Relative-Residual-Based Dynamic Schedule For Decoding Of Ldpc Codes,
2022
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
Relative-Residual-Based Dynamic Schedule For Decoding Of Ldpc Codes, Fatang Chen, Hebin Li, Zhihao Zhang, Zhiqiang Mei
Journal of System Simulation
Abstract: In order to solve the problems of the oscillation phenomenon and greedy characteristics in the dynamic scheduling decoding algorithm for low-density parity-check (LDPC) codes, the relative-residual-based dynamic schedule (RRB-BP) algorithm is proposed based on variable-to-check residual belief propagation (VC-RBP) algorithm. The variable nodes are grouped, then the relative residual value of the message passed by the variable nodes to the check node is taken as a reference, and the node with the largest relative residual value is updated in priority to accelerate the decoding convergence speed. For variable nodes oscillating in the decoding process, the posterior LLR (log likelihood …
Energy-Efficient Scheduling Of Multi-Objective Flexible Job Shop Considering Interval Processing Time,
2022
School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243032, China;
Energy-Efficient Scheduling Of Multi-Objective Flexible Job Shop Considering Interval Processing Time, Hongliang Zhang, Renman Ding, Gongjie Xu
Journal of System Simulation
Abstract: Based on the comprehensive consideration of economic indicators and environmental factors, the energy-efficient scheduling problem of multi-objective flexible job shop with uncertain processing time is studied. The interval number is used to describe uncertain processing time of the workpiece, and the optimization model for energy-efficient problem of interval flexible job shop scheduling is established to minimize the maximum interval completion time and total energy consumption. According to the domination relation of interval possibility degree, an effective interval multi-objective evolutionary algorithm is designed. The simulation experiments of the interval multi-objective evolutionary algorithm, SPEA-II and NSGA-II are carried out through 15 …
Particle Swarm Algorithm For Solving Emergency Material Dispatch Considering Urgency,
2022
School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China;
Particle Swarm Algorithm For Solving Emergency Material Dispatch Considering Urgency, Li Zhang, Huizhen Zhang, Dong Liu, Yuxin Lu
Journal of System Simulation
Abstract: In the early stage of major public health events, medical supplies are rapidly consumed and severely insufficient. In order to distribute medical supplies in a reasonable and efficient manner, research on the distribution of emergency medical materials is carried out. The entropy method is introduced to determine the urgency of demand points, thus could give priority to the demand points with high urgency and make the distribution routing as short as possible on that basis to realize the construction of a split delivery and multi-objective emergency medical materials scheduling model based on different urgency of demand points. Meanwhile the …
Design Of Optical Compound Eye Simulation Software For Small Aircraft Applications,
2022
1.National University of Defense Technology, National Key Laboratory of Science and Technology on ATR, Changsha 410073, China;2.Unit 32139 of the Chinese PLA, Beijing 101200, China;
Design Of Optical Compound Eye Simulation Software For Small Aircraft Applications, Qiming Qi, Ruigang Fu, Ping Wang, Min Wang, Hongqi Fan
Journal of System Simulation
Abstract: Optical compound eye has the advantages of large field of view, multiple viewing angles and high resolution. With another advantage that it can conformal combine with small aircraft, optical compound eye has application value in reconnaissance and surveillance, target detection, image navigation and other aspects. An optical compound eye simulation software for small aircraft is designed for the current situation of long development period of optical compound eye design and high cost of flight test in practical applications. The software integrates compound eye imaging, aircraft simulation and data management, and each functional module is extensible. The simulation results show …
Mesoscopic Modeling And Simulation Of Mixed Traffic Flow Of Buses And Vehicles,
2022
1.School of Intelligent Systems Engineering, Sun Yan-sen University, Guangzhou 510006, China;2.Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou 510006, China;
Mesoscopic Modeling And Simulation Of Mixed Traffic Flow Of Buses And Vehicles, Yiting Zhu, Yun Yan, Zhaocheng He
Journal of System Simulation
