Artificial Intelligence and Robotics Commons^™

Smart Street Light Control: A Review On Methods, Innovations, And Extended Applications, Fouad Agramelal, Mohamed Sadik, Youssef Moubarak, Saad Abouzahir

Computer Vision Faculty Publications

As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each …

Limitations And Possibilities Of Digital Restoration Techniques Using Generative Ai Tools: Reconstituting Antoine François Callet’S Achilles Dragging Hector’S Body Past The Walls Of Troy, Charles O'Brien, James Hutson, Trent Olsen, Jay Ratican

Faculty Scholarship

Digital restoration offers new avenues for conserving historical artworks, yet presents unique challenges. This research delves into the balance between traditional restoration methods and the use of generative artificial intelligence (AI) tools, using Antoine François Callet’s portrayal of Achilles Dragging Hector’s Body Past the Walls of Troy as a case study. The application of Easy Diffusion and Stable Diffusion 2.1 technologies provides insights into AI-driven restoration methods such as inpainting and colorization. Results indicate that while AI can streamline the restoration process, repeated inpainting can compromise the painting’s color quality and detailed features. Furthermore, the AI approach occasionally introduces unintended …

Novus Ex Machina: Realise Your Organisation’S Creative Potential With Ai, Adam Tatarynowicz, Utz Claassen

Asian Management Insights

Innovation managers must learn how to harness AI’s transformative potential.

Healthaichain: Improving Security And Safety Using Blockchain Technology Applications In Ai-Based Healthcare Systems, Naresh Kshetri, James Hutson, Revathy G

Faculty Scholarship

Blockchain as a digital ledger for keeping records of digital transactions and other information, it is secure and decentralized technology. The globally growing number of digital population every day possesses a significant threat to online data including the medical and patients’ data. After bitcoin, blockchain technology has emerged into a general-purpose technology with applications in medical industries and healthcare. Blockchain can promote highly configurable openness while retaining the highest security standards for critical data of medical patients. Referred to as distributed record keeping for healthcare systems which makes digital assets unalterable and transparent via a cryptographic hash and decentralized network. …

Digitizing The Cultural Capital: Harnessing Digital Humanities For Heritage Preservation In Bujumbura, Burundi, James Hutson, Pace Ellsworth, Matt Ellsworth, Jean Bosco Ntungirimana

Faculty Scholarship

In an era where the erosion of cultural heritage is increasingly prevalent, there exists a critical imperative to explore and implement innovative methods for the preservation and revitalization of cultural identities, as exemplified by the urgent situation in Bujumbura, Burundi. Central to this study is the exploration of innovative digital methodologies for archiving a wide spectrum of cultural artifacts, including both notable and everyday heritage elements, in Bujumbura. Traditional approaches to biographical and historical profiling have predominantly focused on official records and significant events, often neglecting the richness of personal experiences and everyday interactions that substantially shape cultural identities. To …

Individuality And The Collective In Ai Agents: Explorations Of Shared Consciousness And Digital Homunculi In The Metaverse For Cultural Heritage, James Hutson, Jay Ratican

Faculty Scholarship

The confluence of extended reality (XR) technologies, including augmented and virtual reality, with large language models (LLM) marks a significant advancement in the field of digital humanities, opening uncharted avenues for the representation of cultural heritage within the burgeoning metaverse. This paper undertakes an examination of the potentialities and intricacies of such a convergence, focusing particularly on the creation of digital homunculi or changelings. These virtual beings, remarkable for their sentience and individuality, are also part of a collective consciousness, a notion explored through a thematic comparison in science fiction with the Borg and the Changelings in the Star Trek …

A Review Of Cyber Attacks On Sensors And Perception Systems In Autonomous Vehicle, Taminul Islam, Md. Alif Sheakh, Anjuman Naher Jui, Omar Sharif, Md Zobaer Hasan

School of Mathematical & Statistical Sciences Faculty Publications

Vehicle automation has been in the works for a long time now. Automatic brakes, cruise control, GPS satellite navigation, etc. are all common features seen in today's automobiles. Automation and artificial intelligence breakthroughs are likely to lead to an increase in the usage of automation technologies in cars. Because of this, mankind will be more reliant on computer-controlled equipment and car systems in our daily lives. All major corporations have begun investing in the development of self-driving cars because of the rapid advancement of advanced driver support technologies. However, the level of safety and trustworthiness is still questionable. Imagine what …

