Engineering Commons^™

A Method For Classifying Ecg Signals With Different Possible States On A Multilayer Perceptron, Sherzod Nematov, Y Talatov

Technical science and innovation

To automatically determine the state of the cardiovascular system based on the recorded ECG signals, an artificial neural network is trained to classify signals into various possible states. At the same time, the parameters of heart rate variability (HRV) were extracted from the ECG signals and used as input functions for the neural network. HRV is the fluctuation in the time intervals between adjacent heartbeats. For this, the architecture of a neural network based on a multilayer perceptron and a method for obtaining the necessary parameters in the learning process have been developed, and the classification efficiency has been checked …

Neural Network Model Of Information Fusion For Coal Storage And Kinetic Energy Of Ball Mill, Bai Yan, He Fang

Journal of System Simulation

Abstract: A dynamic mathematical model of coal pulverizing system was analyzed. Simulation experiments on mill operation process were conducted by PFC3D software platform based on discrete element method. The associated data between different coal quality, coal storage and balls' motion were obtained under certain quantitative optimized operating parameters configuration. Neural network model of information fusion for coal storage and kinetic energy of ball mill was established by using an adaptive combination learning algorithm. Coal storage in mill cylinder was predicted from the energy point of view. The results indicate that there is a close relationship between coal storage, pulverizing efficiency …

Neural Network Inverse Control For The Output Voltage Of Energy Storage Inverter In Micro-Grid, Weiliang Liu, Yongjun Lin, Changliang Liu, Wenying Chen, Liangyu Ma

Journal of System Simulation

Abstract: In order to improve the output voltage waveform quality of energy storage inverter in micro-grid, an inverse control method was proposed based on BP neural network. Mathematical model of the energy storage inverter was established, and the main factors affecting the output voltage were analyzed, and then the expansion inverse model of the system was established based on BP neural network. In order to overcome the local optimum disadvantage in BP training algorithm, gravity algorithm was adopted to optimize the network initial parameters. The neural network inverse model was put in series with its original model to form a …

Soft Sensor Of Particle Size Of Grinding Process Based On Improved Csapso Neural Networks, Zhou Ying, Huimin Zhao, Chen Yang, Wang Long

Journal of System Simulation

Abstract: Aiming at the problems that the particle size can’t be measured online and the offline analysis by lab sample existing in large-time delay, by combining the characteristics of the one stage grinding circuit, the soft sensor model of particle size was proposed by the combination of improved chaotic self-adaptive particle swarm optimization and BP neural network algorithm. Taking advantages of chaotic theory ergodicity and PSO global optimal searching ability, the algorithm above couldadjust the weights of BP network adaptively and avoid falling into the local optimum. As a result of MATLAB simulation, the measurement accuracy of the improved CSAPSO-BP …

Uav Takeoff Decision Based On Neural Network Model Of Takeoff Capability, Yongtao Peng, Yueping Wang, Xiaoting Wang

Journal of System Simulation

Abstract: To enhance the safety in case of engine flameout failure, a new type of UAV takeoff decision based on neural network capacity model was proposed. Two capacity parameters of takeoff safety in case of engine flameout failure were defined, one is the maximum velocity for a safe takeoff and the other is the minimum velocity for a safe shut down. A calculation method based on iterative simulations for those parameters under multiple flight conditions was introduced. Double layer neural networks were used to model the relationship between flight conditions and the capacity parameters, to realize the compressive storage and …

Admittance Method For Estimating Local Field Potentials Generated In A Multi-Scale Neuron Model Of The Hippocampus, Clayton S. Bingham, Javad Paknahad, Christopher Bc Girard, Kyle Loizos, Jean-Marie C. Bouteiller, Dong Song, Gianluca Lazzi, Theodore W. Berger

Engineering Faculty Articles and Research

Significant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric fields are a first-order approximation that may be suitable for model validation, but they are computationally expensive and cannot accurately capture boundary conditions in heterogeneous tissue. While there are many appropriate numerical methods of solving electric fields in neural tissue models, there isn't an established standard for mesh geometry nor a well-known rule for handling any mismatch in spatial resolution. Moreover, the …

Boiler Combustion Optimization Based On Bayesian Neural Network And Genetic Algorithm, Haiquan Fang, Huifeng Xue, Li Ning, Fei Xi

Journal of System Simulation

Abstract: Neural network and genetic algorithm have been extensively used in boiler combustion optimization problems. But the traditional Back Propagation neural network's generalization ability is poor. The Bayesian regularization can improve the neural network's generalization ability. A boiler combustion multi-objective optimization method combining Bayesian regularization BP neural network and genetic algorithm （Bayes NN-GA）was researched. A number of field test data from a boiler was used to simulate the Bayesian neural network model. The results show that the thermal efficiency and NOx emissions predicted by the Bayesian neural network model show good agreement with the measured, and the optimal results show …

Secure Mobile Computing By Using Convolutional And Capsule Deep Neural Networks, Rui Ning

Electrical & Computer Engineering Theses & Dissertations

Mobile devices are becoming smarter to satisfy modern user's increasing needs better, which is achieved by equipping divers of sensors and integrating the most cutting-edge Deep Learning (DL) techniques. As a sophisticated system, it is often vulnerable to multiple attacks (side-channel attacks, neural backdoor, etc.). This dissertation proposes solutions to maintain the cyber-hygiene of the DL-Based smartphone system by exploring possible vulnerabilities and developing countermeasures.

First, I actively explore possible vulnerabilities on the DL-Based smartphone system to develop proactive defense mechanisms. I discover a new side-channel attack on smartphones using the unrestricted magnetic sensor data. I demonstrate that attackers can …

Application Of Pso-Bp Algorithm In Hydraulic System Fault Diagnosis, Handong Zhang, Liusong Tao

Journal of System Simulation

Abstract: It is of great significance to monitor, forecast and diagnose hydraulic systems’ fault timely and accurately. First, this paper describes the basic fault model theoretical knowledge of BP neural neystem failure neural network modeling has created and simulated. PSO-BP neural network has been raised, this paper has established PSO optimize model of the BP neural system fault diagnosis. BP network has been created and simulated in Plunger pump hydraulic system failure. The correct results indicate that this mixed PSO-BP algorithm is better than the improved BP algorithm, and can meet the requirements of Hydraulic system fault diagnosis.

Greentpu: Predictive Design Paradigm For Improving Timing Error Resilience Of A Near-Threshold Tensor Processing Unit, Pramesh Pandey, Prabal Basu, Koushik Chakraborty, Sanghamitra Roy

Electrical and Computer Engineering Faculty Publications

The emergence of hardware accelerators has brought about several orders of magnitude improvement in the speed of the deep neural-network (DNN) inference. Among such DNN accelerators, the Google tensor processing unit (TPU) has transpired to be the best-in-class, offering more than 15\times speedup over the contemporary GPUs. However, the rapid growth in several DNN workloads conspires to escalate the energy consumptions of the TPU-based data-centers. In order to restrict the energy consumption of TPUs, we propose GreenTPU - a low-power near-threshold (NTC) TPU design paradigm. To ensure a high inference accuracy at a low-voltage operation, GreenTPU identifies the patterns in …

Evaluating And Improving The Seu Reliability Of Artificial Neural Networks Implemented In Sram-Based Fpgas With Tmr, Brittany Michelle Wilson

Theses and Dissertations

Artificial neural networks (ANNs) are used in many types of computing applications. Traditionally, ANNs have been implemented in software, executing on CPUs and even GPUs, which capitalize on the parallelizable nature of ANNs. More recently, FPGAs have become a target platform for ANN implementations due to their relatively low cost, low power, and flexibility. Some safety-critical applications could benefit from ANNs, but these applications require a certain level of reliability. SRAM-based FPGAs are sensitive to single-event upsets (SEUs), which can lead to faults and errors in execution. However there are techniques that can mask such SEUs and thereby improve the …

Two Power Sliding Mode Neural Network Compensation Control For Space Robot After Target Capturing, Cheng Jing, Chen Li

Journal of System Simulation

Abstract: The impact analyses of space robot capturing a target and stability control problem in the post-impact process were discussed. The dynamic models of space robot system and target were derived by multi-body theory. The impact effect of rigidcouplingmodel was analyzed by applying geometric relationship and principle of momentum conservation. Atwo power sliding mode neural network control scheme was proposed for the combined system after acquiring with uncertain system parameters and external disturbance. The convergence speed of the control system was guaranteed by applyingtwo power sliding mode reaching raw, and the uncertain part was compensated by using neural …

Adaptive Control For Hydraulic Servo Position System With Bounded Input, Jianfei Shi, Shujuan Yi

Journal of System Simulation

Abstract: An adaptive state feedback controller based on neural network fitting was proposed for hydraulic servo position systems containing parameter uncertainties, external disturbance and bounded input problem. Taking the saturation characteristic into account sufficiently, the adaptive state feedback trajectory tracking controller was designed with an adaptive law to real-timely adjust the disturbance parameters and the bounded hyperbolic tangent functions to promise the bounded of the control law. Moreover, the complete stability and performance analysis were presented using Lyapunov theory. Simulation results show the effectiveness of the designed controller for the trajectory tracking in the present of actuators saturation.

Human Facial Emotion Recognition System In A Real-Time, Mobile Setting, Claire Williamson

Honors Theses

The purpose of this project was to implement a human facial emotion recognition system in a real-time, mobile setting. There are many aspects of daily life that can be improved with a system like this, like security, technology and safety.

There were three main design requirements for this project. The first was to get an accuracy rate of 70%, which must remain consistent for people with various distinguishing facial features. The second goal was to have one execution of the system take no longer than half of a second to keep it as close to real time as possible. Lastly, …

Ahead: Automatic Holistic Energy-Aware Design Methodology For Mlp Neural Network Hardware Generation In Proactive Bmi Edge Devices, Nan-Sheng Huang, Yi-Chung Chen, Jørgen Christian Larsen, Poramate Manoonpong

Electrical and Computer Engineering Faculty Research

The prediction of a high-level cognitive function based on a proactive brain–machine interface (BMI) control edge device is an emerging technology for improving the quality of life for disabled people. However, maintaining the stability of multiunit neural recordings is made difficult by the nonstationary nature of neurons and can affect the overall performance of proactive BMI control. Thus, it requires regular recalibration to retrain a neural network decoder for proactive control. However, retraining may lead to changes in the network parameters, such as the network topology. In terms of the hardware implementation of the neural decoder for real-time and low-power …

Optimization Study Of An Image Classification Deep Neural Network, Rose Ault

Honors Projects

Machine Learning is an important and growing field within Artificial Intelligence. It is particularly useful in situations where developing an algorithm to perform the task in a conventional way would be extremely difficult. Instead of being programmed specifically to complete a task, a program embodies a trained model that can recognize patterns present in given example data, and is able use that model to make predictions on future data. Neural networks are a prominent example of machine learning models used for this purpose. Neural networks are models that are based on how brains work, with massive numbers of connected processing …

Analysis Of Feature Extraction In Knee Cartilage Semantic Segmentation Convolutional Neural Networks, Logan Thorneloe

Undergraduate Honors Theses

Recent advances in deep learning and convolutional neural networks (CNNs) have shown promise for automatic segmentation in magnetic resonance images. However, because of the stochastic nature of the training process, it is difficult to interpret what information networks learn to represent. This study explores multiple difference metrics between networks to determine semantic relationships between knee cartilage tissues. It explores how differences in learned weights and output activations between networks can be used to express these relationships. These findings are further supported by training multi-class networks to segment multiple tissues to compare network accuracy across different tissue combinations. This study shows …

Construction Labor Productivity Modeling And Use Of Neural Networks: A Bibliometric Survey, Shalaka Hire, Sayali Sandbhor

Library Philosophy and Practice (e-journal)

Productivity of a project has a major impact on its cost and profitability. In spite of construction being labor intensive field with labor cost adding up to 30% to 50% of overall project cost, the productivity of labor is one of the least studied areas in the construction industry. It requires to be given due attention to the issues affecting labor productivity and design solution using soft computing techniques to improve the overall performance of the industry. This research paper aims to conduct a bibliographic survey of the literature available in the domain of Labor Productivity (LP) as well as …

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 …

Design, Modeling And Optimization Of Reciprocating Tubular Permanent Magnet Linear Generators For Free Piston Engine Applications, Jayaram Subramanian

Graduate Theses, Dissertations, and Problem Reports

Permanent Magnet Linear Generators (PMLG) are electric generators which convert the linear motion into electricity. One of the applications of the PMLG system is with free piston engines. Here, the piston is moved by the expander using an internal combustion engine or a Stirling engine. Other applications of the PMLG are wave energy conversion, micro energy harvesters, and supercritical CO₂ expander systems. The most common technology of the electric generators is a rotary electric generator. The current technology of the engine-generators (GENSET) is of a rotary type which uses a crankshaft to convert the linear motion to rotary motion …

Fault Detection And Classification Of A Single Phase Inverter Using Artificial Neural Networks, Ayomikun Samuel Orukotan

All Graduate Theses, Dissertations, and Other Capstone Projects

The detection of switching faults of power converters or the Circuit Under Test (CUT) is real-time important for safe and efficient usage. The CUT is a single-phase inverter. This thesis presents two unique methods that rely on backpropagation principles to solve classification problems with a two-layer network. These mathematical algorithms or proposed networks are able to diagnose single, double, triple, and multiple switching faults over different iterations representing range of frequencies. First, the fault detection and classification problems are formulated as neural network-based classification problems and the neural network design process is clearly described. Then, neural networks are trained over …

Instructor Activity Recognition Using Smartwatch And Smartphone Sensors, Zayed Uddin Chowdhury

Electronic Theses and Dissertations

During a classroom session, an instructor performs several activities, such as writing on the board, speaking to the students, gestures to explain a concept. A record of the time spent in each of these activities could be valuable information for the instructors to virtually observe their own style of instruction. It can help in identifying activities that engage the students more, thereby enhancing teaching effectiveness and efficiency. In this work, we present a preliminary study on profiling multiple activities of an instructor in the classroom using smartwatch and smartphone sensor data. We use 2 benchmark datasets to test out the …