Open Access. Powered by Scholars. Published by Universities.®

Artificial Intelligence and Robotics Commons™

Open Access. Powered by Scholars. Published by Universities.®

2025

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 301 - 330 of 1405

Full-Text Articles in Artificial Intelligence and Robotics

Lane Detection In Dark Light Based On Instance Association, Yanji Jiang, Yingyang Zhang, Hao Dong, Xiaoguang Zhang, Meihui Wang Sep 2025

Lane Detection In Dark Light Based On Instance Association, Yanji Jiang, Yingyang Zhang, Hao Dong, Xiaoguang Zhang, Meihui Wang

Journal of System Simulation

Abstract: In current research on lane detection, existing algorithms can efficiently detect lane lines under good lighting conditions. However, lane detection in low light still faces the challenge of a high false negative rate. A detection algorithm called Instance Association Net(IANet) is proposed to address this issue by utilizing the structural relationships between lane lines, which is helpful for low light conditions. The algorithm first generates unique masks for different lane lines using features at the starting points of the lane lines and a global feature map, achieving instance-level feature separation of the lane lines. It employs an instance-level attention …

Go to article

Anylogic-Based Platform-Enterprise Collaborative Scheduling Simulation System For Cloud Manufacturing, Linxuan Wang, Yongkui Liu, Lin Zhang, Tingyu Lin, Lihui Wang Sep 2025

Anylogic-Based Platform-Enterprise Collaborative Scheduling Simulation System For Cloud Manufacturing, Linxuan Wang, Yongkui Liu, Lin Zhang, Tingyu Lin, Lihui Wang

Journal of System Simulation

Abstract: Aiming at the lack of research on collaborative scheduling between a cloud manufacturing platform and associated enterprises, as well as the lack of simulation systems to simulate scheduling strategy combinations and to visualize dynamic scheduling processes, a simulation system that supports visualization of cloud manufacturing platform-enterprise collaborative dynamic scheduling processes is designed and developed. System requirements are analyzed in detail, and then a scalable platform-enterprise collaborative scheduling model and system functional architecture based on hierarchical multi-agents is proposed. Combined with a case of supply chain of industrial robots, considering random selection, time optimal strategy in the cloud manufacturing …

Go to article

Digital Twin Modeling Method For Bulk Cargo Stacks Based On 2d Lidar, Houjun Lu, Yifei Zhu, Yanping Rong, Wanghui Zhang Sep 2025

Digital Twin Modeling Method For Bulk Cargo Stacks Based On 2d Lidar, Houjun Lu, Yifei Zhu, Yanping Rong, Wanghui Zhang

Journal of System Simulation

Abstract: Due to the characteristics of large equipment, harsh working environment and time-varying shape of the material pile in bulk cargo terminal, there are some disadvantages such as low data accuracy and poor stability when building the storage yard model, which affects the unmanned and intelligent operation control. In this paper, we use two-dimensional laser radar combined with equipment mechanism motion to scan material pile point cloud data, present a digital twin modeling method for bulk storage yard, which includes static scene construction of storage yard and real-time modeling of material pile. Prefabricated models are used for the static scenes …

Go to article

Digital Imaging Simulation Of Complex Scene Of Space-Based Space Small Target, Pengfei Li, Wei Xu, Yongjie Piao, Yinghong Fang, Dunpan Shi Sep 2025

Digital Imaging Simulation Of Complex Scene Of Space-Based Space Small Target, Pengfei Li, Wei Xu, Yongjie Piao, Yinghong Fang, Dunpan Shi

Journal of System Simulation

Abstract: In response to the universal demand for space target detection technology research in space image data sources, this study focuses on the problems of insufficient training data for intelligent algorithms and the use of single data for traditional algorithms, with the goal of generating dynamic digital sequence images of small space targets in complex scenes. A visible light digital imaging simulation system based on a space observation platform is designed. A small target imaging model is proposed, which is based on two-dimensional shape feature point description and imaging analysis model to carry out digital modeling and imaging simulation of …

