Artificial Intelligence and Robotics Commons^™

Optimized Scheduling Of Distribution Network With Distributed Generation Based On Coronavirus Herd Immunity Optimizer Algorithm, Xiaomeng Wu, Rongze Yuan, Yingliang Li, Qi Zhu

Journal of System Simulation

Abstract: Following the large-scale entry of distributed new energy into the network, the uncertainty factor of the distribution network increases significantly, and the difficulty of reactive power optimization scheduling increases accordingly. Traditional optimization solutions have many limitations and shortcomings, and a dynamic reactive power optimization scheme for active distribution networks based on a multi-scenario approach is proposed. The mathematical modeling is carried out separately for the uncertainty of new energy and load, and the multi-scenario method is used to transform the uncertainty problem into a deterministic problem. A mathematical model is constructed on the distribution network side to pursue the …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Development Of Several Typical Virtual Reality Fusion Technologies, Qiqi Feng, Zhiming Dong, Wencheng Peng, Yi Dai, Bingshan Si

Journal of System Simulation

Abstract: Virtual reality fusion can realize the two-way interaction, mapping and linkage between virtual world and physical world, which attracts the attention of countries in the world. In order to sort out and make statistics on concept connotation, academic status and application of the related new technologies, digital twin, cyber-physical systems, metaverse and live-virtual-constructive simulation are taken as representatives. The comparison on the development process, functional characteristics, target trends, etc. is carried out.

Data Simulation Testing Framework For Complex Process Equipment Software, Jinkun Zhang, Longfei Shi, Chi Hu, Hao Zhang, Yonghui Yang

Journal of System Simulation

Abstract: Due to the complex task, tight coupling, strict timing, and a large amount of interchange data, the technical threshold of automated testing of bus communication equipment software is high, and the implementation is difficult. The ideas of data-driven testing and keyword-driven testing are introduced, and a data simulation testing framework is proposed. Configuration rules are formulated and implemented in the framework. Testers can simulate peripheral data for complex process equipment software and implement automated testing by only focusing on the task analysis, and configuring interchange data and keywords. There is no need to develop test scripts, which reduces the …

Urban Uav Path Planning Based On Improved Beetle Search Algorithm, Qingqing Yang, Minyi Deng, Yi Peng

Journal of System Simulation

Abstract: An improved SABAS is proposed to improve the safety and path smoothing of UAV missions in urban multi-obstacle environments and to obtain the shortest path. The algorithm no longer completely depends on the difference of odor concentration between the left and the right tentacles of beetle when exploring the path for position update. Instead, it makes full use of the strong searching ability of BAS algorithm, and introduces the annealing algorithm to add the neighborhood position solution of the next position, and finally selects the next best position from the neighborhood position solution. Metropolis criterion of annealing algorithm is …

Research On Network Public Opinion Propagation Model Of Major Epidemics Under Cross-Infection Of Double Emotions, Yaming Zhang, Yanyuan Su, Guiru Zhao, Xiaoyu Guo

Journal of System Simulation

Abstract: Major epidemics provoke a variety of netizens' emotions. To some degree, the interaction of netizens' intense emotions determine the development direction of public opinion. Considering the complexity and dual emotional contagion, the impact of emotional factors in network public opinion is quantified to three dimensions indicators, emotional enhancement, differences and conversion rates. SIPINR public opinion propagation model is constructed. The equilibrium points and the transmission threshold are estimated and the stability is proved. The law of network public opinion propagation during major epidemics is revealed through numerical simulation. The results show that the dual emotional contagion would lead to …

Guilty Machines: On Ab-Sens In The Age Of Ai, Dylan Lackey, Katherine Weinschenk

Critical Humanities

For Lacan, guilt arises in the sublimation of ab-sens (non-sense) into the symbolic comprehension of sen-absexe (sense without sex, sense in the deficiency of sexual relation), or in the maturation of language to sensibility through the effacement of sex. Though, as Slavoj Žižek himself points out in a recent article regarding ChatGPT, the split subject always misapprehends the true reason for guilt’s manifestation, such guilt at best provides a sort of evidence for the inclusion of the subject in the order of language, acting as a necessary, even enjoyable mark of the subject’s coherence (or, more importantly, the subject’s separation …