Abstract: Aiming at the problem that the existing mesoscopic simulation models only convert buses into several standard vehicles and ignore the movement difference between buses and vehicles, a mesoscopic simulation model of mixed traffic flow is proposed. In the process of road driving, on the one aspect, we consider the feature that bus speed is usually lower than vehicle speed, and correspondingly establish the reduction function of bus speed; on the other aspect, we consider the influences of bus-station queue overflow on the adjacent lanes, and correspondingly construct the lane-based speed model of mixed flow.Moreover, we use the …
Electrical Resistance Tomography And Flow Pattern Identification Method Based On Deep Residual Neural Network,
2022
Department of Automation, North China Electric Power University, Baoding 071003, China;
Electrical Resistance Tomography And Flow Pattern Identification Method Based On Deep Residual Neural Network, Weiguo Tong, Shichao Zeng, Lifeng Zhang, Zhe Hou, Jiayue Guo
Journal of System Simulation
Abstract: Aiming at the low accuracy of inverse problem imaging and flow pattern recognition in electrical resistance tomography (ERT), a two-phase flow electrical resistance tomography and flow pattern recognition method based on the deep residual neural network is proposed. The finite element method is used to model the ERT forward problem to construct the "boundary voltage-conductivity distribution-flow pattern category" dataset of various gas-liquid two-phase flow distributions. The residual neural network for ERT image reconstruction and flow pattern identification of gas-liquid two-phase flow is built and trained. The two outputs of the residual neural network are processed respectively to obtain …
Study On Invulnerability Of Urban Agglomeration Passenger Traffic Network Considering Time Characteristics,
2022
School of Transportation, Inner Mongolia University, Hohhot 010070, China;
Study On Invulnerability Of Urban Agglomeration Passenger Traffic Network Considering Time Characteristics, Chengbing Li, Yunfei Li, Peng Wu
Journal of System Simulation
Abstract: The cascading failure invulnerability study of comprehensive passenger transport network in urban agglomeration is helpful to improve the safety and transportation efficiency of intercity travel. In order to be consistent with the actual situation, a comprehensive passenger transport network model for urban agglomerations is constructed based on multi-layer complex network theory and actual passenger flow. The passenger transport network cascading failure invulnerability model considering time characteristics is established with unit time step. The spatial and temporal evaluation indexes are put forward to analyze the network situation in each period. Taking Hu-Bao-E-Yu urban agglomeration as an example, the results show …
Research On Ofdm Signal Detection Method Based On Intrawell Stochastic Resonance Of Bistable System,
2022
Faculty of Automation and Information Engineering, Xi'an University of Technology, xi’an 710048, China;
Research On Ofdm Signal Detection Method Based On Intrawell Stochastic Resonance Of Bistable System, Gaohui Liu, Ying Liang
Journal of System Simulation
Abstract: In order to solve the problem of weak OFDM (orthogonal frequency division multiplexing)signal detection at the receiver in OFDM transmission system, the intrawell stochastic resonance of bistable system is combined with the OFDM signal enhancement and demodulation process. Analytical expression is derived for the time required to change from zero state to potential well state for the intrawell stochastic resonance system under the excitation of multicarrier signals, and the energy loss of multicarrier signals in one symbol caused by the transient response is analyzed. The steady-state output equation of system is derived, and the problem of superimposing the …
Architecture Design And Prototype Verification Of Railway Vehicle Dynamics Cloud Platform,
2022
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China;
Architecture Design And Prototype Verification Of Railway Vehicle Dynamics Cloud Platform, Junjie Sheng, Zhao Tang, Shaodi Dong, Shuyang Wu, Hao Liang
Journal of System Simulation
Abstract: Since almost all the software in the railway vehicle field is controlled by foreign capital, it is difficult to catch up with the development of independent vehicle system software based on single machine deployment mode in a short time. In view of this, a set of autonomous and controllable vehicle system dynamics software architecture based on cloud platform is proposed. Based on the railway vehicle system dynamics and cloud services, a cloud platform with automatic process modeling, cloud computing,post-processing analysis is built. A simulation model of a trailer caris applied in the platform, and compared with the SIMPACK …