Go to article

Control Strategy For Uav Cluster Formation Rendezvous Based On Lde-Maddpg Algorithm, Wei Xiao, Jiabo Gao, Xueliang Ke Sep 2025

Control Strategy For Uav Cluster Formation Rendezvous Based On Lde-Maddpg Algorithm, Wei Xiao, Jiabo Gao, Xueliang Ke

Journal of System Simulation

Abstract: To solve the problem of difficulty in UAV cluster formation rendezvous based on MADDPG algorithm, an autonomous collaborative control strategy based on LDE-MADDPG algorithm is proposed. To address the issues of weak generalization, poor scalability, and slow cluster training process of MADDPG algorithm, LDE-MADDPG algorithm was proposed by designing a state feature learning network and a decoupled Critical network. By integrating LDE-MADDPG algorithm with strategy generation elements such as the decoupled reward function, cluster state space, and UAV action space, a control strategy for UAV cluster formation endezvous that can adapt to diverse formations and varying quantities has been …

Go to article

Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang Sep 2025

Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang

Journal of System Simulation

Abstract: An improved A-DDQN algorithm is proposed to address the challenges of reward sparsity and the inability to distinguish sample importance in traditional DQN algorithms during robot path planning. Building on the original DQN, an enhancement is made by incorporating the Double-DQN approach, which updates the predictive Q-value network based on actions selected by the Q network, rather than directly using the predicted Q-values for action selection, thereby mitigating overestimation issues. Secondly, the concept of artificial potential field (APF) is introduced to design specific rewards for each step of the robot's movement, guiding the robot and addressing the problem of …

Go to article

Research On Real-Time Cgf Maneuvering State Generation Method Based On Random Finite Set, Xiaoyan Zhang, Ge Li, Peng Wang Sep 2025

Research On Real-Time Cgf Maneuvering State Generation Method Based On Random Finite Set, Xiaoyan Zhang, Ge Li, Peng Wang

Journal of System Simulation

Abstract: With the rapid development of sensor networks and other technologies, the acquisition of measurement data in the real physical space has become easier. How to utilize the measurement data from the real battlefield space to improve the accuracy and credibility of CGF simulation is the key issue to realize the CGF simulation combining virtual and real. The method is studied of using real measurement data to generate CGF model maneuvering state data in real time, in order to realize the virtual-real synchronization and real-time mapping between the real battlefield and CGF simulation system, and to provide environmental inputs for …

Go to article

Benefit Distribution Optimization Model And Simulation For Multi-Mode Operation Of Industrial Software Platforms, Rongyu Guo, Xiaobin Li, Pei Jiang, Chuanjiang Li, Shanhui Liu, Jun Ma Sep 2025

Benefit Distribution Optimization Model And Simulation For Multi-Mode Operation Of Industrial Software Platforms, Rongyu Guo, Xiaobin Li, Pei Jiang, Chuanjiang Li, Shanhui Liu, Jun Ma

Journal of System Simulation

Abstract: Industrial software service platforms, characterized by low-cost investment, customized services, and rapid application deployment, have been widely adopted in small and medium-sized industrial clusters. The benefit distribution mechanism under multi-mode operation is crucial to the sustainable development of such platforms. To address the current challenges of single-operation models and the difficulty in adapting to diverse service scenarios, this study focuses on two core stakeholders that users and software developers to analyze the core service components and cooperation mechanisms of industrial software service platforms in a multi-mode operational environment. By integrating the function point method, a multi-mode user demand quantification …

Go to article

Second-Order Cone Optimization Modeling And Simulation For Three-Phase Unbalanced Active Distribution Networks, Yiran Zhao, Yong Xue, Haoxin Tian, Ruixin Zhang, Zhi Zhang, Yanbo Chen Sep 2025

Second-Order Cone Optimization Modeling And Simulation For Three-Phase Unbalanced Active Distribution Networks, Yiran Zhao, Yong Xue, Haoxin Tian, Ruixin Zhang, Zhi Zhang, Yanbo Chen

