Distributed sensor networks (DSNs) are being developed for a wide range of applications. A sensor networks consist of a large number of nodes performing distributed sensing/event detection. Sensor nodes are energy-con...
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Distributed sensor networks (DSNs) are being developed for a wide range of applications. A sensor networks consist of a large number of nodes performing distributed sensing/event detection. Sensor nodes are energy-constrained, efficient routing is essential for increasing the lifetime of a sensor network. A DSN requires interoperability, low latency, and low power consumption in order to operate for long periods of time. Due to power constraints, DSN should be able to operate at low data rates and still delivering acceptable quality of service. In a DSN the energy required for communications tasks is usually much greater than the energy required for computational tasks. To reduce the data communication between nodes and to maximize the network lifetime, we propose to use the mobile-agent paradigm. In mobile-agent based DSN9 instead of moving data from an individual sensor node to a processing center as in the client-server based computing, mobile agents are dispatched from the processing center to the sensor nodes and process data locally. By moving code to the data, a mobile agent can reduce latency, bandwidth and vulnerability to network disconnection.
Learning distributed object grasp by a group of robots with redundant members is the main focus of this paper. In Elahibakhsh, A. H., et al. (2004), we tackled the problem of learning form closure grasp for planar con...
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Learning distributed object grasp by a group of robots with redundant members is the main focus of this paper. In Elahibakhsh, A. H., et al. (2004), we tackled the problem of learning form closure grasp for planar convex objects by multiple non-communicating robots without any information about the shape of objects. In this paper, the problem in presence of redundant agents is investigated. Agents' states and actions are designed such that the group learns grasping different objects using Q-learning method. As the environment is not intelligent enough to assess each agent's effect on the team performance, a credit assignment algorithm based on knowledge evaluation is designed. The proposed method considers the environment credit for the team, number of redundant agents, and the expertness level of each agent in its credit assignment. Applicability of the designed approach is verified through a set of simulations. It is shown that the team learns grasping different objects. Therefore, it is expected that the proposed method can be extended for distributed grasp of deformable objects
Robust asymptotic stability for hybrid systems is considered. For this purpose, a generalized solution concept is developed. The first step is to characterize a hybrid time domain that permits an efficient description...
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作者：
ERIC L. SEVIGNYJONATHAN P. CAULKINSEric L. Sevigny
is a Ph.D. student in the Graduate School of Public and International Affairs at the University of Pittsburgh. Mr. Sevigny's research interests lie in the area of drug policy with an emphasis on enforcement sanctioning international control and treatment/harm reduction. His previous experience includes substance abuse counseling and substance abuse treatment needs research of special populations including prisoners the homeless and adolescents. Mr. Sevigny received a B.A. in Psychology from Middlebury College. Jonathan P. Caulkins
Ph.D. is Professor of Operations Research and Public Policy at Carnegie Mellon University's Heinz School of Public Policy. Dr. Caulkins specializes in mathematical modeling and systems analysis of social policy problems with a particular focus on issues pertaining to drugs crime violence and prevention. Dr. Caulkins has also published on airline operations sulfur dioxide pollution trading markets Internet-based advertising flexible manufacturing systems and personnel performance evaluation among other topics. At RAND he has been a consultant visiting scientist codirector of RAND's Drug Policy Research Center (1994–1996) and founding director of RAND's Pittsburgh office (1999–2001). Dr. Caulkins received a B.S. and M.S. in Systems Science from Washington University and an S.M. in Electrical Engineering and Computer Science and Ph.D. in Operations Research both from M.I.T.
Research Summary: Drug policy reformers and defenders contest the extent to which low-level drug offenders are being sent to prison and for how long. Using data from the Survey of Inmates in Federal and State Correcti...
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Research Summary: Drug policy reformers and defenders contest the extent to which low-level drug offenders are being sent to prison and for how long. Using data from the Survey of Inmates in Federal and State Correctional Facilities, 1997 (BJS, 2000), we assess the seriousness of incarcerated drug offenders along dimensions of dangerousness, culpability, and harm—specifically, functional role and drug group participation, type and amount of drugs, firearms involvement, and criminal conviction and arrest history. We find that only about 1.6% of federal and 5.7% of state inmates can be described as “unambiguously low-level.” Alternatively, not many are “kingpins.” Rather, most fall into a middle spectrum representing different degrees of seriousness that depend on what factors are emphasized. Policy Implications: Our findings dampen hopes of dramatically reducing prison populations by getting out of prison those who are unambiguously low-level drug offenders. They simply do not represent the majority of incarcerated drug offenders. In particular, most played some role in distribution, so eliminating prison terms for users (decriminalization) would not have affected many now in prison. Indeed, if decriminalization increased demand, it could plausibly increase prison populations by increasing the number of suppliers still subject to imprisonment. On the other hand, “drug courier exceptions” to sentencing laws that apply to minor role offenders possessing large quantities could have a greater prison reduction impact.
Robust asymptotic stability for hybrid systems is considered. For this purpose, a generalized solution concept is developed. The first step is to characterize a hybrid time domain that permits an efficient description...
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Robust asymptotic stability for hybrid systems is considered. For this purpose, a generalized solution concept is developed. The first step is to characterize a hybrid time domain that permits an efficient description of the convergence of a sequence of solutions. Graph convergence is used. Then a generalized solution definition is given that leads to continuity with respect to initial conditions and perturbations of the system data. This property enables new results on necessary conditions for asymptotic stability in hybrid systems.
This paper proposes an Adaptive Critic Based Neuro-Fuzzy controller (ACBNFC) to Thyristor controlled Series Capacitor (TCSC), which might have a significant impact on power system dynamics. The function of the ACBNFC ...
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The rapid growth of the Internet and increased desmand to use the Internet for voice and video applications necessitate the design and utilization of new Internet architectures with effective congestion control algori...
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Using unbounded time-varying scaling of the states we design C/sup 1/ feedback laws for power integrator triangular systems which globally asymptotically stabilize (GAS) the origin despite the uncontrollability of the...
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Using unbounded time-varying scaling of the states we design C/sup 1/ feedback laws for power integrator triangular systems which globally asymptotically stabilize (GAS) the origin despite the uncontrollability of the linearization. With bounded scaling the feedback laws achieve global practical stability (GPS). For a trade-off between GAS/GPS of the origin and unboundedness/boundedness of the scaling we construct a dynamic version of these feedback laws.
In the practical path-following problem formulated in this paper, it is required that the error between the system output and the desired geometric path be less then any prespecified constant. If in a nonlinear MIMO s...
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In the practical path-following problem formulated in this paper, it is required that the error between the system output and the desired geometric path be less then any prespecified constant. If in a nonlinear MIMO system the output derivatives do not enter the zero dynamics, a geometric condition on the path is given under which a solution to this problem exists. The solution is obtained by combining input-to-state stability and switched-system methodology.
We present robust stability results for discrete-time nonlinear systems using certainty equivalence output feedback, particularly those that employ a model predictive control (MPC) formulation to generate the feedback...
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We present robust stability results for discrete-time nonlinear systems using certainty equivalence output feedback, particularly those that employ a model predictive control (MPC) formulation to generate the feedback control law. To this end, we discuss nominal robustness properties of general discrete-time nonlinear systems, including those that use discontinuous control laws in the feedback loop. This is important for systems employing MPC since the method can, and sometimes necessarily does, result in discontinuous control laws. Coupling assumptions of nominal robustness with certain uniform observability or detectability assumptions (for each of which we give an observer), we assert that, in particular, MPC is robustly globally asymptotically stabilizing when used in a certainty equivalence output feedback structure. Finally, we give an example to illuminate our results.
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