6,612 research outputs found
Native p-type transparent conductive CuI via intrinsic defects
No full textThe ability of CuI to be doped p-type via the introduction of native defects has been investigated using first-principles pseudopotential calculations based on density functional theory. The Cu vacancy has a lower formation energy than any of the other native defects, which include I vacancy (V(I)), Cu interstitial (Cu(i)), I interstitial (I(i)), Cu antisite (Cu(I)), and I antisite (I(Cu)). Combined with its shallow acceptor level, it offers sufficient hole concentrations in CuI. The natural band alignments as compared to zinc-blende ZnS, ZnSe, and ZnTe have also been calculated in order to further identify the p-type dopability of CuI. It is found that CuI has a relatively high valence band maximum and conduction band minimum, which also makes it easy to dope CuI p-type in terms of the doping limit rule. In addition, the small effective mass of the light hole-about 0.303m(0)-can provide high mobility and p-type conductivity in CuI. All of these results make CuI an ideal candidate for native p-type materials (C) 2011 American Institute of Physics. [doi:10.1063/1.3633220
Youthhood
No full textTESTING-GROUND issue 03, Youthhood, examines worlds through youthful eyes, makes evident young ambitions, and questions how we can better empower young people to design cities, landscapes, and a planet that works for them. The issue includes contributions from: Carmel Keren, Jude Daniel Smith, Claire Edwards, Kazeem Kuteyi, Emmanuel Adarkwah, Reza Nik, Dan Cui, Kristofer Cullum-Fernandez, Fida Sassi, Simeon Shtebunaev, Daze Aghaji, Averill Dimabuyu, Sarri Elfaitouri, Rebecca McDonald-Balfour, and Ed Wall.
Rebecca McDonald-Balfour (Author), Jude Daniel Smith (Author), Daze Aghaji (Author), Carmel Keran (Author), Alexis Liu (Author), Dan Cui (Author), Kristofer Cullum-Fernandez (Author), Fida Sassi (Author), Averill Dimabuyu (Author), Ed
Impact damage of composite laminates with high-speed waterjet
No full textRain erosion may cause substantial damage to aircrafts during supersonic flight. Such event is investigated here via high-speed waterjet impact on composite laminates. An experimental setup is developed to produce waterjets with the speed up to 700m/s and a finite element model of the waterjet-composite impact event is established. The consistency of experiment and simulation results validates the adopted numerical methods. The distribution of the water-hammer pressure is non-uniform and the maximum pressure occurs near the contact periphery when the water is about to eject laterally. After a high-speed (300∼560m/s) waterjet impacts a composite laminate, the impacted surface depression is observed, and the typical surface damage presents a central region with no visible surface damage surrounded by a faded “failure ring” with resin removal, matrix cracking and minor fiber fracture. Delamination occurs at the interfaces of adjacent layers with unequal dimensions and longitudinal matrix cracking appears on the back surface. Both the velocity and the diameter of waterjets are crucial factors on CFRP damage extents. Water-hammer pressure, the stagnation pressure and propagation of stress waves are failure mechanisms for most matrix damage in CFRP impacted by waterjets.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Structural Integrity & Composite
Sampling and Reconstruction of Signals on Product Graphs
No full textIn this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The proposed scheme is particularly useful for processing signals on large-scale product graphs. The sampling sets are designed using a low-complexity greedy algorithm and can be proven to be near-optimal. To illustrate the developed theory, numerical experiments based on real datasets are provided for sampling 3D dynamic point clouds and for active learning in recommender systems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Ban dao ti yi zhi jie gou zai guang cui hua he guang dian cui hua zhong de yan jiu
No full textLi, Qian = 半導體异质结构在光催化和光電催化中的研究 / 李乾.Thesis Ph.D. Chinese University of Hong Kong 2015.Includes bibliographical references (leaves 145-162).Abstracts also in Chinese.Title from PDF title page (viewed on 30, December, 2016).Li, Qian = Ban dao ti yi zhi jie gou zai guang cui hua he guang dian cui hua zhong de yan jiu / Li Qian
A Conversational User Interface for Instructional Maintenance Reports
No full textMaintaining a complex system, such as a modern production line, is a knowledge-intensive task. Many firms use maintenance reports as a decision support tool. However, reports are often poor quality and tedious to compile. A Conversational User Interface (CUI) could streamline the reporting process by validating the user's input, eliciting more valuable information, and reducing the time needed. In this paper, we use a Technology Probe to explore the potential of a CUI to create instructional maintenance reports. We conducted a between-groups study (N = 24) in which participants had to replace the inner tube of a bicycle tire. One group documented the procedure using a CUI while replacing the inner tube, whereas the other group compiled a paper report afterward. The CUI was enacted by a researcher according to a set of rules. Our results indicate that using a CUI for maintenance reports saves a significant amount of time, is no more cognitively demanding than writing a report, and results in maintenance reports of higher quality. Internet of ThingsHuman-Centred Artificial Intelligenc
