Engineering Commons^™

Low-Complexity Apit Algorithm And Its Opnet Simulation Of Underwater Acoustic Sensor Networks, Jiahui Xu, Keyu Chen, En Chen

Journal of System Simulation

Abstract: Due to the energy limitations of underwater acoustic sensor networks, low-complexity location algorithms are more suitable for underwater acoustic sensor networks. The traditional APIT algorithm can obtain better location accuracy with less control overhead, which is beneficial to the location of underwater sensor networks, but it has high complexity and large redundancy errors. This paper proposes a low-complexity APIT algorithm replaced the traditional grid SCAN algorithm with a point scanning method, and builds an underwater acoustic sensor network environment on the OPNET platform, and elaborates the implementation process of the location algorithm in underwater sensor network. Simulation results …

Study On Infrared Radiation Of Nmp Recovery System In Lithium Battery Pole Piece, Yanjun Xiao, Yang Huan, Yanping Kang

Journal of System Simulation

Abstract: The structure design of the NMP recovery system in the lithium battery pole piece coating process has many technology difficulties, including the vacuum and infrared radiation heating technology. For vacuum system, after the analysis of its impact on the coating process, and the drying needs of the NMP recovery system, through the analytic hierarchy process, the most suitable infrared radiation heater type can be determined. The process of recovering gaseous NMP is numerically simulated, and the simulation results of the system flow performance are obtained. The parameters of the drying time, arrangement mode and other parameters are determined by …

Research On The Mvc-Based Generation Of Test Paper And The Algorithm Of Subjective Criterion, Cuicui Zhang, Guoxiang Zhou, Yu Lei, Shi Lei, Qingqing Wang

Journal of System Simulation

Abstract: At home and abroad, the formed test system has a mature algorithm to the objective problem. However, there are still some problems in the subjective questioning. Therefore, it is feasible to design a MVC(Model View Controller) framework for the dynamic generation of papers, and to propose an automatic algorithm. In the paper volume generation system, the paper page is generated dynamically by the distributed view and the component loading technique. In the subjective automatic questioning algorithm, a bidirectional traversal space model algorithm is proposed, which uses the key words bidirectional matching and vector space model to calculate the answer …

Research On Evacuation Simulation Method Considering Social Behavior, Yuanyuan Deng, Liping Zheng, Ruiwen Cai

Journal of System Simulation

Abstract: In an emergency evacuation scenario, the typical social attributes of an individual impact their evacuation behavior. Two kinds of social factors, such as individual familiarity to the environment and the individual group, are introduced and applied in crowd evacuation simulation. An evacuation simulation method is proposed. The real-time collision avoidance technique of RVO library is used to simulate the dynamic motion of the population. The local target points and its selection mechanism are used to simulate the different social behaviors of the population. Experiments show that the familiarity to the environment and group factors have influence on the evacuation …

Research On Flexray Network Optimization Based On Switched Message Scheduling Algorithm, Yinan Xu, Xiangqi Kong, Mengzhuo Liu

Journal of System Simulation

Abstract: The development of vehicle electronic technology needs advanced in-vehicle communication network. Because of the high transmission speed, reliability and the flexible topology structure, the FlexRay network has become the most popular in-vehicle communication protocol in recent years. In order to meet the demand of network development, a scheduling algorithm based on switched FlexRay network was designed, and a new method that could calculate the Static segment and the worst case response time of Dynamic segment was put forward. The result of the simulation experiment shows that the transmission speed improves 26%, the slot number decreases by 44% and …

Cruise Missile Path Planning Based On Aco Algorithm And Bezier Curve Optimization, Shi Yan, Lihua Zhang, Shouquan Dong, Jue Wang

Journal of System Simulation

Abstract: For the low-altitude penetration of cruise missile, there is a large number of steering points and a larger steering angle in missile path planning based on ant colony algorithm. In order to solve this problem, a three-dimensional path planning method based on ant colony algorithm and Bezier curve optimization is proposed. The planning path node generated by ant colony algorithm was used as the control point to generate the flight path of Bezier curve, and then the curve was changed to be broken lines path. In order to avoid the unnavigable section, using the breadth first search algorithm to …

