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Articles 8281 - 8310 of 11188

Full-Text Articles in Computer Sciences

Control Improvement And Simulation Of Dfig Under Power Grid Failure, Hongyang Zhang, Zhentang Shi, Zhifeng Zhang Feb 2020

Control Improvement And Simulation Of Dfig Under Power Grid Failure, Hongyang Zhang, Zhentang Shi, Zhifeng Zhang

Journal of System Simulation

Abstract: Following the increasing scale of installed wind power, the wind turbines, as an important part of the future energy internet, its ability to cope with the fault of the power grid becomes more and more important. Aiming at the transient characteristics of doubly fed induction generator (DFIG) under power grid fault, a comprehensive control strategy, based on the classification of power grid faults, the optimization of controller parameters and auxiliary equipments, is proposed. Compared with the traditional control method, the large control error and response lag is overcome, and the fine control is realized. Based on MATLAB and VC++ …

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Shielding Modeling And Simulation Of Coastal Building To Vts Radar, Chunhui Zhou, Zhou Ling, Langxiong Gan, Xiaodong Cheng, Junjie Gao Feb 2020

Shielding Modeling And Simulation Of Coastal Building To Vts Radar, Chunhui Zhou, Zhou Ling, Langxiong Gan, Xiaodong Cheng, Junjie Gao

Journal of System Simulation

Abstract: In order to study the shielding influence of coastal building on the VTS radar, a mathematical expression model of shielding area is proposed. The expression model are divided into finite continuous 3D shielding area, infinite continuous 3D shielding area, finite discontinuous 3D shielding area and infinite discontinuous 3D shielding area. The range and height of the shielding area are calculated, according to the expression model and spatial geometric relation. In addition, 3ds MAX is used in the modeling and simulation of shielding area. Taking the influence of Prince's Bay Cruise Terminal Building on the radar Shekou radar as an …

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Interference Management Mechanism In Next Generation Dense Wlan, Ronghui Hou, Xiaoyao Ma, Liu Yi, Hongyan Li Feb 2020

Interference Management Mechanism In Next Generation Dense Wlan, Ronghui Hou, Xiaoyao Ma, Liu Yi, Hongyan Li

Journal of System Simulation

Abstract: Aiming at the unfairness and low throughput in a wireless local area network with dense access points and terminals deployment, a user-centered resource allocation method which can configure the specific channel and power level for each user is proposed. This method can maximize the network throughput while ensuring the minimum throughput requirement for each user. The proposed interference management mechanism and user identification method can be easily implemented in the current wireless local area network system. Simulation results show that the proposed resource allocation scheme enhances about 47.3% throughput to a basic service set. In particular, 190% improvement can …

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Cuckoo Search Algorithm With Dynamic Step And Discovery Probability, Jingsen Liu, Xiaozhen Liu, Li Yu Feb 2020

Cuckoo Search Algorithm With Dynamic Step And Discovery Probability, Jingsen Liu, Xiaozhen Liu, Li Yu

Journal of System Simulation

Abstract: In order to further improve the low accuracy and slow convergence speed of algorithm search, a cuckoo search algorithm with dynamic step size and probability of discovery is proposed. The algorithm dynamically constrains the Levy's moving step of each generation by introducing the step adjustment factor, which makes the Levy's flight mechanism adaptive. In the probability of finding, the random inertia weight with uniform distribution and F distribution is used to change the fixed value of the probability of discovery, to strengthen the diversity of the population and to keep the balance between global search and local exploration. The …

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Improving The Efficiency Of Transport Systems Using Simulation, Bushuev Sergey, Kovalev Igor, Permikin Vadim, Anashkina Nataliia Feb 2020

Improving The Efficiency Of Transport Systems Using Simulation, Bushuev Sergey, Kovalev Igor, Permikin Vadim, Anashkina Nataliia

Journal of System Simulation

Abstract: The article describes the possibilities of application of simulation modeling for the analysis of infrastructure and technology of transport services of enterprises. The main technological and possible economic effects for the enterprises arising at performance of modeling of a transport component of their work are resulted.

