Digital Commons Network^™

Image Self-Enhancement De-Hazing Algorithm Combined With Generative Adversarial Network, Wanjun Liu, Yuqian Cheng, Haicheng Qu

Journal of System Simulation

Abstract: To solve the problem that existing dehazing models are prone to over fitting after training with synthetic hazy image data sets, an image self-enhancement dehazing algorithm is proposed in combination with generative adversarial network. The depth information of an image is estimated while combining two Generative Adversarial Networks. The first GAN uses a clear image to learn the process of image hazing, and then adopts the hazed image generated by it as the input of the second GAN to guide the second GAN to correct dehazing. In order to reduce the difference before and after image processing, the consistency …

Cooperative And Optimal Control Of Multiple Mimo Objects Under Information Sharing, Jiong Shao, Xinchun Jie, Meng Wu

Journal of System Simulation

Abstract: Aiming at the problems of low cooperative efficiency and slow convergence of control algorithm for multiple MIMO objects in cyber-physical system (CPS), a cooperative optimization control strategy for multiple MIMO objects under the condition of information sharing is proposed. A new network control system structure with only physical layer and information layer is used to realize real-time sharing of state variables, control and detection information of multiple MIMO objects. Under the condition of information sharing, based on the performance indexes assigned by CPS and the physical constraints between each object, the cooperative controller designed by minimum principle realizes the …

Research On Collaborative Optimization Method Of Multi-Uav Task Allocation And Path Planning, Peng Xiao, Feng Xie, Haihong Ni, Min Zhang, Zhili Tang, Ni Li

Journal of System Simulation

Abstract: Aiming at the task requirements of multi-UAV to perform multi-target collaborative reconnaissance, a collaborative optimization method of multi-machine and multi-objective task allocation and path planning is proposed. Based on the partheno genetic algorithms (PGA), a cost function combined with the actual path cost is constructed through the Dubins curve. To further reduce the calculation cost, a clustering algorithm based on UAV detection distance is proposed, and the generated clustering point is used as a new waypoint of UAV. The simulation results show that considering the dangerous area and the large number of reconnaissance points, the algorithm can effectively complete …

Tri-Training Algorithm Based On Density Peaks Clustering, Yuhang Luo, Runxiu Wu, Zhihua Cui, Yiying Zhang, Yeshen He, Jia Zhao

Journal of System Simulation

Abstract: Tri-training can effectively improve the generalization ability of classifiers by using unlabeled data for classification, but it is prone to mislabeling unlabeled data, thus forming training noise. Tritraining (Tri-training with density peaks clustering, DPC-TT) algorithm based on density peaks clustering is proposed. The DPC-TT algorithm uses the density peaks clustering algorithm to obtain the class cluster centers and local densities of the training data, and the samples within the truncation distance of the class cluster centers are identified as the samples with better spatial structure, and these samples are labeled as the core data, and the classifier is updated …

Deep Learning Based Local Path Planning Method For Moving Robots, Zesen Liu, Sheng Bi, Chuanhong Guo, Yankui Wang, Min Dong

Journal of System Simulation

Abstract: In order to integrate visual information into the robot navigation process, improve the robot's recognition rate of various types of obstacles, and reduce the occurrence of dangerous events, a local path planning network based on two-dimensional CNN and LSTM is designed, and a local path planning approach based on deep learning is proposed. The network uses the image from camera and the global path to generate the current steering angle required for obstacle avoidance and navigation. A simulated indoor scene is built for training and validating the network. A path evaluation method that uses the total length and the …

Path Planning Of Unmanned Delivery Vehicle Based On Improved Q-Learning Algorithm, Xiaokang Wang, Jie Ji, Yang Liu, Qing He

Journal of System Simulation

Abstract: To solve the traditional Q-learning algorithm for unmanned vehicle path planning suffers from the problems of low planning efficiency and slow convergence speed, for this reason, a path planning algorithm for unmanned delivery vehicles based on the improved Q-learning algorithm is proposed. Learning from the energy iteration principle of the simulated annealing algorithm, adjusts the greedy factor ε to make it change dynamically during the training process, so as to balance the relationship between exploration and utilization, and thus improve the planning efficiency. The reward value in the reward mechanism is changed from a discrete value to a continuous …

Research On Simulation Methods For Forest Fire Extinguishing Using Water Mist, Bing Xiang, Xiaohong Dong, Yang Li