The Logic of Knowledge-Based Cooperation in the Social Dilemma
No full textComputer Science, Artificial IntelligenceComputer Science, Theory & MethodsCPCI-S(ISTP)
CUHK electronic theses & dissertations collection
No full textCui, Yan.Thesis Ph.D. Chinese University of Hong Kong 2015.Includes bibliographical references (leaves 236-251).Abstracts also in Chinese.Title from PDF title page (viewed on 15, September, 2016)
Wood decay fungi: an analysis of worldwide research
No full textPurpose: Wood decay fungi are the only forms of life capable of degrading wood to its initial constituents, greatly contributing to the soil ecosystem. This study summarizes the current research status and development characteristics of global wood decay fungi research, in order to better understand their role in soils. Methods: A bibliometric analysis was applied to the literature from 1913 to 2020, based on data from the Web of Science (WOS) Core Collection. For this, various bibliometric analysis methods, R (Biblioshiny package), and VOSviewer were applied. Results: A total of 8089 documents in this field were identified in the WOS Core Collection. The annual number of publications tended to increase, with exponential growth after 2008. Researchers in this field were mainly concentrated in North Europe, the USA, and China. Biotechnology, applied microbiology, environmental sciences, and microbiology were the most popular WOS categories. Bioresource Technology and Applied Environmental Microbiology were the top two journals with the most citations. The top three authors with the most published papers were Dai YC, Martinez AT, and Cui BK. Co-occurrence analysis of author keywords identified six clusters, mainly divided into three categories: the classification and diversity, the degradation mechanisms, and the ecological functions of wood decay fungi. Clustering results further showed that the lignin degradation process and the application of wood decay fungi in industrial production and soil contamination remediation are current research hotspots. Conclusions: We present a comprehensive and systematic overview of research related to wood decay fungi and provide a deep perspective to understand the associated research progress. This is important for facilitating the development of a profound understanding of the contribution of wood decay fungi to soil systems and the degradation of soil contaminants.Full Tex
Apprentissage à base de noyaux sur représentations d'images arborescentes : applications à la classification des images de télédétection
No full textHierarchical image representations have been widely used in the image classification context. Such representations are capable of modeling the content of an image through a tree structure, where objects-of-interest (represented by the nodes of the tree) can be revealed at various scales, and where the topological relationship between objects (e.g. A is part of B, or B consists of A) can be easily captured thanks to the edges of the tree. However, for fully benefiting from this key information, dedicated machine learning methods that can directly learn on hierarchical representations and handle the induced structured data need to be developed. In this thesis, we investigate kernel-based strategies that make possible taking input data in tree-structured and capturing the topological patterns inside each structure through designing structured kernels. We apply the designed kernel to remote sensing image classification tasks, allowing the discovery of complex cross-scale patterns in hierarchical image representations. We develop a structured kernel dedicated to unordered tree and path (sequence of nodes) structures equipped with numerical features, called Bag of Subpaths Kernel (BoSK). BoSK is an instance of a convolution kernel relying on subpath substructures, more precisely a bag of all paths and single nodes. It is formed by summing up kernels computed on all pairs of subpaths of the same length between two bags. The direct computation of BoSK can be done through an iterative scheme, yielding a quadratic complexity w.r.t. both structure size (number of nodes) and amount of data (training size). However, such complexity prevents BoSK to be used on real world large-scale problems, where the tree can have more than hundreds of nodes and the available training data can consist in more than ten thousands samples. Therefore, we propose a fast version of the algorithm, called Scalable BoSK (SBoSK for short), using Random Fourier Features to map the structured data in a randomized finite-dimensional Euclidean space, where inner product of the transformed feature vector approximates BoSK. It brings down the complexity from quadratic to linear w.r.t. structure size and amount of data, making the kernel compliant with the large-scale machine learning context. Thanks to (S)BoSK, we can learn from cross-scale patterns