-BASED MULTI-SOURCE TRANSFER LEARNING APPROACH FOR TIME SERIES CLASSIFICATION

This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance records collectively lead to heterogeneous distribution for data collected from different machines. This will significantly limit the performance of conventional ML algorithms in PdM. Moreover, labeling data is generally costly and time-consuming. Finally, industrial processes incorporate complex conditions, and unpredictable breakdown modes lead to extreme complexities for PdM. In this study, similarity-based multi-source transfer learning (SiMuS-TL) approach is proposed for real-time classification of time series signals. A new domain, called "mixed domain," is established to model the hidden similarities among the multiple sources and the target. The proposed SiMuS-TL model mainly includes three key steps: 1) learning group-based feature patterns, 2) developing group-based pre-trained models, and 3) weight transferring. The proposed SiMuS-TL model is validated by observing the state of the rotating machinery using a dataset collected on the Skill boss manufacturing system, publicly available standard bearing datasets, Case Western Reserve University (CWRU), and Paderborn University (PU) bearing datasets. The results of the performance comparison demonstrate that the proposed SiMuS-TL method outperformed conventional Support Vector Machine (SVM), Artificial Neural Network (ANN), and Transfer learning with neural networks (TLNN) without similarity-based transfer learning methods

In-Situ Layer-Wise Quality Monitoring for Laser-Based Additive Manufacturing Using Image Series Analysis

Quality assurance has been one of the major challenges in laser-based additive manufacturing (AM) processes. This study proposes a novel process modeling methodology for layer-wise in-situ quality monitoring based on image series analysis. An image-based autoregressive (AR) model has been proposed based on the image registration function between consecutively observed thermal images. Image registration is used to extract melt pool location and orientation change between consecutive images, which contains sensing stability information. Subsequently, a Gaussian process model is used to characterize the spatial correlation within the error matrix. Finally, the extracted features from the aforementioned processes are jointly used for layer-wise quality monitoring. A case study of a thin wall fabrication by a Directed Laser Deposition (DLD) process is used to demonstrate the effectiveness of the proposed methodology.Mechanical Engineerin

Layer-Wise Profile Monitoring of Laser-Based Additive Manufacturing

Additive manufacturing (AM) is a novel fabrication technique capable of producing highly complex parts. Nevertheless, a major challenge is improving the quality of fabricated parts. While there are a number of ways of approaching this problem, developing data-driven methods that use AM process signatures to identify these part anomalies can be rapidly applied to improve overall part quality during build. The objective of this study is to build a new layer-wise process signature model to create the thermal-microstructure relationship. In this study, we derive novel key process signatures for each layer (from melt pool thermal images), which are reduced using multilinear principal component analysis (MPCA) and are directly correlated with layer-wise quality of the part. Using these key process signatures, a Gaussian SVM classifier model is trained to detect the existence of anomalies inside a layer. The proposed models are validated through a case study of real-world direct laser deposition experiment where the layer-wise quality of the part is predicted on the fly. The accuracy of the predictions is calculated using three measures (recall, precision, and f-score), showing reasonable success of the proposed methodology in predicting layer-wise quality. The ability to predict layer-wise quality enables process correction to eliminate anomalies and to ultimately improve the quality of the fabricated part.Mechanical Engineerin

Using K-means Clustering to Create Training Groups for Elite American Football Student-athletes Based on Game Demands

Background: American football and the athletes that participate have continually evolved since the sport’s inception. The fluidity of the sport, as well as the growth of the body of knowledge pertaining to American football, requires evolving training techniques. While performance data is being garnered at very high rates by elite level sports organizations, the limiting factor to the value of data can be the limited known uses for the data. Objective: This study introduces a technique that can be used in tandem with data collected from wearable technology to better inform training decisions. Method: The K-means clustering technique was used to group athletes from two seasons worth of data from an NCAA Division 1 American football team that is in the “Power 5.” The data was obtained using Catapult Sports OPTIMEYE S5 TM in games played against only other “Power 5” programs. This data was then used to create average game demands of each student-athlete, which was then used to create training groups based upon individual game demands as previously mentioned. Results: The resultant groupings from the single-season analyses of seasons one and two showed results that were similar to traditional groupings used for training in American football, which worked as validation of the results, while also offering insights on individuals that may need to consider training in a non-traditional group based upon their game demands. Conclusion: This technique can be brought to `athletic training and be useful in any organization that is dealing with training multitudes of athletes

