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Artificial Intelligence and Robotics

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Articles 4621 - 4650 of 11311

Full-Text Articles in Computer Sciences

A Simulation Method Of Airborne Radar Real-Time Detection Based On Three-Dimensional Subdivision, Ying Xu, Shuai Zhang, Zhige Xie, Xinhai Xu, Manhui Sun, Ning Guo Feb 2023

A Simulation Method Of Airborne Radar Real-Time Detection Based On Three-Dimensional Subdivision, Ying Xu, Shuai Zhang, Zhige Xie, Xinhai Xu, Manhui Sun, Ning Guo

Journal of System Simulation

Abstract: The emergence and rapid development of UAVs make the target detection of UAV airborne radar in combat simulation great research valuable. In the existing combat simulation platforms at home and abroad, the detection relationship between radar and target is pairwise interactive, and the calculation overhead increases linearly or ultra-linear following the increase of entity numbers, which is difficult to carry out the large-scale real-time combat simulation. Based on the concept of three-dimensional meshing, an airborne radar target detection simulation method is proposed, which can quickly judge the success or failure of detection by making a detection template before simulation …

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Design And Implementation Of Industrial Robot Remote Monitoring System In Cloud Manufacturing, Yongkui Liu, Lin Zhang, Yingfu Liu, Jianyong Feng, Bo Yu, Wenbo Niu Feb 2023

Design And Implementation Of Industrial Robot Remote Monitoring System In Cloud Manufacturing, Yongkui Liu, Lin Zhang, Yingfu Liu, Jianyong Feng, Bo Yu, Wenbo Niu

Journal of System Simulation

Abstract: Considering the lack of the existing research on cloud manufacturing monitoring system and the lack of scalability and flexibility of existing remote monitoring system, and taking deep reinforcement learning-based industrial robot intelligent grasping as an application scenario, a micro-service architecture-based remote monitoring system for cloud manufacturing is developed, to carry out the requirement analysis and design of the monitoring system, and the remote monitoring of industrial robot intelligent grasping processes is realized. Test results show that the system can meet the monitoring requirements of resource providers, platform operator(s) and service consumers.

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Design Of System Combat Simulation Platform For Complex Electromagnetic Environment, Baiyuan Ding, Fuling Mu, Yunpeng Li, Zhongkuan Chen, Chengyu Liu Feb 2023

Design Of System Combat Simulation Platform For Complex Electromagnetic Environment, Baiyuan Ding, Fuling Mu, Yunpeng Li, Zhongkuan Chen, Chengyu Liu

Journal of System Simulation

Abstract: War gaming and simulation can be divided into four levels of strategy、campaign、tactics and technique. Existing system combat simulation platforms of campaign and tactics at home and abroad cannot drive the special model of electromagnetic equipment in technique level, resulting in the low fidelity of battlefield complex electromagnetic environment simulation. To solve the problem, flexible analysis modeling and exercise system(FLAMES) model architecture as the reference, based on the simulation engine library of a discrete event system simulator(ADEVS), a flexible operation simulation platform(FOSim) is designed and developed, which integrates three levels of campaign, tactics and technology, supports three levels independence simulation …

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Machine Learning-Based Simulation Research Of On-Line Subway Pedestrian Flow Control, Jiajie Shi, Peng Yang, Yannan Pi Feb 2023

Machine Learning-Based Simulation Research Of On-Line Subway Pedestrian Flow Control, Jiajie Shi, Peng Yang, Yannan Pi

Journal of System Simulation

Abstract: In recent years, a large number of digital experiments have been carried out in the field of space launch, such as digital design verification, digital joint training and simulation training, and rocket-ground joint simulation evaluation, all of which involve the space launch information visualization. Through virtual reality technology, system simulation technology, data visualization technology, etc., on the basis of multi-thread, multi-module architecture design idea, and message queue system interaction mode, the space launch visual simulation analysis technology platform with functions of data management, scenario management, calculation management, and script management is constructed. The application cases of visual simulation analysis …

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Takeout Distribution Routes Optimization Considering Order Clustering Under Dynamic Demand, Houming Fan, Fushan Xian, Huaiqi Wang Feb 2023

Takeout Distribution Routes Optimization Considering Order Clustering Under Dynamic Demand, Houming Fan, Fushan Xian, Huaiqi Wang

Journal of System Simulation

Abstract: Takeout distribution optimization includes order allocation and route planning. Aiming at dynamic order and rider position change, with the goal of minimizing the overtime order proportion, average delivery time and average travel distance,a two-stage mathematical model is established based on the idea of pre-optimization and dynamic adjustment. In the pre-optimization stage, an improved variable neighborhood search algorithm is designed to obtain the initial distribution scheme. In the dynamic adjustment stage, a periodic optimization strategy is adopted to transform the problem into a virtual distribution center vehicle problem for solution. In each stage,different clustering methods are used to optimize the …

