Digital Commons Network^™

Modeling And Simulation Of Power Communication Networking Based On Quality Of Service, Xueyi Zhang, Juhui Gu, Meiqin Zhou

Journal of System Simulation

Abstract: For the complex network topology and channel time-varying characteristics of power line carrier communication in low voltage distribution grid, there is a phenomenon of slow convergence and stagnation when solving the routing problem with traditional ant colony algorithm. The algorithm used is easy to fall into premature and local optimization. An automatic networking algorithm based on transmission delay and the load factor of the evaluation factors was proposed and a genetic algorithm was joined at a later stage. The optimal solution was obtained by using the improved crossover operators, and the convergence of optimal algorithm solution was accelerated by …

Simulation On Global Demand Information Sharing Value In B2b Supply Chain, Dongbo Hu, Shen Yue, Rudan Heng, Binbin Zhang

Journal of System Simulation

Abstract: In B2B e-commerce environment, a multi-agent simulation model based on the supply chain with multi suppliers was established to study the incentive effects of the global demand information sharing on suppliers and the effects of different parameters on these incentive effects. The research results are as follows: Compared with the local demand information sharing, the global demand information sharing can bring significant incentive effect to the supplier. The global demand information sharing is more valuable with the number of selected suppliers getting closer to the number of alternative suppliers, and an optimal number of alternative suppliers do exist. Based …

Reachable Set Modeling Approach For Autonomous Vehicle's Dynamic Conflict Control, Cao Kai, Xiufang Liu, Yang Xu, Shen Peng

Journal of System Simulation

Abstract: Aiming at the problem of active safety control modeling for autonomous vehicles, a combined method using the formal modeling and the dynamic non-cooperative game theory was proposed for discussing a control strategy of trajectory tracking and collision avoidance under dynamic conflict condition between autonomous vehicles. And it was proposed to divide the system control into three modes: trajectory tracking, collision avoidance and switching between trajectory tracking and collision avoidance. Taking into account the complexity as a hybrid systems and the existence of the trajectory tracking error for autonomous vehicle, a modeling approach based on reachable set was proposed for …

Improved Particle Swarm Optimization Based On Lévy Flights, Rongyu Li, Wang Ying

Journal of System Simulation

Abstract: The particle swarm optimization (PSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. The Lévy particle swarm optimization (Lévy PSO) was proposed. In the particle position updating formula, Lévy PSO eliminated the impact of speed on the convergence rate, and used Levy flight to change the direction of particle positions movement to prevent particles getting into local optimum value, and then using greedy strategy to update the evaluation and choose the best solution to obtain the global optimum. The experimental results show that Lévy PSO can effectively …

Performance Modeling Of Cryptographic Service System Virtualization Based On Issm, Songhui Guo, Qingbao Li, Sun Lei, Xuerong Gong, Tianchi Yang

Journal of System Simulation

Abstract: The complicated architecture of cryptographic service system virtualization raised the difficulty of performance modeling. A performance modeling approach based on ISSMs was proposed. The approach divided the execution process into two stages, host preprocessing and arithmetic-module calculating, and built two sub-models based on queuing theory. On this basis, the effectiveness of this approach was verified. The results show that this method can analyze the impacts on system performance caused by task arrival rates, host and cryptographic card configurations quantitatively, and also be helpful for providing reasonable solutions to deploy virtualized cryptographic service system on cloud computing platforms.

Modeling And Analysis Of Trust Attack Based On Object-Oriented Petri Net, Guangqiu Huang, Bai Lu

Journal of System Simulation

Abstract: On the basis of trust attack graph, the object-oriented trust attack Petri net model was put forward, the dynamic model described the attack behaviors among components of trust entity object. By introducing the object Petri net, the trust attack relations between trust entity objects and components were shown in the Petri net, simulating the collaborative work between the components. According to the new trust relation reconstruction rules, a trust attack path inference algorithm was proposed to make the attacker knowing the changes of harmfulness when attacking the components, thus the attacker could decide the attack direction. The maximal …

Simulation Of Three-Region Commutation Torque Ripple Reduction For Brushless Dc Motor, Junjie Zhu, Haoran Liu

Journal of System Simulation

Abstract: Aiming at brushless DC motor torque ripple during commutation section, new three-step three phases PWM method was proposed based on voltage balance principle, improving existing two phase PWM suppression method and suitable for all speed section. In this new method, the principle of non-commutation phase voltage and the neutral point voltage variation was researched. Voltage difference was remained the same by three-step PWM control strategy before and after commutation in order to suppression current ripple. Comparing with existing current slope research method, this new method is with a series of advantages: simple parameters, computing, and sampling circuit design. …

Study On Visual Function Of Human Computer Interaction In Parachuting Simulator, Han Liang, Gong Xuan, Jiangyun Wang, Yanghua Du

Journal of System Simulation

Abstract: The parachuting training system consists of software and hardware, to simulate the parachute training through head mounted display and operating equipment, combined with system modeling, kinematic computation, visual display and expert evaluation. Principle methods of head mounted display with human-computer interactive function and exploitation of Vega Prime inventive exploration on the information collection of viewpoint are conducted. It provides a real time training environment through the configuration of environmental effects, scene control, collision detection and driving information.

Observer-Based Integral Backstepping Control For Permanent Magnet Synchronous Motor, Yonghong Lan, Liangliang Wang, Caixue Chen

Journal of System Simulation

Abstract: For the speed tracking control problem of Permanent Magnet Synchronous Motor (PMSM), an observer-based back-stepping speed tracking control method was presented. To reconstruct the motor speed and stator axis current, a full order Luenberger observer for PMSM was constructed. By using Lyapunov stability theory, the linear matrix inequality (LMI) based design method of observer was obtained. Through the design of the virtual control input that include the reconstruction variables, using back-stepping control strategy and integrating with tracking errors, the controller of the closed-loop system was proposed. The obtained controller can achieve high precision speed tracking. The …

Stability Of Electrically Conducting Fluid Flow In Channel Under Magnetic Field, Dong Shuai, Lishuai Liu, Xuemin Ye

Journal of System Simulation

Abstract: The stability of conducting fluid flow in a channel under a vertical magnetic field was simulated by non-normal modal stability analysis. The perfect conductivity assumption of the channel wall was employed in the model, and its effect on the stability problem was considered. The results show that when the Hartmann number Ha is larger than 9, the two opposite Hartmann boundary layers are essentially independent of each other; however, when the Hartmann number Ha is smaller than 9, there are still residual interactions between the two opposite Hartmann boundary layers, which has influences on the stability of the flow. …

Interaction Of Particle-Particle And Particle-Bubble In Water:Molecular Dynamics Simulation, Qingqun Luo, Jieming Yang

Journal of System Simulation

Abstract: Graphene and a bulk of gas were used to represent a part of particle and a part of bubble, respectively, and their interactions in liquid water with dissolved gas were simulated. Changes of the structural phase diagram, the gas density, and the potential of mean force were analyzed. The results show that the interactions of particle-particle and particle-bubble are both related to the nanobubble bridges therein. The forming processes of nanobubble bridges were shown in details. The range of nanobubble bridges and the energy change of the system were quantitatively calculated.

A Way Of Integrated Navigation Fault Detection Of Near Space Hypersonic Cruising Aircraft, Hailin Li, Bin Zhang, Dewei Wu, Lu Hu

Journal of System Simulation

Abstract: The doppler shift is augmented, that causes acquisition and tracking of GNSS losing, error of CNS's ray propagation also causes the problem of celestial body tracking when aircraft is hypersonically flighting. A way of INS/GNSS/CNS integrated navigation fault detection of the hypersonic cruising aircraft based on the residual chi-square-Fuzzy ARTMAP (Adaptive Resonance Theory Map) fast neural networks was proposed. The fault diagnostic elements of INS/GNSS/CNS integrated navigation system of the hypersonic cruising aircraft were given; the detection function formula of residual chi-square test and Fuzzy ARTMAP fast neural networks arithmetic was deduced; the realizing way was studied. The …

