To enable uplink inter-cell interference coordination in cellular networks, estimates of the powers generated by the own serving cell, the neighbour cells and the thermal noise processes need to be made available. An ...
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To enable uplink inter-cell interference coordination in cellular networks, estimates of the powers generated by the own serving cell, the neighbour cells and the thermal noise processes need to be made available. An algorithm for such high-bandwidth interference power splitting is derived in the study. The algorithm does not require inter-base station communication. The resulting estimates support self-organising network interference management and coordinated scheduling, both technologies being important in new heterogeneous networks. A new method for G-matrix tracking is also outlined, based on the estimates obtained from the proposed algorithm. The performance evaluation, performed for a 3G wideband code division multiple access setup of the algorithm, shows that the estimation inaccuracy's of the proposed algorithm is below 10-15% in the relevant operating region.
作者：
Hsu, Chia-YuYuan Ze Univ
Dept Informat Management Taoyuan 32003 Taiwan Yuan Ze Univ
Innovat Ctr Big Data & Digital Convergence Taoyuan 32003 Taiwan
Wafer bin map (WBM) represents specific defect pattern that provides information for diagnosing root causes of low yield in semiconductor manufacturing. In practice, most semiconductor engineers use subjective and tim...
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Wafer bin map (WBM) represents specific defect pattern that provides information for diagnosing root causes of low yield in semiconductor manufacturing. In practice, most semiconductor engineers use subjective and time-consuming eyeball analysis to assess WBM patterns. Given shrinking feature sizes and increasing wafer sizes, various types of WBMs occur;thus, relying on human vision to judge defect patterns is complex, inconsistent, and unreliable. In this study, a clustering ensemble approach is proposed to bridge the gap, facilitating WBM pattern extraction and assisting engineer to recognize systematic defect patterns efficiently. The clustering ensemble approach not only generates diverse clusters in data space, but also integrates them in label space. First, the mountain function is used to transform data by using pattern density. Subsequently, k-means and particle swarm optimization (PSO) clustering algorithms are used to generate diversity partitions and various label results. Finally, the adaptive response theory (ART) neural network is used to attain consensus partitions and integration. An experiment was conducted to evaluate the effectiveness of proposed WBMs clustering ensemble approach. Several criterions in terms of sum of squared error, precision, recall, and F-measure were used for evaluating clustering results. The numerical results showed that the proposed approach outperforms the other individual clustering algorithm.
Unmanned Aerial Vehicles (UAVs) have received considerable attention from the academic community and technology solutions companies, given their civilian and military applications. The autopilot system shall be thorou...
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ISBN:
(纸本)9781467395205
Unmanned Aerial Vehicles (UAVs) have received considerable attention from the academic community and technology solutions companies, given their civilian and military applications. The autopilot system shall be thoroughly tested in lab because an accident may cause irreversible damage to the UAV. This article presents a proposal of a Hardware-In-the-Loop platform for testing and validation of a small fixed-wing aircraft. Besides a communication system, it was developed a deflection measurement platform of the aircraft control surfaces. This measurement platform was validated, enabling future work on implementing embedded control algorithms.
Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW duration for diverse groups of devices would increase upli...
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ISBN:
(纸本)9781509013296
Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW duration for diverse groups of devices would increase uplink energy consumption, delay and lower down the data rate. In this paper, we study a RAW optimization problem with a novel retransmission scheme that utilizes the next empty slot for retransmission in the uplink. The problem is formulated based on overall energy efficiency and delay of each RAW by applying probability theory and Markov Chain. To jointly optimize energy efficiency and delay, an energy-delay aware window control algorithm is proposed to adapt RAW size by estimating the number of time slots and internal slot duration in one RAW for different groups. The optimal solution is derived by applying Gradient Descent approach. Simulation results show that our proposed algorithm improves up to 113.3% energy efficiency and reduces 53.4% delay compared to the existing RAW.
This paper solves the robust control problem of a plant with distributed delay using a reference model. The plant is subjected to bounded exogenous disturbances and the parameters of its mathematical model appear unkn...
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This paper solves the robust control problem of a plant with distributed delay using a reference model. The plant is subjected to bounded exogenous disturbances and the parameters of its mathematical model appear unknown. The author suggests a control algorithm for compensating the a priori parametric uncertainty and the bounded exogenous disturbances with a desired accuracy. A numerical example with corresponding simulation results is provided.
In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a mu...
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In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a multi-PCA model is used for fault detection in steady state process under different conditions, while a weighted algorithm is applied to transition process. Then, describe the faults quantitatively and use the optimization method to derive the fault amplitude under the sense of fault reconstruction. Fault amplitude drifts under different conditions even if the same fault occurs. To solve the above problem, consistent estimation algorithm of fault amplitude under different conditions has been studied. Last, employ the support vector machine (SVM) to predict the trend of the fault amplitude. Effectiveness of the algorithms proposed in this paper has been verified using Tennessee Eastman process as the study object.
This paper presents a reconstruction formula in general shift-invariant signal spaces that improve the rate of A-P iterative algorithm. We use the algorithm to show reconstruction of signals from weighted samples and ...
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This paper presents a reconstruction formula in general shift-invariant signal spaces that improve the rate of A-P iterative algorithm. We use the algorithm to show reconstruction of signals from weighted samples and also show that there is better convergence than the old one. We study the algorithm with emphasis on its implementation in field of signal processing, which the signal spaces is sufficiently large to accommodate a large number of possible models. Numerical examples results are furnished to illustrate our results. (C) 2015 Elsevier B.V. All rights reserved.
For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate syste...
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For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate systemis deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.
Generic programming is a programming paradigm for creation of highly resuable software components through decoupling algorithms from specific data structures which are being processed. The rise of research on ways of ...
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Generic programming is a programming paradigm for creation of highly resuable software components through decoupling algorithms from specific data structures which are being processed. The rise of research on ways of handling generic programming in various programming languages took place last years. We analyze and develop a number of generic programming features, in particular associated types and constraint propagation, for the Scala programming language designed by Martin Odersky in A parts per thousand cole Polytechnique F,d,rale de Lausanne.
For solving the problem that the conversion rate of vinyl chloride monomer (VCM) is hard for real-time online measurement in the polyvinyl chloride (PVC) polymerization production process, a soft-sensor modeling metho...
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For solving the problem that the conversion rate of vinyl chloride monomer (VCM) is hard for real-time online measurement in the polyvinyl chloride (PVC) polymerization production process, a soft-sensor modeling method based on echo state network (ESN) is put forward. By analyzing PVC polymerization process ten secondary variables are selected as input variables of the soft-sensor model, and the kernel principal component analysis (KPCA) method is carried out on the data preprocessing of input variables, which reduces the dimensions of the high-dimensional data. The k-means clustering method is used to divide data samples into several clusters as inputs of each submodel. Then for each submodel the biogeography-based optimization algorithm (BBOA) is used to optimize the structure parameters of the ESN to realize the nonlinear mapping between input and output variables of the soft-sensor model. Finally, the weighted summation of outputs of each submodel is selected as the final output. The simulation results show that the proposed soft-sensor model can significantly improve the prediction precision of conversion rate and conversion velocity in the process of PVC polymerization and can satisfy the real-time control requirement of the PVC polymerization process.
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