Carbon Footprint Analysis And Low-Carbon Optimization Method Simulation Study Of Power Transformer Based On Digital Twin Technology,
2024
Economic and Technical Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110870, China
Carbon Footprint Analysis And Low-Carbon Optimization Method Simulation Study Of Power Transformer Based On Digital Twin Technology, Dongxue Li, Yan Liu, Boyao Shen, Yongteng Jing, Qiang Ma, Ran Liu
Journal of System Simulation
Abstract: Power transformers are the main energy-consuming equipment for substations. According to the goal of “carbon peak, carbon neutralization” in China, it is of great significance to accurately calculate the carbon footprint of transformers and seek low-carbon optimization methods. A method for constructing a digital twin model of power transformer magnetic characteristics is proposed. Based on the three-dimensional electromagnetic time-harmonic field finite element analysis method, a threedimensional model of SZ11-31.5MVA/66kV power transformer is established. The transformer loss map is obtained under fluctuating load condition, and the transformer digital twin model is constructed. The carbon footprint of the transformer is analyzed, …
Uav Online Track Planning Based On Dmoea-Aptc Algorithm,
2024
College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
Uav Online Track Planning Based On Dmoea-Aptc Algorithm, Erchao Li, Shenghui Zhang
Journal of System Simulation
Abstract: In order to solve the dynamic multi-objective optimization problem with time correlation, this paper introduces the concept of time correlation feature and establishes the model of UAV timecorrelation dynamic multi-objective optimization problem moedl on the basis of UAV online track planning problem, and proposes a dynamic multi-objective double-layer optimization algorithm using adaptive predictive response mechanism and time-correlation optimization mechanism (DMOEA-APTC). The intensity of environmental change was judged according to the correlation of environmental change and different response mechanisms were used to quickly adapt to environmental change. In the optimization process, the least square method was used to learn the …
Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds,
2024
College of Engineering, South China Agricultural University, Guangzhou 510000, China
Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds, Hanlin Huo, Xiangjun Zou, Yan Chen, Xinzhao Zhou, Mingyou Chen, Chengen Li, Yaoqiang Pan, Yunchao Tang
Journal of System Simulation
Abstract: In response to the large and complex data volume and high redundancy of visual point cloud obstacle recognition in complex unstructured orchard environments, which severely impacts the real-time performance and efficiency of harvesting operations, a point cloud compression algorithm is proposed based on point cloud segmentation to enhance the efficiency of point cloud obstacle recognition and environmental adaptability. An Informed RRT* based approach is used combined with an inverse projection algorithm, mapping-based informed RRT*(M-Informed RRT*) to solve the harvesting path problem. By constructing a highly real-time and robust integrated robot system for sampling, perception, and obstacle avoidance, efficient obstacle …
An Improved Path Planning Algorithm For Mobile Robots,
2024
Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China; Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming 650500, China
An Improved Path Planning Algorithm For Mobile Robots, Haijie Sun, Hongjun San, Le Xiao, Dexin Yao, Jiupeng Chen, Xiaoyuan Yang
Journal of System Simulation
Abstract: To solve the problems of invalid sampling and non-optimal paths of the RRT, the quasi-stream avoidance algorithm is proposed. The RRT algorithm is introduced to specify the sampling interval to limit the sampling points and enhance the goal-oriented nature of sampling. The quasi-stream avoidance algorithm incorporating the A* algorithm (QSA*) is used to quickly bypass the obstacle when it is encountered. A path optimization algorithm is used to smooth the searched path. The simulation results show that compared with the RRT algorithm, the computation time of the RRT-QSA* algorithm is reduced by 96.83%~99.88%, the number of search nodes is …
Deep Learning Approach In Melanoma Stage Classification,
2024
Copperbelt University
Deep Learning Approach In Melanoma Stage Classification, Frank Lemba Lemba, Clopas Kwenda
African Conference on Information Systems and Technology
