Search

Dynamic isotropy is a condition where eigenfrequencies of a robot are equal, which could be equivalent to the maximization of the lowest eigenfrequency. Accordingly, dynamic isotropy can be considered as an effective criterion to optimize dynamic performance of a robot. In this paper, we firstly present the mathematical conditions in order to obtain dynamic isotropy in hexapods. These conditions are presented for two cases (with and without considering the strut inertia). Then, it is proven that complete dynamic isotropy is physically impossible to achieve in hexapods where the platform is a single (rigid) body, but a semi-complete dynamic isotropy is feasible. It is also analytically proven that the dynamic isotropy leads to the maximization of the lowest eigenfrequency, even for the semi-complete dynamic isotropy. In a generalized approach, to obtain isotropy or near-isotropy solutions, we have established an analytical tool named “analytical index of dynamic isotropy” in order to directly obtain solutions as close as possible to isotropy. The developed method can be applied to all forms of isotropy and is not limited to dynamic isotropy in hexapods. This work is a continuation of the PhD thesis by the first author. [ABSTRACT FROM AUTHOR]

The principle of kinematic design has a wide range of applications e.g. from optical mirror mounts to parallel robots. Despite the importance of dynamic isotropy in the optimization of dynamic performance, a thorough analysis of dynamic isotropy in different kinematic arrangements has not yet been addressed in the literature. Dynamic isotropy, leading to equal eigenfrequencies, is a powerful optimization measure. In this paper, we present fully-parametric solutions for obtaining dynamic isotropy in general 3D platforms kinematically constrained by three elastic nodal joints in 6 DOFs. It is analytically shown that there exist two possible kinematic arrangements which are described by 3-2-1 and 2-2-2 kinematic node spaces. Both kinematic arrangements are studied with respect to their Jacobian formulation, Jacobian singularity and stiffness decoupling. It is proven that decoupling of stiffness matrices and accordingly dynamic isotropy for both kinematic arrangements are possible. Subsequently, conditions concerning geometry, stiffness and inertia in order to obtain dynamic isotropy are parametrically established. Finally, it is numerically demonstrated that the presented formulation is general enough even for being directly used, as a novel and efficient approach, in order to design dynamically isotropic 6-6 Gough–Stewart platforms (6-6 hexapods). [ABSTRACT FROM AUTHOR]

Modal behavior of a Gough–Stewart Platform (GSP) is sensitive to several variables related to its inertia, damping and stiffness as well as its complex 3-D geometry. To optimize its dynamical performance, due to the complications of this system, it is crucial to have the equations parametrically at the neutral configuration. However, in the literature, no complete parametric solution to this problem is presented. In this paper, we establish a fully-parametric and closed-form model for the damped vibrations of GSPs. In particular, this analytical model can be used in order to design, optimize and control GSPs in high-precision/bandwidth applications. Parametric expressions of the damped eigenfrequencies and the corresponding eigenvectors as well as the Jacobian, stiffness and damping matrices are developed. Interestingly, despite the complexity of the system, it is shown how well-structured algebraic expressions are obtained using the Cartesian-space approach. Having analytically studied the eigenvectors, the conditions for decoupled vibrations are also analytically formulated. Finally, using a reference GSP, the sensitivity of the damped eigenfrequencies to stiffness and damping variations are investigated accompanied by a cross-check with an ABAQUS® simulation. [ABSTRACT FROM AUTHOR]