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Demand Forecasting Method Of Emergency Materials Based On Metabolic Gray Markov, Long Ma, Baodong Qin, Na Lu, Meng Kou Feb 2023

Demand Forecasting Method Of Emergency Materials Based On Metabolic Gray Markov, Long Ma, Baodong Qin, Na Lu, Meng Kou

Journal of System Simulation

Abstract: In order to improve the prediction accuracy of the demand for emergency materials of people affected by the disaster, a forecasting method based on metabolism-gray Markov's is proposed. To realize the dynamic prediction of the number of people affected by the disaster, according to demand forecast ideas, the prediction model of metabolism-gray Markov fused is constructed progressively through gray, Markov and metabolism theories. A flexible demand forecasting model for emergency supplies is built through safety stock theory to complete the balance of supply and demand between people number and the materials demand. The prediction results of different …

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Se(3)-Based Finite-Time Fault-Tolerant Control Of Spacecraft Integrated Attitude-Orbit, Yafei Mei, Ying Liao, Kejie Gong, Xingyu Zheng Feb 2023

Se(3)-Based Finite-Time Fault-Tolerant Control Of Spacecraft Integrated Attitude-Orbit, Yafei Mei, Ying Liao, Kejie Gong, Xingyu Zheng

Journal of System Simulation

Abstract: For relative motion spacecraft, when the actuator fails and the external disturbance and system uncertainty occur simultaneously, a finite-time fault-tolerant control method is proposed on the basis of the robustness of sliding mode control. A single-rigid spacecraft integrated attitude-orbit model is established based on Lie Group SE(3), and the relative motion spacecraft error dynamic equation is derived in exponential coordinates. A class of non-singular fast terminal sliding surface is designed, and the equivalent adaptive method is adopted to design the controller to estimate and compensate the total disturbance. The proposed fault-tolerant control algorithm can be independent from fault diagnosis …

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Research On Image Super-Resolution Reconstruction Based On Loss Extraction Feedback Attention Network, Hong Sun, Yuxiang Zhang, Yuelan Ling Feb 2023

Research On Image Super-Resolution Reconstruction Based On Loss Extraction Feedback Attention Network, Hong Sun, Yuxiang Zhang, Yuelan Ling

Journal of System Simulation

Abstract: Since the first application of convolutional neural network to the field of super-resolution image reconstruction (super-resolution convolutional neural network, SRCNN), a large number of studies have proved that deep learning can improve the effect of image reconstruction. Aiming at the too many parameters in the image super-resolution network and the insufficient utilization of image features resulting in less available high-frequency information, a loss extraction feedback attention network (LEFAN) is proposed to reuse parameters in a circular way and increase the reuse of low-resolution image features to capture more high-frequency information. The loss caused in the reconstruction process is extracted …

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Research On Cooperative Path Planning Model Of Multiple Unmanned Vehicles In Real Environment, Guohui Zhang, Xuan Wang, Yanan Zhang, Ang Gao Feb 2023

Research On Cooperative Path Planning Model Of Multiple Unmanned Vehicles In Real Environment, Guohui Zhang, Xuan Wang, Yanan Zhang, Ang Gao

Journal of System Simulation

Abstract: The cluster combat application of unmanned ground vehicles(UVS) is a hot research issue of the intersection of artificial intelligence and battle command. Aiming at the cooperative path planning multiple unmanned vehicles not meeting the dynamic threat condition requirement, by combining the global path planning algorithm A-STAR with the local path planning algorithm RL, from the perspective of perception to behavioral decision making, the cooperative path planning model of multiple unmanned vehicles is studied. The cooperative combat situation threat algorithm, state and action space, reward function and sphere of influence function are designed, the sub-models of formation configuration strategy generation …

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Dqn-Based Joint Scheduling Method Of Heterogeneous Tt&C Resources, Naiyang Xue, Dan Ding, Yutong Jia, Zhiqiang Wang, Yuan Liu Feb 2023

Dqn-Based Joint Scheduling Method Of Heterogeneous Tt&C Resources, Naiyang Xue, Dan Ding, Yutong Jia, Zhiqiang Wang, Yuan Liu