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Modelling And Simulation For NoX Emission Concentration Of Scr Denitrification System, Dong Ze, Laiqing Yan Feb 2020

Modelling And Simulation For NoX Emission Concentration Of Scr Denitrification System, Dong Ze, Laiqing Yan

Journal of System Simulation

Abstract: The selective catalytic reduction (SCR) denitrification system has the features of non-linearity, large lag and strong disturbance, when the operating condition changes. Based on mutual information (MI) and Kernel-based Orthogonal Projections to Latent Structures (KOPLS), the model for NOx emission concentration is proposed. The time-delay of each input variable is estimated by mutual information, and phase space construction is performed, KOPLS is utilized to modelling. KOPLS shows the merits of strong generalization, nonlinear fitting and anti-noise in the simulation of benchmark datasets. According to field data analysis, RMSE of MI-KOPLS in training and test are reduced by 17% …

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Research On Approximate Reference Algorithm Of Svdbn Based On Sliding Window, Haiyang Chen, Chai Bing, Ruilan Wang, Cao Lu Feb 2020

Research On Approximate Reference Algorithm Of Svdbn Based On Sliding Window, Haiyang Chen, Chai Bing, Ruilan Wang, Cao Lu

Journal of System Simulation

Abstract: Structure-variable dynamic Bayesian networks (SVDBN) have the special advantage in dealing with the uncertainty of the unstable processes. In order to overcome the disadvantage that the inference algorithms of the SVDBN are unable to apply online, introducing the concepts of SVDBN sliding window and the window width, the online approximate inference mechanism of structure-variable dynamic Bayesian networks based on sliding window is explained, and two online algorithms are proposed, that is the recursive inference algorithm of structure-variable discrete dynamic Bayesian networks (SVDDBN) based on sliding window and the fast inference algorithm of SVDDBN based on sliding window. Experimental simulations …

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Optimization Model And Method Of Urban Road Traffic Signal Control Under Rainfall Environment, Shaohu Tang, Zhou Jin, Chunlin Shang, Guorong Zheng Feb 2020

Optimization Model And Method Of Urban Road Traffic Signal Control Under Rainfall Environment, Shaohu Tang, Zhou Jin, Chunlin Shang, Guorong Zheng

Journal of System Simulation

Abstract: The efficiency of urban road traffic operation fell obviously during a rain, and the existing road traffic signal control has not yet established a relevant signal optimization scheme. Considering the traffic operation scenario under the influence of rainfall and road water, an urban road traffic cyber physical systems is designed, a framework of urban road traffic control based on cyber physical systems is built, an optimization model of traffic signal control is established, and further more, the solution method of the model is designed by using BP neural network. Building a traffic simulation model of the example intersection, comparing …

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Simulation Research And Application On Urban Rail Transit Fully Automatic Operation System, Du Heng, Chunhai Gao, Huang Qing, Huaiming Cang Feb 2020

Simulation Research And Application On Urban Rail Transit Fully Automatic Operation System, Du Heng, Chunhai Gao, Huang Qing, Huaiming Cang

Journal of System Simulation

Abstract: Following the development of the key technologies for the Fully Automatic Operation (FAO) of urban rail transit and the application of autonomous fully automatic operation system demonstration projects, more and more urban rail transit lines choose the FAO system. High availability, high reliability and high security also need high requirements on the testing and verification before the line is operated. Through the simulation research of the fully automatic operation system, combined with the comprehensive debugging and verification management platform of the Yanfang Line's fully automatic independent demonstration project, the scenario-based FAO system simulation model is established, the key technologies …

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Research On Glide Width Simulation System Of Instrument Landing System, Chunying Jiang, Yuxiang Kang, Xiaofeng You, Xiaoxin Zhang, Changlong Ye Feb 2020

Research On Glide Width Simulation System Of Instrument Landing System, Chunying Jiang, Yuxiang Kang, Xiaofeng You, Xiaoxin Zhang, Changlong Ye