Tiny Machine Learning For Underwater Image Enhancement: Pruning And Quantizaition Approach, Dr Khaled Nagaty, The British University In Egypt, Andreas Pester Dr

Computer Science

Many people have expressed an interest in underwater image processing in a variety of fields, including underwater vehicle control, archaeology, marine biological studies, etc. Underwater exploration is becoming an increasingly important element of our lives, with applications ranging from underwater marine and creature research to pipeline and communication logistics, military use, touristic and entertainment use. Underwater images suffer from poor visibility, distortion, and poor quality for a variety of causes, including light propagation. The major issue arises when these images must be captured at depths greater than 500 feet and artificial lighting needs to be provided. Efficient algorithms and models …

Smart Applications And Resource Management In Internet Of Things, Zeinab Akhavan

Computer Science ETDs

Internet of Things (IoT) technologies are currently the principal solutions driving smart cities. These new technologies such as Cyber Physical Systems, 5G and data analytic have emerged to address various cities' infrastructure issues ranging from transportation and energy management to healthcare systems. An IoT setting primarily consists of a wide range of users and devices as a massive network interacting with different layers of the city infrastructure resulting in generating sheer volume of data to enable smart city services. The goal of smart city services is to create value for the entire ecosystem, whether this is health, education, transportation, energy, …

Context-Driven Behavior: Improved Contextual Reasoning For Context-Aware Agents, Christian L. Wilson

Electronic Theses and Dissertations

Over the last three decades, a considerable amount of research has been dedicated to improving an artificial agent's ability to recognize and deal effectively with context. In this paper, I discuss a framework for a novel form of contextual reasoning. Unlike existing contextual reasoning frameworks, which allow an agent to apply its contextual knowledge after it is operating in an instance of a known context, the model I discuss allows an agent to reason about context proactively. With a proactive model, an agent forecasts the future contexts it will encounter, then takes steps to ensure its behaviors are appropriate for …

Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang

Electronic Thesis and Dissertation Repository

This research investigates the mortality risk of COVID-19 patients across different variant waves, using the data from Centers for Disease Control and Prevention (CDC) websites. By analyzing the available data, including patient medical records, vaccination rates, and hospital capacities, we aim to discern patterns and factors associated with COVID-19-related deaths.

To explore features linked to COVID-19 mortality, we employ different techniques such as Filter, Wrapper, and Embedded methods for feature selection. Furthermore, we apply various machine learning methods, including support vector machines, decision trees, random forests, logistic regression, K-nearest neighbours, na¨ıve Bayes methods, and artificial neural networks, to uncover underlying …

Probing And Enhancing The Reliance Of Transformer Models On Poetic Information, Almas Abdibayev

Dartmouth College Ph.D Dissertations

Transformer models have achieved remarkable success in the widest variety of domains, spanning not just a multitude of tasks within natural language processing, but also those in computer vision, speech, and reinforcement learning. The key to this success is largely attributed to the self-attention mechanism, particularly its ability to scale in performance as it grows in the number of parameters. Extensive effort has been underway to study the major linguistic properties learned by these models during the course of their pretraining. However, the role of certain finer linguistic phenomena present in language and their utilization by Transformers has not been …

Deep Learning Uncertainty Quantification For Clinical Text Classification, Alina Peluso, Ioana Danciu, Hong-Jun Yoon, Jamaludin Mohd Yusof, Tanmoy Bhattacharya, Adam Spannaus, Noah Schaefferkoetter, Eric B. Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi, Shang Gao

School of Public Health Faculty Publications

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …

The Transformative Integration Of Artificial Intelligence With Cmmc And Nist 800-171 For Advanced Risk Management And Compliance, Mia Lunati

Cybersecurity Undergraduate Research Showcase

This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating …

Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson

Theses and Dissertations

The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model …

Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded

Theses and Dissertations

Historical manuscripts are an essential source of original content. For many reasons, it is hard to recognize these manuscripts as text. This thesis used a state-of-the-art Handwritten Text Recognizer, TrOCR, to recognize a 16^th-century manuscript. TrOCR uses a vision transformer to encode the input images and a language transformer to decode them back to text. We showed that carefully preprocessed images and designed augmentations can improve the performance of TrOCR. We suggest an ensemble of augmented models to achieve an even better performance.

Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser

Theses and Dissertations

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail

College of Computing and Digital Media Dissertations

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning.

Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct …