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

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

A wide-angle broadband absorber in graphene-based hyperbolic metamaterials

A wide-angle broadband absorber which is realized by periodic structures containing graphene-based hyperbolic metamaterials (GHMM) and isotropic medium is theoretically investigated. The GHMM is composed of monolayer graphene and conventional dielectric, which the refractive index can be tuned by the chemical potential, the thickness of dielectric and phenomenological scattering rates, respectively. A periodic structure of GHMM can obtain a broadband absorption which is shown to absorb roughly 70% (relative bandwidth is larger than 45%) of all available electromagnetic wave in absorption bandwidth at normal incident angle. Compared with some previous designs, our proposed structure has a relative bandwidth over a broad frequency range in mid-infrared. This kind periodic structures offer additional opportunities to design novel optoelectronic devices

Switching behavior induced by different substituents of group in single molecular device

We investigate the electronic transport properties of photochromic azobenzene-based molecular devices with Au electrodes using non-equilibrium Green’s function and density functional theory. A reversible switching behavior between cis and trans isomerization is found in the device. In addition, the substituent of −NH2 on the right end hydrogen atom of azobenzene molecule reduces the switching ratio of current, consequently the disappearance of switching behavior, while the substituent of −NO2 improves the switching ratio of current. We discuss the different electronic transport induced by different substituents through the transmission spectra, localized density of states, molecular projected self-consistent Hamiltonian and transmission pathways. The observed polarization effect under bias is explained by the evolution of molecular projected self-consistent Hamiltonian of LUMO level. The results indicate that the electron-withdrawing group −NO2 substituting right terminal hydrogen of azobenzene molecule becomes a candidate for improving the performance of molecular device

Economic optimization for configuration and sizing of micro integrated energy systems

Abstract Based on analysis of construction and operation of micro integrated energy systems (MIES), this paper presents economic optimization for their configuration and sizing. After presenting typical models for MIES, a residential community MIES is developed by analyzing residential direct energy consumption within a general design procedure. Integrating with available current technologies and local resources, the systematic design considers a prime mover, fed by natural gas, with wind power, photovoltaic generation, and two storage devices serving thermal energy and power to satisfy cooling, heating and electricity demands. Control strategies for MIES also are presented in this study. Multi-objective formulas are obtained by analyzing annual cost and dumped renewable energy to achieve optimal coordination of energy supply and demand. According to historical load data and the probability distribution of distributed generation output, clustering methods based on K-means and discretization methods are employed to obtain typical scenarios representative of uncertainties. The modified non-dominated sorting genetic algorithm is applied to find the Pareto frontier of the constructed multi-objective formulas. In addition, aiming to explore the Pareto frontier, the dumped energy cost ratio is defined to check the energy balance in different MIES designs and provide decision support for the investors. Finally, simulations and comparision show the appropriateness of the developed model and the applicability of the adopted optimization algorithm

AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal