In this paper, we generalize the block splitting preconditioner presented by Cao to a two-parameter preconditioner for solving generalized saddle point linear systems and prove that the eigenvalues of the correspondin...
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In modern avionics system Testing and Verification are very important criteria. With the advantage of Field Programmable Gate Array (FPGA), power sub systems are more intelligent with advance features. Advance power s...
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A vast amount of research has been done on vehicular networking because it is continuously required for infotainment, safety, efficient traffic management, and normalization. As the vehicles are installed with various...
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In this paper, we consider the distributed time-varying optimization problem with coupled equality constraints over a connected undirected network. To address this issue, we design a novel distributed constraint optim...
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ISBN:
(纸本)9798350354416;9798350354409
In this paper, we consider the distributed time-varying optimization problem with coupled equality constraints over a connected undirected network. To address this issue, we design a novel distributed constraint optimization algorithm, and establish its ISS stability with external disturbances and tracking errors as the input and state, respectively. Moreover, the obtained result includes distributed constrained optimization with static objective functions as a special case. In comparison to existing relevant works, the proposed algorithm demonstrates exponential convergence for cases involving static objective functions. Finally, the theoretical results are validated via a numerical example.
This survey paper delves into the effective utilization of virtual machines (VMs) in cloud computing. It underscores the significance of VM usage in both cloud computing technology and virtualization. The paper encomp...
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In the future, autonomous systems such as self-driving cars must robustly and flexibly handle various fault situations. Static models of faults and countermeasures are standard in classical approaches;however, such st...
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ISBN:
(纸本)9783031477232;9783031477249
In the future, autonomous systems such as self-driving cars must robustly and flexibly handle various fault situations. Static models of faults and countermeasures are standard in classical approaches;however, such static models are no longer efficient as the complexity of fault scenarios tremendously increased. The bio-inspired concept of organic computing applies biological concepts to technical systems. Organic computing utilizes an artificial hormone system as a decentralized mechanism, i.e., a middleware that continuously monitors and organizes task allocations to computing nodes in distributed real-time embedded systems. By introducing different types of artificial hormones for the tasks, task allocations are realized by constantly establishing hormone balances via distributed closed control loops. This process handles the increasing complexity of e.g., distributed controlsystems by enabling self-configuration, -adaptation, improvement and -healing. Such an organic computing environment inherently overcomes system-level faults, such as computation node failures, since missing (i.e., non-executed) tasks directly lead to hormone imbalances that are compensated for, thereby restoring these tasks. However, faults in the artificial hormone system, e.g., due to incorrect hormone values, are currently not covered by the organic computing environment and can result in adverse and critical system behavior and even complete system failure. To address these types of faults and thus improve the capabilities and safety of current organic computingsystems, this paper presents a fault injection framework to analyze the effects of an extended range of fault cases, including faults in the artificial hormone system. Such analyses are important as they mark the foundation for future fault-handling strategies and safety features in next-generation organic computingsystems for autonomous systems. The statistical analyses and results based on the fault injection framework reveal
E-learning systems improve day by day. Therefore, it is important to monitor and evaluate student performance to provide targeted content. This paper focuses on a model for intelligent E-learning systems that can iden...
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The autonomous anti-sway control of the ship-mounted crane systems is a tough issue due to the under-actuated characteristic, strong coupling and base excitation. Furthermore, the ship-mounted crane systems are also s...
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ISBN:
(纸本)9789819770007;9789819770014
The autonomous anti-sway control of the ship-mounted crane systems is a tough issue due to the under-actuated characteristic, strong coupling and base excitation. Furthermore, the ship-mounted crane systems are also suffering from unknown dynamics and frictions etc. In some conditions, the ship-mounted cranes may exhibit spherical pendulum effects. These factors make the control problem even more challenging. To solve the above problems, this paper designed a Radical Basis Function Neural Network (RBFNN) based feedback control method for a 5-DOF ship-mounted rotary crane. Specially, the adaptive RBFNN is established to approximate the unknown dynamics online. After that, eschewing any simplification of the dynamic model, a feedback anti-sway control is designed to ensure the cargo could reach to desired position and dampening the payload spherical swing simultaneously. The closed-loop stability is analyzed and the effectiveness of the control method is validated via simulation experiment results.
The system uses the man-machine interface and PLC programming function to precisely quantify the feed taken from each storage warehouse according to the target. The feed is then delivered in time to a specific feeding...
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This work presents a Fleet Manager for a fleet of Autonomous Mobile Robots (AMRs) that perform material handling tasks in a shared environment. The Fleet Manager assigns AMRs to newly released tasks, computes paths fo...
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ISBN:
(纸本)9798350358513;9798350358520
This work presents a Fleet Manager for a fleet of Autonomous Mobile Robots (AMRs) that perform material handling tasks in a shared environment. The Fleet Manager assigns AMRs to newly released tasks, computes paths for them to travel to the task's locations, and schedules their travel along the computed paths so that conflicts with other AMRs are avoided. The objective is for each AMR to complete its task as quickly as possible, to then be assigned a new task. The Fleet Manager works online, assigning a released task to the AMR closest to the task's location, and then computing the path and schedule to fit in with the already assigned and executing AMRs. Conflicts occur when, in order to reach their targets, AMRs would have to simultaneously occupy the same space. Resolving this is done by appropriate scheduling, or by moving idle AMRs out of the way. For fleet management to be practicable, the computation time for assigning an AMR to a task and computing its path and schedule must be negligible compared to other system times. Tests were conducted to evaluate the performance of the Fleet Manager on a number of benchmark problem instances, counting up to hundreds of AMRs. The results show that the presented Fleet Manager can handle these systems quickly enough to be practically useful in real industrial scenarios.
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