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1.Computational modelling of titanium particles in warm spray

Author:Tabbara, H;Gu, S;McCartney, DG

Source:COMPUTERS & FLUIDS,2011,Vol.44

Abstract:A warm spray system has been computationally investigated by introducing a centrally located mixing chamber into a HVOF thermal spray gun. The effects of injecting a cooling gas on the gas and particle dynamics are examined. The gas phase model incorporates liquid fuel droplets which heat, evaporate and then exothermically combust with oxygen within the combustion chamber producing a realistic compressible, supersonic and turbulent jet. The titanium powder is tracked using the Lagrangian approach including particle heating, melting and solidification. The results present an insight into the complex interrelations between the gas and particle phases, and highlight the advantage of warm spray, especially for the deposition of oxygen sensitive materials such as titanium. This work also demonstrates the effectiveness of a computational approach in aiding the development of thermal spray devices. (C) 2011 Elsevier Ltd. All rights reserved.

2.The Effects of Financial Regulations on Market Qualities and Informed Traders

Author:DAI Xiaoting;ZHANG Jie;JIANG Qinfeng

Source:The Theory and Practice of Finance and Economics,2022,Vol.43

Abstract:利用一个基于代理的人工股票市场,考察了卖空禁令、涨跌幅限制、交易税和T+1结算周期对市场质量和知情交易者的影响。通过平行测定几个市场程式化事实,将系统校准到真实的金融市场后,发现这四种监管都不同程度地降低了市场波动、价格扭曲和交易量。买卖价差在卖空禁令、交易税和T+1结算周期下增加,但与涨跌幅限制存在非单调关系。当涨跌幅限制政策较为紧时,买卖价差很小,随着涨跌幅限制的放宽,买卖价差增大。一旦市场上不考虑涨跌幅限制,它仍然会下降。此外,在卖空禁令、涨跌幅限制或T+1结算周期的政策下,知情交易者的交易量比例和财富份额均下降。与之相反,交易税给出了相反的结论,即在交易税下,知情交易者的活动百分比增加...

3.Examining the effects of the built environment on topological properties of the bike-sharing network in Suzhou, China

Author:Wu, CL;Chung, H;Liu, ZY;Kim, I

Source:INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION,2021,Vol.15

Abstract:In recent years, many cities around the world have implemented bike-sharing programs. A number of studies on the relationship between the built environment and bike usage have provided important insights into understanding bike-sharing systems. However, the effects of the built environment on the structural properties of bike-sharing networks are seldom discussed in the literature. This research proposes a novel and interdisciplinary framework to explore how built environment factors affect the topological properties of bike-sharing networks. Firstly, this research applies a complex network approach to quantify the importance of bike stations in the network. Then, multisource data are utilized to identify comprehensive built environment attributes. Finally, spatial regression models are used to reveal the relationship between the importance of bike stations and built environment. In this study, the bike-sharing system in Suzhou, China, is taken as a case study. The empirical result shows that the importance of bike stations displays strong spatial dependence. Also, built environment attributes such as resident population, accessibility to subway stations, the capacity of bike stations, and the total length of main roads within a catchment area have different effects on the importance of bike stations. It should be noted that the floating population and the number of bus stops surrounding bike stations do not have strong correlations with the importance of bike stations. The findings of this study can guide urban planners and operators to improve the service quality and resilience of bike-sharing systems.

4.Multi-source social media data sentiment analysis using bidirectional recurrent convolutional neural networks

Author:Abid, F;Li, C;Alam, M

Source:COMPUTER COMMUNICATIONS,2020,Vol.157

Abstract:Subjectivity detection in the text is essential for sentiment analysis, which requires many techniques to perceive unanticipated means of communication. Few accomplishments adapted to capture the syntactic, semantic, and contextual sentimental information via distributed word representations (DWRs)(1). This paper, concatenating the DWRs through a weighted mechanism on Recurrent Neural Network (RNN) variants joint with Convolutional Neural network (CNN) distinctively involving weighted attentive pooling (WAP)(2). Whereas, CNNs with traditional pooling operations comprise many layers merely able to capture enough features. Our considerations empower the sentiment analysis over DWRs contains Word2vec, FastText, and GloVe to produce dense efficient concatenated representation (DECR)(3) to hold long term dependencies on a single RNN layer acquired by Parts of Speech Tagging (POS) explicitly with verbs, adverbs, and noun only. Then use these representations gained in a way, inputted to CNN contain single convolution layer engaging WAP on multi-source social media data to handle the issues of syntactic and semantic regularities as well as out of vocabulary (OOV) words. Experimentations demonstrate that DWRs together with proposed concatenation qualified in resolving the mentioned issues by moderate hyper-parameter configurations. Our architecture devoid of stacking multiple layers achieved modest accuracy of 89.67%% by DECR-Bi-GRU-CNN (WAP) on IMDB as compared to random initialization 81.11%% on SST.

