Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Formation of Uniform Fe3O4 Hollow Spheres Organized by Ultrathin Nanosheets and Their Excellent Lithium Storage Properties

Abstract: Hierarchical Fe3 O4 hollow spheres constructed by nanosheets are obtained from solvothermally synthesized Fe-glycerate hollow spheres. With the unique structural features, these hierarchical Fe3 O4 hollow spheres exhibit excellent electrochemical lithium-storage performance.

Progress in concentrated solar power technology with parabolic trough collector system: A comprehensive review

Abstract: Advanced solar energy utilization technology requires high-grade energy to achieve the most efficient application with compact size and least capital investment recovery period Concentrated solar power (CSP) technology has the capability to meet thermal energy and electrical demands Benefits of using CSP technology with parabolic trough collector (PTC) system include promising cost-effective investment, mature technology, and ease of combining with fossil fuels or other renewable energy sources This review first covered the theoretical framework of CSP technology with PTC system Next, the detailed derivation process of the maximum theoretical concentration ratio of the PTC was initially given Multiple types of heat transfer fluids in tube receivers were reviewed to present the capability of application Moreover, recent developments on heat transfer enhancement methods for CSP technology with PTC system were highlighted As the rupture of glass covers was frequently observed during application, methods of thermal deformation restrain for tube receivers were reviewed as well Commercial CSP plants worldwide with PTC system were presented, including those that are in operation, under construction, and announced Finally, possible further developments of CSP plants with PTC system were outlined Besides, suggestions for future research and application guidance were also illustrated

Deep Feature Fusion for VHR Remote Sensing Scene Classification

Abstract: The rapid development of remote sensing technology allows us to get images with high and very high resolution (VHR) VHR imagery scene classification has become an important and challenging problem In this paper, we introduce a framework for VHR scene understanding First, the pretrained visual geometry group network (VGG-Net) model is proposed as deep feature extractors to extract informative features from the original VHR images Second, we select the fully connected layers constructed by VGG-Net in which each layer is regarded as separated feature descriptors And then we combine between them to construct final representation of the VHR image scenes Third, discriminant correlation analysis (DCA) is adopted as feature fusion strategy to further refine the original features extracting from VGG-Net, which allows a more efficient fusion approach with small cost than the traditional feature fusion strategies We apply our approach to three challenging data sets: 1) UC MERCED data set that contains 21 different areal scene categories with submeter resolution; 2) WHU-RS data set that contains 19 challenging scene categories with various resolutions; and 3) the Aerial Image data set that has a number of 10 000 images within 30 challenging scene categories with various resolutions The experimental results demonstrate that our proposed method outperforms the state-of-the-art approaches Using feature fusion technique achieves a higher accuracy than solely using the raw deep features Moreover, the proposed method based on DCA fusion produces good informative features to describe the images scene with much lower dimension

Template‐Based Engineering of Carbon‐Doped Co3O4 Hollow Nanofibers as Anode Materials for Lithium‐Ion Batteries

Abstract: Co3O4 anode materials exhibit poor conductivity and a large volume change, rendering controlling of their nanostructure essential to optimize their lithium storage performance. Carbon-doped Co3O4 hollow nanofibers (C-doped Co3O4 HNFs), for the first time are synthesized using bifunctional polymeric nanofibers as template and carbon source. Compared with undoped Co3O4 HNFs and solid Co3O4 NFs, C-doped Co3O4 HNFs feature a remarkably high specific capacity, excellent cycling stability, and superior rate capacity as anode materials for lithium-ion batteries. The superior performance of C-doped Co3O4 HNFs electrodes can be attributed to their structural features, which confer enhanced electron transportation and Li+ ion diffusion due to C-doping, and tolerance for volume change due to the 1D hollow structure. Density functional theory calculations provide a good explanation of the observed enhanced conductivity in C-doped Co3O4 HNFs.

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA.

Abstract: N6-Methyladenosine (m6A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N6-methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m6A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m6A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m6A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m6A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the state-of-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server.malab.cn/Gene2vec/.