Accurate and efficient classification of melanoma stages is crucial for effective treatment planning and improved patient outcomes. Traditional diagnostic methods are often time-consuming and subjective, highlighting the need for advanced computational approaches. This study proposes a self-supervised learning framework combined with a convolutional neural network (CNN) to classify melanoma stages more effectively. Initially, features are extracted from unlabeled skin images using pre-trained VGG16 and ResNet50 models. These features are combined and reduced in dimensionality using Principal Component Analysis (PCA). Subsequently, K-means and DBSCAN clustering is applied to pseudo-label the data, providing a foundation for pre-training a CNN model. This pre-trained …
Trends From 20 Years Of Artificial Intelligence In Financial Services In Africa,
2024
University of Johannesburg
Trends From 20 Years Of Artificial Intelligence In Financial Services In Africa, Nthabiseng Moela, Lerato Matlala, Jackie Ma, Dipuo Maphutha, Hossana Twinomurinzi
African Conference on Information Systems and Technology
The need for financial inclusion in Africa, particularly for marginalised groups like women and small businesses, highlights the importance of leveraging Artificial Intelligence (AI). This study provides a bibliometric analysis of AI's integration into African financial services from 2003 to 2023. The key results show a significant increase in AI use, particularly in fraud detection, credit risk prediction, and stock market volatility forecasting, with 49% of the research coming from South Africa, Nigeria, and Tunisia. However, areas like financial development management, inflation control, and gender disparities in loan access remain underexplored. The emphasis has been on the technical implementation of …
Review Of Data Bias In Healthcare Applications,
2024
Vellore Institute of Technology
Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra
Physical Therapy Faculty Articles and Research
In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …
Exploring Artificial Intelligence: A Collaborative Small Group Analysis And Application,
2024
University of North Dakota
Exploring Artificial Intelligence: A Collaborative Small Group Analysis And Application, Ellamarie Powell
AI Assignment Library
In this small group project, students will collaborate to explore the principles and applications of Artificial Intelligence (AI). Each group will research, analyze, and present on a specific AI topic, highlighting its real-world implications and ethical considerations. The project involves team members contributing to various roles, including research, technical analysis, and presentation. The final deliverable will be a video presentation integrating individual contributions, showcasing a comprehensive understanding of AI and its impact on society. This assignment fosters teamwork, critical thinking, and effective communication skills.
Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning,
2024
Penn State University
Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari
Faculty Publications
Foundation models have emerged as powerful tools, exhibiting extraordinary performance across various tasks, such as language processing, visual recognition, code generation, and human-centered engagement. However, recent studies have highlighted their limitations when grounded, abstract, and generalized reasoning capabilities are required. Complex tasks often involve multiple hierarchical reasoning steps, which are typical features of human thinking processes. In fact, in this chapter we claim that cognitively-inspired computational models, such as the so-called Common Model of Cognition, are key to enable complex reasoning within foundation model-based artificial intelligence (AI) systems. We investigate neurosymbolic approaches for mapping AI system components to those of …
A Primer On How Al Algorithms Control You,
2024
Husson University
A Primer On How Al Algorithms Control You, Russell Fulmer
Journal of Technology in Counselor Education and Supervision
Artificial intelligence (AI) algorithms can control you by exerting heavy influence on your worldview. Your worldview is akin to your personal philosophy, which affects how you perceive and label social systems and structures, groups of people, and politics. Algorithms impact your decision-making, beliefs, mood, relationships, and more. My rhetoric is intentionally strong when discussing algorithms, and I invite you to assess its merit by reviewing related literature and thinking critically.