Forecasting Of Short-Term Power Load Of Secrpso-Svm Based On Data-Driven, Hairong Sun, Bixia Xie, Tian Yao, Zhuoqun Li

Journal of System Simulation

Abstract: For the parameter selection of support vector machine in modeling, a particle swarm optimization algorithm based on second-order oscillation and repulsion factor was proposed to optimize the parameter of SVM. The algorithm employed the nonlinear decreasing weight to balance the global and local search ability. Second-order oscillation factor could maintain the population diversity. The repulsion factor was introduced to make the swarm even distribution in search space, which could avoid local optimum. For the complex characteristics of nonlinearity, time-varying and multifactorial of electric power load, a support vector machine forecasting model based on data was proposed, and the influence …

Ect Image Reconstruction Algorithm Based On Generalized Regularization, Ma Min, Guo Qi, Chaoqi Yan

Journal of System Simulation

Abstract: Aiming at the numerical instability caused by the singular value decomposition algorithm and the over-smooth caused by the Tiknonov regularization in the image reconstruction of electrical capacitance tomography (ECT) system, a more generalized regularization algorithm was proposed. The penalty phase of the regularized objective function was modified by the positive definite matrix so that it could reconstruct the image with non smooth information, In the process of solving the objective function, the diagonal weight matrix was introduced, and the data items based on l₂-norm were improved. By comparing the image quality, the relative error of the image …

Talk Like Somebody Is Watching: Understanding And Supporting Novice Live Streamers, Terrance Mok, Colin Matthew Au Yueng, Anthony Tang, Lora Oehlberg

Research Collection School Of Computing and Information Systems

We built a chatbot system–Audience Bot–that simulates an audience for novice live streamers to engage with while streaming. New live streamers on platforms like Twitch are expected to perform and talk to themselves, even while no one is watching. We ran an observational lab study on how Audience Bot assists novice live streamers as they acclimate to multitasking–simultaneously playing a video game while performing for a (simulated) audience.

Sensing, Computing, And Communications For Energy Harvesting Iots: A Survey, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, Sajal K. Das

Research Collection School Of Computing and Information Systems

With the growing number of deployments of Internet of Things (IoT) infrastructure for a wide variety of applications, the battery maintenance has become a major limitation for the sustainability of such infrastructure. To overcome this problem, energy harvesting offers a viable alternative to autonomously power IoT devices, resulting in a number of battery-less energy harvesting IoTs (or EH-IoTs) appearing in the market in recent years. Standards activities are also underway, which involve wireless protocol design suitable for EH-IoTs as well as testing procedures for various energy harvesting methods. Despite the early commercial and standards activities, IoT sensing, computing and communications …

Transfer Learning: Bridging The Gap Between Deep Learning And Domain-Specific Text Mining, Chaoran Cheng

Dissertations

Inspired by the success of deep learning techniques in Natural Language Processing (NLP), this dissertation tackles the domain-specific text mining problems for which the generic deep learning approaches would fail. More specifically, the domain-specific problems are: (1) success prediction in crowdfunding, (2) variants identification in biomedical literature, and (3) text data augmentation for domains with low-resources.

In the first part, transfer learning in a multimodal perspective is utilized to facilitate solving the project success prediction on the crowdfunding application. Even though the information in a project profile can be of different modalities such as text, images, and metadata, most existing …

Efficient Hardware Implementations Of Bio-Inspired Networks, Anakha Vasanthakumaribabu

Dissertations

The human brain, with its massive computational capability and power efficiency in small form factor, continues to inspire the ultimate goal of building machines that can perform tasks without being explicitly programmed. In an effort to mimic the natural information processing paradigms observed in the brain, several neural network generations have been proposed over the years. Among the neural networks inspired by biology, second-generation Artificial or Deep Neural Networks (ANNs/DNNs) use memoryless neuron models and have shown unprecedented success surpassing humans in a wide variety of tasks. Unlike ANNs, third-generation Spiking Neural Networks (SNNs) closely mimic biological neurons by operating …

Analysis Of Gameplay Strategies In Hearthstone: A Data Science Approach, Connor W. Watson

Theses

In recent years, games have been a popular test bed for AI research, and the presence of Collectible Card Games (CCGs) in that space is still increasing. One such CCG for both competitive/casual play and AI research is Hearthstone, a two-player adversarial game where players seeks to implement one of several gameplay strategies to defeat their opponent and decrease all of their Health points to zero. Although some open source simulators exist, some of their methodologies for simulated agents create opponents with a relatively low skill level. Using evolutionary algorithms, this thesis seeks to evolve agents with a higher skill …

Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng

Theses

In the biological field, the smallest unit of organisms in most biological systems is the single cell, and the classification of cells is an everlasting problem. A central task for analysis of single-cell RNA-seq data is to identify and characterize novel cell types. Currently, there are several classical methods, such as K-means algorithm, spectral clustering, and Gaussian Mixture Models (GMMs), which are widely used to cluster the cells. Furthermore, typical dimensional reduction methods such as PCA, t-SNE, and ZIDA have been introduced to overcome “the curse of dimensionality”. A more recent method scDeepCluster has demonstrated improved and promising performances in …

Evaluating Driving Performance Of A Novel Behavior Planning Model On Connected Autonomous Vehicles, Keyur Shah

Honors Scholar Theses

Many current algorithms and approaches in autonomous driving attempt to solve the "trajectory generation" or "trajectory following” problems: given a target behavior (e.g. stay in the current lane at the speed limit or change lane), what trajectory should the vehicle follow, and what inputs should the driving agent apply to the throttle and brake to achieve this trajectory? In this work, we instead focus on the “behavior planning” problem—specifically, should an autonomous vehicle change lane or keep lane given the current state of the system?

In addition, current theory mainly focuses on single-vehicle systems, where vehicles do not communicate with …

Predicting Students’ Performance By Learning Analytics, Sandeep Subhash Madnaik

Master's Projects

The field of Learning Analytics (LA) has many applications in today’s technology and online driven education. Learning Analytics is a multidisciplinary topic for learn- ing purposes that uses machine learning, statistic, and visualization techniques [1]. We can harness academic performance data of various components in a course, along with the data background of each student (learner), and other features that might affect his/her academic performance. This collected data then can be fed to a sys- tem with the task to predict the final academic performance of the student, e.g., the final grade. Moreover, it allows students to monitor and self-assess …

Evidence-Based Detection Of Pancreatic Canc, Rajeshwari Deepak Chandratre

Master's Projects

This study is an effort to develop a tool for early detection of pancreatic cancer using evidential reasoning. An evidential reasoning model predicts the likelihood of an individual developing pancreatic cancer by processing the outputs of a Support Vector Classifier, and other input factors such as smoking history, drinking history, sequencing reads, biopsy location, family and personal health history. Certain features of the genomic data along with the mutated gene sequence of pancreatic cancer patients was obtained from the National Cancer Institute (NIH) Genomic Data Commons (GDC). This data was used to train the SVC. A prediction accuracy of ~85% …

Using Machine Learning To Optimize Predictive Models Used For Big Data Analytics In Various Sports Events, Akhil Kumar Gour

Master's Projects

In today’s world, data is growing in huge volume and type day by day. Historical data can hence be leveraged to predict the likelihood of the events which are to occur in the future. This process of using statistical or any other form of data to predict future outcomes is commonly termed as predictive modelling. Predictive modelling is becoming more and more important and is trending because of several reasons. But mainly, it enables businesses or individual users to gain accurate insights and allows to decide suitable actions for a profitable outcome.

Machine learning techniques are generally used in order …

Fallen Objects: Collaborating With Artificial Intelligence In The Field Of Graphic Design, Harrison S. Gerard

University Honors Theses

In this paper, I discuss the creation, execution and reception of my digital art series Fallen Objects, in which I collaborate with a neural net to create pseudo-found objects. I explore how artists might collaborate with Artificial Intelligence obliquely, not by having the AI generate the images themselves, but instead generate input for the artists to make the images. While many artists are focused on training neural nets to replicate their own art inputs, I instead focus on working with an AI trained on external, easily-accessible data and creating images from the prompts it delivers. In this way, the AI …

Ship Detection Feature Analysis In Optical Satellite Imagery Through Machine Learning Applications, Sylvia Charchut

LSU New Orleans Theses and Dissertations

Ship detection remains an important challenge within the government and the commercial industry. Current research has focused on deep learning and has found high success with large labeled datasets. However, deep learning becomes insufficient for limited datasets as well as when explainability is required. There exist scenarios in which explainability and human-in-the-loop processing are needed, such as in naval applications. In these scenarios, handcrafted features and traditional classification algorithms can be useful. This research aims at analyzing multiple textures and statistical features on a small optical satellite imagery dataset. The feature analysis consists of Haar-like features, Haralick features, Hu moments, …

Pattern Analysis And Prediction Of Mild Cognitive Impairment Using The Conn Toolbox, Meenakshi Anbukkarasu

Master's Projects

Alzheimer's is an irreversible neurodegenerative disorder described by dynamic psychological and memory defalcation. It has been accounted for that the pervasiveness of Alzheimer's is to increase by 4 times in a few years, where one in every 75 people will have this disorder. Hence, there is a critical requirement for the analysis of Alzheimer's at its beginning stage to diminish the difficulty of the overall medical complications. The initial state of Alzheimer’s is called Mild cognitive impairment (MCI), and hence it is a decent target for premature diagnosis and treatment of Alzheimer's. This project focuses on coordinating numerous imaging modalities …

Detection Of Mild Cognitive Impairment Using Diffusion Compartment Imaging, Matthew Jones

Master's Projects

The result of applying the Neurite Orientation Density and Dispersion Index (NODDI) algorithm to improve the prediction accuracy for patients diagnosed with MCI is reported. Calculations were carried out using a collection of 68 patients (34 control and 34 with MCI) gathered from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). Patient data includes the use of high-resolution Magnetic Resonance Images as with as Diffusion Tensor Imaging. A Linear Regression accuracy of 83% was observed using the added NODDI summary statistic: Orientation Dispersion Index (ODI). A statistically significant difference in groups was found between control patients and patients with MCI with …

Probabilistic And Machine Learning Enhancement To Conn Toolbox, Gayathri Hanuma Ravali Kuppachi

Master's Projects

Clinical depression is a state of mind where the person suffers from persevering and overpowering sorrow. Existing examinations have exhibited that the course of action of arrangement in the brain of patients with clinical depression has a weird framework topology structure. In the earlier decade, resting-state images of the brain have been under the radar a. Specifically, the topological relationship of the brain aligned with graph hypothesis has discovered a strong connection in patients experiencing clinical depression. However, the systems to break down brain networks still have a couple of issues to be unwound. This paper attempts to give a …

Malware Classification Based On Hidden Markov Model And Word2vec Features, Aparna Sunil Kale

Master's Projects

Malware classification is an important and challenging problem in information security. Modern malware classification techniques rely on machine learning models that can be trained on a wide variety of features, including opcode sequences, API calls, and byte ��-grams, among many others. In this research, we implement hybrid machine learning techniques, where we train hidden Markov models (HMM) and compute Word2Vec encodings based on opcode sequences. The resulting trained HMMs and Word2Vec embedding vectors are then used as features for classification algorithms. Specifically, we consider support vector machine (SVM), ��-nearest neighbor

(��-NN), random forest (RF), and deep neural network (DNN) classifiers. …