Journal of System Simulation

Abstract: As a highly hazardous natural disaster, the occurrence and spread of forest fires are usually affected by a variety of complex factors such as climate, terrain, vegetation, combustible materials, etc., which makes it difficult to accurately simulate the spread and extinguishing process of forest fires. The spread of forest fires and the process of water mist fire extinguishing are physically modele. The spread model adopts a tree module structure to simulate the pyrolysis reaction of tree burning, and considers the effects of temperature, wind field,mass loss rate and other factors on the spread of tree burning.In the fire extinguishing …

Time Slot Allocation Method Of Data Link Based On Improved Difference Algorithm, Yuting Zhu, Huankun Su, Xiaodong Feng, Shijie Lei, Yanfang Fu

Journal of System Simulation

Abstract: Aiming at the problems of single algorithm, being prone to local optima, and weak generalization ability of the current strategies, based on an improved differential evolutionary algorithm, a chaos algorithm, an adaptive variational crossover algorithm, and a problem solution processing mechanism, a time slot allocation strategy is proposed. The chaos algorithm is used to initialize the population to increase the diversity and avoid the premature convergence. The selection probability parameter is then used to make the crossover and variation process more flexible, expanding the search range in early to increase the possibility of global optima in late. The experiment …

Research On Verification Method Of Missile Elastic Suppression Based On Frequency Compensation, Rixin Su, Ou Zhang

Journal of System Simulation

Abstract: For the elastic model of missile body in six degree of freedom mathematical simulation, the research on the verification method of elastic vibration suppression is carried out.. The notch filter used for elastic vibration suppression is introduced, the verification idea for filter design in boost-phase and passive-phase stages of missile flight is analyzed, and the problem currently existing in mathematical simulation verification is pointed out. Based on the frequency modulation phenomenon, an online verification method of frequency compensation for the notch filter is put forward, and it can be found that the function can be applied to any order …

“Use” As A Conscious Thought: Towards A Theory Of “Use” In Autonomous Things, Gohar Khan, A Karim Feroz

All Works

The way users perceive and use information systems artefacts has been mainly studied from the notion of behavioral beliefs, deliberate cognitive efforts, and physical actions performed by human actors to produce certain outcomes. The next generation of information systems, however, can sense, respond, and adapt to environments without necessitating similar cognitive efforts, physical contact, or explicit instructions to operate. Therefore, by leveraging theories of consciousness and technology use, this research aims to advance an alternative understanding of the "use" associated with the next generation of IS artefacts that do not require deliberate cognitive efforts, physical manipulation, or explicit instructions to …

An Innovative Approach On Yao’S Three-Way Decision Model Using Intuitionistic Fuzzy Sets For Medical Diagnosis, Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Florentin Smarandache, Hamza Ghazanfar Toor, Faiza Asif

Neutrosophic Systems with Applications

In the realm of medical diagnosis, intuitionistic fuzzy data serves as a valuable tool for representing information that is uncertain and imprecise. Nevertheless, decision-making based on this kind of knowledge can be quite challenging due to the inherent vagueness of the data. To address this issue, we employ power aggregation operators, which prove effective in combining several sources of data, such as expert thoughts and patient information. This allows for a more correct diagnosis; a particularly crucial aspect of medical practice where precise and timely diagnoses can significantly impact medication policy and patient results. In our research, we introduce a …

Towards Machine Proficiency With Semantic Underspecification, Zachary S. Gottesman

Dartmouth College Master’s Theses

Human natural language communication frequently relies on extra-linguistic information to fill in gaps in the linguistic signal left by semantic underspecification, or the omission of details that can be inferred from prior knowledge or other modalities. Underspecification is particularly common in conversations between acquaintances, since these interlocutors share context. Underspecification is a key and beneficial feature of natural language that improves efficiency, although it can cause communication to fail if it is not resolved correctly. For language models to communicate effectively and in a human-like fashion, they must learn how to recognize and utilize underspecified language. This thesis argues that …

Gradient-Based Deep Reinforcement Learning Interpretation Methods, Yuan Wang, Lin Xu, Xiaoze Gong, Yongliang Zhang, Yongli Wang

Journal of System Simulation

Abstract: The learning process and working mechanism of deep reinforcement learning methods such as DQN are not transparent, and their decision basis and reliability cannot be perceived, which makes the decisions made by the model highly questionable and greatly limits the application scenarios of deep reinforcement learning. To explain the decision-making mechanism of intelligent agents, this paper proposes a gradient based saliency map generation algorithm SMGG. It uses the gradient information of feature maps generated by high-level convolutional layers to calculate the importance of different feature maps. With the known structure and internal parameters of the model, starting from the …

