Recently, many researchers have demonstrated that computation by DNA tile self-assembly may be scalable and it is considered as a promising technique in nanotechnology. In this paper, we show how the tile self-assembl...
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Recently, many researchers have demonstrated that computation by DNA tile self-assembly may be scalable and it is considered as a promising technique in nanotechnology. In this paper, we show how the tile self-assembly process can be used for solving the 0-1 multi-objective knapsack problem by mainly constructing four small systems which are nondeterministic guess system, multiplication system, addition system and comparing system, by which we can probabilistically get the feasible solution of the problem. Our model can successfully perform the 0-1 multi-objective knapsack problem in polynomial time with optimal Theta(1) distinct tile types, parallely and at very low cost.
Attribute exploration is a tool for implication relation research between attributes in formal concept analysis. Currently, attribute exploration is mainly focused on the implication between attributes based on pseudo...
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Attribute exploration is a tool for implication relation research between attributes in formal concept analysis. Currently, attribute exploration is mainly focused on the implication between attributes based on pseudo connotation. Based on the implication relation between real pre-condition attributes, another attribute exploration method is used to discuss the implication relation to obtain concept connotation. Through an analysis on the corresponding relationship between attribute implications composed by the real pre-condition and pseudo connotation and the reciprocal transformation between the two classes' attribute implications, a method to generate real pre-condition is analyzed. Result shows that the attribute implication expressed by real pre-condition and the implication between attributes based on pseudo connotation are essentially identical and completely equivalent, and can be converted. Moreover, the implication relation between attributes based on real pre-condition can provide a new method for attribute exploration, and the attribute implication expressed by real pre-condition is more concise and more conducive to further analyzing data.
To reduce the downtime of wind turbine caused by the fault of pitch system that usually has a high failure rate, a fault early warning method based on long short-term memory (LSTM) neural network is proposed. Accordin...
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A robust appearance model is usually required in visual tracking, which can handle pose variation, illumination variation, occlusion and many other interferences occurring in video. So far, a number of tracking algori...
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ISBN:
(纸本)9781479958306
A robust appearance model is usually required in visual tracking, which can handle pose variation, illumination variation, occlusion and many other interferences occurring in video. So far, a number of tracking algorithms make use of image samples in previous frames to update appearance models. There are many limitations of that approach: 1) At the beginning of tracking, there exists no sufficient amount of data for online update because these adaptive models are data-dependent and 2) in many challenging situations, robustly updating the appearance models is difficult, which often results in drift problems. In this paper, we proposed a tracking algorithm based on compressive sensing theory and particle filter framework. Features are extracted by random projection with data-independent basis. Particle filter is employed to make a more accurate estimation of the target location and make much of the updated classifier. The robustness and the effectiveness of our tracker have been demonstrated in several experiments.
control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, it provides a simple and computationally efficient way to obtain safe ...
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Object tracking is an important capability for robots tasked with interacting with humans and the environment, and it enables robots to manipulate objects. In object tracking, selecting samples to learn a robust and e...
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Object tracking is an important capability for robots tasked with interacting with humans and the environment, and it enables robots to manipulate objects. In object tracking, selecting samples to learn a robust and efficient appearance model is a challenging task. Model learning determines both the strategy and frequency of model updating, which concerns many details that can affect the tracking results. In this paper, we propose an object tracking approach by formulating a new objective function that integrates the learning paradigm of self-paced learning into object tracking such that reliable samples can be automatically selected for model learning. Sample weights and model parameters can be learned by minimizing this single objective function under the framework of kernelized correlation filters. Moreover, a real-valued error-tolerant self-paced function with a constraint vector is proposed to combine prior knowledge, i.e., the characteristics of object tracking, with information learned during tracking. We demonstrate the robustness and efficiency of our object tracking approach on a recent object tracking benchmark data set: OTB 2013.
Ant colony optimization (ACO) is a population-based meta-heuristic for combinatorial optimization problems such as communication network routing problem (CNRP). This paper proposes an improved ant colony optimization ...
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Ant colony optimization (ACO) is a population-based meta-heuristic for combinatorial optimization problems such as communication network routing problem (CNRP). This paper proposes an improved ant colony optimization (IACO), which adapts a new strategy to update the increased pheromone, called ant-weight strategy, and a mutation operation, to solve CNRP. The simulation result for a benchmark problem is reported and compared to the simple ant colony optimization (ACO).
Generative adversary networks (GANs) have recently led to highly realistic image synthesis results. In this work, we describe a new method to expose GAN-synthesized images using the locations of the facial landmark po...
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Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more ...
Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks.
The cutoff frequency of a LC output filter for dynamic voltage restorers (DVR) limits the control bandwidth of a DVR system and the attenuation factor against the inverter switching ripples. For a selected cutoff freq...
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ISBN:
(纸本)0780383990
The cutoff frequency of a LC output filter for dynamic voltage restorers (DVR) limits the control bandwidth of a DVR system and the attenuation factor against the inverter switching ripples. For a selected cutoff frequency of a LC output filter, infinite number of L-C combinations is possible. Although different L-C combination has different filter characteristics, the filter design on L-C combination has been depended on field experiences without clear analysis. This paper proposes a design criterion and a design example for the L-C filter combination considering the control characteristics and the size of DVRs. An experimental DVR system based on the proposed LC output filter design methodology is built and tested.
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