Artificial Intelligence and Robotics Commons^™

Time Domain Vertical Dynamics Model Of Electric Vehicle Considering Electric Motor Vibration, Guoying Tian, Pengyi Deng, Shulei Sun, Yiqiang Peng, Haiying Lu

Journal of System Simulation

Abstract: On the basis of considering the electric motor vibration, a time domain vertical dynamics model of the electric vehicle with 11 DOF is built, and the road roughness input model and the numerical method are given. Aiming at an electric passenger vehicle, the basic characteristics of the typical vehicle dynamics responses under the condition of the impulse and random road roughness inputs are calculated, the influence of the different mount stiffness, damping and motor mass on the motor and the vehicle vibration is analyzed under the condition of the impulse input. The results show that the proposed model is …

Trajectory Planning Of Wheeled Mobile Robot Based On Model Predictive Control, You Bo, Mingrui Wang, Li Zhi, Ding Liang

Journal of System Simulation

Abstract: Aiming at the underactuation of the Wheeled Mobile Robot (WMR) and considering the various limitations and constraints during the actual operation, a trajectory planning method based on the Model Predictive Control (MPC) is proposed.This method can deal with the kinematics constraints, physical constraints and obstacle avoidance constraints unifiedly and effectively. It can generate the feasible trajectories that conform to the characteristics of the car body model and satisfy the various constraints. It fully guarantees the feasibility, safety and efficiency of the autonomous driving of wheeled mobile robots. The simulation results fully verify the effectiveness of the proposed method.

Fault Diagnosis Method Of Vehicle Power Supply Based On Deep Learning And Sequential Test, Li Wei, Bingxiang Zhou, Dongnian Jiang

Journal of System Simulation

Abstract: Focus on the health maintenance of vehicle power supply, a fault diagnosis method of vehicle power supply is proposed, which is based on the long and short time memory LSTM(Long Short Time Memory) network and the sequential probability ratio test SPRT(Sequential Probability Ratio Test). Based on the LSTM network, the multivariate time series model of vehicle power supply is established, and the SPRT method is used to perform the adaptive multi-sample fault diagnosis. The experiment on the vehicle power supply simulation system shows that the LSTM diagnosis model has stronger learning and mapping capabilities, and the fault diagnosis method …

Research On Flight Ground Service Time Prediction Based On Deep Neural Network, Zhiwei Xing, Li Biao, Zhu Hui, Luo Qian

Journal of System Simulation

Abstract: Flight ground service time prediction is one of the key issues in improving the airport operational efficiency and decision making capacity. Taking into account the complexity, particularity and uncertainty of the service process, a Gaussian probability model of flight ground service resource in place time is established, a flight ground service time prediction model based on the deep neural network is proposed. According to the regular changes of operational data, the model parameters are adjusted to reducet the generalization error caused by other factors. The research results show that the average absolute error of time prediction under single …

Unmanned Crane Dispatching System Based On Grid Method In Steel Works, Weigang Li, Wang Xiao, Yuntao Zhao, Zixiang Li

Journal of System Simulation

Abstract: A grid method is used to model a storeroom to solve the problem of the unmanned crane automatic scheduling of the iron and steel industry. An improved A* algorithm is proposed to make the path planning. Different moving costs are assigned for the different route sections in storeroom and the different moving modes of cranes, and the path with the minimum total cost which can avoid the slab obstacles alongside is calculated by the algorithm. A set of intelligent dispatching rules are designed to solve the inefficiency of the manual operation, which can effectively deal with the problems about …

Vehicle Coordination Strategy For Semi-Active Suspension Vehicles, Quanmin Guo, Haowen Zhang, Wang Yan

Journal of System Simulation

Abstract: In order to realize the coordinated control of the vertical, pitch and roll vibration of vehicle, a coordinated control strategy of vehicle semi-active suspension is proposed. In the strategy, the fuzzy PID controllers estimate three adjustment forces according to the vibration state in vertical, pitch and roll directions. Through the designed force coordinator with the control parameters of the front and back axes and the steering control strategy, the expected adjustment forces are coordinated to the four magnetorheological dampers to output the adjustable damping forces and realize the coordinated control of the vehicle vibration in three directions. The experimental …

Simplification And Compression Service Construction Of 3d Model For Complex Products, Junjie Xue, Guoqiang Shi, Junhua Zhou, Huiyang Qu, Tao Luan, Ruiying Pu

Journal of System Simulation

Abstract: A simplification and compression (S&C) service construction method of the 3D model for the complex products is proposed. An improved method of the model data export and spatial index generation is adopted to preserve the assembly tree and label information of the model in the process of model S&C. An adaptive mesh simplification algorithm based on the mesh density is designed and implemented, and the meshes can be simplified adaptively on condition of satisfying the requirement of error for the geometric model. And a model data compression algorithm with the high compression ratio and efficiency is adopted to reduce …

3d Tree Model Matching Based On Tree Shape Space, Liang Shuang, Zuoteng Zhu, Jinyuan Jia

Journal of System Simulation

Abstract: The retrieval of the 3D tree models cannot get the higher accuracy, retrieving efficiency and descripting match. In order to solve the problems, a three-dimensional tree model matching method based on tree space is proposed. By constructing the tree space of the three-dimensional tree model dataset, the method computes the difference between the Euclidean distance and the geodesic distance in the tree space by means of spindle matching, contour matching, and branch matching, and gradually matches the corresponding model. The matching implementation results are displayed in the general model dataset and the self-designed model dataset respectively. …