Prediction Of Converter Gas Generation Based On Intermission Production Improved Elman, Jiajie Fei, Dinghui Wu, Junyan Fan, Jing Wang

Journal of System Simulation

Abstract: Aiming at large fluctuations of intermission and low prediction accuracy in iron and steel industry, based on the classification of intermission characteristics, a converter gas generation predicting model(CPSO-Elman) based on Elman neural network(ENN) optimized by chaotic PSO(CPSO) algorithm is proposed. The intermittent characteristics of converter gas generation time series are extracted and raw data is classified according to intermittent duration. The PSO algorithm improved by chaotic disturbance is introduced to optimize the initial weight and threshold of ENN and inertia weight of nonlinear updating is designed to balance global search ability and local search ability. Construct the combined prediction …

Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning, Charles Woodrum, Torrey J. Wagner, David E. Weeks

Faculty Publications

Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level …

A Robust Decision-Making Model For Medical Supplies Via Selecting Appropriate Unmanned Aerial Vehicle, Amira Salam, Mai Mohamed, Rui Yong, Jun Ye

Neutrosophic Systems with Applications

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In …

The Classification Of Internet Memes Through Supervised And Unsupervised Machine Learning Algorithms, William H. Little

Symposium of Student Scholars

Memes, those captivating internet phenomena, effortlessly deliver online entertainment. By leveraging time-series data from Google Trends, we can vividly illustrate and dissect the dynamic trends in meme popularity. Previous studies have discerned four distinct post-peak popularity patterns— "smoothly decaying," "spikey decaying," "leveling off," and "long-term growth"—and elegantly modeled these using ordinary differential equations.

This research introduces a programmatic approach that harnesses both supervised and unsupervised machine learning algorithms. The dataset, now expanded to over 2000 elements, becomes the canvas for exploration. The K-means algorithm identifies clusters, which then serve as labels for the supervised SVC algorithm. The overarching goal is …

Latent Auto-Recursive Composition Engine: A Generative System For Creative Expression In Human-Ai Collaboration, Yenkai Huang

Computer Science Senior Theses

This thesis investigates the shifting boundaries of art in the era of Generative AI, crit-
ically examining the essence of art and the legitimacy of AI-generated works. Despite
significant advancements in the quality and accessibility of art through generative
AI, such creations frequently encounter skepticism regarding their status as authentic
art. To address this skepticism, the study explores the role of creative agency in var-
ious generative AI workflows and introduces an ”artist-in-the-loop” system tailored
for image generation models like Stable Diffusion. This system aims to deepen the
artist’s engagement and understanding of the creative process. Additionally, a novel
tool, …

Inchi Isotopologue And Isotopomer Specifications, Hunter N. B. Moseley, Philippe Rocca-Serra, Reza M. Salek, Masanori Arita, Emma L. Schymanski

Markey Cancer Center Faculty Publications

This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically‑resolved chemi‑ cal entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically‑ resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry.

Scientific contribution

This new extension to the InChI standard …

Tree Recovery By Dynamic Programming, Gustavo Alberto Gratacos

McKelvey School of Engineering Graduate Student Theses & Dissertations

Tree-like structures are common, naturally occurring objects that are of interest to many fields of study, such as plant science and biomedicine. Analysis of these structures is typically based on skeletons extracted from captured data, which often contain spurious segments or cycles that need to be removed. We propose a dynamic programming algorithm which seeks to recover directed trees from these noisy skeletons. Our method recovers trees by removing edges and duplicating nodes while adhering to edge-label constraints. Our algorithm proceeds by iteratively merging graph nodes, such that the solution on the original graph can be obtained from those on …

Scalable Pythagorean Mean-Based Incident Detection In Smart Transportation Systems, Md Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das

Computer Science Faculty Research & Creative Works

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. to materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. in this article, we first propose a scalable data-driven anomaly-Based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. …

Improved Models Of Elastic Scheduling, Marion Baumli Sudvarg

McKelvey School of Engineering Graduate Student Theses & Dissertations

In real-time computing systems, \textit{timely} execution is a requirement of \textit{correct} execution. Such systems are widely found in robotics and autonomous vehicle applications, mobile spectrometry of atmospheric aerosols, real-time hybrid simulation for natural hazards engineering, and in prompt localization of transients such as gamma-ray bursts for time-domain and multi-messenger astrophysics. \textit{Elastic scheduling} provides a framework to adjust computational rates and workloads in systems for which timeliness cannot otherwise be guaranteed. While originally proposed for periodic tasks executing on a single processor, elastic scheduling has since been extended to sequential and parallel execution on multiple processors and to earliest deadline first …