in hierarchical image representations. (S)BoSK operates on paths, thus allowing modeling the context of a pixel (leaf of the hierarchical representation) through its ancestor regions at multiple scales. Such a model is used within pixel-based image classification. (S)BoSK also deals with trees, making the kernel able to capture the composition of an object (top of the hierarchical representation) and the topological relationships among its subparts. This strategy allows tile/sub-image classification. Further relying on (S)BoSK, we introduce a novel multi-source classification approach that performs classification directly from a hierarchical image representation built from two images of the same scene taken at different resolutions, possibly with different modalities. Evaluations on several publicly available datasets illustrate the superiority of (S)BoSK compared to state-of-the-art remote sensing classification methods in terms of classification accuracy, and experiments on a urban classification task show the effectiveness of the proposed multi-source classification approach.La représentation d'image sous une forme hiérarchique a été largement utilisée dans un contexte de classification. Une telle représentation est capable de modéliser le contenu d'une image à travers une structure arborescente, où les objets d'intérêt (représentés par les noeuds de l'arbre) peuvent être appréhendés à différentes échelles et où la relation topologique entre les objets (par exemple A fait partie de B, ou B se compose de A) peut être facilement décrite grâce aux arêtes de l'arbre. Cependant, pour bénéficier pleinement de ces informations-clés, des méthodes d'apprentissage statistiques doivent être développées pour traiter directement les données structurées sous leur forme hiérarchique. Dans cette thèse, nous considérons les méthodes à noyaux qui permettent de prendre en entrée des données sous une forme structurée et de tenir compte des informations topologiques présentes dans chaque structure en concevant des noyaux structurés. Nous appliquons le noyau que nous avons développé aux tâches usuelles de classification des images de télédétection, permettant ainsi de découvrir des modèles complexes dans les représentations hiérarchiques des images.Nous présentons un noyau structuré dédié aux structures telles que des arbres non ordonnés et des chemins (séquences de noeuds) équipés d'attributs numériques. Le noyau proposé, appelé Bag of Subpaths Kernel (BoSK), est une instance du noyau de convolution et s'appuie sur l'extraction de sous-structures de sous-chemins, plus précisement un sac de tous les chemins et des n{\oe}uds simples. Il est formé en sommant les noyaux calculés sur toutes les paires de sous-chemins de même longueur entre deux sacs. Le calcul direct de BoSK peut se faire selon un schéma itératif, amenant à une complexité quadratique par rapport à la taille de la structure (nombre de n{\oe}uds) et la quantité de données (taille de l'ensemble d'apprentissage). Cependant, une telle complexité ne permet pas d'utiliser BoSK pour résoudre des problèmes à grande échelle, où la structure peut contenir des centaines de n{\oe}uds et les données d'apprentissage disponibles peuvent comporter plus de dix milliers d'échantillons. Par conséquent, nous proposons également une version rapide de notre algorithme, appelé Scalable BoSK (SBoSK), qui s'appuie sur les Random Fourier Features pour projeter les données structurées dans un espace euclidien, où le produit scalaire du vecteur transformé est une approximation de BoSK. Cet algorithme bénéficie d'une complexité non plus quadratique mais linéaire par rapport aux tailles de la structure et de l'ensemble d'apprentissage, rendant ainsi le noyau adapté aux situations d'apprentissage à grande échelle.Grâce à (S)BoSK, nous sommes en mesure d'effectuer un apprentissage à partir d'informations présentes à plusieurs échelles dans les représentations hiérarchiques d'image. (S)BoSK fonctionne sur des chemins, permettant ainsi de tenir compte du contexte d'un pixel (feuille de la représentation hiérarchique) par l'intermédiaire de ses régions ancêtres à plusieurs échelles. Un tel modèle est utilisé dans la classification des images au niveau pixel. (S)BoSK fonctionne également sur les arbres, ce qui le rend capable de modéliser la composition d'un objet (racine de la représentation hiérarchique) et les relations topologiques entre ses sous-parties. Cette stratégie permet la classification des tuiles ou parties d'image. En poussant plus loin l'utilisation de (S)BoSK, nous introduisons une nouvelle approche de classification multi-source qui effectue la classification directement à partir d'une représentation hiérarchique construite sur deux images de la même scène prises à différentes résolutions, éventuellement selon différentes capteurs. Les évaluations sur plusieurs jeux de données de télédétection disponibles dans la communauté illustrent la supériorité de (S)BoSK par rapport à l'état de l'art en termes de précision de classification, et les expériences menées sur une tâche de classification urbaine montrent la pertinence de l'approche de classification multi-source proposée
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