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Articles 91 - 120 of 965
Full-Text Articles in Artificial Intelligence and Robotics
Distilled Siamese Networks For Visual Tracking, Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi
Distilled Siamese Networks For Visual Tracking, Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi
Research Collection School Of Computing and Information Systems
In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students), which capture critical knowledge from large Siamese trackers (teachers) by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher versus multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module …
End-To-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
End-To-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Research Collection School Of Computing and Information Systems
Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hierarchy of policies that uses them to reach the goals. Recently introduced end-to-end HRL methods accomplish this by using the higher-level policy in the hierarchy to directly search the useful subgoals in a continuous subgoal space. However, learning such a policy may be challenging when the subgoal space is large. We propose integrated discovery of salient subgoals (LIDOSS), an end-to-end HRL method with an integrated …
A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer
A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer
Research Collection School Of Computing and Information Systems
We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (QoS) constraints. The key challenge lies in the fact that the QoS functions have no closed-form and need to be approximated by simulation. In this paper we propose a new way to approximate the QoS functions by logistic functions and design a new algorithm that combines logistic regression, cut generations and logistic-based local search to efficiently find good staffing solutions. We report computational results using examples up to 65 call types and 89 agent groups …
Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai
Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai
Research Collection School Of Computing and Information Systems
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …
Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing
Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing
Research Collection School Of Computing and Information Systems
Out-of-distribution (OOD) settings are used to measure a model’s performance when the distribution of the test data is different from that of the training data. NLU models are known to suffer in OOD settings (Utama et al., 2020b). We study this issue from the perspective of causality, which sees confounding bias as the reason for models to learn spurious correlations. While a common solution is to perform intervention, existing methods handle only known and single confounder, but in many NLU tasks the confounders can be both unknown and multifactorial. In this paper, we propose a novel interventional training method called …
Conversation Disentanglement With Bi-Level Contrastive Learning, Chengyu Huang, Zheng Zhang, Hao Fei, Lizi Liao
Conversation Disentanglement With Bi-Level Contrastive Learning, Chengyu Huang, Zheng Zhang, Hao Fei, Lizi Liao
Research Collection School Of Computing and Information Systems
Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, a huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our …
Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati
Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati
Research Collection School Of Computing and Information Systems
In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements …
Towards Reinterpreting Neural Topic Models Via Composite Activations, Jia Peng Lim, Hady Wirawan Lauw
Towards Reinterpreting Neural Topic Models Via Composite Activations, Jia Peng Lim, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Most Neural Topic Models (NTM) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. These topics are interpreted through the selection of the top activated words via the weights or reconstructed vector of the decoder that are directly connected to each neuron. In this paper, we present a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. Firstly, building on the original information from a trained NTM, we generate a pool of potential candidate “composite topics” by exploiting possible co-occurrences within the original set of …
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Research Collection School Of Computing and Information Systems
Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predicts different sentiments for similar texts that differ only in the characteristic of individuals described. To automatically uncover bias in SA systems, this paper presents BiasFinder, an approach that can discover biased predictions in SA systems via metamorphic testing. A key feature of BiasFinder is the automatic curation of suitable templates from any given …
S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong
S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong
Research Collection School Of Computing and Information Systems
State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only …
Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang
Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang
Research Collection School Of Computing and Information Systems
Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pretrained RoBERTa language model for hateful meme classification. …
A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang
A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang
Research Collection School Of Computing and Information Systems
Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment largescale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with fewshot data. The experiments on a target dataset across multiple domains show …
Supply Regulation Under The Exclusion Policy In A Ride-Sourcing Market, Xiaonan Li, Xiangyong Li, Hai Wang, Junxin Shi, Yash P. Aneja
Supply Regulation Under The Exclusion Policy In A Ride-Sourcing Market, Xiaonan Li, Xiangyong Li, Hai Wang, Junxin Shi, Yash P. Aneja
Research Collection School Of Computing and Information Systems
On-demand ride-sourcing platforms have quickly emerged and become ubiquitous in our daily lives. Motivated by the rising public concern about service quality in the ride-sourcing market, this paper aims to examine the impact of exclusion policy that can serve as both quality management and supply regulation strategy. With an exclusion policy, the platform excludes low-quality service providers/drivers from the ride-sourcing market by setting a quality threshold of admission (QTA). We propose a model to describe and analyze the market equilibrium under the exclusion policy and present our analytical and numerical results – some of which are non-intuitive and intriguing. Considering …
An Investigation Into Whitening Loss For Self-Supervised Learning, Xi Weng, Lei Huang, Lei Zhao, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan
An Investigation Into Whitening Loss For Self-Supervised Learning, Xi Weng, Lei Huang, Lei Zhao, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan
Computer Vision Faculty Publications
A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of positive pairs under the conditioning that the embeddings from different views are whitened. In this paper, we propose a framework with an informative indicator to analyze whitening loss, which provides a clue to demystify several interesting phenomena as well as a pivoting point connecting to other SSL methods. We reveal that batch whitening (BW) based methods do not impose whitening constraints on the embedding, but they only require the embedding to be full-rank. This full-rank constraint …
Contribution Rate Calculation Method To System-Of-Systems Based On Interval-Valued Intuitionistic Fuzzy Number Anp, Zejian Ding, Songtao Sun, Zhiwen He, Fei Liu
Contribution Rate Calculation Method To System-Of-Systems Based On Interval-Valued Intuitionistic Fuzzy Number Anp, Zejian Ding, Songtao Sun, Zhiwen He, Fei Liu
Journal of System Simulation
Abstract: Contribution rate to system-of-systems (CRSoS) is mainly used to measure the contribution of an equipment to system of systems (SoS) in system construction. In order to solve some problems in the calculation of CRSoS, a multi-level equipment indicator architecture of "task-ability-indicator- equipment" is proposed. At the same time, considering the characteristics of the equipment indicator architecture, ANP (analytic network process) and IVIFN (interval-valued intuitionistic fuzzy number), a IVIF-ANP calculation method is proposed to obtain more accurate CRSoS. Experiments show that this method can not only solve the problem of the calculation formula of CRSoS, but also obtain more …
Simulation And Effectiveness Evaluation System For Joint Delivery Mission Planning Of Airlift Fleets, Guochen Wang
Simulation And Effectiveness Evaluation System For Joint Delivery Mission Planning Of Airlift Fleets, Guochen Wang
Journal of System Simulation
Abstract: Airlift fleet plays an important role in modern war. Compared with other countries such as the USA and Russia, China's airlift fleet still has obvious shortcomings and deficiencies. To analysis and optimize the future fleet alternatives, a software tool is established with the modules of model construction and management, scenarios editing, mission planning, simulation deduction, effectiveness analysis. This tool mainly focuses on the interactive relationship between the transport aircraft and cargo, airport and so on, as well as the cooperative relationship of different types of aircraft, which can realize the functions of automatic generation of loading schemes, automatic planning …