Journal of System Simulation

Abstract: Based on the principle of the formation of the instrumental landing system ILS(Instrument Landing System) gliding DDM(Different in the Depth of Modulation) index, and the factors that affect DDM are found as the amplitude of SBO(Sideband Only) signal. The relationmodel between the amplitude and the glide width, of the Half-width SBO signal in the linear variation range of DDM are obtained by the control variable method. And Based on the measured data, the correctness of the model is verified. Based on the mathematical model and combined with the NM7000 equipment, a simulation system is built to dynamically …

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Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li Feb 2020

Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li

Journal of System Simulation

Abstract: Monocular camera mark-less pose estimation system suffers low accuracy, robustness and efficiency due to variety of action, self-occlusion of human body. A method of feature exaction from point clouds was proposed, in which a single-to-multiple (S2M) feature regressor and a joint position regressor were designed to quickly and accurately predict the 3D positions of body joints from a single depth image without any temporal information. Experiment result shows that the estimation accuracy is superior to that of state-of-the-arts and multi-camera based methods.

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Simulation And Optimization Of Aircraft Sliding Path, Zhiwei Xing, Mingyi Xu, Luo Xiao, Luo Qian Feb 2020

Simulation And Optimization Of Aircraft Sliding Path, Zhiwei Xing, Mingyi Xu, Luo Xiao, Luo Qian

Journal of System Simulation

Abstract: Variable taxiing time is an important indicator to the characteristics of the airport traffic flow assessment, which affects the airport operating efficiency, the passenger satisfaction for the airline and the pollution emissions. For a large hub airport, according to the principle of cellular automata and the congestion of traffic flow, the airport taxiing area is regarded as the network topology of nodes and links, and the conflict between taxi rules and aircraft is taken as the constraint. The simulation analysis is carried out on the basis of constructing the model of airplane departure traffic flow, using Monte Carlo algorithm …

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Situation Cognition And Decision Modeling Method For Submarine Operation Based On Discriminant Matrix, Dongjun Zhang, Weiping Wang, Li Xiao, Zhang Lei, Xiaobo Li, Guojie Liu Feb 2020

Situation Cognition And Decision Modeling Method For Submarine Operation Based On Discriminant Matrix, Dongjun Zhang, Weiping Wang, Li Xiao, Zhang Lei, Xiaobo Li, Guojie Liu

Journal of System Simulation

Abstract: Submarine operations have the characteristics of dynamic game confrontation in complex environments. Situation cognition and decision (SCD) behavior of combatants have an important impact on the effectiveness of submarine operations. Aiming at the SCD modeling in submarine engagement-level operational experiments, based on analyzing the SCD process, the representative variable set is refined, and a SCD modeling method for submarine operation based on the discrimination matrix is proposed. The submarine engagement-level confrontation is used as an example to study the SCD process in the given scenarios, and the feasibility and effectiveness of the method are verified. The established model …

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Responsive Economic Model Predictive Control For Next-Generation Manufacturing, Helen Durand Feb 2020

Responsive Economic Model Predictive Control For Next-Generation Manufacturing, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

There is an increasing push to make automated systems capable of carrying out tasks which humans perform, such as driving, speech recognition, and anomaly detection. Automated systems, therefore, are increasingly required to respond to unexpected conditions. Two types of unexpected conditions of relevance in the chemical process industries are anomalous conditions and the responses of operators and engineers to controller behavior. Enhancing responsiveness of an advanced control design known as economic model predictive control (EMPC) (which uses predictions of future process behavior to determine an economically optimal manner in which to operate a process) to unexpected conditions of these types …

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A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik Feb 2020

A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik

Augsburg Honors Review

The intent of this work is to explore the interactions of artificial neural networks and digital games. It details the development of an artificial neural network trained upon a brick breaking game like the Atari game Breakout. This network was designed with the goals of not dropping the ball and maximizing the game score. Full game and network integration was not completed. However, two versions of the network were developed to move the paddle to the right or left based on the ball's point of impact on the paddle. In preliminary testing using manual inputs, these networks eventually learned to …

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Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar Feb 2020

Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar

SDSU Data Science Symposium

Data obtained from social media microblogging websites such as Twitter provide the unique ability to collect and analyze conversations of the public in order to gain perspective on the thoughts and feelings of the general public. Sentiment and volume analysis techniques were applied to the dataset in order to gain an understanding of the amount and level of sentiment associated with certain disaster-related tweets, including a topical analysis of specific terms. This study showed that disaster-type events such as a hurricane can cause some strong negative sentiment in the period of time directly preceding the event, but ultimately returns quickly …

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A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela Feb 2020

A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela

Faculty Publications

We propose a novel approach to the ’reality gap’ problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are …

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Singapore’S National Ai Strategy, Singapore Management University Feb 2020

Singapore’S National Ai Strategy, Singapore Management University

Perspectives@SMU

The island state is banking on industry-wide projects and building an AI ecosystem to transform its economy

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Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali Feb 2020

Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali

Dissertations

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an …

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Zero-Shot Ingredient Recognition By Multi-Relational Graph Convolutional Network, Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua Feb 2020

Zero-Shot Ingredient Recognition By Multi-Relational Graph Convolutional Network, Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recognizing ingredients for a given dish image is at the core of automatic dietary assessment, attracting increasing attention from both industry and academia. Nevertheless, the task is challenging due to the difficulty of collecting and labeling sufficient training data. On one hand, there are hundred thousands of food ingredients in the world, ranging from the common to rare. Collecting training samples for all of the ingredient categories is difficult. On the other hand, as the ingredient appearances exhibit huge visual variance during the food preparation, it requires to collect the training samples under different cooking and cutting methods for robust …

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Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen Feb 2020

Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen

Research Collection School Of Computing and Information Systems

The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems, both in cloud applications and infrastructure layers. These problems are centered around empowering individually resource-limited devices to exhibit intelligent behavior, both in sensing and control, thanks to a judicious utilization of cloud resources. Cloud services will enable learning from data, performing inference, and executing control, all with assurances on outcomes. The paper discusses such emerging services and outlines five resulting new research directions towards enabling …

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Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh Feb 2020

Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh

Research Collection School Of Computing and Information Systems

Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack of a quantitative guarantee about the quality of output approximate Nash equilibria (NE). A natural quantitative guarantee for such an approximate NE is the regret in the game (i.e. the best deviation gain). We formulate this deviation gain computation as a multi-armed bandit problem, with a new optimization goal unlike those studied in prior work. We propose an efficient algorithm Super-Arm UCB (SAUCB) for the problem and a number of variants. We present sample complexity results as well as extensive …

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Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman Feb 2020

Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman

Research Collection School Of Computing and Information Systems

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.

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Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe Feb 2020

Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe

Research Collection School Of Computing and Information Systems

Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at …

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Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw Feb 2020

Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the topk items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named SRPR, which factors in the stochasticity of …

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Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw Feb 2020

Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Oftentimes documents are linked to one another in a network structure,e.g., academic papers cite other papers, Web pages link to other pages. In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve both textual content and network structure. On the basis of reconstructing not only the input document but also its adjacent neighbors, we develop two neural encoder architectures. Adjacent-Encoder, or AdjEnc, induces competition among documents for topic propagation, and reconstruction among neighbors for semantic capture. Adjacent-Encoder-X, or AdjEnc-X, extends this to also encode the network structure …

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Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang Feb 2020

Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang

Research Collection School Of Computing and Information Systems

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We …

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Multi-Level Fine-Scaled Sentiment Sensing With Ambivalence Handling, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria Feb 2020

Multi-Level Fine-Scaled Sentiment Sensing With Ambivalence Handling, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and …

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Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu Feb 2020

Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu

Research Collection School Of Computing and Information Systems

Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vectorrepresentation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary

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Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He Feb 2020

Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Photorealistic multi-view face synthesis from a single image is an important but challenging problem. Existing methods mainly learn a texture mapping model from the source face to the target face. However, they fail to consider the internal deformation caused by the change of poses, leading to the unsatisfactory synthesized results for large pose variations. In this paper, we propose a Gated Deformable Face Synthesis Network to model the deformation of faces that aids the synthesis of the target face image. Specifically, we propose a dual network that consists of two modules. The first module estimates the deformation of two views …

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