Smartphone Based Object Detection For Shark Spotting, Darrick W. Oliver

Master's Theses

Given concern over shark attacks in coastal regions, the recent use of unmanned aerial vehicles (UAVs), or drones, has increased to ensure the safety of beachgoers. However, much of city officials' process remains manual, with drone operation and review of footage still playing a significant role. In pursuit of a more automated solution, researchers have turned to the usage of neural networks to perform detection of sharks and other marine life. For on-device solutions, this has historically required assembling individual hardware components to form an embedded system to utilize the machine learning model. This means that the camera, neural processing …

Optimizing Uncertainty Quantification Of Vision Transformers In Deep Learning On Novel Ai Architectures, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja

Computer Science: Faculty Publications and Other Works

Deep Learning (DL) methods have shown substantial efficacy in computer vision (CV) and natural language processing (NLP). Despite their proficiency, the inconsistency in input data distributions can compromise prediction reliability. This study mitigates this issue by introducing uncertainty evaluations in DL models, thereby enhancing dependability through a distribution of predictions. Our focus lies on the Vision Transformer (ViT), a DL model that harmonizes both local and global behavior. We conduct extensive experiments on the ImageNet-1K dataset, a vast resource with over a million images across 1,000 categories. ViTs, while competitive, are vulnerable to adversarial attacks, making uncertainty estimation crucial for …

Optimized Uncertainty Estimation For Vision Transformers: Enhancing Adversarial Robustness And Performance Using Selective Classification, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja

Computer Science: Faculty Publications and Other Works

Deep Learning models often exhibit undue confidence when encountering out-of-distribution (OOD) inputs, misclassifying with high confidence. The ideal outcome, in these cases, would be an "I do not know" verdict. We enhance the trustworthiness of our models through selective classification, allowing the model to abstain from making predictions when facing uncertainty. Rather than a singular prediction, the model offers a prediction distribution, enabling users to gauge the model’s trustworthiness and determine the need for human intervention. We assess uncertainty in two baseline models: a Convolutional Neural Network (CNN) and a Vision Transformer (ViT). By leveraging these uncertainty values, we minimize …

The Impact Of Generative Ai In The Financial Sector [El Impacto De La Inteligencia Artificial Generativa En El Sector Financiero], Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

No abstract provided.

Examining The Externalities Of Highway Capacity Expansions In California: An Analysis Of Land Use And Land Cover (Lulc) Using Remote Sensing Technology, Serena E. Alexander, Bo Yang, Owen Hussey, Derek Hicks

Mineta Transportation Institute

There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution optical camera. In many instances, the IR camera …

A Smart Chatbot System For Digitizing Service Management To Improve Business Continuity, Asraa Mohammed Albeshr

Theses

Chatbots, also called digital systems that require a natural language-based interface for user interaction, are increasingly being integrated into our daily lives. These chatbots respond intelligently to voice and text and function as sophisticated entities. Its functioning includes the recognition of multiple human languages through the application of Natural Language Processing (NLP) techniques. These chatbots find applications in various areas such as e-commerce services, medical assistance, recommendation systems, and educational purposes. This reflects the versatility and widespread adoption of this technology. AI chatbots play a crucial role in improving IT support in IT Service Management (ITSM) for better business continuity. …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel. Chen, Nan Hu, Peng. Liang, Morgan. Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Typesqueezer: When Static Recovery Of Function Signatures For Binary Executables Meets Dynamic Analysis, Ziyi Lin, Jinku Li, Bowen Li, Haoyu Ma, Debin Gao, Jianfeng Ma

Research Collection School Of Computing and Information Systems

Control-Flow Integrity (CFI) is considered a promising solutionin thwarting advanced code-reuse attacks. While the problem ofbackward-edge protection in CFI is nearly closed, effective forward-edge protection is still a major challenge. The keystone of protecting the forward edge is to resolve indirect call targets, which although can be done quite accurately using type-based solutionsgiven the program source code, it faces difficulties when carriedout at the binary level. Since the actual type information is unavailable in COTS binaries, type-based indirect call target matching typically resorts to approximate function signatures inferredusing the arity and argument width of indirect callsites and calltargets. Doing so …

Designing Depaul

DePaul Magazine

DePaul’s comprehensive, collaborative plan creates a road map that positions the university for monumental impact.