5.Automatic Building and Floor Classification using Two Consecutive Multi-layer Perceptron

Author:Cha, J;Lee, S;Kim, KS

Source:2018 18TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS),2018,Vol.2018-October

Abstract:Key issues of indoor localization is taking full advantages and overcoming its disadvantages. indoor localization based on Wi-Fi fingerprinting attracts researchers' attentions since it does not require new infrastructure and devices. Many devices such as smart phones and laptops, which have a function to capture Wi-Fi signals, can be used for Wi-Fi fingerprinting. However, due to unreliable Wi-Fi signals, there are still difficulty to achieve high positioning accuracy. The unreliable signal disturbs devices to find their locations. As a result, getting localization with devices sometimes makes a wrong decision in building classification. It is useless for people to find a destination floor if they are in different building. In this paper, we propose two consecutive multi-layer perceptrons to get more precise localization. With sumple structure, we get better performance and show precise decision results in building classification, which is critical in Wi-Fi fingerprinting. We use UJIndoorLoc dataset which is open dataset.

6.Electrical and Electronic Technologies in More-Electric Aircraft: A Review

Author:Ni, K;Liu, YJ;Mei, ZB;Wu, TH;Hu, YH;Wen, HQ;Wang, YG

Source:IEEE ACCESS,2019,Vol.7

Abstract:This paper presents a review of the electrical and electronic technologies investigated in more-electric aircraft (MEA). In order to change the current situation of low power efficiency, serious pollution, and high operating cost in conventional aircraft, the concept of MEA is proposed. By converting some hydraulic, mechanical, and pneumatic power sources into electrical ones, the overall power efficiency is greatly increased, and more flexible power regulation is achieved. The main components in an MEA power system are electrical machines and power electronics devices. The design and control methods for electrical machines and various topologies and control strategies for power electronic converters have been widely researched. Besides, several studies are carried out regarding energy management strategies that intend to optimize the operation of MEA power distribution systems. Furthermore, it is necessary to investigate the system stability and reliability issues in an MEA, since they are directly related to the safety of passengers. In terms of machine technologies, power electronics techniques, energy management strategies, and the system stability and reliability, a review is carried out for the contributions in the literature to MEA.

7.Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

Author:Yan, K;Wang, XD;Du, Y;Jin, N;Huang, HC;Zhou, HX

Source:ENERGIES,2018,Vol.11

Abstract:Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household's personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

8.Comproportionation Reaction Synthesis to Realize High-Performance Water-Induced Metal-Oxide Thin-Film Transistors

Author:Liu, QH;Zhao, C;Mitrovic, IZ;Xu, WY;Yang, L;Zhao, CZ

Source:ADVANCED ELECTRONIC MATERIALS,2020,Vol.6

Abstract:Solution-processed metal-oxide thin films have been widely studied in low-power and flexible electronics. However, the high temperature required to form a condensed and uniform film limits their applications in flexible and low-cost electronics. Here, a novel and environmental-friendly comproportionation reaction synthesis (CRS) is presented to obtain amorphous aluminum oxide (AlOx) thin films for solution-processed thin-film transistors (TFTs) employing water as the precursor solvent. The thermal decomposition of CRS-AlO(x)precursor is completed at approximate to 300 degrees C, which is 100 degrees C lower than that of the conventional water-induced AlOx. The morphological, optical, compositional, and electrical properties of CRS-AlO(x)dielectric films are studied systematically. Meanwhile, TFTs based on water-induced In(2)O(3)metal oxide semiconductor layers deposited on these dielectrics at low temperatures are formed and characterized. Compared with TFTs based on conventional AlO(x)showing low mobility and low clockwise hysteresis, In2O3TFTs based on CRS-AlO(x)exhibit improved electrical performance and counterclockwise hysteresis in the transfer curves. Water-induced TFTs fabricated on CRS-AlO(x)formed at a low temperature of 250 degrees C have average mobility of 98 cm(2)V(-1)s(-1). Through chemical composition characterization and electrical characterization, the high mobilities of TFTs based on CRS-AlO(x)dielectrics are correlated to trap states, which resulted in counterclockwise hysteresis in the transfer curves.