Interoperability In Deep Learning: A User Survey And Failure Analysis Of Onnx Model Converters,
2024
Purdue University
Interoperability In Deep Learning: A User Survey And Failure Analysis Of Onnx Model Converters, Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George Thiruvathukal, James C. Davis
Computer Science: Faculty Publications and Other Works
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models between frameworks and to runtime environments. Conversion errors compromise model quality and disrupt deployment. However, the failure characteristics of DL model converters are unknown, adding risk when using DL interoperability technologies. This paper analyzes failures in DL model converters. We survey software engineers about DL interoperability tools, use cases, and pain points (N=92). Then, we characterize failures in model converters associated with the main interoperability tool, ONNX (N=200 issues in PyTorch and TensorFlow). Finally, we formulate …
Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning,
2024
Air Force Institute of Technology
Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning, Benjamin F. Akers, Kristina O. F. Williams
Faculty Publications
A numerical method for evolving the nonlinear Schrödinger equation on a coarse spatial grid is developed. This trains a neural network to generate the optimal stencil weights to discretize the second derivative of solutions to the nonlinear Schrödinger equation. The neural network is embedded in a symmetric matrix to control the scheme’s eigenvalues, ensuring stability. The machine-learned method can outperform both its parent finite difference method and a Fourier spectral method. The trained scheme has the same asymptotic operation cost as its parent finite difference method after training. Unlike traditional methods, the performance depends on how close the initial data …
Adaptive Worlds: Generative Ai In Game Design And Future Of Gaming, And Interactive Media,
2024
Lindenwood University
Adaptive Worlds: Generative Ai In Game Design And Future Of Gaming, And Interactive Media, Jay Ratican, James Hutson
Faculty Scholarship
Generative AI is revolutionizing the field of game design, introducing unprecedented adaptability and personalization in gameplay. The latest advancements in AI-driven engines enable real-time content creation, offering dynamic, player-driven experiences that diverge from traditional pre-programmed narratives. This shift marks a transition toward "choose your own adventure" formats, with an unlimited number of variations in levels, enemies, collectibles, and weaponry, tailored to each player's decisions. Google's GameNGen, for example, showcases AI's capacity to recreate classic games like DOOM, learning and generating gameplay in real time. These innovations are not restricted to gaming alone; they extend to edutainment, television, and film, where …
Multimodal Mixing Convolutional Neural Network And Transformer For Alzheimer’S Disease Recognition,
2024
Chapman University
Multimodal Mixing Convolutional Neural Network And Transformer For Alzheimer’S Disease Recognition, Junde Chen, Yun Wang, Adnan Zeb, M. D. Suzauddola, Yuxin Wen
Engineering Faculty Articles and Research
Early recognition of Alzheimer’s disease (AD) and its precursor state, mild cognitive impairment (MCI), is pivotal in interrupting the progression of the disease and providing suitable treatment. Recent development in deep learning techniques has drawn great research attention for improving the efficacy of AD recognition. However, numerous current methods solely utilize data from a single auxiliary domain, limiting their ability to harness valuable intrinsic insights from multiple domains. To cope with the challenge, this paper is devoted to establishing an innovative multimodal medical data fusion model, termed as MMDF, to perform Alzheimer’s disease recognition. Multimodal data including clinical records and …
Regulating Algorithmic Harms,
2024
University of Michigan Law School
Regulating Algorithmic Harms, Sylvia Lu
Law & Economics Working Papers
In recent years, the rapid expansion of artificial intelligence (AI) innovations has led to a rise in algorithmic harms—harms emerging from AI operations that pose significant threats to civil rights and democratic values in today’s technological landscape. A facial recognition system for improving criminal detection wrongly collected sensitive personal data and flagged racial minorities as shoplifters. A risk-prediction algorithm adopted to identify patients denied medical treatment to Black individuals with poor health conditions. A social media algorithm intended to boost social engagement exacerbated addictive behavior and mental illness in teenagers. These harms are becoming increasingly ubiquitous yet often manifest in …
Rethinking Retrieval Augmented Fine-Tuning In An Evolving Llm Landscape,
2024
Southern Methodist University
