In this paper, it will be shown that the minimum mean square error (MMSE) for predicting a stationary stochas-tic time series from its past observations is not generally Turing computable, even if the spectral density...
In this paper, it will be shown that the minimum mean square error (MMSE) for predicting a stationary stochas-tic time series from its past observations is not generally Turing computable, even if the spectral density of the stochastic process is differentiable with a computable first derivative. This implies that for any approximation sequence that converges to the MMSE there does not exist an algorithmic stopping criterion that guarantees that the computed approximation is sufficiently close to the true value of the MMSE. Furthermore, it will be shown that under the same conditions on the spectral density, it is also the case that coefficients of the optimal prediction filter are not generally Turing computable.
In this work, we propose a novel parameter identifier for linear time-invariant systems, which has the fastest possible convergence in principal (i.e., dead-beat). The identifier applies where states and derivatives o...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
In this work, we propose a novel parameter identifier for linear time-invariant systems, which has the fastest possible convergence in principal (i.e., dead-beat). The identifier applies where states and derivatives of states can be measured. The identifier is applied to the certainty-equivalence (indirect) model reference adaptive control that relaxes the conventional assumptions about open-loop stability and persistent excitation in MRAC. Furthermore, in conventional MRAC the parameter convergence rate is proportional to the tracking error, whereas our proposed adaptation law has dead-beat dynamics. This method is previously applied to a Delta robot. In this paper, we present a theoretical analysis of the method by solving the identifier dynamics to get its closed-form solution and examine the effect of perturbations such as white additive noise, uncertainty, and disturbance. The linearized model of an actuated inverted pendulum with unknown parameters is used as a benchmark example.
Automatic defect detection has important implications to intelligent manufacturing which could be used for the precise quality control of different products. However, the diverse aluminium profile surface defects pres...
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ISBN:
(纸本)9781450385855
Automatic defect detection has important implications to intelligent manufacturing which could be used for the precise quality control of different products. However, the diverse aluminium profile surface defects present the characteristics of micro defects and different sizes. Conventional handcrafted-based methods and machine learning-based methods have limited feature expression ability which cause relatively poor detection performance. Recently, with the stronger feature extraction ability, deep learning has got wide applications on defect detection and recognition. Due to the loss information caused by pooling operations, it still exists a certain drawbacks on multi-scale object detection. To address this issue, with the residual neural network (ResNet), a new deep defect recognition network is proposed in this paper for aluminium profile surface defects to construct an end-to-end defect detection scheme. An attention fusion model is proposed to improve the detection precision on multi-scale defects. Experiments show that the proposed defect detection method shows a better detection performance compared with other advanced detection models.
Slice isolation is a key feature of the network slicing technique and refers to protecting slices from negative impact of fault, attack or workload increase in other slices. Dynamic resource slicing policies, although...
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The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarc...
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As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of...
As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.
This paper explores the H∞ state estimation problem for a category of discrete-time complex-valued memristive neural networks (CVMNNs). Regarding the studied CVMNNs, the phenomena of the distributed delay and time-va...
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DC collection systems offer advantages by reducing the weight and size of DC cables without requiring reactive power compensation. This enables the replacement of the bulky 50/60 Hz transformers typically used in AC c...
DC collection systems offer advantages by reducing the weight and size of DC cables without requiring reactive power compensation. This enables the replacement of the bulky 50/60 Hz transformers typically used in AC collection systems on offshore platforms with smaller medium-frequency transformers in DC collection configurations. Nonetheless, challenges persist in implementing high-power DC-DC converters with high-voltage transformation ratios and DC protection methods in DC collection systems. It is worth noting that while HVDC (High Voltage DC) transmission can transfer offshore wind power from collection systems to onshore grids, DC collection systems do not always result in fewer power conversion stages compared to AC collection systems. To tackle these challenges, this paper conducts a comparative analysis of the technological, economic, and environmental aspects of DC and AC collection systems for offshore wind farms, using a wind farm in China as an illustrative example. Our approach involves an innovative method for estimating losses and a technical comparison. Simulation results validate that DC collection systems exhibit higher total losses than AC collection systems. We also explore the impact of collection bus voltages on these losses in DC systems. Additionally, we develop an economic cost assessment method, and the sensitivity analysis results confirm that cost reductions primarily stem from the reduced size of DC cables and offshore platforms rather than improvements in DC protective devices and DC-DC converters. Lastly, we investigate the environmental implications of these systems.
This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders a...
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This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.
The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote s...
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