9.A cold plasma-activated in situ AgCo surface alloy for enhancing the electroreduction of CO2 to ethanol

Author:Zhang, Q;Tao, SH;Du, J;He, A;Yang, Y;Tao, CY

Source:JOURNAL OF MATERIALS CHEMISTRY A,2020,Vol.8

Abstract:With regard to using the electric energy generated by renewable sources, the CO2 electroreduction reaction (CO2RR) for the production of fuels is helpful for creating an artificial carbon cycle. Herein, for the first time, we prepared in situ AgCo surface alloy electrocatalysts at room temperature by the cold H-2-plasma activation method; these electrocatalysts showed high activity for the CO2RR to ethanol with an excellent faradaic efficiency of ethanol (72.3%%) and current density (7.4 mA cm(-2) at -0.80 V). Based on experiments and DFT calculations, this high intrinsic activity was attributed to the selective suppression of hydrogen evolution and C1 production due to the distortion of the Ag lattice induced by the formation of a {111}Ag + Co surface alloy to reduce the energy barrier for *CO2 delta- formation; this increased the coverage of CO* and resulted in a C-C coupling reaction to form *OC-CO* on Ag atoms (CO* pool sites), which was further converted to CH3CH2OH. Thus, this result showed that promoting the Ag surface with small amounts of Co is a promising way to improve ethanol selectivity during the CO2RR.

10.Clustering learning model of CCTV image pattern for producing road hazard meteorological information

Author:Lee, J;Hong, B;Jung, S;Chang, V

Source:FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2018,Vol.86

Abstract:A method for real-time estimation of weather, especially the amount of rainfall, by analyzing CCTV images, is much cheaper than one using the existing expensive weather observation equipment. In this paper, we propose a method to find an estimation model function whose input is CCTV images and output is the amount of rainfall. Using CCTV images, we propose an algorithm for selecting the number and size of the region of interest optimized for rainfall estimation, generating a data pattern graph showing a clear distinction from the number of regions of interest, clustering the pattern data graphs, and estimating the amount of rainfall. Experiments using real CCTV images show that the estimation accuracy is greater than 80%%. (C) 2018 Elsevier B.V. All rights reserved.

11.Review on Non-Volatile Memory with High-k Dielectrics: Flash for Generation Beyond 32 nm

Author:Zhao, C;Zhao, CZ;Taylor, S;Chalker, PR

Source:MATERIALS,2014,Vol.7

Abstract:Flash memory is the most widely used non-volatile memory device nowadays. In order to keep up with the demand for increased memory capacities, flash memory has been continuously scaled to smaller and smaller dimensions. The main benefits of down-scaling cell size and increasing integration are that they enable lower manufacturing cost as well as higher performance. Charge trapping memory is regarded as one of the most promising flash memory technologies as further down-scaling continues. In addition, more and more exploration is investigated with high-k dielectrics implemented in the charge trapping memory. The paper reviews the advanced research status concerning charge trapping memory with high-k dielectrics for the performance improvement. Application of high-k dielectric as charge trapping layer, blocking layer, and tunneling layer is comprehensively discussed accordingly.

12.Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning

Author:Duan, HB;Li, P;Shi, YH;Zhang, XY;Sun, CH

Source:IEEE TRANSACTIONS ON EDUCATION,2015,Vol.58

Abstract:This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the concepts taught in the course and motivate them to explore relevant issues of bio-inspired optimization algorithms through interactive and collaborative learning processes. BOLE differs from other similar tools in that it places greater emphasis on fundamental concepts than on complex mathematical equations. The learning tasks using BOLE can be classified into four steps: introduction, recognition, practice, and collaboration, according to task complexity. It complements traditional classroom teaching, enhancing learning efficiency and facilitating the assessment of student achievement, as verified by its practical application in an undergraduate course "Bio-Inspired Computing." Both objective and subjective measures were evaluated to assess the learning effectiveness.

13.Multiview video quality enhancement without depth information

Author:Jammal, S;Tillo, T;Xiao, JM

Source:SIGNAL PROCESSING-IMAGE COMMUNICATION,2019,Vol.75

Abstract:The past decade has witnessed fast development in multiview 3D video technologies, such as Three-Dimensional Video (3DV), Virtual Reality (VR), and Free Viewpoint Video (FVV). However, large information redundancy and a vast amount of multiview video data needs to be stored or transmitted, which poses a serious problem for multiview video systems. Asymmetric multiview video compression can alleviate this problem by coding views with different qualities. Only several viewpoints are kept with high-quality and other views are highly compressed to low-quality. However, highly compressed views may incur severe quality degradation. Thus, it is necessary to enhance the visual quality of highly compressed views at the decoder side. Exploiting similarities among the multiview images is the key to efficiently reconstruct the multiview compressed views. In this paper, we propose a novel method for multiview quality enhancement, which directly learns an end-to-end mapping between the low-quality and high-quality views and recovers the details of the low-quality view. The mapping process is realized using a deep convolutional neural network (MVENet). MVENet takes a low-quality image of one view and a high-quality image of another view of the same scene as inputs and outputs an enhanced image for the low-quality view. To the best of our knowledge, this is the first work for multiview video enhancement where neither a depth map nor a projected virtual view is required in the enhancement process. Experimental results on both computer graphic and real datasets demonstrate the effectiveness of the proposed approach with a peak signal-to-noise ratio (PSNR) gain of up to 2dB over low-quality compressed views using HEVC and up to 3.7dB over low-quality compressed views using JPEG on the benchmark Cityscapes.