Journal of System Simulation

Abstract: Guided by the carbon peaking and carbon neutrality goals, and propelled by the development of new type power systems, the significance of distribution networks as key energy infrastructure has been increasingly underscored. Amidst the burgeoning rise of distributed photovoltaics, electric vehicles, and novel energy storage technologies, distribution networks are transitioning from passive entities to active systems capable of bidirectional interaction, heralding the advent of active distribution networks with a critical mission. This research tackles the optimal power flow issue in three-phase unbalanced active distribution networks, incorporating inter-phase coupling relationships. By employing dimensionality lifting and rank relaxation, along with the …

Go to article

Station Layout Optimization Method And Simulation For Non-Cooperative Target In Angle Of Arrival Positioning, Yida Ning, Jiongqi Wang, Juhui Wei, Zhenzu Bai, Zhangming He Sep 2025

Station Layout Optimization Method And Simulation For Non-Cooperative Target In Angle Of Arrival Positioning, Yida Ning, Jiongqi Wang, Juhui Wei, Zhenzu Bai, Zhangming He

Journal of System Simulation

Abstract: In the context of angle of arrival (AOA) positioning system for non-cooperative target tracking and positioning, accurately determining true location of the target poses a significant challenge. Conventional station deployment indicators like geometric dilution of precision (GDOP) fail to provide effective guidance for optimization station layout. To address the issue, this study introduces a novel indicator for station optimization and evaluation based on factors that influence positioning accuracy within an angle measurement system. These factors encompass angular differencing, baseline intersection angles, and the observer-target line distance. Moreover, this indicator encompasses the challenges associated with data conformity in "air to …

Go to article

Research On Strong Real-Time Synchronisation Algorithm For Lvc Co-Simulation, Junhui Li, Songtao Sun, Fei Liu Sep 2025

Research On Strong Real-Time Synchronisation Algorithm For Lvc Co-Simulation, Junhui Li, Songtao Sun, Fei Liu

Journal of System Simulation

Abstract: Live, virtual, and constructive(LVC) joint simulation has become a hot research topic of current military simulation; however, existing time management strategies usually fail to meet the needs of strict real-time performance of LVC. A LVC joint simulation synchronization algorithm is proposed that starts with a window sliding-based median smoothing strategy and real time drift rate-based clock compensation strategy for effective node synchronization. A novel hybrid timing strategy is introduced combining long and short cycles implemented in software, which balances precision and efficiency. A simulation catch-up strategy is proposed to address software delays, which combined with the highprecision timing strategy, …

Go to article

Algorithm Simulation Of Multi-Targets Track Correlation Based On Spectral Feature, Zhenping Ding, Huidong Guo Sep 2025

Algorithm Simulation Of Multi-Targets Track Correlation Based On Spectral Feature, Zhenping Ding, Huidong Guo

Journal of System Simulation

Abstract: In order to solve the problems of multi-targets track correlation in dense scenes, a method of track sequential real-time processing for multi-source track correlation system modeling is proposed. By calculating the absolute and relative position of the spectral features, the unified correlation matrix can be defined based on fuzzy decision theory, and the multi-target track correlation can be realized. Numerical simulations have shown the effectiveness of the track correlation algorithm on the basis of spectral features. Especially, the accuracy of correlation is much larger than that of the nearest-neighbor distance algorithm under the condition of dense target environment …

Go to article

Unveiling The Interplay Of Electronic And Phononic Excitations In Laser-Induced Oxygen Activation On Ru(0001), Xiangrui Wang, Jiamin Wang, Paul Spiering, Liping Liu, Jörg Meyer, Jerry L. Larue, Hongliang Xin Sep 2025

Unveiling The Interplay Of Electronic And Phononic Excitations In Laser-Induced Oxygen Activation On Ru(0001), Xiangrui Wang, Jiamin Wang, Paul Spiering, Liping Liu, Jörg Meyer, Jerry L. Larue, Hongliang Xin

