Hasil untuk "Cybernetics"

J. Howard

Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer engineering. The modern field of AI began at a small summer workshop at Dartmouth College in 1956. Since then, AI applications made possible by machine learning (ML), an AI subdiscipline, include Internet searches, e-commerce sites, goods and services recommender systems, image and speech recognition, sensor technologies, robotic devices, and cognitive decision support systems (DSSs). As more applications are integrated into everyday life, AI is predicted to have a globally transformative influence on economic and social structures similar to the effect that other general-purpose technologies, such as steam engines, railroads, electricity, electronics, and the Internet, have had. Novel AI applications in the workplace of the future raise important issues for occupational safety and health. This commentary reviews the origins of AI, use of ML methods, and emerging AI applications embedded in physical objects like sensor technologies, robotic devices, or operationalized in intelligent DSSs. Selected implications on the future of work arising from the use of AI applications, including job displacement from automation and management of human-machine interactions, are also reviewed. Engaging in strategic foresight about AI workplace applications will shift occupational research and practice from a reactive posture to a proactive one. Understanding the possibilities and challenges of AI for the future of work will help mitigate the unfavorable effects of AI on worker safety, health, and well-being.

Helin Cao, Sven Behnke

Autonomous driving perception faces significant challenges due to occlusions and incomplete scene data in the environment. To overcome these issues, the task of semantic occupancy prediction (SOP) is proposed, which aims to jointly infer both the geometry and semantic labels of a scene from images. However, conventional camera-based methods typically treat all categories equally and primarily rely on local features, leading to suboptimal predictions, especially for dynamic foreground objects. To address this, we propose Object-Centric SOP (OC-SOP), a framework that integrates high-level object-centric cues extracted via a detection branch into the semantic occupancy prediction pipeline. This object-centric integration significantly enhances the prediction accuracy for foreground objects and achieves state-of-the-art performance among all categories on SemanticKITTI.

Jeong-Woo Park, Seong-Whan Lee

Composed Image Retrieval (CIR) is the task of retrieving a target image from a gallery using a composed query consisting of a reference image and a modification text. Among various CIR approaches, training-free zero-shot methods based on pre-trained models are cost-effective but still face notable limitations. For example, sequential VLM-LLM pipelines process each modality independently, which often results in information loss and limits cross-modal interaction. In contrast, methods based on multimodal large language models (MLLMs) often focus exclusively on applying changes indicated by the text, without fully utilizing the contextual visual information from the reference image. To address these issues, we propose multi-faceted Chain-of-Thought with re-ranking (MCoT-RE), a training-free zero-shot CIR framework. MCoT-RE utilizes multi-faceted Chain-of-Thought to guide the MLLM to balance explicit modifications and contextual visual cues, generating two distinct captions: one focused on modification and the other integrating comprehensive visual-textual context. The first caption is used to filter candidate images. Subsequently, we combine these two captions and the reference image to perform multi-grained re-ranking. This two-stage approach facilitates precise retrieval by aligning with the textual modification instructions while preserving the visual context of the reference image. Through extensive experiments, MCoT-RE achieves state-of-the-art results among training-free methods, yielding improvements of up to 6.24% in Recall@10 on FashionIQ and 8.58% in Recall@1 on CIRR.

Wei Xiang, Muchen Li, Jie Yan et al.

Level 3 automated driving systems allows drivers to engage in secondary tasks while diminishing their perception of risk. In the event of an emergency necessitating driver intervention, the system will alert the driver with a limited window for reaction and imposing a substantial cognitive burden. To address this challenge, this study employs a Large Language Model (LLM) to assist drivers in maintaining an appropriate attention on road conditions through a "humanized" persuasive advice. Our tool leverages the road conditions encountered by Level 3 systems as triggers, proactively steering driver behavior via both visual and auditory routes. Empirical study indicates that our tool is effective in sustaining driver attention with reduced cognitive load and coordinating secondary tasks with takeover behavior. Our work provides insights into the potential of using LLMs to support drivers during multi-task automated driving.

Thanh Thi Nguyen, Saeid Nahavandi, Imran Razzak et al.

