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Real-time Group Route Diversion Control Considering Dynamic Response Behavior

Real-time Group Route Diversion Control Considering Dynamic ...

引用

IEEE international conference on Intelligent Transportation Engineering (ICITE)

作者： Tiandong Xu Yuan Hao Yao Yu Huibing Li Xinshuo Xu College of Transport and Communications Shanghai Maritime University Shanghai China University of Florida Gainesville USA Urban Transport Institute China Academy of Urban Planning and Design Beijing China

ISBN: (数字)9781728194097

ISBN: (纸本)9781728194103

This paper proposes a real-time group route diversion control (GRDC) methodology to determine route diversion strategies based on the controller's objectives and drivers' dynamic response behavior. The GRDC optimization procedure is to update the GRDC strategies to minimize the difference between the desired diversion rates and the diversion rates obtained from the drivers' route diversion behavior (DRDB) model, which comprises a GRDC model that determines the desired diversion rates to satisfy some system objectives of the GRDC controller, and a controller-estimated drivers' route diversion behavior (DRDB) model that predicts the likely driver route choice decisions in light of the updated GRDC strategies. The experimental results show that the group route diversion control considering dynamic response behavior can effectively provide guidance for drivers, improve the accuracy of traffic state prediction, and obtain better system performance over the same period of operations.

关键词： Predictive models Real-time systems Roads data models Traffic control Prediction algorithms Optimization

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Real-Time TCAD: a new paradigm for TCAD in the artificial intelligence era

Real-Time TCAD: a new paradigm for TCAD in the artificial in...

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international conference on Simulation of Semiconductor Processes and Devices (SISPAD)

作者： Sanghoon Myung Jinwoo Kim Yongwoo Jeon Wonik Jang In Huh Jaemin Kim Songyi Han Kang-hyun Baek Jisu Ryu Yoon-Suk Kim Jiseong Doh Jae-ho Kim Changwook Jeong Dae Sin Kim Computational Science and Engineering Team Data and Information Technology Center Device Solution Business Samsung Electronics Co. Ltd Korea

ISBN: (数字)9784863487635

ISBN: (纸本)9781728173542

This paper presents a novel approach to enable real-time device simulation and optimization. State-of-the-art algorithms which can describe semiconductor domain are adopted to train deep learning models whose input and output are process condition and doping profile / electrical characteristic, respectively. Our framework enables to update automatically deep learning models by estimating the uncertainty of the model prediction. Our Real-Time TCAD framework is validated on 130nm processes for display driver integration circuit (DDI), and 1) prediction time was 530,000 times faster than conventional TCAD, and time spent for process optimization was reduced by 300,000 times compared to human expert, 2) the model achieved average accuracy of 99% compared to TCAD simulation results, and thus, 3) process development time for DDI was reduced by 8 weeks.

关键词： Deep learning Uncertainty Predictive models Real-time systems Semiconductor process modeling Integrated circuit modeling Optimization

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Fela: Incorporating Flexible Parallelism and Elastic Tuning to Accelerate Large-Scale DML

Fela: Incorporating Flexible Parallelism and Elastic Tuning ...

引用

international conference on data Engineering

作者： Jinkun Geng Dan Li Shuai Wang Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Distributed machine learning (DML) has become the common practice in industry, because of the explosive volume of training data and the growing complexity of training model. Traditional DML follows data parallelism but causes significant communication cost, due to the huge amount of parameter transmission. The recently emerging model-parallel solutions can reduce the communication workload, but leads to load imbalance and serious straggler problems. More importantly, the existing solutions, either data-parallel or model-parallel, ignore the nature of flexible parallelism for most DML tasks, thus failing to fully exploit the GPU computation power. Targeting at these existing drawbacks, we propose Fela, which incorporates both flexible parallelism and elastic tuning mechanism to accelerate DML. In order to fully leverage GPU power and reduce communication cost, Fela adopts hybrid parallelism and uses flexible parallel degrees to train different parts of the model. Meanwhile, Fela designs token-based scheduling policy to elastically tune the workload among different workers, thus mitigating the straggler effect and achieve better load balance. Our comparative experiments show that Fela can significantly improve the training throughput and outperforms the three main baselines (i.e. dataparallel, model-parallel, and hybrid-parallel) by up to 3.23×, 12.22×, and 1.85× respectively.

关键词： Parallel processing Graphics processing units Training Computational modeling Task analysis data models Throughput

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BLAC: A Named Entity Recognition Model Incorporating Part-of-Speech Attention in Irregular Short Text

BLAC: A Named Entity Recognition Model Incorporating Part-of...