Research On Active Training Compliance Control Of Ankle Rehabilitation Robot, Yanbin Liu, Xiangyuan Pang, Yanbin Zhang, Bingjing Guo, Jianhai Han

Journal of System Simulation

Abstract: In order to ensure that ankle rehabilitation robot can accurately supply arbitrary characteristic training force for patient during active training, the pneumatic muscle redundant parallel driving ankle rehabilitation robot was taken as research objects, the zero error force tracking method and the compliance control strategy for active training were researched. The dynamics model of the ankle rehabilitation robot were set up, based on the impedance control theory, the trajectory planning method for the zero error force tracking was researched, and based on the Lyapunov’s stability theory, the pneumatic muscle redundant parallel driving compliance control strategy was proposed. Rehabilitation training …

Machine Learning In Manufacturing: Review, Synthesis, And Theoretical Framework, Ajit Sharma, Zhibo Zhang, Rahul Rai

Business Administration Faculty Research Publications

There has been a paradigmatic shift in manufacturing as computing has transitioned from the programmable to the cognitive computing era. In this paper we present a theoretical framework for understanding this paradigmatic shift in manufacturing and the fast evolving role of artificial intelligence. Policy, Strategic and Operational implications are discussed. Implications for the future of strategy and operations in manufacturing are also discussed. Future research directions are presented.

Deep Reinforcement Learning For The Optimization Of Building Energy Control And Management, Jun Hao

Electronic Theses and Dissertations

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC).

We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in …

Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale

Electronic Theses and Dissertations

The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …

Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal

Electronic Theses and Dissertations

The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Fault Identification On Electrical Transmission Lines Using Artificial Neural Networks, Christopher W. Asbery

Theses and Dissertations--Electrical and Computer Engineering

Transmission lines are designed to transport large amounts of electrical power from the point of generation to the point of consumption. Since transmission lines are built to span over long distances, they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. When faults occur, protection systems installed near the faulted transmission lines will isolate these faults from the …

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …

Exploring Strategies For Adapting Traditional Vehicle Design Frameworks To Autonomous Vehicle Design, Alex Munoz

Walden Dissertations and Doctoral Studies

Fully autonomous vehicles are expected to revolutionize transportation, reduce the cost of ownership, contribute to a cleaner environment, and prevent the majority of traffic accidents and related fatalities. Even though promising approaches for achieving full autonomy exist, developers and manufacturers have to overcome a multitude of challenged before these systems could find widespread adoption. This multiple case study explored the strategies some IT hardware and software developers of self-driving cars use to adapt traditional vehicle design frameworks to address consumer and regulatory requirements in autonomous vehicle designs. The population consisted of autonomous driving technology software and hardware developers who are …

A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan

Open Access Theses & Dissertations

Soft robotics is a growing field in robotics research. Heavily inspired by biological systems, these robots are made of softer, non-linear, materials such as elastomers and are actuated using several novel methods, from fluidic actuation channels to shape changing materials such as electro-active polymers. Highly non-linear materials make modeling difficult, and sensors are still an area of active research. These issues have rendered typical control and modeling techniques often inadequate for soft robotics. Reinforcement learning is a branch of machine learning that focuses on model-less control by mapping states to actions that maximize a specific reward signal. Reinforcement learning has …

Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi

Doctoral Dissertations

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In …

Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment, Francesco Alessandro Stefano Mikulis-Borsoi

Honors Theses and Capstones

In this paper, I develop a hierarchical Markov Decision Process (MDP) structure for completing the task of vertical rocket landing. I start by covering the background of this problem, and formally defining its constraints. In order to reduce mistakes while formulating different MDPs, I define and develop the criteria for a standardized MDP definition format. I then decompose the problem into several sub-problems of vertical landing, namely velocity control and vertical stability control. By exploiting MDP coupling and symmetrical properties, I am able to significantly reduce the size of the state space compared to a unified MDP formulation. This paper …

A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal

Theses and Dissertations--Electrical and Computer Engineering

Advances in computing power in recent years have facilitated developments in autonomous robotic systems. These robotic systems can be used in prosthetic limbs, wearhouse packaging and sorting, assembly line production, as well as many other applications. Designing these autonomous systems typically requires robotic system and world models (for classical control based strategies) or time consuming and computationally expensive training (for learning based strategies). Often these requirements are difficult to fulfill. There are ways to combine classical control and learning based strategies that can mitigate both requirements. One of these ways is to use a gravity compensated torque control with reinforcement …

Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang

Publications

Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …

Estimating Free-Flow Speed With Lidar And Overhead Imagery, Armin Hadzic

Theses and Dissertations--Computer Science

Understanding free-flow speed is fundamental to transportation engineering in order to improve traffic flow, control, and planning. The free-flow speed of a road segment is the average speed of automobiles unaffected by traffic congestion or delay. Collecting speed data across a state is both expensive and time consuming. Some approaches have been presented to estimate speed using geometric road features for certain types of roads in limited environments. However, estimating speed at state scale for varying landscapes, environments, and road qualities has been relegated to manual engineering and expensive sensor networks. This thesis proposes an automated approach for estimating free-flow …

Compound Vision Approach For Autonomous Vehicles Navigation, Michael Mikhael

Open Access Theses & Dissertations

An analogy can be made between the sensing that occurs in simple robots and drones and that in insects and crustaceans, especially in basic navigation requirements. Thus, an approach in robots/drones based on compound eye vision could be useful. In this research, several image processing algorithms were used to detect and track moving objects starting with images upon which a grid (compound eye image) was superimposed, including contours detection, the second moments of those contours along with the grid applied to the original image, and Fourier Transforms and inverse Fourier Transforms. The latter also provide information about scene or camera …

Brian Valdez - Dynamics And Control Of A 3-Dof Manipulator With Deep Learning Feedback, Brian Orlando Valdez

Open Access Theses & Dissertations

With the ever-increasing demands in the space domain and accessibility to low-cost small satellite platforms for educational and scientific projects, efforts are being made in various technology capacities including robotics and artificial intelligence in microgravity. The MIRO Center for Space Exploration and Technology Research (cSETR) prepares the development of their second nanosatellite to launch to space and it is with that opportunity that a 3-DOF robotic arm is in development to be one of the payloads in the nanosatellite. Analyses, hardware implementation, and testing demonstrate a potential positive outcome from including the payload in the nanosatellite and a deep learning …

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean …

Red Blood Cell Segmentation And Classification From Microscopic Images Using Machine Learning, Korranat Naruenatthanaset

Chulalongkorn University Theses and Dissertations (Chula ETD)

Red blood cell morphology analysis plays an essential role in diagnosing many diseases caused by RBC disorders. This manual inspection is a long process and requires practice and experience. Since recent computer vision and image processing in the medical imaging area can provide efficient tools, it can help hematologists to automatically analyze images from a microscope in a reduced time and cost. This research presents a new method to segment and classify RBCs from blood smear images. The process started from data collection, which a new application was created for precisely labeling. The normalization was done to reduce the color …

Wind Power Forecasting Methods Based On Deep Learning: A Survey, Xing Deng, Haijian Shao, Chunlong Hu, Dengbiao Jiang, Yingtao Jiang

Electrical & Computer Engineering Faculty Research

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of …

Real-Time Traffic Sign Detection And Classification Based On A Video Feed, Victor Ciuntu

Graduate Research Theses & Dissertations

Autonomous vehicle development is currently progressing at a very fast pace and traffic sign detection and classification has an important role in it. This thesis looks at the history of autonomous vehicles as well as different implementations for traffic sign detection and classification. Multiple possible approaches are analyzed with the final goal of doing this task in real-time using a portable system.

To accomplish this task, the final solution uses a convolutional neural network for detection and classification combined with a custom optical character recognition algorithm for speed limit signs. The optical character recognition algorithm is built from the ground …

Satellite Constellation Deployment And Management, Joseph Ryan Kopacz

Electronic Theses and Dissertations

This paper will review results and discuss a new method to address the deployment and management of a satellite constellation. The first two chapters will explorer the use of small satellites, and some of the advances in technology that have enabled small spacecraft to maintain modern performance requirements in incredibly small packages.

The third chapter will address the multiple-objective optimization problem for a global persistent coverage constellation of communications spacecraft in Low Earth Orbit. A genetic algorithm was implemented in MATLAB to explore the design space – 288 trillion possibilities – utilizing the Satellite Tool Kit (STK) software developers kit. …