The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with their environments. This survey provides a comprehensive overview of traditional approaches as well as the recent advancements in DRL applied to path planning tasks, focusing on autonomous vehicles, drones, and robotic platforms. Key algorithms across both conventional and learning-based paradigms are categorized, with their innovations and practical implementations highlighted. This is followed by a thorough discussion of their respective strengths and limitations in terms of computational efficiency, scalability, adaptability, and robustness. The survey concludes by identifying key open challenges and outlining promising avenues for future research. Special attention is given to hybrid approaches that integrate DRL with classical planning techniques to leverage the benefits of both learning-based adaptability and deterministic reliability, offering promising directions for robust and resilient autonomous navigation.

Haofeng Wang, Fangtao Zhou, Qi Zhang et al.

RGB-IR(RGB-Infrared) image pairs are frequently applied simultaneously in various applications like intelligent surveillance. However, as the number of modalities increases, the required data storage and transmission costs also double. Therefore, efficient RGB-IR data compression is essential. This work proposes a joint compression framework for RGB-IR image pair. Specifically, to fully utilize cross-modality prior information for accurate context probability modeling within and between modalities, we propose a Channel-wise Cross-modality Entropy Model (CCEM). Among CCEM, a Low-frequency Context Extraction Block (LCEB) and a Low-frequency Context Fusion Block (LCFB) are designed for extracting and aggregating the global low-frequency information from both modalities, which assist the model in predicting entropy parameters more accurately. Experimental results demonstrate that our approach outperforms existing RGB-IR image pair and single-modality compression methods on LLVIP and KAIST datasets. For instance, the proposed framework achieves a 23.1% bit rate saving on LLVIP dataset compared to the state-of-the-art RGB-IR image codec presented at CVPR 2022.

Yihan Sun, Yuqi Cheng, Yunkang Cao et al.

3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detection due to insufficient global information. To address this, we propose Multi-View Reconstruction (MVR), a method that losslessly converts high-resolution point clouds into multi-view images and employs a reconstruction-based anomaly detection framework to enhance global information learning. Extensive experiments demonstrate the effectiveness of MVR, achieving 89.6\% object-wise AU-ROC and 95.7\% point-wise AU-ROC on the Real3D-AD benchmark.

Giuseppe Silano, Daniel Bonilla Licea, Hajar El Hammouti et al.

This paper introduces a Nonlinear Model Predictive Control (NMPC) framework for communication-aware motion planning of Multi-Rotor Aerial Vehicles (MRAVs) using Free-Space Optical (FSO) links. The scenario involves MRAVs equipped with body-fixed optical transmitters and Unmanned Ground Vehicles (UGVs) acting as mobile relays, each outfitted with fixed conical Field-of-View (FoV) receivers. The controller integrates optical connectivity constraints into the NMPC formulation to ensure beam alignment and minimum link quality, while also enabling UGV tracking and obstacle avoidance. The method supports both coplanar and tilted MRAV configurations. MATLAB simulations demonstrate its feasibility and effectiveness.

Dariusz Krawczyk, Alina Yefimenko, Iryna Pozovna et al.

A well-capitalised bank system is a key element for securing macroeconomic stability. By applying a comprehensive approach to managing the capitalisation of banks, policymakers, regulators, and financial institutions can strengthen the resistance of the financial system, reduce system risks, and contribute to macroeconomic stability. The goal of the research is to develop an international functional benchmarking model for managing bank capital in the context of securing macroeconomic stability for 34 European countries with different population income levels from 2010 to 2022 based on World Bank data. The aim is achieved through the implementation of the defined stages of benchmarking modelling. The international functional benchmarking model for bank capital management in the context of macroeconomic stability has been developed by defining the qualitative and quantitative characteristics of the leading countries, chosen based on the corresponding ranking. The three groups of benchmarks are identified: institutional and innovative approaches (based on Swiss and Luxembourg practices), monetary and credit approaches (based on Sweden and Iceland’s practices), and preventive and regulatory approaches (based on Norway and Finland’s practices). The research results can be used in the processes of a bank’s risk management and formation and regulation of capital adequacy by bank management, as well as when developing state socioeconomic and financial policies.

Василь Абрамчук, Ігор Абрамчук

Визначені необхідні умови, за яких рівняння може мати розв’язок у цілих додатних числах. Параметри рівняння  узгоджені з  і належать обмеженим замкненим множинам. Показники степенів і змінні розділені на класи. Доведено, що у просторі змішаних змінних, зв’язаних рівнянням, де одна із змінних дійсна, а інші цілі числа, значення дійсної змінної ірраціональне, що є достатньою умовою нерозв’язності рівняння у цілих додатних числах для всіх показників степенів більших трьох. На кривих Ферма існує лише дві раціональні точки. Побудована матрична (лінійна) модель степенів цілих додатних чисел.  