Achieving Obfuscation Through Self-Modifying Code: A Theoretical Model, Heidi Waddell

Senior Honors Theses

With the extreme amount of data and software available on networks, the protection of online information is one of the most important tasks of this technological age. There is no such thing as safe computing, and it is inevitable that security breaches will occur. Thus, security professionals and practices focus on two areas: security, preventing a breach from occurring, and resiliency, minimizing the damages once a breach has occurred. One of the most important practices for adding resiliency to source code is through obfuscation, a method of re-writing the code to a form that is virtually unreadable. …

Storage Management Strategy In Mobile Phones For Photo Crowdsensing, En Wang, Zhengdao Qu, Xinyao Liang, Xiangyu Meng, Yongjian Yang, Dawei Li, Weibin Meng

Department of Computer Science Faculty Scholarship and Creative Works

In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to …

Simulation Environment For Object Manipulation With Soft Robots In Shared Autonomy, Devin Hunter, Fabio Stroppa, Allison Okamura

Showcase of Osprey Advancements in Research and Scholarship (SOARS)

The robots of today have grown to be of much more significant use than their predecessors. Robots are now being used in industries outside of the factory setting which can be seen primarily in the medical, transportation, and social fields. With robots taking on all of these new roles within our society, the establishment of robust human-robot collaboration is crucial in order for robots to be able to successfully complete desired tasks without becoming a hinderance to nearby humans. We explored this concept by implementing a shared-autonomy algorithm named MBSA (Motion Based Smart Assistance) to a soft robot simulation and …

Artificial Intelligence Towards The Wireless Channel Modeling Communications In 5g, Saud Mobark Aldossari

USF Tampa Graduate Theses and Dissertations

Channel prediction is a mathematical predicting of the natural propagation of the signal that helps the receiver to approximate the affected signal, which plays an important role in highly mobile or dynamic channels. The standard wireless communication channel modeling can be facilitated by either deterministic or stochastic channel methodologies. The deterministic approach is based on the electromagnetic theories and every single object in that environment has to be known in that propagation space and an example of this method is ray tracing. While the stochastic modeling method is based on measurements that involve statistical distributions of the channel parameters and …

Book Genre Classification By Its Cover Using A Multi-View Learning Approach, Chandra Shakhar Kundu

Masters Theses & Specialist Projects

An interesting topic in the visual analysis is to determine the genre of a book by its cover. The book cover is the very first communication to the reader which shapes the reader’s expectation about the type of the book. Each book cover is carefully designed by the cover designers and typographers to convey the visual representation of its content. In this study, we explore several different deep learning approaches for predicting the genre from the cover image alone, such as MobileNet V1, MobileNet V2, ResNet50, Inception V2. Moreover, we add an extra modality by extracting text from the cover …

Finding Critical And Gradient-Flat Points Of Deep Neural Network Loss Functions, Charles Gearhart Frye '09

Doctoral Dissertations

Despite the fact that the loss functions of deep neural networks are highly non-convex, gradient-based optimization algorithms converge to approximately the same performance from many random initial points. This makes neural networks easy to train, which, combined with their high representational capacity and implicit and explicit regularization strategies, leads to machine-learned algorithms of high quality with reasonable computational cost in a wide variety of domains.

One thread of work has focused on explaining this phenomenon by numerically characterizing the local curvature at critical points of the loss function, where gradients are zero. Such studies have reported that the loss functions …

A Survey Of Feature Extraction And Fusion Of Deep Learning For Detection Of Abnormalities In Video Endoscopy Of Gastrointestinal-Tract, Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani, Farhan Riaz

Publications

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for …

Data Science Meets Compliance, Christian Clarke

Petersheim Academic Exposition

No abstract provided.

Topics In Artifical Intelligence, Hunter Mcnichols, Nyc Tech-In-Residence Corps

Open Educational Resources

Syllabus for the course "CSC 59974: Special Topics in Artificial Intelligence" delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

Two Can Play That Game: An Adversarial Evaluation Of A Cyber-Alert Inspection System, Ankit Shah, Arunesh Sinha, Rajesh Ganesan, Sushil Jajodia, Hasan Cam

Research Collection School Of Computing and Information Systems

Cyber-security is an important societal concern. Cyber-attacks have increased in numbers as well as in the extent of damage caused in every attack. Large organizations operate a Cyber Security Operation Center (CSOC), which forms the first line of cyber-defense. The inspection of cyber-alerts is a critical part of CSOC operations (defender or blue team). Recent work proposed a reinforcement learning (RL) based approach for the defender’s decision-making to prevent the cyber-alert queue length from growing large and overwhelming the defender. In this article, we perform a red team (adversarial) evaluation of this approach. With the recent attacks on learning-based decision-making …

Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne

Electrical & Computer Engineering Theses & Dissertations

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …

Does Applying Deep Learning In Financial Sentiment Analysis Lead To Better Classification Performance?, Tao Wang, Changhe Yuan, Cuiyuan Wang

Publications and Research

Using a unique data set from Seeking Alpha, we compare the deep learning approach with traditional machine learning approaches in classifying financial text. We apply the long short-term memory (LSTM) as the deep learning method and Naive Bayes, SVM, Logistic Regression, XGBoost as the traditional machine learning approaches. The results suggest that the LSTM model outperforms the conventional machine learning methods on all metrics. Based on the tSNE graph, the success of the LSTM model is partially explained as the high-accuracy LSTM model distinguishes between positive and negative important sentiment words while those words are chosen based on SHAP values …