Exploring Neural Networks For Breast Cancer Tissue Classification, Stephen Jacobs, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use …

Analysis And Computation Of Constrained Sparse Coding On Emerging Non-Von Neumann Devices, Kyle Henke

Mathematics & Statistics ETDs

This dissertation seeks to understand how different formulations of the neurally inspired Locally Competitive Algorithm (LCA) represent and solve optimization problems. By studying these networks mathematically through the lens of dynamical and gradient systems, the goal is to discern how neural computations converge and link this knowledge to theoretical neuroscience and artificial intelligence (AI). Both classical computers and advanced emerging hardware are employed in this study. The contributions of this work include:

1. Theoretical Work: A comprehensive convergence analysis for networks using both generic Rectified Linear Unit (ReLU) and Rectified Sigmoid activation functions. Exploration of techniques to address the binary …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Building A Data Pipeline And Machine Learning Model For Insurance Data, Connor Weyers

Honors Program: Senior Projects (Public)

Insurance telematics is an emerging and exciting field. It combines the advancements in GPS tracking, computational analytics, data processing, and machine learning into a useful tool to help insurance companies make the best product for their consumers. This is why National Indemnity looked to implement a telematics portion to their business processes of underwriting insurance policies and sponsored a School of Computing Senior Design project. In this report, we will first review existing solutions that been used to solve problems and subproblems similar to that we are given in this project. We then propose designs for the data pipeline and …

Mending Trust In Ai: Trust Repair Policy Interventions For Large Language Models In Visual Data Journalism, Hangxiao Zhu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Trust in Large Language Models (LLMs) emerged as a pivotal concern. This is because, despite the transformative potential of LLMs in enhancing the interpretability and interactivity of complex datasets, the opacity of these models and instances of inaccuracies or biases have led to a significant trust deficit among end-users. Moreover, there is a tendency for people to personify AI tools that utilize these LLMs, attributing abilities and sensibilities that they do not truly possess. This thesis exploits this personification and proposes a comprehensive framework of trust repair policies tailored to address the challenges inherent in LLM annotations within data journalism …

Accord: Constraint-Driven Mediation Of Multi-User Conflicts In Cloud Services, Abhiroop Tippavajjula, Primal Pappachan, Anna Squicciarini, Jose Such

Computer Science Faculty Publications and Presentations

When multiple users adopt collaborative cloud services like Google Drive to work on a shared resource, incorrect or missing permis- sions may cause conflicting or inconsistent access or use privileges. These issues (or conflicts) compromise resources confidentiality, integrity, or availability leading to a lack of trust in cloud services. An example conflict is when a user with editor permissions changes the permissions on a shared resource without consent from the orig- inal resource owner. In this demonstration, we introduce ACCORD, a web application built on top of Google Drive able to detect and resolve multi-user conflicts. ACCORD employs a simulator …

Fea Simulations For Thermal Distributions Of Large Scale 3dic Packages, Suxia Chen, Qiang Wu, Wayne Xun, Jiachen Zhang, Jianping Xun

Computer Science Faculty Publications and Presentations

As the market increases for Artificial Intelligence and High-Performance Computing applications, the geometry of 3-Dimensional Integrated Circuit packages becomes more complicated; therefore, predicting the thermal distributions of the structures becomes not only more important but also more challenging. The physics governing the thermal distribution is a 3-dimensional partial differential equation. In order to predict the thermal distributions, various approaches such as the layer modeling method have been invented. While practical, these approaches solve a simplified version of the differential equation placing an inherent limitation on their capabilities which may be improved upon. In this research we solve the actual differential …

Stochastic Algorithms For Simulating Protein Assembly In Biological Cells, Advait Shukla

Theses - ALL

Understanding membrane protein assembly using computational methods is critical in developing drug delivery strategies for tight junction diseases such as Alzheimer’s and Crohn’s diseases. While experimental methods, such as freeze-fracture micrographs, provide microscopic details of the protein strand networks, they cannot offer molecular-level information, which is necessary for drug design. This work attempts to close the gap by computationally predicting strand networks of the claudin family of proteins that form tight junctions across neighboring cells in epithelial and endothelial tissues. Using claudin dimer data collected by the Protein AssociatioN Energy Landscape (PANEL) method coupled with some Markovian approximations, we built …