Artificial Intelligence and Robotics Commons^™

Research On Network Attack And Defense Situation Based On Game Theory Model And Netlogo Simulation, Xiaohu Liu, Hengwei Zhang, Yuchen Zhang, Ma Zhuang, Wenlei Lü

Journal of System Simulation

Abstract: Aiming at the problem that the existing modeling methods lack of the analysis on the behavior and trend of attack and defense game, the characteristics of network attack and defense game from the perspective of confrontation are analyzed Based on the static game theory of non- cooperative incomplete information, the network attack and defense game model is established, and the revenue quantification, game equilibrium calculation and decision-making of strategic confrontation result are given. The multi-agent simulation model of network attack and defense game is constructed. The simulation experiments under different strategy combinations and different initial numbers of players are …

Chaos Control Of Permanent Magnet Synchronous Motor Based On Finite Time Lasalle Invariant Set, Zhang Yun, Wang Cong, Hongli Zhang, Ma Ping

Journal of System Simulation

Abstract: In order to effectively restrain the chaotic behavior of permanent magnet synchronous motor, an adaptive controller is designed based on finite time theory and LaSalle invariant set theorem. The chaotic dynamics characteristics of the permanent magnet synchronous motor system are analyzed, and the parameter fields of the system in different motion states are determined. It is proved theoretically that the controller can stabilize to the equilibrium point in finite time and can automatically track the equilibrium point of the system. Simulation results show that the control scheme is concise, faster and more stable. The research results are of great …

Simulation On Spatial-Temporal Dynamic Change Of Ocean Environment In Marine Simulators, Qianfeng Jing, Helong Shen, Zhengli Gao, Yin Yong

Journal of System Simulation

Abstract: Marine structures are subject to environmental interference all the time, and the simulation of the ocean environment significantly affects the realism of the marine simulators. The ocean environmental fields are generated by the numerical wave model, and the real ocean databases are developed based on SQLite. The actual wind, current, and wave information are obtained from the databases to keep consistency with the actual sea. The environmental disturbances are modeled and both the spatiotemporal-varying features and the coupling effects are brought into the simulation. The real voyage cases are reproduced by the proposed simulation method. The measured data …

Prediction Of N-Acetylglucosamine Content Based On Rf-Ga-Bp Neural Network, Wenfeng Yang, Wang Yan, Zhichen Ji

Journal of System Simulation

Abstract: In order to solve the problem that the content of N-acetylglucosamine (GlcNAc) in the process of preparing glucocosamine (GlcN) by microbial fermentation is difficult to measure online, an improved prediction algorithm based on stochastic forest algorithm, genetic algorithm and neural network algorithm is proposed. The algorithm utilizes the feature of decreasing average impurity in random forest algorithm to analyze the relevance of the input characteristics. The initial weights and thresholds of the neural networks are optimized by the genetic algorithm. A prediction model based on the RF-GA-BP algorithm is established based on the data from the fermentation process of …

A Photovoltaic Power Forecasting Method Based On Da-Rkelm Algorithm, Mingqi Wei, Tianrui Zhang, Xiuxiu Gao, Shumei Wang

Journal of System Simulation

Abstract: Aiming at the power grid safety problems caused by the fluctuation and randomness of photo-voltaic power generation, a method for predicting photo-voltaic power generation of a regular nuclear limit learning machine based on the optimization of a dragonfly algorithm was proposed. Through correlation analysis, the key factors affecting the photo-voltaic power generation are determined, and the photo-voltaic power prediction model is constructed. Dragonfly algorithm is used to obtain the optimal weight and threshold value of the network, and regularization function and kernel function are introduced based on the standard limit learning machine to avoid the over …

Research On Simulation Optimization Of Intelligent Storage Robot Configuration Under Multiple Constraints, Guo Di, Danlan Xie, Ji Yuan

Journal of System Simulation

Abstract: Aiming at the intelligent warehouse storage robot configuration, a discrete event simulation model based on queuing theory is constructed. Considering the influence of faults and the reliability and service intensity of the system, aiming at minimizing the total cost of distance cost, time cost, idle cost and purchase cost, a based on FlexSim simulation model is proposed. Discrete event simulation optimization method of the platform. By analyzing the system average team length, order average stay time and other indicators, the intuitionistic bottleneck of the system is combined with the actual operation data, and the configuration strategy of minimum system …

Optimization And Simulation Of Offshore Edge Computing Network For E-Pilotage, Bin Lin, Chenchen Song, Yajing Zhang, Jianli Duan

Journal of System Simulation

Abstract: In order to improve the safety of the ship's piloting process, an INA-based offshore edge computing network (IOECN) architecture is proposed to provide navigation assistance information. The Layout Optimization Problem (LOP) of network element nodes in the network is mainly studied. The mathematical model is used to convert the LOP into an Integer Linear Programming (ILP) problem. On condition of the required network coverage and connectivity, aiming to minimize the network cost, being solved by Gurobi and simulated and demonstrated by Matlab, the network optimization on different scales is obtained and the correctness and scalability of …

Artificial Intelligence: A Prospective Or Real Option For Education?, Maha Sourani

Al Jinan الجنان

The education sector is grappling with a plethora of challenges that have compelled scholars and practitioners to begin looking for a solution. Artificial intelligence (AI) is among the proposed solutions that is having significant attention lately. However, its adoption in the education sector remains low because of challenges, such as lack of proper trials, testing, and recommendation for its applicability in the sector. This article seeks to explore the role and potentiality of AI in improving education. A systematic review design is used for this purpose. The methodology corresponds with the rapid survey protocol of Khangura (2012) which provides an …

The Limits Of Machine Learning, Ma. Mercedes T. Rodrigo

Magisterial Lectures

In this lecture, Dr. Rodrigo discusses how machine-learned models are constrained by the data on which they are based and by the human beings who control them.