Rethinking Retrieval Augmented Fine-Tuning In An Evolving Llm Landscape, Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, Joaquin Dominguez
SMU Data Science Review
This study explores the utilization of Retrieval Augmented Fine-Tuning (RAFT) to enhance the performance of Large Language Models (LLMs) in domain-specific Retrieval Augmented Generation (RAG) tasks. By integrating domain-specific information during the retrieval process, RAG aims to reduce hallucination and improve the accuracy of LLM outputs. We investigate the use of RAFT, an approach that enhances LLMs by incorporating domain-specific knowledge and effectively handling distractor documents. This paper validates previous work, which found that RAFT can considerably improve the performance of Llama2-7B in specific domains. We also expand upon previous work into new state-of-the-art open-source models and other datasets with …
Assessing The Accuracy And Utility Of Chatgpt Responses To Patient Questions Regarding Posterior Lumbar Decompression,
2024
Thomas Jefferson University
Assessing The Accuracy And Utility Of Chatgpt Responses To Patient Questions Regarding Posterior Lumbar Decompression, Alec Giakas, Rajkishen Narayanan, Teeto Ezeonu, Jonathan Dalton, Yunsoo Lee, Tyler Henry, John Mangan, Gregory Schroeder, Alex Vaccaro, Christopher Kepler
Department of Orthopaedic Surgery Faculty Papers
Aim: To examine the clinical accuracy and applicability of ChatGPT answers to commonly asked questions from patients considering posterior lumbar decompression (PLD). Methods: A literature review was conducted to identify 10 questions that encompass some of the most common questions and concerns patients may have regarding lumbar decompression surgery. The selected questions were then posed to ChatGPT. Initial responses were then recorded, and no follow-up or clarifying questions were permitted. Two attending fellowship-trained spine surgeons then graded each response from the chatbot using a modified Global Quality Scale to evaluate ChatGPT’s accuracy and utility. The surgeons then analyzed each question, …
Pooling And Winsorizing Machine Learning Forecasts To Predict Stock Returns With High-Dimensional Data,
2024
University of Denver
Pooling And Winsorizing Machine Learning Forecasts To Predict Stock Returns With High-Dimensional Data, Erik Mekelburg, Jack Strauss
Finance: Faculty Scholarship
We evaluate US market return predictability using a novel data set of several hundred ag- gregated firm-level characteristics. We apply LASSO, Elastic Net, Random Forest, Neural Net, Extreme Gradient Boosting, and Light Gradient Boosting Machine methods and find these models experience large prediction errors that lead to forecast failures. However, winsorizing and pooling machine learning model forecasts provides consistent out-of-sample predictability. To assess robustness, we apply machine learning methods to high-dimensional data for Canada, China, Germany and the UK as well as the Goyal-Welch data. All machine learning models we consider, except for the ensemble pooled methods, fail to significantly …
Recasting The Mould – Librarianship Of The Future: Leveraging Automation, Apis, And Ai,
2024
Singapore Management University
Recasting The Mould – Librarianship Of The Future: Leveraging Automation, Apis, And Ai, Samantha Seah
Research Collection Library
With leaps in artificial intelligence made in recent years redefining the information landscape and introducing new means of information production, librarianship also must evolve to include new literacies. One way librarians can equip and empower ourselves is by understanding the building blocks of how machines and automation work. Perhaps more important than learning specific programming languages, learning computational thinking provides us with more ways to spot and evaluate problems and devise solutions without extensive coding knowledge. My presentation will take the improvement of membership processing as an example using Power Automate, a low-code Microsoft tool mimicking block programming. The tool …
Self-Replication Via Tile Self-Assembly,
2024
University of Arkansas, Fayetteville
Self-Replication Via Tile Self-Assembly, Andrew Alseth, Daniel Hader, Matthew J. Patitz
Computer Science and Computer Engineering Faculty Publications and Presentations
In this paper we present a model containing modifications to the Signal-passing Tile Assembly Model (STAM), a tile-based self-assembly model whose tiles are capable of activating and deactivating glues based on the binding of other glues. These modifications consist of an extension to 3D, the ability of tiles to form “flexible” bonds that allow bound tiles to rotate relative to each other, and allowing tiles of multiple shapes within the same system. We call this new model the STAM*, and we present a series of constructions within it that are capable of self-replicating behavior. Namely, the input seed assemblies to …
