Artificial Intelligence and Robotics Commons^™

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.

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. …

Research On Natural Gas Explosion Rules For Cuboid Obstacles In Offshore Oil Platform, Pengcheng Wang, Yuqing Sun

Journal of System Simulation

Abstract: Quantities and blockage ratios of equipment on the platform has a great effect on the peak overpressure and temperature. The loss caused by explosion can be reduced to the minimum through the reasonable distribution of equipment, and the finite element method has been widely used in the simulation of gas cloud explosion. Offshore oil platform models with different cuboid obstacles were established by the finite element method and cuboid obstacles were distributed in 15 different situations. Mathematical relationships between overpressure and cuboid obstacles' quantities and blockage ratios were obtained. The results show that with the increase of quantities and …

Research On Picking Robot Vision Localization Based On Semi-Physical Simulation, Keyin Chen, Xiangjun Zou, Hongxing Peng, Haiying Liang, Yuanchuang Hu

Journal of System Simulation

Abstract: Aiming at the problems of accuracy and stability of the vision localization based on physical picking robot, which were easy to make mistakes, long cycle, and not easy to be carried out indoors, a picking robot vision localization method based on the semi-physical simulation technology was proposed combining with the robot vision localization mechanism and robot kinematics. This method adopted the virtual picking robot to instead of the physical robot, and studied the vision localization of the fruit target in the virtual simulated environment, which was as the vision localization based on semi-physical simulation. The test results show that: …

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 …

Improved Threshold Function Simulation Research In Vibration Signal Denoising, Hongxing Sun, Zhang Yang

Journal of System Simulation

Abstract: Filtering noise component of mechanical vibration signal effectively can observe the characteristics of vibration signal more clearly. So based on the wavelet threshold de-noising method, a new improved threshold function was proposed. The coefficient of wavelet transform mechanical vibration signals were estimated by the threshold selection method based on kurtosis value. Not only new threshold function conforms to the distribution characteristics of the vibration signal, but also new threshold function is continuous in the threshold point. And the new threshold function overcomes the inherent deviation which traditional threshold function brings. The research of noise reduction on the simulation signal …

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 …

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 …

Simulation And Analysis Of Ultra High Frequency Induction Heating Circuit Based On Current Source, Mangyuan Ma, Xinchun Shi, Wang Hui, Jianhui Meng

Journal of System Simulation

Abstract: The operating frequency range, operating mode and output power are determined by the circuit. A suitable circuit is very important for induction heating power supply. On the basis of Class-E and Boost Chopper circuit, a new current source was analyzed with parallel resonant load circuit by energy conservation law and Fourier decomposition, which derived mathematical relation among the parameters and provided application theory. This circuit was studied by using Matlab/Simulink with variable parameters, which obtained the operating waves with different parameters and values. Comparing the operating waves with the theoretical analysis, the simulation results are completely consistent with the …

Modeling Method Of Air Command And Security Work Process Service Oriented, Yongliang Luo, Yuanhui Qin

Journal of System Simulation

Abstract: A modeling method of air command and security work process service oriented was proposed combined with the typical process equipment air command and support building requirements. Air command and process characteristics was analyzed systematically. On this basis, a complex process modeling method was proposed. From the concept of process meta-model, process formalized description mechanism was studied. A prototype flow modeling tool was developed, and the rationality of the proposed method was analyzed combined with the application example.

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 …

Self-Trained Deep Ordinal Regression For End-To-End Video Anomaly Detection, Guansong Pang, Cheng Yan, Chunhua Shen, Anton Van Den Hengel, Xiao Bai

Research Collection School Of Computing and Information Systems

Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. However, they are challenged by the relative scarcity of instances of depression in the data. In this work we introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks, namely one-class metric learning and anomaly ranking. The auxiliary tasks introduce an inductive bias that improves the classification model’s generalizability on small depression …

Learning Transferable Deep Convolutional Neural Networks For The Classification Of Bacterial Virulence Factors, Dandan Zheng, Guansong Pang, Bo Liu, Lihong Chen, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have a very limited number of samples available.Results: We first construct a large VF dataset, covering 3446 VF classes with 160 495 sequences, and then propose deep convolutional neural network models for VF classification. We show that (i) for common VF classes with sufficient samples, …

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.

Greenwatch-Shing: Using Ai To Detect Greenwashing, Theodor Florian Cojoianu, Andreas G. F. Hoepner, Georgiana Ifrim, Yanan Lin

Research Collection College of Integrative Studies

The rise of fake news has resulted in a wide discrepancy between the attention given to the scientific understanding of an issue versus misinformed and sometimes purposefully disinformed claims (false information that is purposely spread to deceive people) coming from outside the scientific community. One such example relates to climate change, where a nature study shows that climate change contrarians are featured in 49% more media articles than scientists, despite the overwhelming consensus in the scientific community over the significance of anthropogenic climate change. In addition, many companies worldwide make inaccurate and often misleading claims about their environmental and social …

Integrating Finance Dictionary In Lexicon-Based Approach With Machine Learning Algorithm To Analyse The Impact Of Opec News Sentiment On Financial Market, Ling Wu

Student Works (2020-2029)

Since last few decades, machine learning algorithm which trains computers to learn from experience, is one of the most rapidly developing techniques which settles in the intersection research field of statistics and computer science. This research aims to build a properly trained machine learning classifier to study the impact of Organization of Petroleum Exporting Countries (OPEC) news sentiment on stock prices of six Malaysian public listed companies (energy sector) in the main board of Bursa Malaysia. The data used in this research are collected during the period 2012-2017. To carry out the research, firstly, lexicon-based approach is used to analyze …

A Machine Learning Approach For Vulnerability Curation, Yang Chen, Andrew E. Santosa, Ming Yi Ang, Abhishek Sharma, Asankhaya Sharma, David Lo

Research Collection School Of Computing and Information Systems

Software composition analysis depends on database of open-source library vulerabilities, curated by security researchers using various sources, such as bug tracking systems, commits, and mailing lists. We report the design and implementation of a machine learning system to help the curation by by automatically predicting the vulnerability-relatedness of each data item. It supports a complete pipeline from data collection, model training and prediction, to the validation of new models before deployment. It is executed iteratively to generate better models as new input data become available. We use self-training to significantly and automatically increase the size of the training dataset, opportunistically …

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 …

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, …

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 …

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 …

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 …

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 …