Speaker: Ma Mercedes T Rodrigo is a professor at the Department of Information Systems and Computer Science, the head of the Ateneo Laboratory for the Learning Sciences, and the Executive Director of Arete. Her areas of specialization are educational technology, artificial intelligence in education, and educational data mining.

Video Game Genre Classification Based On Deep Learning, Yuhang Jiang

Masters Theses & Specialist Projects

Video games have played a more and more important role in our life. While the genre classification is a deeply explored research subject by leveraging the strength of deep learning, the automatic video game genre classification has drawn little attention in academia. In this study, we compiled a large dataset of 50,000 video games, consisting of the video game covers, game descriptions and the genre information. We explored three approaches for genre classification using deep learning techniques. First, we developed five image-based models utilizing pre-trained computer vision models such as MobileNet, ResNet50 and Inception, based on the game covers. Second, …

Asymptotically-Optimal Topological Nearest-Neighbor Filtering, Read Sandström, Jory Denny, Nancy M. Amato

Department of Math & Statistics Faculty Publications

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to a significant computational bottleneck for problems which require many configurations to find a solution. In this work, we develop a method of mapping configurations of a jointed robot to neighborhoods in the workspace that supports fast search for configurations in nearby neighborhoods. This expedites nearest-neighbor search by locating a small set of the most likely candidates for connecting to the query with a local plan. We show that this filtering technique can preserve asymptotically-optimal …

Topology-Guided Roadmap Construction With Dynamic Region Sampling, Read Sandström, Diane Uwacu, Jory Denny, Nancy M. Amato

Department of Math & Statistics Faculty Publications

Many types of planning problems require discovery of multiple pathways through the environment, such as multi-robot coordination or protein ligand binding. The Probabilistic Roadmap (PRM) algorithm is a powerful tool for this case, but often cannot efficiently connect the roadmap in the presence of narrow passages. In this letter, we present a guidance mechanism that encourages the rapid construction of well-connected roadmaps with PRM methods. We leverage a topological skeleton of the workspace to track the algorithm's progress in both covering and connecting distinct neighborhoods, and employ this information to focus computation on the uncovered and unconnected regions. We demonstrate …

Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh

Research Collection School Of Computing and Information Systems

Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such …

Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools, Murat Kuzlu, Umit Cali, Vinayak Sharma, Özgür Güler

Engineering Technology Faculty Publications

Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a ‘‘black-box’’ due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using …

Co-Design And Evaluation Of An Intelligent Decision Support System For Stroke Rehabilitation Assessment, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Badia

Research Collection School Of Computing and Information Systems

Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist's evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspectives with quantitative information on patient's exercise motions). As a result, we co-designed and developed an intelligent decision support system that automatically identifies salient features of assessment using reinforcement learning to assess the quality of motion and generate patient-specific analysis. We evaluated this system with …

We Mind Your Well-Being: Preventing Depression In Uncertain Social Networks By Sequential Interventions, Aye Phye Phye Aung, Xinrun Wang, Bo An, Xiaoli Li

Research Collection School Of Computing and Information Systems

Mental health has become a major concern according to WHO who estimates that more than 350 million people worldwide are affected by depression. Studies have shown that interventions and social support can reduce stress and depression. However, counselling centers do not have enough resources to provide counselling and social support to all the participants in their interest. This paper helps social support organizations (e.g., university counselling centers) sequentially select the participants for interventions. Unfortunately, previous works do not consider emotion propagation from other neighbours of the influencees and initial uncertainties of mental states and influence. Moreover, they fail to scale …

Reinforcement Learning For Zone Based Multiagent Pathfinding Under Uncertainty, Jiajing Ling, Tarun Gupta, Akshat Kumar

Research Collection School Of Computing and Information Systems

We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applications of autonomous vehicles such as drone traffic management, we present zone-based path finding (or ZBPF) where agents move among zones, and agents' movements require uncertain travel time. Furthermore, each zone can accommodate multiple agents (as per its capacity). We also develop a simulator for ZBPF which provides a clean interface from the …

Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose …

Modular Neural Networks For Low-Power Image Classification On Embedded Devices, Abhinav Goel, Sara Aghajanzadeh, Caleb Tung, Shuo-Han Chen, George K. Thiruvathukal, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

Embedded devices are generally small, battery-powered computers with limited hardware resources. It is difficult to run deep neural networks (DNNs) on these devices, because DNNs perform millions of operations and consume significant amounts of energy. Prior research has shown that a considerable number of a DNN’s memory accesses and computation are redundant when performing tasks like image classification. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Instead of using one large DNN for the classifier, this architecture uses multiple smaller DNNs (called modules) to progressively classify images …

The Future Of Work Now: Ai-Driven Transaction Surveillance At Dbs Bank, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I co-authored the story.