• Investigated opponent modeling in automated negotiation. • Fuzzifyed the stakeholders evaluation models based on weighted preference limits. • Proposed a recursive learning approach to learn the parameters of these models. • A probabilistic model is applied that uses the learned criteria to find a proposal. Automated negotiation is a toolset to model human interactions during a negotiation process with the aim of improving the efficiency and quality of decision-making using advanced information analytics. During the negotiation, the participants share their viewpoints and concerns about the negotiation issues. However, in reality, they usually do not reveal the details of their preferences to one another. Therefore, modeling and learning opponents' behavior is a crucial component of automated negotiation. In this paper, we propose an estimation technique based on recursive Bayesian filtering to facilitate opponent-modeling and -learning in the context of multi-participant, multi-issue negotiations. In the proposed technique, opponents' preference profiles are modeled using fuzzy functions, which are very close to the way humans evaluate alternatives. As the negotiation progresses, the agents can recursively learn the parameters of these models in real time. The only required information for this learning process includes the feedback and the arguments the participants may provide in support of their decisions. At each round, a probabilistic graphical model is also implemented that utilizes the learned preference limits of the participants to offer a new proposal with a high probability of satisfying the participants and reaching an agreement. The proposed methodology is examined in two different negotiation contexts: energy-system development and real estate service. The experiments show that the proposed opponent modeling/learning approach increases the efficiency of the negotiation up to 85% and facilitates reaching an agreement in fewer rounds of negotiation without requiring any prior understanding of the negotiation participants. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer among Canadian women and the second cause of death from cancer. Fine needle aspirate (FNA) is a technology used to investigate early breast tumors to detect cancer. In this paper, we demonstrate the application of a new ordered weighted averaging operator (OWA) to the problem of breast tumor classification. The OWA operator employs the Laplace distribution to calculate the weight vector to aggregate the uncertain information about the breast tumors. The aggregated information is used along with the tumor label, i.e., benign or malignant, to train a nearest neighbor, support vector machine, and logistic regression classifiers. The result of this study based on the nearest neighbor classifier achieves 99.71% accuracy that outperforms other studies that utilize other OWA operators using the same dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized network-level traffic signal control method addressing the effects of queue spillbacks. The method is traffic-responsive, does not require data communication between intersections' controllers, uses lane-based queue measurements, and is acyclic. Each traffic controller operating at an intersection aims at maximizing the effective outflow rate locally and independently with the goal of maximizing global throughput of the entire network. At each intersection, the signal control method estimates and adopts the maximum possible phase time in which all active movements discharge at their full capacity. This is modeled using a shockwave based queue length estimation model while capturing the spillback at the downstream links. The method demands real-time data including, the queue lengths, the arrival flows, and the downstream queue lengths in all the lanes at the control decision times. The proposed method results in a feasible solution in all conditions in the entire network with any scale within a short amount of time. A stability concept for the traffic network is defined, and asymptotic stability of the controlled traffic network are verified. Moreover, a sufficient condition for the optimality of the proposed control algorithm for maximizing the instantaneous total throughput of the network intersections is demonstrated. Numerical results show that the proposed method outperforms benchmark methods in both isolated intersection and network configurations. • Real-time decentralized acyclic isolated and network-level traffic signal control. • Seeks to maximize network global throughput by maximizing the effective outflow rate locally. • Theoretical proof of network-wide traffic stability and optimality properties. • Efficiently controls long queues and spillbacks. • Provides a fast, low-cost, scale-free, and feasible solution in all conditions in networks. • Experiments show that the proposed method outperforms well-known benchmark methods. [ABSTRACT FROM AUTHOR]

Improvement of traffic signal control (TSC) efficiency has been found to lead to improved urban transportation and enhanced quality of life. Recently, the use of reinforcement learning (RL) in various areas of TSC has gained significant traction; thus, we conducted a systematic literature review as a systematic, comprehensive, and reproducible review to dissect all the existing research that applied RL in the network-level TSC domain, called as RL in NTSC or RL-NTSC for brevity. The review only targeted the network-level articles that tested the proposed methods in networks with two or more intersections. This review covers 160 peer-reviewed articles from 30 countries published from 1994 to March 2020. The goal of this study is to provide the research community with statistical and conceptual knowledge, summarize existence evidence, characterize RL applications in NTSC domains, explore all applied methods and major first events in the defined scope, and identify areas for further research based on the explored research problems in current research. We analyzed the extracted data from the included articles in the following seven categories: (i) publication and authors' data, (ii) method identification and analysis, (iii) environment attributes and traffic simulation, (iv) application domains of RL-NTSC, (v) major first events of RL-NTSC and authors' key statements, (vi) code availability, and (vii) evaluation. This paper provides a comprehensive view of the past 26 years of research on applying RL to NTSC. It also reveals the role of advancing deep learning methods in the revival of the research area, the rise of using non-commercial microscopic traffic simulators, a lack of interaction between traffic and transportation engineering practitioners and researchers, and a lack of proposal and creation of testbeds which can likely bring different communities together around common goals. • A review on Reinforcement Learning in the network-scale Traffic Signal Control area. • Presents a comprehensive systematic literature review of 160 included articles. • Consolidates and characterizes the existing research on the defined area. • Explores the methods, applications, domains, and first events in the defined scope. • Identifies past and present trends and directions for further research in the area. [ABSTRACT FROM AUTHOR]