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IEEE international conference on Real-time Computing and Robotics (RCAR)

作者： Ming Zhu Huakang Li Xiaoyu Sun Zhuo Yang School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Urban Land Resources Monitoring and Simulation Shenzhen China Suzhou Privacy Information Technology Company Suzhou China School of Computers Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Irregular text refers to text with incomplete sequence information of the sentence or text that does not meet normal grammatical specifications, such as Weibo text. Existing named entity recognition algorithms recognize such text, the effect is poor due to the lack of context information. Because the attention mechanism has advantages in obtaining contextual information, we merge part-of-speech attention with the BI-LSTM-CRF model and propose a BLAC model. We tested on several public datasets and compared the results with the basic model BI-LSTM-CRF. The results show that the method we proposed has a certain improvement in the entity recognition of irregular short text.

关键词： Hidden Markov models Text recognition Task analysis Computational modeling Neural networks Deep learning data models

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Simulating State-Dependent Systems with Partial Aging in Standby

Simulating State-Dependent Systems with Partial Aging in Sta...

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international IEEE conference on Intelligent Systems, IS

作者： Natalia Nikolova Guixin Fan Mark Symes Kiril Tenekedjiev Australian Maritime College University of Tasmania Launceston TAS Australia Nikola Vaptsarov Naval Academy Varna Bulgaria

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

We analyze a two-component standby system with failures in standby (2SBS). Typically, such a system is interpreted as a state-dependent system with time-dependent failure rates. When the backup component is aging in standby as if it is working, the 2SBS is denoted as 2SBS with full aging. Such a system can be modelled with a system of ODEs and may have a numerical solution. Alternatively, simulation modelling can generate the probability of the system and estimate the important reliability characteristics. However, when the backup component does not age in standby (2SBS with no aging), or when the backup component ages in standby slower than in operation (2SBS with partial aging), then there is no system of ODEs because the system is not state dependent. We develop simulation algorithms to solve the 2SBS with no aging and with partial aging. The partial aging model assumes that the aging uses reliability as a proxy variable for aging and introduces the equivalent aging time T * as the starting time for the conditional reliability function of the backup component in standby. An extensive numerical example is provided.

关键词： Aging Reliability Numerical models Mathematical model Australia Markov processes data models

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Prediction of Student’s Academic Performance using Feedforward Neural Network Augmented with Stochastic Trainers

Prediction of Student’s Academic Performance using Feedforw...

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international conference on Application of Information and Communication Technologies (AICT)

作者： Thaer Thaher Rashid Jayousi Al-Quds University Abu Dies Palestine

ISBN: (数字)9781728173863

ISBN: (纸本)9781728173870

The academic performance of students is of great interest to tutors and decision-makers in educational institutions. The extensive use of information technology systems in education generates an enormous amount of data, which is challenging to analyze and extract valuable information. Therefore, Educational data Mining (EDM) concept emerges to adapt data Mining (DM) techniques to extract the hidden and valuable educational knowledge that improves the learning process. The primary purpose of this paper is to introduce an efficient student's performance prediction model. For this purpose, a feed-forward Multi-Layer Perceptron approach boosted with stochastic training algorithms is proposed. The proposed model is benchmarked and assessed using three public educational datasets gathered from UCI and Kaggle repositories. Synthetic Minority Oversampling Technique (SMOTE) is utilized to handle the imbalanced data problem. The performance of the proposed model is evaluated by a set of classifiers, namely, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Logistic Regression, Linear Discriminant Analysis, and Random Forest. The comparative study revealed that the MLP achieved promising prediction quality on the majority of datasets compared to other traditional classifiers, as well as those in previous works.

关键词： Training Support vector machines Adaptation models Stochastic processes data mining Regression tree analysis Random forests

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An optimization-based process mining approach for explainable classification of timed event logs

An optimization-based process mining approach for explainabl...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Hugo De Oliveira Vincent Augusto Baptiste Jouaneton Ludovic Lamarsalle Martin Prodel Xiaolan Xie Mines Saint-Étienne Univ Clermont Auvergne CNRS UMR 6158 LIMOS Centre CIS F - 42023 Saint-Étienne France HEVA 186 avenue Thiers Lyon France Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper addresses the problem of supervised classification of time event logs of two classes: positive and negative population. The key idea of this paper to explain classification is to determine some process model that fits well the positive event logs and poorly the negative ones. More specifically, we introduce formal definitions of event logs, process models and a replayability score that measures the fitness of a process model for a given event log. We then set the event log classification as an optimization problem for the determination of a process model that maximizes its replayability for the positive population and minimizes its replayability for the negative one. A tabu search algorithm is then proposed to solve this problem. The proposed algorithm is compared with three state-of-the-art classification algorithms on test cases of various complexity. It is shown to provide superior performances and a graphic representation of the process model of the positive event logs.