A High Resolution Reconstruction Method Of Temperature Distribution In Acoustic Tomography,
2022
Department of Automation, North China Electric Power University, Baoding 071003, China;
A High Resolution Reconstruction Method Of Temperature Distribution In Acoustic Tomography, Lifeng Zhang, Yu Miao
Journal of System Simulation
Abstract: Accurate measurement temperature distribution is important for industrial production. In order to solve the number of mesh divisions will impact reconstruction accuracy in acoustic tomography, the TR-RBF (Tikhonov regularization-radial basis function) reconstruction algorithm is rebuilt to reconstruct the temperature field with high resolution. The Tikhonov regularization is used to reconstruct the ultrasound time of flight (TOF) to obtain a temperature distribution on coarse grids, and use local weighted regression method to smooth processing; use RBF neural networks to predict the temperature distribution on fine grids. Through numerical simulation with and without noise, compared with ART,SVD and Tikhonov, the proposed …
Research On Cooperative Adaptive Cruise Control Strategy Based On Improved Mpc,
2022
1.School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
Research On Cooperative Adaptive Cruise Control Strategy Based On Improved Mpc, Qiming Wang, Jiangyue Jiang, Zhichao Lü, Hanzu Zhang
Journal of System Simulation
Abstract: To solve the problems of environmental interference, sensor noise and poor tracking stability of time-varying speed, an improved MPC (model predictive control) algorithm based on KF (kalman filtering) is proposed. The longitudinal kinematics model of CACC(cooperative adaptive cruise control)between vehicles is established and the discrete state space equation is created. KF is used to reduce the noise of state variables, and at the same time, the prediction model is designed for robustness. The CACC control objectives are analyzed under different working conditions and the objective optimization functions are created. Verify by building Simulink and CarSim co-simulation model, the simulation …
Simulation Of The Market Exclusive Competition Between Platforms,
2022
School of Management, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China;
Simulation Of The Market Exclusive Competition Between Platforms, Wen Zheng, Zhe Zhang, Jingyi Zhu
Journal of System Simulation
Abstract: As for the problem of an exclusive competition between the platforms, 2 competing PlatformsAgent, 100 ConsumersAgent and 300 SellersAgent are introduced and encapsulated into a closed market environment in BarriersModelSwarm. A two-sided market system is constructed with the cross-network externality. Through BarriersObserverSwarm, the Agents attribute information and behavior strategy are cross-called, and the virtual connection class Orderand ArrayList class in the Virtual Connection Classes are generated to run cyclically. The unilateral dependence degree of Consumers/SellersAgent is triggered, which restores the exclusive of the two-sided market competition in comparison with the platform transaction scale, market concentration, and platform cumulative capital. …
Turbofan Engine Fault Prediction Based On Evidential Reasoning And Belief Rule Base,
2022
1.College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China;
Turbofan Engine Fault Prediction Based On Evidential Reasoning And Belief Rule Base, Hailong Zhu, Ruxia Jia, Liang Zhang, Wei He
Journal of System Simulation
Abstract: Aiming at the fault prediction problem of a turbofan engine, a fault prediction model based on evidential reasoning (ER) and belief rule base (BRB) is proposed. In order to describe the health state of turbofan engine, ER algorithm is adopted to fuse the state information. Combined with prior knowledge, a hybrid driven simulation prediction of BRB model is established. Projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model parameters. The validity of the model is verified by experiments. Experimental results show that the proposed method not only accurately predicts the probability of failure …
Modulation Recognition Algorithm Based On Truncated Migration And Parallel Resnet,
2022
1.School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing University of Information Science & Technology, Nanjing 210044, China;
Modulation Recognition Algorithm Based On Truncated Migration And Parallel Resnet, Yecai Guo, Qingwei Wang
Journal of System Simulation