Generative Ai-Based Non-Person Character (Npc) For Navigating Virtual Worlds, Ananth Ramaseri-Chandra

Computer Science Posters and Presentations

An innovative approach to virtual world interactions through generative AI-based Non-person Characters (NPCs). These AI-driven NPCs significantly advance over traditional, scripted characters by providing more realistic, adaptive, and dynamic interactions in various virtual environments. The work details the development process of these NPCs, from algorithm design to data integration and iterative refinement, ensuring their seamless integration into game environments. Additionally, the poster explores the wide-ranging applications of these AI NPCs, including enhancing gaming experiences, offering realistic training environments, and facilitating personalized virtual learning experiences. This research marks a substantial leap in virtual interaction, pushing the boundaries of immersion and realism …

Search-Based Fairness Testing: An Overview, Hussaini Mamman, Shuib Basri, Abdullateef Balogun, Abdullahi Abubakar Imam, Ganesh Kumar, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasizing the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems’ biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.

Electromagnetic Transient Equivalent Modeling Method For Wind Power Clusters Adapted To Expected Faults, Dongsheng Li, Ye Liu, Yankan Song, Chen Shen

Journal of System Simulation

Abstract: Based on an existing equivalent modeling method for individual wind farm, an iterative simulation-based equivalent modeling method for wind power clusters is proposed and a software development for equivalent modeling of wind power clusters is completed by using CloudPSS-XStudio suite. The system integrates expected fault selection, equivalent parameter calculation and result analysis, which provides support for dynamic security assessment of power systems with large-scale wind power clusters. The equivalent method takes the average wind speed of each wind farm and the expected faults as input, and obtains the cluster index of each wind turbine based on the iterative simulation …

Key Technology And Application Of Digital Twin Modeling For Mri, Shanshan Chen, Hongzhi Wang, Tian Xia

Journal of System Simulation

Abstract: With the accelerating digitalization in education, the construction of digital resources and application platforms has caught increasing attention. The framework of MRI equipment digital twin fivedimensional model is constructed to solve the problems in teaching and training for magnetic resonance imaging (MRI). A modeling and simulation method based on the mechanism model is proposed. The multi-dimensional physical data are obtained to perform digital human modeling, and the virtual acquisition and image reconstruction method is proposed to generate images. The digital twin data are adopted for iterative optimization to implement the whole process of the three-dimensional visual operation including preparation …

A Hybrid Empirical Method For Fast Modeling Of Ship Manoeuvring Motion, Peng Wu, Zongmo Yang, Qianfeng Jing, Yulin Li

Journal of System Simulation

Abstract: Simulation testing is an important means to verify the functions of intelligent ships. Ship maneuvering motion modeling and simulation is the key theoretical basis for the intelligent collision avoidance of multiple vessels in complex sea areas. To address the problem that the calculation of the hydrodynamic coefficients required for ship maneuvering modeling is complex and difficult to obtain, a hybrid empirical method is proposed, a combination method of the existing regression methods is established, the comprehensive performance indicators are constructed, the optimal hydrodynamic coefficients groups are selected by simulated maneuvering experiments, and a rapid modeling program code is developed …

Reliability Evaluation Method Of Radar Simulation Model Based On Air Combat Mechanism, Chenguang Wang, Jinpeng Bai, Tingting Li, Lifeng Miao, Kaifeng Wang

Journal of System Simulation

Abstract: In modern air combat simulation, the reliability of radar simulation model is very important. Based on simulation model VV&A theory, a cropped and suitable for engineering applications simulation model reliability evaluation method is proposed. Based on the analysis of radar use mechanism in medium and long range air combat and short range air combat is analyzed,, the requirement of radar model in air combat simulation system, the evaluation index system is established, and the reliability quantification method based on JS dispersion is proposed, which further enriches the base of basic method. The test case is designed, the simulation test …

Learning And Analysis Of Dynamic Models For Grid Discrete Events Based On Log Information, Danlong Zhu, Yunqi Yan, Ying Chen, Jiaqi Zhang, Longxing Jin, Wei Fu

Journal of System Simulation

Abstract: With the increasing scale of power grid, the massive amount of log information generated bydevices in the power grid poses a challenge to the manual analysis of abnormal grid conditions. The log information generated during the operation of the power grid has the typical discrete sequential characteristics. By analyzing the log information of grid alarm messages, a station event transition probability model and an event sequence risk calculation method are proposed to effectively model and analyze the abnormal operation level of primary and secondary systems in substations. The proposed method not only successfully identifies the event sequences corresponding to …

Research On Multi-Aircraft Air Combat Behavior Modeling Based On Hierarchical Intelligent Modeling Methods, Yukun Wang, Ze Wang, Liwei Dong, Ni Li