Tarik Crnovrsanin, Calvin Yu, Dane Hankamer et al.

Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing. Our approach effectively applies a Long-Short Term Memory (LSTM) neural network for the purpose of time series classification. We performed experiments to test the effects of changing the timestamp sampling method and addressing the imbalance in the class distribution. Through these experiments, we identified the top-performing sampling and class imbalance fixing methods. Averaging the macro f-scores across 10 folds of data, we found that the majority quadrotor class was predicted well (98.16%), and, despite an extreme class imbalance, the model could also predicted a majority of fixed-wing flights correctly (73.15%). Hexarotor instances were often misclassified as quadrotors due to the similarity of multirotors in general (42.15%). However, results remained relatively stable across certain methods, which prompted us to analyze and report on their tradeoffs. The supplemental material for this paper, including the code and data for running all the experiments and generating the results tables, is available at https://osf.io/mnsgk/.

Guebum Han, Kanav Khosla, Kieran T. Smith et al.

Abstract Devising an approach to deterministically position organisms can impact various fields such as bioimaging, cybernetics, cryopreservation, and organism‐integrated devices. This requires continuously assessing the locations of randomly distributed organisms to collect and transfer them to target spaces without harm. Here, an aspiration‐assisted adaptive printing system is developed that tracks, harvests, and relocates living and moving organisms on target spaces via a pick‐and‐place mechanism that continuously adapts to updated visual and spatial information about the organisms and target spaces. These adaptive printing strategies successfully positioned a single static organism, multiple organisms in droplets, and a single moving organism on target spaces. Their capabilities are exemplified by printing vitrification‐ready organisms in cryoprotectant droplets, sorting live organisms from dead ones, positioning organisms on curved surfaces, organizing organism‐powered displays, and integrating organisms with materials and devices in customizable shapes. These printing strategies can ultimately lead to autonomous biomanufacturing methods to evaluate and assemble organisms for a variety of single and multi‐organism‐based applications.

Andrey Ivashko , Andrey Zuev , Dmytro Karaman et al.

The purpose of the study is to develop methods for synthesizing a Gaussian filter that ensures simplified hardware and software implementation, particularly filters with powers-of-two coefficients. Such filters can provide effective denoising of images, including landscape maps, both natural and synthetically generated. The study also involves analyzing of methods for FPGA implementation, comparing their hardware complexity, performance, and noise reduction with traditional Gaussian filters. Results. An algorithm for rounding filter coefficients to powers of two, providing optimal approximation of the constructed filter to the original, is presented, along with examples of developed filters. Topics covered include FPGA implementation, based on the Xilinx Artix-7 FPGA. Filter structures, testing methods, simulation results, and verification of the scheme are discussed. Examples of the technological placement of the implemented scheme on the FPGA chip are provided. Comparative evaluations of FPGA resources and performance for proposed and traditional Gaussian filters are carried out. Digital modeling of the filters and noise reduction estimates for noisy images of the terrain surface are presented. The developed algorithm provides approximation of Gaussian filter coefficients as powers of two for a given window size and maximum number of bits with a relative error of no more than 0.18. Implementing the proposed filters on FPGA results in a hardware costs reduction with comparable performance. Computer simulation show that Gaussian filters both traditional and proposed effectively suppress additive white noise in images. Proposed filters improve the signal-to-noise ratio within 5-10 dB and practically match the filtering quality of traditional Gaussian filters.

Marco Faroni, Alessandro Umbrico, Manuel Beschi et al.

Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

Evsey V. Morozov, Irina V. Peshkova, Alexander S. Rumyantsev

In this research, a single-server <i>M</i>-class retrial queueing system (orbit queue) with constant retrial rates and Poisson inputs is considered. The main purpose is to construct the upper and lower bounds of the stationary workload in this system expressed via the stationary workloads in the classical <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mo>/</mo><mi>G</mi><mo>/</mo><mn>1</mn></mrow></semantics></math></inline-formula> systems where the service time has <i>M</i>-component mixture distributions. This analysis is applied to establish the extreme behaviour of stationary workload in the retrial system with Pareto service-time distributions for all classes.