This paper consider the problem of reconfiguring VLSI/WSI arrays via the degradation approach. In this approach, all elements are treated uniformly and no elements are dedicated as spares. The goal is to derive a faul...
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The authors consider the problem of reconfiguring VLSI/WSI arrays via the degradation approach. In this approach, all elements are treated uniformly and no elements are dedicated as spares. The goal is to derive a fau...
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The authors consider the problem of reconfiguring VLSI/WSI arrays via the degradation approach. In this approach, all elements are treated uniformly and no elements are dedicated as spares. The goal is to derive a fault-free subarray T from the defective host array such that the dimensions of T are larger than some specified minimum. This problem has been shown to be NP-complete under various switching and routing constraints. However, it is shown that a special case of the reconfiguration problem with row bypass and column rerouting capabilities, is solvable in polynomial time using network flows. Using this result, a new fast and efficient reconfiguration algorithm is proposed. Empirical study shows that the new algorithm indeed produces good results in terms of the percentages of harvest and degradation of VLSI/WSI arrays.
Cellular neural networks (CNNs) are considered here as cellular analog programmable multidimensional processing arrays with distributed logic and memory. The interconnecting weights between the neighbouring processing...
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computer Integrated Manufacturing (CIM) systems having determining role in the modern industry. These systems contain essentially two different equipments for transporting materials between workstations: - conveyors c...
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A method for the path-control of automated guided vehicles (AGVs) in computer integrated manufacturing (CIM) systems that combines the flexibility and easy installation of optical methods with the simplicity and robus...
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A method for the path-control of automated guided vehicles (AGVs) in computer integrated manufacturing (CIM) systems that combines the flexibility and easy installation of optical methods with the simplicity and robustness of the inductive method is proposed. Using a new computing paradigm, the cellular neural network (CNN), and a related device, the VLSI CNN chip, a very high speed solution that is less expensive than the conventional methods can be achieved. This AGV control complies with the requirements of CIM systems. Further advantages of the proposed system are as follows: fault tolerance and the ability to give instructions along the path, and the use of a simple local control.< >
This paper describes the meta-level control system of a program (Dominic) for parametric design of mechanical components by iterative redesign. We view parametric design as search, and thus Dominic is a hill climbing ...
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The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers pre...
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ISBN:
(数字)9783642211058
ISBN:
(纸本)9783642211041
The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.
Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and ex...
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Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and explanation as separate, ignoring the fact that explanation content can assist in enhancing fake news detection. To overcome this gap, we present a new solution: the End-to-end Explainable Fake News Detection Network (\(EExpFND\)). Our model includes an evidence-claim variational causal inference component, which not only utilizes explanation content to improve fake news detection but also employs a variational approach to address the distributional bias between the ground truth explanation in the training set and the prediction explanation in the test set. Additionally, we incorporate a masked attention network to detail the nuanced relationships between evidence and claims. Our comprehensive tests across two public datasets show that \(EExpFND\) sets a new benchmark in performance. The code is available at https://***/r/EExpFND-F5C6.
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