关键词： Hidden Markov models Medical services data mining Linear programming Feature extraction Optimization Predictive models

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Interplay of Machine Learning and Software Engineering for Quality Estimations

Interplay of Machine Learning and Software Engineering for Q...

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2020 international conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)

作者： Hamza Abubakar M. S. Obaidat Aaryan Gupta Pronaya Bhattacharya Sudeep Tanwar Institute of Technology Nirma University Ahmedabad Gujarat India College of Computing and Informatics University of Sharjah Sharjah 27272 UAE King Abdullah II School of Information Technology University of Jordan Amman 11942 Jordan University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728173153

ISBN: (纸本)9781728173160

In this era, the agile mindset has innovated the traditional software engineering (SE) process through the integration of DevOps flow engines, scrum iterations, and automation of continuous integration (CI) and continuous deployment (CD) cycles. However, the CI/CD integration requires manual code-revisions and refactoring at large scales. Recently, machine-learning (ML) is employed in SE that allows legacy codes to be highly dynamic, less coupling in related modules, automatic code versioning, and refactoring, with less coupling among related modules. However, over time, ML models tend to become bulky, with increasing monotonic losses during model training. To address this, SE techniques like code revisions are employed over ML codes to allow low-order training losses, that enables seamless and precise workflow structures. Motivated from the aforementioned discussions, the paper presents a systematic review of the close interplay of SE and ML and possible interactions in different applications. Suitable research questions and case studies are presented for possible adoption scenarios that depict the close ML-SE interplay share with each other, with the concluding remarks. The paper forms useful insights for ML engineers, data science practitioners, and SE quality estimators towards the building of efficient and scalable software solutions.

关键词： Software Maximum likelihood estimation data models Software algorithms Training Task analysis Mathematical model

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Research on Lean Operations Management Method of Railway Marshalling Yard

Research on Lean Operations Management Method of Railway Mar...

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IEEE international conference on Intelligent Transportation Engineering (ICITE)

作者： Wenqiao Sun Qigang Liu Xiang Li Min Chen Yan Zhang Transportation and Economics Research Institute China Academy of Railway Sciences Corporation Limited Beijing China China Railway Nanning Group Corporation Limited Liuzhou China China Railway Nanning Group Corporation Limited Nanning China

ISBN: (数字)9781728194097

ISBN: (纸本)9781728194103

Freight railway plays a more and more important role in China freight logistics industry. The traffic volume is increasing at an annual growth rate of more than 6%, which enhances the importance of increasing carrying capacity of marshalling yards. By using intelligent technologies like satellite positioning, image recognition, and Iot (Internet of Things), methods of accurate marshalling-yards operation data obtaining, transmission and processing are illustrated in the paper. Cargo wagon loading situation inspection is taken as a case study, and explains how to establish an accurate real-time data base for lean operations management, provides an optimized operation process, and displays a control query interface, which increases both safety and efficiency of the operation process.

关键词： Rail transportation Safety Inspection Real-time systems data models Satellites Loading

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Network Update Compression for Federated Learning

Network Update Compression for Federated Learning

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IEEE Visual Communications and Image processing (VCIP)

作者： Birendra Kathariya Li Li Zhu Li Lingyu Duan Shan Liu University of Missouri-Kansas City Kansas City USA Peking University Beijing China Tencent America Palo Alto USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In federated learning setting, models are trained in a variety of edge-devices with locally generated data and each round only updates in the current model rather than the model itself are sent to the server where they are aggregated to compose an improved model. These edge devices, however, reside in highly uneven nature of network with higher latency and lower-throughput connections and are intermittently available for training. In addition, a network connection has an asymmetric nature of downlink and uplink. All these contribute to a major challenge while synchronizing these updates to the *** this work, we proposed an efficient coding solution to significantly reduce uplink communication cost by reducing the total number of parameters required for updates. This was achieved by applying Gaussian Mixture Model (GMM) to localize Karhunen-Loève Transform (KLT) on inter-model subspace and representing it with two low-rank matrices. Experiments on convolutional neural network (CNN) models showed the proposed model can significantly reduce the uplink communication cost in federated learning while preserving reasonable accuracy.

关键词： Servers data models Collaborative work Uplink Urban areas Training Matrix decomposition

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