Research On Key Technology Of Uavs Autonomous Landing Based On Relative Precise Point Position, Guohua Kang, Teng Zhao, Yao Fu, Weizheng Xu, Jianyu Wei, Yuhuan Qiu, Junfeng Wu
Research On Key Technology Of Uavs Autonomous Landing Based On Relative Precise Point Position, Guohua Kang, Teng Zhao, Yao Fu, Weizheng Xu, Jianyu Wei, Yuhuan Qiu, Junfeng Wu
Journal of System Simulation
Abstract: In complex sea conditions with wind and waves, the relative motion between unmanned aerial vehicles (UAVs) requiring autonomous landing and ships is highly uncertain. In order to improve the accuracy of relative positioning and control during autonomous landing, and to ensure the safety and reliability of autonomous landing, a relative precise point positioning (RPPP) technique based on differential tropospheric error is proposed. The technology only relies on data link and carrier satellite positioning receiver to eliminate the same error of satellite positioning in the same environment and obtain accurate relative positioning. The combination of proportional navigation and linear quadratic …
Robust Optimal Configuration Of Pv-Energy Storage In Industrial Parks Considering The Uncertainty Of Photovoltaics, Guiting Xue, Boya Shan, Ti Wang, Xiao Wang, Wei Xing, Weiqing Sun
Robust Optimal Configuration Of Pv-Energy Storage In Industrial Parks Considering The Uncertainty Of Photovoltaics, Guiting Xue, Boya Shan, Ti Wang, Xiao Wang, Wei Xing, Weiqing Sun
Journal of System Simulation
Abstract: Research on using rooftop resources in industrial parks to develop photovoltaic projects and reasonable configuration of energy storage will help improve the park's energy economy. To obtain the optimal PV-storage configuration scheme, an industrial park with three types of load demand, namely, cold, heat and electricity, is selected, and a robust optimization allocation model of park PV-storage is established with optimal operating profit as the objective function, considering the increased cost of power purchase caused by the PV uncertainty. The model uses the box uncertainty set in robust optimization for PV intensity, and linearizes the model using pairwise …
Evacuation Model Considering The Restricted View Of The Sign, Yinghua Song, Zheqian Zhang, Feizhou Huo, Danhui Fang
Evacuation Model Considering The Restricted View Of The Sign, Yinghua Song, Zheqian Zhang, Feizhou Huo, Danhui Fang
Journal of System Simulation
Abstract: In order to study the influence of restricted vision on the process of pedestrian evacuation, a cellular automata model of pedestrian evacuation under restricted vision is established. In the model, the evacuation space is divided into three different areas according to the field of view radius, pedestrians have different ways of moving in different areas. Different income parameters are defined to calculate the pedestrian movement income matrix and determine the target position of the pedestrian in the next time step. An evacuation scene is established to simulate the initial density of different pedestrians, the change of the field of …
Simulation Of O2o Platform Transaction Considering The Constituted Information, Wen Zheng, Ke Shi, Jingyi Zhu
Simulation Of O2o Platform Transaction Considering The Constituted Information, Wen Zheng, Ke Shi, Jingyi Zhu
Journal of System Simulation
Abstract: In the transactions concluded with the help of APP, the information intervenes into the transaction process in the form of constituted information. The constituted information is classified into three degrees in a two-sided market. By introducing the three degrees of the constituted information: information acceptance (IA), information diversity (ID) and information loss (IL), the Swarm Class Library and the interactive Agents are designed to construct a systematic O2O (online to offline)platform transaction model, which encapsulates the Consumer/Seller/PlatformAgents. The constituted information acts as the systematic conditions of invoking and judging, and the graphical user interface (GUI) outputs the …
Multi-Uav Trajectory Planning Based On Adaptive Segmented Potential Field Method, Guangjian Tian, Jiyang Dai, Jin Ying, Ning Wang
Multi-Uav Trajectory Planning Based On Adaptive Segmented Potential Field Method, Guangjian Tian, Jiyang Dai, Jin Ying, Ning Wang
Journal of System Simulation
