Hasil untuk "Systems engineering"

Y. Pei, Heng Wang, G. J. Snyder

S. Vlierberghe, P. Dubruel, E. Schacht

M. Wooldridge

R. Haug

F. Hayes-Roth

J. Bronzino

Physiological systems bioelectric phenomena biomechanics biomaterials biosensors biomedical signal analysis imaging medical instruments and devices biological effects of non-ionizing biotechnology tissue engineering human performance engineering physiological modelling, simulation and control clinical engineering and artificial intelligence. (Part contents).

Drew Endy

N. Engheta, R. Ziolkowski

F. Zambonelli, N. Jennings, M. Wooldridge

J. Larminie, A. Dicks

J. Barth, G. Costantini, K. Kern

I. Portugal, P. Alencar, D. Cowan

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.

J. Roncali

Sanket A. Salunkhe, George P. Kontoudis

Multi-robot systems require scalable and federated methods to model complex environments under computational and communication constraints. Gaussian Processes (GPs) offer robust probabilistic modeling, but suffer from cubic computational complexity, limiting their applicability in large-scale deployments. To address this challenge, we introduce the pxpGP, a novel distributed GP framework tailored for both centralized and decentralized large-scale multi-robot networks. Our approach leverages sparse variational inference to generate a local compact pseudo-representation. We introduce a sparse variational optimization scheme that bounds local pseudo-datasets and formulate a global scaled proximal-inexact consensus alternating direction method of multipliers (ADMM) with adaptive parameter updates and warm-start initialization. Experiments on synthetic and real-world datasets demonstrate that pxpGP and its decentralized variant, dec-pxpGP, outperform existing distributed GP methods in hyperparameter estimation and prediction accuracy, particularly in large-scale networks.

D. Buede

Bao-ping Cai, M. Xie, Yonghong Liu et al.

Several resilience metrics have been proposed for engineering systems (e.g., mechanical engineering, civil engineering, critical infrastructure, etc.); however, they are different from one another. Their difference is determined by the performances of the objects of evaluation. This study proposes a new availability-based engineering resilience metric from the perspective of reliability engineering. Resilience is considered an intrinsic ability and an inherent attribute of an engineering system. Engineering system structure and maintenance resources are principal factors that affect resilience, which are integrated into the engineering resilience metric. A corresponding dynamic-Bayesian-network-based evaluation methodology is developed on the basis of the proposed resilience metric. The resilience value of an engineering system can be predicted using the proposed methodology, which provides implementation guidance for engineering planning, design, operation, construction, and management. Some examples for common systems (i.e., series, parallel, and voting systems) and an actual application example (i.e., a nine-bus power grid system) are used to demonstrate the application of the proposed resilience metric and its corresponding evaluation methodology.

Qi Zhang

To solve the problem of lack of science and poor reusability of experience in traditional engineering scheme decision-making, which leads to the increase of time and cost of pre-decision-making, the study first uses case-based reasoning and ontology to construct a solution library for engineering solution decision-making system, and standardizes the cases using methods such as eigenfrequency. In addition, a retrieval mechanism based on residual similarity is designed to achieve effective retrieval of similar cases. The experiment outcomes denoted that the resource utilization rate of the traditional scheme was 75% before implementation, but decreased to 72% after implementation, a decrease of 3%. The resource utilization rate of the decision-making system scheme was 75% before implementation, and increased to 80% after implementation, an increase of 5%. The results indicated that the decision system scheme designed by the research performed better in terms of resource utilization, could more efficiently utilize resources, and reduce waste. The average decision accuracy of integrating CBR and BIM systems was 92%, significantly higher than the 84% of traditional decision systems. The CBR technology improved the scientificity and reliability of decision-making through continuous updating and optimization of the case library.

Jun Sung Seo, Mungyeong Song, Hee Soon Choi et al.

Abstract Constitutive promoters such as CaMV 35S and ubiquitin are commonly utilized in crop genome editing. However, their ectopic overexpression patterns may lead to off-target effects. To address this limitation, tissue-specific or developmentally regulated promoters offer promising alternatives. The RIBOSOMAL PROTEIN S5A (RPS5A) promoter has demonstrated superior editing efficiency compared to the 35S and ubiquitin promoters in dicotyledonous species, yet its potential application in monocots remains unexplored. In this study, we identified and functionally characterized the Oryza sativa RPS5 (OsRPS5) promoters and evaluated their utility in CRISPR/Cas9-mediated genome editing. The activities of the OsRPS5 promoters were assessed through GFP reporter expression in rice protoplasts, and their genome editing capability was validated by targeting two endogenous genes, OsPDS and OsBADH2. Genome editing driven by the OsRPS5 promoter targeting OsPDS resulted in albino phenotypes in approximately 50% of the transgenic lines, with insertion/deletion mutations confirmed through sequencing analysis. Notably, the genome editing efficiency driven by the OsRPS5 promoter was comparable to that of the widely used constitutive promoters in monocots. These findings suggest the OsRPS5 promoter as a potentially more precise and efficient alternative to constitutive promoters for genome editing applications in monocot crops.

Soham Ghosh, Gaurav Mittal

Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate this new paradigm. Our paper addresses this gap by providing a comprehensive review that establishes a precise definition and taxonomy for "agentic AI," with the aim of distinguishing it from previous AI paradigms. The concepts are gradually introduced, starting with a highlight of its diverse applications across the broader field of engineering. The paper then presents four detailed, state-of-the-art use case applications specifically within electrical engineering. These case studies demonstrate practical impact, ranging from an advanced agentic framework for streamlining complex power system studies and benchmarking to a novel system developed for survival analysis of dynamic pricing strategies in battery swapping stations. Finally, to ensure robust deployment, the paper provides detailed failure mode investigations. From these findings, we derive actionable recommendations for the design and implementation of safe, reliable, and accountable agentic AI systems, offering a critical resource for researchers and practitioners.