14.Energy Dissipation During Impact of an Agglomerate Composed of Autoadhesive Elastic-Plastic Particles

Author:Liu, LF;Thornton, C;Shaw, SJ

Source:PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON DISCRETE ELEMENT METHODS,2017,Vol.188

Abstract:Discrete Element Method is used to simulate the impact of agglomerates consisting of autoadhesive, elastic-plastic primary particles. In order to explain the phenomenon that the elastic agglomerate fractures but the elastic-plastic agglomerate disintegrates adjacent to the impact site for the same impact velocity, we increase the impact velocity and lower the yield strength of the constituent particles of the agglomerate. We find that increasing the impact velocity can lead to the increased number of yielded contacts, and cause the elastic-plastic agglomerate to disintegrate faster. Mostly importantly, the energy dissipation process for the elastic-plastic agglomerate impact has been investigated together with the evolutions of the yielding contacts, and evolutions of velocity during impact.

15.Identifying the influential spreaders in multilayer interactions of online social networks

Author:Al-Garadi, MA;Varathan, KD;Ravana, SD;Ahmed, E;Chang, V

Source:JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2016,Vol.31

Abstract:Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations.

16.Minimize Reactive Power Losses of Dual Active Bridge Converters using Unified Dual Phase Shift Control

Author:Wen, HQ;Su, B

Source:JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY,2017,Vol.12

Abstract:This paper proposed an unified dual-phase-shift (UDPS) control for dual active bridge (DAB) converters in order to improve efficiency for a wide output power range. Different operating modes of UDPS are characterized with respect to the reactive current distribution. The proposed UDPS has the same output power capability with conventional phase-shift (CPS) method. Furthermore, its implementation is simple since only the change of the leading phase-shift direction is required for different operating power range. The proposed UDPS control can minimize both the inductor rms current and the circulating reactive current for various voltage conversion ratios and load conditions. The optimal phase-shift pairs for two bridges of DAB converter are derived with respect to the comprehensive reactive power loss model, including the reactive components delivered from the load and back to the source. Simulation and experimental results are illustrated and explained with details. The effectiveness of the proposed method is verified in terms of reactive power losses minimization and efficiency improvement.

17.On the theoretical distribution of the wind farm power when there is a correlation between wind speed and wind turbine availability

Author:Kan, C;Devrim, Y;Eryilmaz, S

Source:RELIABILITY ENGINEERING & SYSTEM SAFETY,2020,Vol.203

Abstract:It is important to elicit information about the potential power output of a wind turbine and a wind farm consisting of specified number of wind turbines before installation of the turbines. Such information can be used to estimate the potential power output of the wind farm which will be built in a specific region. The output power of a wind turbine is affected by two factors: wind speed and turbine availability. As shown in the literature, the correlation between wind speed and wind turbine availability has an impact on the output of a wind farm. Thus, the probability distribution of the power produced by the farm depending on the wind speed distribution and turbine availability can be effectively used for planning and risk management. In this paper, the theoretical distribution of the wind farm power is derived by considering the dependence between turbine availability and the wind speed. The theoretical results are illustrated for real wind turbine reliability and wind speed data.

18.An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model

Author:Li, Yuming ; Ni, Pin ; Chang, Victor

Source:COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk,2019,Vol.

Abstract:The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield. © 2019 International Conference on Complexity, Future Information Systems and Risk.

19.BEYOND CODES AND PIXELS

Author:Fischer, T;De Biswas, K;Ham, JJ;Naka, R;Huang, WX

Source:PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED ARCHITECTURAL DESIGN RESEARCH IN ASIA (CAADRIA 2012): BEYOND CODES AND PIXELS,2012,Vol.

20.A multi-objective optimization model for bike-sharing    

Author:Shan, Yu ; Xie, Dejun ; Zhang, Rui

Source:ACM International Conference Proceeding Series,2019,Vol.

Abstract:The study proposes a multi-objective optimization model for bike-sharing industry by monitoring, with high accuracy, the user demand and providing the suitable number of bikes at selected stations. One of the key factors for designing an optimized bike sharing system is to balance the demand of pick-ups (drop-offs) around a given station and the number of available bikes (vacant lockers) in the station throughout the day. The model optimizes the location of bicycle stations and the number of parking slots that each station should have by taking account of the main contributing factors including the total budget of the bike sharing system, the popularity of riding in the city, and the expected proximity of the stations. A case study using the bike-sharing in New York was conducted to test theeffectiveness of themodel. © 2019 Association for Computing Machinery.
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