Abstract: To solve the problems that the traditional artificial potential field method is prone to fall into the local extreme value, target unreachability and excessive curvature of the planned trajectory curvature in the application of UAV trajectory planning, on the basis of the layered potential field method, a method of adding a second local attractive field at the target point and an attractive set composed of the target attractive field is proposed. This method overcomes the defects of unreachable targets and easy falling into local extremes. In addition, a piecewise function is introduced into the original layered potential field method, …
Nonlinear System Identification Based On Combined Signal Sources, Tian Zheng, Feng Li, Naibao He, Ya Gu
Nonlinear System Identification Based On Combined Signal Sources, Tian Zheng, Feng Li, Naibao He, Ya Gu
Journal of System Simulation
Abstract: Aiming at the interference of noise in the nonlinear system, the identification modeling method of the neuro-fuzzy Hammerstein output error nonlinear system is considered. The combined signal sources are used to realize the parameter identification separation of the linear block and the nonlinear block. The correlation analysis method and the recursive least square identification method based on auxiliary model technique are derived to estimate the parameters of dynamic linear block and nonlinear block, which can effectively suppress the interference of system output noise. Compared with least square algorithm, polynomial model and multi-innovation method, the simulation results demonstrate that the …
A Real-Time Ultrasound Simulation Platform Using Ray Tracing And Its Integration With Virtual Reality, Bo Peng, Qiang Wang, Ruibing Qing, Lixue Yin, Jingfeng Jiang
A Real-Time Ultrasound Simulation Platform Using Ray Tracing And Its Integration With Virtual Reality, Bo Peng, Qiang Wang, Ruibing Qing, Lixue Yin, Jingfeng Jiang
Journal of System Simulation
Abstract: In order to further improve the efficacy of ultrasound training and reduce the cost. An ultrasound training system that is integrated with a VR environment is developed. The main contribution of this study is to incorporate the Ray-tracing based ultrasound image simulation approach into a virtual reality environment, taking advantage of immersive VR experience for medical ultrasound training. The simulated ultrasound images obtained by the proposed method are then compared to images that are simulated using a generative adversarial network (GAN) technique and Field II ultrasound simulator. The data show that the ultrasound simulator can produce high-quality simulated …
A Two-Layer Network Propagation Model Of Awareness Diffusion And Seir Epidemic, Yurong Song, Yulin Bao, Ruqi Li
A Two-Layer Network Propagation Model Of Awareness Diffusion And Seir Epidemic, Yurong Song, Yulin Bao, Ruqi Li
Journal of System Simulation
Abstract: In order to understand the transmission characteristics of epidemics similar to COVID-19 (coronavirus disease 2019) with obvious expose period, a two-layer network transmission model considering time-varying factors is proposed to make corresponding predictions and measures. The UAU (unaware-aware-unaware) information transmission model is used to represent the diffusion process of conscious information about epidemic. In the underlying network, the susceptible-exposed-infected- recovered (SEIR) epidemic-like transmission model with latent state is used to describe the epidemic transmission process affected by conscious information. The MMCA (microscopic Markov chain approach) is used to deduce the transmission threshold of epidemics diseases. By analyzing the key …
Research On Mobile Edge Computing Resource Allocation With Energy Harvesting Device, Changyun Li, Jianbo Li, Xi Xu, Tingli Li
Research On Mobile Edge Computing Resource Allocation With Energy Harvesting Device, Changyun Li, Jianbo Li, Xi Xu, Tingli Li
Journal of System Simulation
Abstract: In order to solve the problem of computing resource allocation of mobile edge computing system with energy gathering ability, an algorithm based on Lyapunov greed optimization (LGO) is proposed. This paper presents a dynamic optimization problem to minimize the combined cost of time delay and energy consumption of mobile devices under the gradual convergence of equipment battery power. Using Lyapunov dynamic optimization theory, the optimization problem is decomposed into three sub-problems of optimal local execution, unloading execution and energy harvesting for each time slot, and the optimal solution of the sub-problems is obtained by linear programming. By selecting the …
Bilevel Distributed Optimal Dispatch Of Active Distribution Network With Multi-Microgrids, Yongjun Lin, Xin Chen, Kai Yang, Shanshan Zhou, Qingfei Bai
Bilevel Distributed Optimal Dispatch Of Active Distribution Network With Multi-Microgrids, Yongjun Lin, Xin Chen, Kai Yang, Shanshan Zhou, Qingfei Bai