Journal of System Simulation

Abstract: Joint scheduling of heterogeneous TT&C resources as research object, a deep Q network (DQN) algorithm based on reinforcement learning is proposed. The characteristics of the joint scheduling problem of heterogeneous TT&C resources being fully analyzied and mathematical language being used to describe the constraints affecting the solution, a resource joint scheduling model is established. From the perspective of applying reinforcement learning, two neural networks with the same structure and the action selection strategies based onεgreedy algorithm are respectively designed after Markov decision process description, and DQN solution framework is established. The simulation results show that DQN-based heterogeneous …

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Online Classification Method For Motor Imagery Eeg With Spatial Information, Fengwei Yang, Peng Chen, Kai Xi, Hualin Pu, Xueyin Liu Feb 2023

Online Classification Method For Motor Imagery Eeg With Spatial Information, Fengwei Yang, Peng Chen, Kai Xi, Hualin Pu, Xueyin Liu

Journal of System Simulation

Abstract: EEG-based BCI system can help the daily life and rehabilitation training of limb movement disorders patients. Due to the low signal-to-noise ratio and large individual differences of EEG signals, the accuracy and efficiency of EEG feature extraction and classification are not high, which affects the wide application of online BCI system. A CNN with spatial information is proposed for the online classification of MI-EEG signals. The reordered MI-EEG is convolved horizontally and vertically respectively. With the contralateral effect of motor imagery ERD/ERS phenomenon, the spatial information in MI-EEG is fully utilized to achieve the real-time acquisition and classification of …

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Simulation Of Occluded Pedestrian Detection Based On Improved Yolo, Nan Xiang, Lu Wang, Chongliu Jia, Yuemou Jian, Xiaoxia Ma Feb 2023

Simulation Of Occluded Pedestrian Detection Based On Improved Yolo, Nan Xiang, Lu Wang, Chongliu Jia, Yuemou Jian, Xiaoxia Ma

Journal of System Simulation

Abstract: Aiming at the high missed detection rates and low accuracy of existing YOLO for occlusion and multi-scale pedestrian targets, an improved pedestrian detection algorithm is proposed. YOLO backbone is modified to enhance the capabilities of cross-scale feature extraction. To increase thepedestrian feature fusion capabilities of different scales, a spatial pyramid pooling module and two attention mechanisms are introduced at different positions in front of YOLO layers. Aiming at the detection performance degradation due to the extreme complexity of network module and to improve the model training efficiency, the network structure is pruned according to the actual …

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Evacuation Dynamics Research Based On Evolutionary Game Theory, Qiaoru Li, Jinxiu Yan, Xiaoyong Tian, Kun Li, Xia Li Feb 2023

Evacuation Dynamics Research Based On Evolutionary Game Theory, Qiaoru Li, Jinxiu Yan, Xiaoyong Tian, Kun Li, Xia Li

Journal of System Simulation

Abstract: In order to study the impact of individual cooperative behavior on the overall pedestrian evacuation efficiency, combined with cellular automata and social force model, the co-evolution of evacuation system dynamics and "cooperative behavior" is studied, and an evacuation dynamics research method based on evolutionary game theory is proposed. The cellular automata model is used as the basic simulation framework, and the social force model is used to represent the psychological repulsion among the practitioners. The evacuation individual strategy is updated through evolutionary game. The simulation results show that when the game gain coefficient exceeds a certain threshold, the …

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Efficient Hmm Map Matching Method Using R-Tree And Trajectory Segmentation, Yanjiao Song, Jiayue Zhou, Longhao Wang, Jing Wu, Rui Li, Xiaoping Rui Feb 2023

Efficient Hmm Map Matching Method Using R-Tree And Trajectory Segmentation, Yanjiao Song, Jiayue Zhou, Longhao Wang, Jing Wu, Rui Li, Xiaoping Rui

Journal of System Simulation

Abstract: In view of the incapability of traditional methods to efficiently process massive trajectory data, an improved HMM (hidden-Markov model) map matching algorithm is proposed. Spatial index for road networks is established through R-tree spatial index. GPS trajectory data are segmented based on the position change rates of trajectory points. R-tree index is used to quickly determine the candidate road section that sub-trajectories belong to, and the key points of the sub-trajectories instead of the entire sub-trajectories are selected to judge which road the sub-trajectories should be matched with. The map matching of each sub-trajectory is carried out on …

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Research On Digital Twin Credibility Assessment Process And Index, Fan Yang, Ping Ma, Wei Li, Ming Yang Feb 2023

Research On Digital Twin Credibility Assessment Process And Index, Fan Yang, Ping Ma, Wei Li, Ming Yang

Journal of System Simulation

Abstract: With the application field expansion of digital twin technology, in order to meet the requirement of digital twin credibility and promote the digital twin credibility assessment, the credibility assessment process and indicators of digital twin are researched. The development process of digital twin is analyzed and the construction method of flexible multi-layer digital twin credibility assessment process model based on IDEF0 is proposed. Two index systems, process-stage-activity layers(P-S-A) and activity-element-feature layers(A-E-F),are proposed to solve the problems of defect backtracking and evaluation of complex objects. Several examples of index system are given.