Journal of System Simulation

Abstract: In response to the problem of the difficulty of decision-making in the game of force under the constraints of high-dimensional state-space in multi-machine air combat confrontation scenarios, a force intelligent agent decision-making generation strategy based on deep reinforcement learning is adopted. The developing situational cognition and reward feedback generation algorithms for force intelligentgame are proposed, a behavior modeling hierarchical framework based on hybrid intelligence modeling method is constructed, which solve the technical difficulty of sparse reward in the reinforcement learning process. It provides an feasible reinforcement learning training method that can solve the large-scale, multi-model, and multi-element air combat …

Dynamic 3d Scene Perception Based On Battlefield Metaverse, Haoyu Wang, Guanghong Gong, Jihong Cai, Bipeng Ye, Zhaofang Zhou, Zheng Mei, Ni Li

Journal of System Simulation

Abstract: Informatization combat needs higher requirements for battlefield situational awareness, and the use of unmanned intelligences to conduct battlefield reconnaissance and perceive target information is particularly important. Facing the needs of complex dynamic environment localization and target recognition, a dynamic 3D scene perception system is proposed and constructed based on battlefield meta-universe target data and operational environment, which uses vision and IMU fusion sensor simulation data as inputs, extracts battlefield target information through instance segmentation and dense optical flow estimation network and uses it as a scene prior, and synchronizes the position estimation of unmanned intelligences in the battlefield with …

Executive Order On The Safe, Secure, And Trustworthy Development And Use Of Artificial Intelligence, Joseph R. Biden

Copyright, Fair Use, Scholarly Communication, etc.

Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

My Administration places the highest urgency …

A Precise Attention Tracking System Based On Computer Vision, Jiyuan Liu, Hanwen Qi, Zhicheng Liu, Minrui Fei, Kun Zhang

Journal of System Simulation

Abstract: A precise attention tracking system based on machine vision is designed to address the difficulty in studying students' attention allocation. The system includes an image capture device and an accurate attention tracking algorithm. The image capture device can capture the clearer ocular images. The pupil center localization algorithm replaces VGG16 with lightweight MobileNetv3 and uses twostage feature fusion and center keypoint prediction techniques to improve the speed and accuracy. The algorithm achieves a speed of up to 36 frames/s and 97.42% accuracy. The gaze tracking algorithm compensates for the head movements to achieve the meticulous gaze tracking. An interactive …

Modeling And Analysis On Scattering Characteristics Automatic Driving Radar Bands In Rainy Environment, Mengfan Zou, Xiaoyu He

Journal of System Simulation

Abstract: The operating frequency band of modern communication and radar systems has extended to millimeter wave and terahertz frequency band, and the analysis on propagation characteristics of electromagnetic signals in rainy environment is important. A calculation model through Mie scattering theory is built to simulate the attenuation and the scattering of electromagnetic signals in rainy environments. Different types of raindrop size distribution function are adopted to analyze the propagation attenuation under different rainfall of frequencies spanning from 1 GHz to 1 THz. Experimental results are compared with international telecommunication union (ITU) half-empirical model to verify the validation of the model. …

Integrated Scheduling Simulation Based On Improved Moth Flame Optimizer, Tianrui Zhang, Huiyuan Niu, Wei Xie

Journal of System Simulation

Abstract: Aiming at the rising cost of manufacturing enterprises, a mathematical programming model of integrated scheduling of production and transportation is established, and a double adaptive weights for moth flame optimizer(DAWMFO) is proposed. A double adaptive weight mechanism is proposed. The spiral function is used to update the population, which improves the convergence speed and accuracy of the algorithm. The benchmark function is tested by the improved algorithm. The results show that the improved algorithm can converge quickly and not easily fall into local optimum. Compared with other algorithms, the optimization ability is also improved. Through the simulation experiment on …

Simulation And Research Of Manipulator Motion Strategy Based On Adaptive Dynamic Programming, Ming Li, Qun Xu, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the difficulty of manipulator to realize high-precision motion tracking in complex and harsh environment, a strategy method based on the combination of adaptive dynamic programming (ADP) and sliding mode admittance control is proposed. The unknown environment is modeled as a linear model and based on quasi, a sliding mode admittance controller is derived to resist disturbance interference. An optimal control method that combines ADP with sliding mode admittance controller is proposed, in which the definition of R-matrix in value function is optimized and improved to further improve the tracking accuracy. The neural network based on ADP is …