Digital Commons Network^™

Modeling Method Of Emergency Response Based On Internal And External Areas Of Disaster Zone, Liang Wei, Yanyan Huang, Jianyu Wang

Journal of System Simulation

Abstract: The insufficient ability of the rescuers and the insufficient self-rescue ability of the victims are the two main reasons of the low efficiency in some conventional event emergency. An emergency response model which combines the rescue ability and the self-rescue ability is presented. The division of the internal and the external system in the disaster field is defined, the basic structure of the model and the emergency response process characteristics are described. MAS (multi-agent system) is used to describe the law of the emergency behavior. The synergistic rescue effect is simulated through an imaginary case. The results show that …

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 …

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 …

Research On Modeling And Analyzing Method Of Task-Oriented Network Information System Of Systems, Zhu Tao, Weitai Liang, Songhua Huang, Jieyong Zhang

Journal of System Simulation

Abstract: As the catalyst and fusion agent of the system combat capability in the information age, the network information system of systems has been changing the form of information-based warfare and the mode of combat effectiveness generation. On the basis of studying the concept of the network information system of systems and introducing association mapping rules between tasks and systems, a new modeling and analyzing method of the network information system of systems with two dimensions and four networks, based on super-network and service-oriented characteristics, is proposed. The definitions of services information flow motif delay, the task execution accuracy …

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 …

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.

Keeping Ai Under Observation: Anticipated Impacts On Physicians' Standard Of Care, Iria Giuffrida, Taylor Treece

Faculty Publications

As Artificial Intelligence (AI) tools become increasingly present across industries, concerns have started to emerge as to their impact on professional liability. Specifically, for the medical industry--in many ways an inherently "risky" business--hospitals and physicians have begun evaluating the impact of Al tools on their professional malpractice risk. This Essay seeks to address that question, zooming in on how AI may affect physicians' standard of care for medical malpractice claims.

Applications Of Machine Learning To Threat Intelligence, Intrusion Detection And Malware, Charity Barker

Senior Honors Theses

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or …

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 …

A Feel For The Game: Ai, Computer Games And Perceiving Perception, Marc A. Ouellette, Steven Conway

English Faculty Publications

I walk into the room and the smell of burning wood hits me immediately. The warmth from the fireplace grows as I step nearer to it. The fire needs to heat the little cottage through the night so I add a log to the fire. There are a few sparks and embers. I throw a bigger log onto the fire and it drops with a thud. Again, there are barely any sparks or embers. The heat and the smell stay the same. They don’t change and I do not become habituated to it. Rather, they are just a steady stream, …

A Cue Adaptive Decoder For Controllable Neural Response Generation, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Research Collection School Of Computing and Information Systems

In open-domain dialogue systems, dialogue cues such as emotion, persona, and emoji can be incorporated into conversation models for strengthening the semantic relevance of generated responses. Existing neural response generation models either incorporate dialogue cue into decoder’s initial state or embed the cue indiscriminately into the state of every generated word, which may cause the gradients of the embedded cue to vanish or disturb the semantic relevance of generated words during back propagation. In this paper, we propose a Cue Adaptive Decoder (CueAD) that aims to dynamically determine the involvement of a cue at each generation step in the decoding. …

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (Lidar) Data, Olcay Sahin

Civil & Environmental Engineering Theses & Dissertations

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources.

Nevertheless, using …

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 …

Neural Network Pruning For Ecg Arrhythmia Classification, Isaac E. Labarge

Master's Theses

Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning …

Artificial Stupidity, Clark D. Asay

William & Mary Law Review

Artificial intelligence is everywhere. And yet, the experts tell us, it is not yet actually anywhere. This is because we are yet to achieve artificial general intelligence, or artificially intelligent systems that are capable of thinking for themselves and adapting to their circumstances. Instead, all the AI hype—and it is constant—concerns narrower, weaker forms of artificial intelligence, which are confined to performing specific, narrow tasks. The promise of true artificial general intelligence thus remains elusive. Artificial stupidity reigns supreme.

What is the best set of policies to achieve more general, stronger forms of artificial intelligence? Surprisingly, scholars have paid little …

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 …

Recipegpt: Generative Pre-Training Based Cooking Recipe Generation And Evaluation System, Helena Huey Chong Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, Lav R. Varshney

Research Collection School Of Computing and Information Systems

Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes. We present RecipeGPT, a novel online recipe generation and evaluation system. The system provides two modes of text generations: (1) instruction generation from given recipe title and ingredients; and (2) ingredient generation from recipe title and cooking instructions. Its back-end text generation module comprises a generative pre-trained language model GPT-2 fine-tuned on a large cooking recipe dataset. Moreover, the recipe evaluation module allows the users to conveniently inspect the quality of the generated recipe …

Securing The Emerging Technologies Of Autonomous And Connected Vehicles, Shahab Tayeb, Matin Pirouz

Mineta Transportation Institute

The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another through facilitation led by road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, research in this area lacks consensus about security schemes and methods of disseminating messages. Furthermore, a current deficiency of information regarding resource optimization prevents further efficient development of this network. This paper takes an interdisciplinary approach to these issues by merging both cybersecurity and data science to optimize and secure the network. …