This paper proposes an immersion and invariance (I&I) fuzzy adaptive image-based visual servoing (IBVS) method for an omnidirectional mobile robot (OMR), ensuring robustness against the uncertainties in feature po...
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This paper proposes an immersion and invariance (I&I) fuzzy adaptive image-based visual servoing (IBVS) method for an omnidirectional mobile robot (OMR), ensuring robustness against the uncertainties in feature point dynamics. Most existing IBVS studies for OMR systems have only addressed the kinematics of OMRs, and studies that consider the dynamics of OMRs have only been conducted recently. In particular, the practical uncertainties caused by dynamic uncertainty, interaction matrix uncertainty, and target motion have not been considered in previous studies. In this paper, feature point dynamics of OMRs are modeled by including all of the dynamic uncertainty, interaction matrix uncertainty, and target motion. To approximate and compensate for these uncertainties, fuzzy logic systems (FLS) with two update laws—the general adaptive law and I&I law—are proposed. Notably, the proposed I&I law eliminates the limitations of general adaptive methods, improving transient response owing to its noncertainty-equivalent adaptive structure. The proposed methods are designed by based on the FLS, I&I law, and integral sliding mode control all together to compensate for the practical uncertainties in feature point dynamics, guaranteeing global asymptotic stability unlike the existing studies. The stability and feasibility of the proposed methods are verified via the Lyapunov stability analysis, simulations, and experiments in an environment with uncertain feature point dynamics. IEEE
With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can b...
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With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.
Urban Air Mobility(UAM)is an emerging aviation sector which the goal is to transform air transportation with safe,on-demand air travel for both passengers and *** flight planning strategically separates flows of aircr...
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Urban Air Mobility(UAM)is an emerging aviation sector which the goal is to transform air transportation with safe,on-demand air travel for both passengers and *** flight planning strategically separates flows of aircraft on intersecting routes vertically by allocating distinct flight levels to them,and aircraft are required to maintain the flight level when crossing the ***,there is a possibility that an aircraft may fail to maintain the assigned flight level,leading to a potential conflict at *** paper aims to address conflicts at intersections in the context of UAM,focusing on decentralized conflict detection and resolution.A novel approach is developed to facilitate information exchange among UAM components,including the provider of services to UAM,UAM operators,and the pilot in command.A receding horizon trajectory planning approach is proposed for the execution of conflict resolution,optimizing trajectory planning by eliminating potential problems and challenges associated with geometric *** proposed trajectory planner considers the model and constraints of UAM aircraft,offering optimal solutions for safe separation at UAM airspace *** significance of the proposed planning framework is demonstrated through simulations considering conflict at intersections by communicating the UAM components through request and replay services and generating resolution maneuvers on-the-fly for each aircraft involved in the conflict.
As renewable energy integration increases, ensuring stability of Inverter-Based Resources (IBRs) in weak grids is crucial, as grid-following (GFL) converters often become unstable under such conditions. Integrating vi...
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During the last 35 years, the community devoted to the design and fabrication of solid-state circuits has benefited from the outstanding scientific contributions of Prof. Michiel Steyaert (Figure 1). The finding of in...
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Designing and/or controlling complex systems in science and engineering relies on appropriate mathematical modeling of systems dynamics. Classical differential equation-based solutions in applied/computational mathema...
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Designing and/or controlling complex systems in science and engineering relies on appropriate mathematical modeling of systems dynamics. Classical differential equation-based solutions in applied/computational mathematics are often computationally demanding. Recently, the connection between reducedordermodels of high-dimensional differential equation systems and surrogate machine learning models has been explored. However, the focus of both existing reduced-order and machine learning models has been how to best approximate the high-fidelitymodel of choice. Due to high complexity and often limited training data, it is critical for the models to have reliable uncertainty quantification. In this paper, we propose such a novel framework of Bayesian reduced-order models naturally equipped with uncertainty quantification as it learns the distributions of the parameters of the reduced-order models instead of their point estimates. Specifically, we develop learnable Bayesian proper orthogonal decomposition (BayPOD) that learns the distributions of both the POD projection bases and the mapping from the system input parameters to the projected scores/coefficients so that the learned BayPOD can help predict high-dimensional systems dynamics/fields with reliable uncertainty estimates. The developed BayPOD inherits the capability of embedding physics constraints when learning the POD-based surrogate models, a desirable feature when studying complex systems with limited available data. Furthermore, the proposed BayPOD is an end-To-end solution, which unlike other surrogate-based methods, does not require separate POD and machine learning steps. The results from a real-world case study of the pressure field around an airfoil shows the potential of learnable BayPOD as a new family of reduced-order models with reliable uncertainty estimates. Impact Statement-Surrogatemachine learning models for complex engineering and science systems have gained popularity with the advancement of ma
The design and implementation of sensing and power mechanisms in microdevices for biomedical applications present significant challenges. In this context, we introduce a new three-layer bi-metallic metamaterial absorb...
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Federated learning (FL) has been an area of active research in recent years. There have been numerous studies in FL to make it more successful in the presence of data heterogeneity. However, despite the existence of m...
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Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, this such assumption may fail...
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Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, this such assumption may fail in different weather conditions. Due to the domain gap, a detection model trained under clear weather may not perform well in foggy and rainy conditions. Overcoming detection bottlenecks in foggy and rainy weather is a real challenge for autonomous vehicles deployed in the wild. To bridge the domain gap and improve the performance of object detection in foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection. The adaptations at both the image-level and objectlevel are intended to minimize the differences in image style and object appearance between domains. Furthermore, in order to improve the model's performance on challenging examples, we introduce a novel adversarial gradient reversal layer that conducts adversarial mining on difficult instances in addition to domain adaptation. Additionally, we suggest generating an auxiliary domain through data augmentation to enforce a new domain-level metric regularization. Experimental findings on public V2V benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios IEEE
Various mobile devices and applications are now used in daily *** devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile...
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Various mobile devices and applications are now used in daily *** devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile ***-security cryptography guarantees that essential data can be transmitted securely;however,it increases energy consumption and reduces data processing ***,this study proposes a low-energy data encryption(LEDE)algorithm based on the Advanced Encryption Standard(AES)for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things(IoT)*** the proposed LEDE algorithm,the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of *** indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES *** reduction in encryption time results in the LEDE algorithm having a 67.4%lower energy consumption and 43.9%shorter encryption time than conventional AES;thus,the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices.
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