Statistical process control for multistage processes with non-repeating cyclic profiles

<p>In many manufacturing processes, process data are observed in the form of time-based profiles, which may contain rich information for process monitoring and fault diagnosis. Most approaches currently available in profile monitoring focus on single-stage processes or multistage processes with repeating cyclic profiles. However, a number of manufacturing operations are performed in multiple stages, where non-repeating profiles are generated. For example, in a broaching process, non-repeating cyclic force profiles are generated by the interaction between each cutting tooth and the workpiece. This article presents a process monitoring method based on Partial Least Squares (PLS) regression models, where PLS regression models are used to characterize the correlation between consecutive stages. Instead of monitoring the non-repeating profiles directly, the residual profiles from the PLS models are monitored. A Group Exponentially Weighted Moving Average control chart is adopted to detect both global and local shifts. The performance of the proposed method is compared with conventional methods in a simulation study. Finally, a case study of a hexagonal broaching process is used to illustrate the effectiveness of the proposed methodology in process monitoring and fault diagnosis.</p

Current situation and progress toward the 2030 health-related Sustainable Development Goals in China: A systematic analysis

Background The Sustainable Development Goals (SDGs), adopted by all United Nations (UN) member states in 2015, established a set of bold and ambitious health-related targets to achieve by 2030. Understanding China’s progress toward these targets is critical to improving population health for its 1.4 billion people. Methods and findings We used estimates from the Global Burden of Disease (GBD) Study 2016, national surveys and surveillance data from China, and qualitative data. Twenty-eight of the 37 indicators included in the GBD Study 2016 were analyzed. We developed an attainment index of health-related SDGs, a scale of 0–100 based on the values of indicators. The projection model is adjusted based on the one developed by the GBD Study 2016 SDG collaborators. We found that China has achieved several health-related SDG targets, including decreasing neonatal and under-5 mortality rates and the maternal mortality ratios and reducing wasting and stunting for children. However, China may only achieve 12 out of the 28 health-related SDG targets by 2030. The number of target indicators achieved varies among provinces and municipalities. In 2016, among the seven measured health domains, China performed best in child nutrition and maternal and child health and reproductive health, with the attainment index scores of 93.0 and 91.8, respectively, followed by noncommunicable diseases (NCDs) (69.4), road injuries (63.6), infectious diseases (63.0), environmental health (62.9), and universal health coverage (UHC) (54.4). There are daunting challenges to achieve the targets for child overweight, infectious diseases, NCD risk factors, and environmental exposure factors. China will also have a formidable challenge in achieving UHC, particularly in ensuring access to essential healthcare for all and providing adequate financial protection. The attainment index of child nutrition is projected to drop to 80.5 by 2025 because of worsening child overweight. The index of NCD risk factors is projected to drop to 38.8 by 2025. Regional disparities are substantial, with eastern provinces generally performing better than central and western provinces. Sex disparities are clear, with men at higher risk of excess mortality than women. The primary limitations of this study are the limited data availability and quality for several indicators and the adoption of "business-as-usual" projection methods. Conclusion The study found that China has made good progress in improving population health, but challenges lie ahead. China has substantially improved the health of children and women and will continue to make good progress, although geographic disparities remain a great challenge. Meanwhile, China faced challenges in NCDs, mental health, and some infectious diseases. Poor control of health risk factors and worsening environmental threats have posed difficulties in further health improvement. Meanwhile, an inefficient health system is a barrier to tackling these challenges among such a rapidly aging population. The eastern provinces are predicted to perform better than the central and western provinces, and women are predicted to be more likely than men to achieve these targets by 2030. In order to make good progress, China must take a series of concerted actions, including more investments in public goods and services for health and redressing the intracountry inequities