Journal of System Simulation
Abstract: With continuous increase of the penetration proportion of renewable energy in the distribution network, the traditional centralized dispatching is facing problems such as high pressure of power flow calculation and difficulty in recycling renewable energy, which makes it difficult to guarantee the operation quality of the system. A distributed optimal two-layer scheduling method for active distribution networks with multiple micro-grids is proposed. The upper-level aims to minimize the loss of regional distribution network, the second-order conical relaxation method and synchronous ADMM (alternating direction method of multipliers) algorithm are used to solve the scheduling instructions of the micro-grid connection lines. …
Transmission Line Insulator Recognition Based On Artificial Images Data Expansion, Yaru Wang, Kai Yang, Yongjie Zhai, Congbin Guo, Wenqing Zhao, Jie Su
Transmission Line Insulator Recognition Based On Artificial Images Data Expansion, Yaru Wang, Kai Yang, Yongjie Zhai, Congbin Guo, Wenqing Zhao, Jie Su
Journal of System Simulation
Abstract: Deep learning method has developed rapidly in the field of computer vision, but relies on a large quantities of training data. In the task of transmission line insulator automatic detection, problems such as insufficient number of aerial insulator images and poor diversity affect the accuracy of insulator recognition. An artificial insulator images data expansion method is proposed. Artificial insulator images are created by modeling software, and a compensation network is constructed. The artificial images are compensated and optimized by compensation network, and the aerial insulator image data set is expanded by the compensated artificial insulator images. The insulator recognition …
A Wind Turbine Fault Diagnosis Method Based On Siamese Deep Neural Network, Jiarui Liu, Guotian Yang, Xiaowei Wang
A Wind Turbine Fault Diagnosis Method Based On Siamese Deep Neural Network, Jiarui Liu, Guotian Yang, Xiaowei Wang
Journal of System Simulation
Abstract: In order to effectively extract the fault features of time series data in supervisory control and data acquisition (SCADA), considering the advantages of one-dimensional convolutional neural network (1-D CNN) for extracting local time series features and the advantages of long-term memory (LSTM) which can extract long-term dependent features, a method for fault diagnosis of wind turbines based on 1-D CNN-LSTM is proposed. To solve the problem of the scarcity of fault samples of wind turbines based on the siamese network architecture, a wind fault diagnosis method based on siamese 1-D CNN-LSTM is proposed. The proposed siamese 1-D CNN-LSTM …
Research On Network Public Opinion Transmission Mechanism Of Inversion Event Based On Integrating Improved Sir Model, Jianrong Tang, Jiatong Bao
Research On Network Public Opinion Transmission Mechanism Of Inversion Event Based On Integrating Improved Sir Model, Jianrong Tang, Jiatong Bao
Journal of System Simulation
Abstract: In order to identify the spreading rules of rumors in vicious news reversal events and make more targeted guiding decisions, a short-term prediction model is proposed to simulate the spread of virus information. This paper improves the traditional susceptible infected removed (SIR) model and solves the problem that the conversion rate is fixed and single due to the limitation of Markov chain when it is combined with systems dynamics (SD) model. The data is validated with the example of "asthmatic girls" . The results show that the model not only effectively simulates the crisis of public opinion communication in …
Design And Simulation-Based Evaluation Of Taxiway Operation Scheme For Multi-Runway Airport Maneuvering Area, Xinping Zhu, Chuan Xu, Jingjing Qu, Tingwen Su
Design And Simulation-Based Evaluation Of Taxiway Operation Scheme For Multi-Runway Airport Maneuvering Area, Xinping Zhu, Chuan Xu, Jingjing Qu, Tingwen Su
Journal of System Simulation
Abstract: The operational scheme of the taxiway system is very important to promote the efficient utilization of airfield resources in multi-runway airports. The design and simulation evaluation methods of the taxiway operation scheme for multi-runway airports are studied. The design principles of "fixed, unidirectional, compliant and circular" taxiway operation scheme and the design paradigm of the operation scheme are presented, and the concepts of taxiway space occupancy index and potential conflict index are proposed. Using Haikou Meilan International Airport as the application scenario, the corresponding optimization scheme of the taxiway system in the maneuvering area is given based on …