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Research On Real-Time Gesture Classification Algorithm Based On Imu And Semg Mixed Signals, Tao Wang, Yingnian Wu, Rui Yang, Yueying Sun Feb 2023

Research On Real-Time Gesture Classification Algorithm Based On Imu And Semg Mixed Signals, Tao Wang, Yingnian Wu, Rui Yang, Yueying Sun

Journal of System Simulation

Abstract: In order to improve the gesture classification accuracy of surface electromyography (sEMG), the mixed signal of attitude and sEMG is collected by inertial measurement unit (IMU) and EMG sensor, and a GRU-BiLSTM double-layer network real-time gesture classification algorithm is proposed. The first layer of gated recurrent unit (GRU) detects the mutation point of the initial mixed signal though energy combination operator feature and locates the starting point of the dynamic data. The second layer Bi-directional long short term memory (BiLSTM) classifies the motion state mixed signal into 10 gestures in two directions though energy kernel phase map feature. …

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Computation Offloading Strategy Based On Stackelberg Game And Drl, Xianwei Zhou, Qixu Gong, Songsen Yu Feb 2023

Computation Offloading Strategy Based On Stackelberg Game And Drl, Xianwei Zhou, Qixu Gong, Songsen Yu

Journal of System Simulation

Abstract: To achieve the optimal computation offloading strategy for two kinds of MEC users in 5G hybrid private network, Stackelberg game is used to build the model of the competition for MEC server resources of two kinds of users, andthe strategies of complete information game and partially incomplete information game are researched respectively. It is proved that there is only one Nash equilibrium solution in the complete information scenario. In the incomplete information scenario, the environment is modeled as POMDP, and a two-stage deep reinforcement learning(TSDRL) is proposed to obtain the optimal computation offloading strategy. Simulation results show the proposed …

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Machine Learning Methods For Computational Phenotyping Using Patient Healthcare Data With Noisy Labels, Praveen Kumar Feb 2023

Machine Learning Methods For Computational Phenotyping Using Patient Healthcare Data With Noisy Labels, Praveen Kumar

Computer Science ETDs

Positive and Unlabeled (PU) learning problems abound in many real-world applications. In healthcare informatics, diagnosed patients are considered labeled positive for a specific disease, but being undiagnosed does not mean they can be labeled negative. PU learning can improve classification performance, and estimate the positive fraction, α, among unlabeled samples. However, algorithms based on the Selected Completely At Random (SCAR) assumption are inadequate when the SCAR assumption fails (e.g., severe cases overrepresented), and when class imbalance is substantial. This dissertation presents and evaluates new algorithms to overcome these limitations. The proposed methods outperform the state-of-art for α-estimation, enhance classification performance, …

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Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle Feb 2023

Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …

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Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle Feb 2023

Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix factorization (TMF) and low-rank tensor factorization. From this method, we propose an expansion of TMF to two-dimensional data: temporal tensor factorization (TTF). The current research aims to interpolate missing values via low-rank tensor factorization, which produces a latent space of the original multilinear time series. We then can perform forecasting in the latent space. We present experimental results of the proposed …

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Deep Learning Architectures For Visual Question Answering On Medical Images, Venkat Ramana Kodali Kodali Feb 2023

Deep Learning Architectures For Visual Question Answering On Medical Images, Venkat Ramana Kodali Kodali

Theses and Dissertations

The purpose of this research is to apply both computer vision and natural language processing techniques for visual question answering (VQA) on a medical image dataset. Deep learning and machine learning libraries were used in the research. The research includes understanding key achievements in the field of visual question answering, identifying techniques applied in general images and applying them along with new techniques to medical images. There are many more articles explaining the application of visual question answering to general images than on applying VQA specifically to medical images. In this research, I initially developed a model of VQA that …

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A Proposed Meta-Reality Immersive Development Pipeline: Generative Ai Models And Extended Reality (Xr) Content For The Metaverse, Jay Ratican, James Hutson, Andrew Wright Feb 2023

A Proposed Meta-Reality Immersive Development Pipeline: Generative Ai Models And Extended Reality (Xr) Content For The Metaverse, Jay Ratican, James Hutson, Andrew Wright

Faculty Scholarship

The realization of an interoperable and scalable virtual platform, currently known as the “metaverse,” is inevitable, but many technological challenges need to be overcome first. With the metaverse still in a nascent phase, research currently indicates that building a new 3D social environment capable of interoperable avatars and digital transactions will represent most of the initial investment in time and capital. The return on investment, however, is worth the financial risk for firms like Meta, Google, and Apple. While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the …