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding laser-induced dynamics on metal surfaces poses significant challenges due to the intricate interplay between electronic and phononic degrees of freedom, which evolve on distinct timescales. In this study, we introduce a machine learning-accelerated approach to molecular dynamics simulations that incorporates anisotropic electronic friction, providing deeper insights into these complex processes. Our framework extends the accessible time and length scales for nonadiabatic dynamics simulations, enabling a detailed investigation of the laser-induced activation of oxygen on the Ru(0001) surface. Statistical analysis reveals that strong electronic excitation dominates the first 800 fs after laser exposure. Beyond this timescale, energy deposited by electronic …

Go to article

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara Sep 2025

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

School of Medicine Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Go to article

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman Sep 2025

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

Go to article

Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues Sep 2025

Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues

School of Medicine Faculty Publications

The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RAS gene functions and elucidate their roles in biological pathways. A total of 39,463 RAS-related publications from PubMed and PMC were processed into text format. Feature matrices were generated using TF-IDF and token processing, followed by dimensionality reduction via Principal Component Analysis (PCA). A Multi-Layer Perceptron (MLP) was applied for multi-label classification, with performance evaluated using Precision, F1-Score, Ranking Loss, and ROC-AUC metrics. The model outperformed …

Go to article

Utilizing Generative Ai To Counter Learner Groupthink By Introducing Controversy In Collaborative Problem-Based Learning Settings, Andrew Wiss, Mary Showstark, Kyle Dobbeck, Jennifer Pattershall-Geide, Elke Zschaebitz, Dawn Joosten-Hagye, Kirsten Potter, Erin Embry Sep 2025

Utilizing Generative Ai To Counter Learner Groupthink By Introducing Controversy In Collaborative Problem-Based Learning Settings, Andrew Wiss, Mary Showstark, Kyle Dobbeck, Jennifer Pattershall-Geide, Elke Zschaebitz, Dawn Joosten-Hagye, Kirsten Potter, Erin Embry

Montclair State University Scholarship & Creative Works

This article highlights the foundational challenge of rapid interprofessional student team formation and the potential challenges that groupthink poses for newly-formed teams participating in collaborative problem-based learning activities. This article describes a mixed-methods study that addresses groupthink by introducing a generative artificial intelligence-based agent (genAI agent) into the small group processes of student teams engaging in a session of a well-established virtual interprofessional education methodology. The integration of this novel genAI tool into each student team was an intentional pedagogical technique, introduced in response to the challenges that newly-formed student teams may encounter as they rapidly come together and potentially …

Go to article

The God Prompt And Deus Ex Machina: Techno-Theological Tropes And Operational Metaphors In Generative Media, James Hutson Sep 2025

The God Prompt And Deus Ex Machina: Techno-Theological Tropes And Operational Metaphors In Generative Media, James Hutson

Faculty Scholarship

This study reframes two durable tropes—the ―God Prompt‖ and the deus ex machina—as analytic lenses for understanding how contemporary generative systems stage beginnings and endings of cultural production. The ―God Prompt‖ denotes command-driven synthesis in which minimal textual instructions instantiate content on demand, crystallizing a production loop of input, model execution, and post hoc evaluation that orients anticipation toward instantaneous yield and controllable variation. The deus ex machina names an externally imposed resolution that interrupts causal development—historically a crane-borne god, functionally an algorithmic override—thereby concentrating attention on closure mechanics rather than world-building continuity. Read together, the pair offers a compact …

Go to article

Exact And Approximate Conformal Inference For Multi-Output Regression, Chancellor Johnstone, Eugene Ndiaye Sep 2025

Exact And Approximate Conformal Inference For Multi-Output Regression, Chancellor Johnstone, Eugene Ndiaye

Faculty Publications

It is common in machine learning to estimate a response y given covariate information x . However, these predictions alone do not quantify any uncertainty associated with said predictions. One way to overcome this deficiency is with conformal inference methods, which construct a set containing the unobserved response with a prescribed probability. Unfortunately, even with a one-dimensional response, conformal inference is computationally expensive despite recent encouraging advances. In this paper, we explore multi-output regression, delivering exact derivations of conformal inference p-values when the predictive model can be described as a linear function of y . Additionally, we introduce a multivariate …

Go to article

Futurescape Libraries Ai Toolkit, Keith Webster Sep 2025

Futurescape Libraries Ai Toolkit, Keith Webster

Copyright, Fair Use, Scholarly Communication, etc.

A toolkit developed to explore scenario-specific strategies and activities that research libraries can undertake to prepare for various possible AI-influenced futures. The toolkit integrates the ARL/CNI AI Scenarios published in spring 2024 along with priorities trialed and refined by strategic thinkers working directly in, or adjacent to, the research library field during a Strategic Implications forum held December 7–8, 2024, in Washington, DC.

Go to article

Applying Artificial Intelligence And Chatbots To Enhance Collection Development In Health Sciences Libraries, Ivan Portillo, David Carson Sep 2025

Applying Artificial Intelligence And Chatbots To Enhance Collection Development In Health Sciences Libraries, Ivan Portillo, David Carson

Library Presentations, Posters, and Audiovisual Materials

The continuous advancement of artificial intelligence (AI) and large language models (LLMs) has presented several opportunities for librarians to reduce their workload and become more efficient. This session will explore the potential of generative AI chatbots in assisting health sciences librarians with collection development. Two methods that will be discussed include the potential of AI to help discover new titles and how AI can evaluate your library collection for any potential gaps based on a college program’s curriculum.

Go to article

Multi-Modal Depth Estimation Using Camera And Mmwave Sensors, Alston D. Devero-Belfon Sep 2025

Multi-Modal Depth Estimation Using Camera And Mmwave Sensors, Alston D. Devero-Belfon

Student Theses

For accurately estimating the depth of environments with varying lighting conditions, reliable methods are limited. By utilizing wireless sensor technology in conjunction with cameras, a wide range of environments can be visualized, and objects within these environments can be tracked and monitored. Such methods offer cost-effective alternatives and provide a more secure, data-at-rest option for individuals with low vision, while also enhancing machine perception. In this work, we develop such a prototype that utilizes wireless sensors and cameras, which act in sync, enabling us to estimate the depth of objects within varying lighting environments to a level that is recognizable …

Go to article

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu Sep 2025

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Go to article

The “Founder’S Gaze”: How The Fourth Amendment Is A Surveillance Technology That Enables Ai To Scale Control Over The Subaltern, Diego H. Alcalá Laboy Sep 2025

The “Founder’S Gaze”: How The Fourth Amendment Is A Surveillance Technology That Enables Ai To Scale Control Over The Subaltern, Diego H. Alcalá Laboy

Michigan Journal of Race and Law

Much has been written about the rise of artificial intelligence and machine learning applications and how the current Fourth Amendment law has been unable to mitigate the privacy harm that these tools produce. This article explores how the development and usage of AI and machine learning models is dependent on the originalism principles of Fourth Amendment Law. Utilizing Critical Surveillance Studies and Anticolonial Theory, I posit that the Fourth Amendment is a surveillance technology that categorizes conduct, persons, and places to impose the material conditions for the subjugation of historically minoritized communities within the United States. Furthermore, this article explores …

Go to article

Reimagining Academic Assessment In The Age Of Ai, Matthew Hammerton Sep 2025

Reimagining Academic Assessment In The Age Of Ai, Matthew Hammerton

Research Collection School of Social Sciences

In ‘Reimagining Academic Assessment in the Age of AI’, Matthew Hammerton examines the challenges and opportunities posed by generative AI for higher education assessment. He critiques common responses like banning AI, reverting to in-class exams, or abandoning essays altogether, arguing that they fail to preserve the deeper pedagogical goals of higher order, independent thinking. Instead, Hammerton proposes a guiding principle of intellectual responsibility: students should be accountable for explaining and defending each major choice in their work—regardless of whether they use AI tools. To operationalise this, he advocates for reintegrating oral examinations (‘vivas’) alongside written essays. In this model, students …