Abstract: A truncated migration data preprocessing algorithm is proposed for the problem of limited time series characteristics of the signal extracted by convolutional neural network. The distance unit at one end of the sampling matrix is truncated, migrated to the other end to form a new matrix, allowing the convolutional neural network to extract more sampling points and compare more symbolic information.An improved parallel ResNet is proposed, which focuses on features in both horizontal and vertical directions simultaneously by two parallel branches. The results show that the algorithm has an accuracy rate of about 10% higher than that of ordinary …
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription,
2022
Rowan University
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
Theses and Dissertations
Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …
An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions,
2022
University of South Florida
An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions, Quintin P. Mcgrath
USF Tampa Graduate Theses and Dissertations
The effective use of Artificial Intelligence (AI) has immediate business benefits for an organization and its stakeholders through efficiency and quality gains, and the potential to explore and implement new business models. However, there are risks of unintended ethical consequences. Enterprise Risk Management (ERM) focuses on managing risk while maximizing business value from exploiting opportunities. Using applied ethics as a basis and the perspective that ethics includes both enabling human flourishing and not violating accepted norms, I argue that greater business value is achieved when an organization simultaneously targets the maximization of benefits and the minimization of harms for the …
Learning Hierarchical Metrical Structure Beyond Measures,
2022
Music X Lab, NYU Shanghai, China & Mohamed bin Zayed University of Artificial Intelligence
Learning Hierarchical Metrical Structure Beyond Measures, Junyan Jiang, Daniel Chin, Yixiao Zhang, Gus Xia
Machine Learning Faculty Publications
Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate …
Led Down The Rabbit Hole: Exploring The Potential Of Global Attention For Biomedical Multi-Document Summarisation,
2022
The University of Melbourne, Australia
Led Down The Rabbit Hole: Exploring The Potential Of Global Attention For Biomedical Multi-Document Summarisation, Yulia Otmakhova, Hung Thinh Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor, Jey Han Lau
Natural Language Processing Faculty Publications
In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration. © 2022, CC BY-SA.
Unsupervised Lexical Substitution With Decontextualised Embeddings,
2022
School of Computing and Information Systems, The University of Melbourne, Australia & RIKEN Center for Advanced Intelligence Project (AIP), Japan
Unsupervised Lexical Substitution With Decontextualised Embeddings, Takashi Wada, Timothy Baldwin, Yuji Matsumoto, Jey Han Lau
Natural Language Processing Faculty Publications
We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language models to predict substitutes, our method retrieves substitutes based on the similarity of contextualised and decontextualised word embeddings, i.e. the average contextual representation of a word in multiple contexts. We conduct experiments in English and Italian, and show that our method substantially outperforms strong baselines and establishes a new state-of-the-art without any explicit supervision or fine-tuning. We further show that our method performs particularly well at predicting low-frequency substitutes, and also generates a diverse list of …
Artificial Intelligence-Driven Design Of Fuel Mixtures,
2022
Clean Combustion Research Center (CCRC), Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Artificial Intelligence-Driven Design Of Fuel Mixtures, Nursulu Kuzhagaliyeva, Samuel Horváth, John Williams, Andre Nicolle, S. Mani Sarathy
Machine Learning Faculty Publications
High-performance fuel design is imperative to achieve cleaner burning and high-efficiency engine systems. We introduce a data-driven artificial intelligence (AI) framework to design liquid fuels exhibiting tailor-made properties for combustion engine applications to improve efficiency and lower carbon emissions. The fuel design approach is a constrained optimization task integrating two parts: (i) a deep learning (DL) model to predict the properties of pure components and mixtures and (ii) search algorithms to efficiently navigate in the chemical space. Our approach presents the mixture-hidden vector as a linear combination of each single component’s vectors in each blend and incorporates it into the …