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Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox Feb 2023

Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox

Faculty Publications

Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of …

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Towards Carbon Neutrality: Prediction Of Wave Energy Based On Improved Gru In Maritime Transportation, Zhihan Lv, Nana Wang, Ranran Lou, Yajun Tian, Mohsen Guizani Feb 2023

Towards Carbon Neutrality: Prediction Of Wave Energy Based On Improved Gru In Maritime Transportation, Zhihan Lv, Nana Wang, Ranran Lou, Yajun Tian, Mohsen Guizani

Machine Learning Faculty Publications

Efficient use of renewable energy is one of the critical measures to achieve carbon neutrality. Countries have introduced policies to put carbon neutrality on the agenda to achieve relatively zero emissions of greenhouse gases and to cope with the crisis brought about by global warming. This work analyzes the wave energy with high energy density and wide distribution based on understanding of various renewable energy sources. This study provides a wave energy prediction model for energy harvesting. At the same time, the Gated Recurrent Unit network (GRU), Bayesian optimization algorithm, and attention mechanism are introduced to improve the model's performance. …

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Uncertaintyfusenet: Robust Uncertainty-Aware Hierarchical Feature Fusion Model With Ensemble Monte Carlo Dropout For Covid-19 Detection, Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhreddine (Fakhri) Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi Feb 2023

Uncertaintyfusenet: Robust Uncertainty-Aware Hierarchical Feature Fusion Model With Ensemble Monte Carlo Dropout For Covid-19 Detection, Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhreddine (Fakhri) Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi

Machine Learning Faculty Publications

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority Such automatic systems are usually based on traditional machine learning or deep learning methods Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of …

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Customised Multi-Energy Pricing: Model And Solutions, Qiuyi Hong, Fanlin Meng, Jian Liu Feb 2023

Customised Multi-Energy Pricing: Model And Solutions, Qiuyi Hong, Fanlin Meng, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralized energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customized multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimization problem. At the upper level, the energy retailer (leader) aims to maximize its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimize their …

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Working With (Not Against) The Technology: Gpt3 And Artificial Intelligence (Ai) In College Composition, James Hutson, Daniel Plate Feb 2023

Working With (Not Against) The Technology: Gpt3 And Artificial Intelligence (Ai) In College Composition, James Hutson, Daniel Plate

Faculty Scholarship

The use of artificial intelligence (AI) for improvement of writing is commonplace with word-processing software and cloudbased writing assistants such as Grammarly and Microsoft Word. However, more and more options are cropping up that move beyond assistance with grammar, spelling, and punctuation to complete essay generation. The free availability of AI essay generators has led to lamenting the coming death of college writing. But AI has been used in the previously noted examples for decades without such a reaction. In fact, the idea that the use of essay generating software is synonymous with academic dishonesty is as passé as worries …

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Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha Feb 2023

Drone Detection Using Yolov5, Burchan Aydin, Subroto Singha

Faculty Publications

The rapidly increasing number of drones in the national airspace, including those for recreational and commercial applications, has raised concerns regarding misuse. Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. In addition, automated drone detection systems need to be trained with ample amounts of data to provide high accuracy. Real-time detection is also necessary, but this requires highly configured devices such as a graphical processing unit (GPU). …

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Safe Delivery Of Critical Services In Areas With Volatile Security Situation Via A Stackelberg Game Approach, Tien Mai, Arunesh Sinha Feb 2023

Safe Delivery Of Critical Services In Areas With Volatile Security Situation Via A Stackelberg Game Approach, Tien Mai, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Vaccine delivery in under-resourced locations with security risks is not just challenging but also life threatening. The COVID pandemic and the need to vaccinate added even more urgency to this issue. Motivated by this problem, we propose a general framework to set-up limited temporary (vaccination) centers that balance physical security and desired (vaccine) service coverage with limited resources. We set-up the problem as a Stackelberg game between the centers operator (defender) and an adversary, where the set of centers is not fixed a priori but is part of the decision output. This results in a mixed combinatorial and continuous optimization …

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Layout Generation As Intermediate Action Sequence Prediction, Huiting Yang, Danqing Huang, Chin-Yew Lin, Shengfeng He Feb 2023

Layout Generation As Intermediate Action Sequence Prediction, Huiting Yang, Danqing Huang, Chin-Yew Lin, Shengfeng He

Research Collection School Of Computing and Information Systems

Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetition emphasizes the reuse of similar visual elements throughout the design. To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the …

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