Go to article

Learning Orientation Field For Osm-Guided Autonomous Navigation, Yuming Huang, Wei Gao, Zhiyuan Zhang, Maani Ghaffari, Dezhen Song, Cheng-Zhong Xu, Hui Kong Sep 2025

Learning Orientation Field For Osm-Guided Autonomous Navigation, Yuming Huang, Wei Gao, Zhiyuan Zhang, Maani Ghaffari, Dezhen Song, Cheng-Zhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, existing methods often struggle with noisy OSM data and incomplete sensor observations, leading to inaccuracies in trajectory planning. These challenges are particularly evident in complex driving scenarios, such as at intersections or facing occlusions. To address these challenges, we propose a robust and explainable two-stage framework to learn an Orientation Field (OrField) for robot navigation by integrating LiDAR scans and OSM routes. In the first stage, we introduce a novel representation, OrField, which can provide orientations for each …

Go to article

Recurrent Autoregressive Linear Model For Next-Basket Recommendation, Tereza Zmeskalova, Antoine Ledent, Martin Spisak, Pavel Kordik, Rodrigo Alves Sep 2025

Recurrent Autoregressive Linear Model For Next-Basket Recommendation, Tereza Zmeskalova, Antoine Ledent, Martin Spisak, Pavel Kordik, Rodrigo Alves

Research Collection School Of Computing and Information Systems

Next-basket recommendation aims to predict the (sets of) items that a user is most likely to purchase during their next visit, capturing both short-term sequential patterns and long-term user preferences. However, effectively modeling these dynamics remains a challenge for traditional methods, which often struggle with interpretability and computational efficiency, particularly when dealing with intricate temporal dependencies and inter-item relationships. In this paper, we propose ReALM, a Recurrent Autoregressive Linear Model that explicitly captures temporal item-to-item dependencies across multiple time steps. By leveraging a recurrent loss function and a closed-form optimization solution, our approach offers both interpretability and scalability while maintaining …

Go to article

Studying Satd In Drone Systems With Human-Ai Collaboration, Leevi Rantala, Lwin Khin Shar, Mäntylä Mika V., Wei Minn, Naing Tun Yan Sep 2025

Studying Satd In Drone Systems With Human-Ai Collaboration, Leevi Rantala, Lwin Khin Shar, Mäntylä Mika V., Wei Minn, Naing Tun Yan

Research Collection School Of Computing and Information Systems

Background: Self-Admitted Technical Debt (SATD) refers to sub-optimal solutions that developers acknowledge within the source code. SATD research originated on Java projects but is expanding to other domains. We focus on SATD in drones, which are used for various critical tasks.Aims: The primary objective is to investigate SATD in drone systems. The second aim is to explore the integration of AI and human collaboration for SATD labelling and classification.Method: We conducted a sample study of SATD comments in drone systems (14 open source, 4 SDKs) to analyse the quantity and types of SATD comments present. Our study incorporates collaboration between …

Go to article

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu Sep 2025

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu

Research Collection School Of Computing and Information Systems

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust …

Go to article

Stable And Fair Cost Allocation In Platform-Enabled Lcl Consolidation, Pang Jin Tan, Shih-Fen Cheng Sep 2025

Stable And Fair Cost Allocation In Platform-Enabled Lcl Consolidation, Pang Jin Tan, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Many logistics platforms enable collaboration between agents to reduce costs, but determining fair pricing remains challenging when agents have pre-existing partnerships. This paper introduces a cooperative game theory framework to model platform-mediated collaboration, modeling the platform as an additional player. We present a novel characteristic function that distinguishes between partial collaborations (existing relationships) and full collaborations (platform-enabled). Using Shapley value, we derive fair cost allocations and platform charges that reflect each participant's contribution. We address stability concerns through an optimization model that minimizes platform subsidies while preventing profitable deviations. The framework is demonstrated through an application in freight forwarding for …

Go to article

First Prev Next Last