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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates

JOURNAL OF MACHINE LEARNING RESEARCH 2015年第1期16卷 3299-3340页

作者： Zhang, Yuchen Duchi, John Wainwright, Martin Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Stanford Univ Dept Stat Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

We study a decomposition-based scalable approach to kernel ridge regression, and show that it achieves minimax optimal convergence rates under relatively mild conditions. The method is simple to describe: it randomly partitions a dataset of size N into m subsets of equal size, computes an independent kernel ridge regression estimator for each subset using a careful choice of the regularization parameter, then averages the local solutions into a global predictor. This partitioning leads to a substantial reduction in computation time versus the standard approach of performing kernel ridge regression on all N samples. Our two main theorems establish that despite the computational speed-up, statistical optimality is retained: as long as m is not too large, the partition-based estimator achieves the statistical minimax rate over all estimators using the set of N samples. As concrete examples, our theory guarantees that the number of subsets m may grow nearly linearly for finite-rank or Gaussian kernels and polynomially in N for Sobolev spaces, which in turn allows for substantial reductions in computational cost. We conclude with experiments on both simulated data and a music-prediction task that complement our theoretical results, exhibiting the computational and statistical benefits of our approach.

关键词： kernel ridge regression divide and conquer computation complexity

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Adaptive directive neural network control for threephase AC/DC PWM converter

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IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS 2001年第5期148卷 425-430页

作者： Cheng, KWE Wang, HY Sutanto, D Hong Kong Polytech Univ Dept Elect Engn Kowloon Hong Kong Peoples R China

A novel control strategy is proposed: adaptive B-spline neural network for three-phase AC DC voltage source converters, which realises a sinusoidal AC input current and unity power factor. Compared with other PWM techniques, neural network control provides an excellent component of a nonlinear system and is adaptive enough to fit the environment change. An on-line B-spline neural network is used because of its local weight updating characteristic. which has the advantages of fast convergence speed and low computation complexity. This is very important for real-time control applications. Both simulation and experimental results are presented to verify the effectiveness of the proposed control strategy.

关键词： splines (mathematics) adaptive control convergence speed computation complexity Power convertors and power supplies to apparatus AC-DC power convertors control system analysis local weight updating characteristic PWM power convertors control performance real-time control applications Neural nets control simulation control design Neurocontrol Interpolation and function approximation (numerical analysis) three-phase AC/DC PWM power converter control system synthesis unity power factor voltage source converters adaptive directive neural network control Control system analysis and synthesis methods neurocontrollers Control of electric power systems sinusoidal AC input current Self-adjusting control systems Voltage control voltage control rectifying circuits adaptive B-spline neural network

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Neuro-Fuzzy Logic for Automatic Animation Scene Generation in Movie Arts in Digital Media Technology

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INTERNATIONAL JOURNAL OF computationAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-15页

作者： Peng, Liu Tianshui Normal Univ Coll Literature & Cultural Commun Tianshui 741000 Peoples R China

Animation scene generation (ASG) is the best digital media tool for lifelike scenes, particularly for movies. Traditional animation methods are laborious, computationally intensive, and scalable. Thus, this work addresses animation production issues using NFL-ASG. Combining fuzzy logic with a convolution neural network may create more realistic animated situations with less human interaction and better learning. Convolutional model training uses animation scenarios' complicated motion patterns, character interactions, and ambient factors. Deep learning and fuzzy logic might change animation by boosting production techniques and releasing digital media technological creativity. After testing the system on the Moana Island scene dataset, it achieved a perception analysis success rate of 0.981% and a minimal processing complexity of (n logn).

关键词： Animation scene generation Convolution neural networks Fuzzy logic Moana island scene computation complexity

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Huddle hierarchy based group key management protocol using gray code

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WIRELESS NETWORKS 2014年第4期20卷 695-704页

作者： Varalakshmi, R. Uthariaraj, V. Rhymend Anna Univ Ramanujan Comp Ctr Chennai 600025 Tamil Nadu India

Secure and reliable group communication is an active area of research. Its popularity is fuelled by the growing importance of group-oriented and collaborative applications. The central research challenge is secure and efficient group key management. The present paper is based on the huddle hierarchy based secure multicast group key management scheme using the most popular absolute encoder output type code named gray code. The focus is of twofolds. The first fold deals with the reduction of computation complexity which is achieved in this protocol by performing fewer multiplication operations during the key updating process. To optimize the number of multiplication operations, the fast Fourier transform, divide and conquer approach for multiplication of polynomial representation of integers, is used in this proposed work. The second fold aims at reducing the amount of information stored in the Group Center and group members while performing the update operation in the key content. Comparative analysis to illustrate the performance of various key distribution protocols is shown in this paper and it has been observed that this proposed algorithm reduces the computation and storage complexity significantly.

关键词： Multicast group key management Huddle hierarchy Fast Fourier transform Polynomial integer multiplications Gray code computation complexity Storage complexity

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Robust measure of non-linearity-based cubature Kalman filter

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第7期11卷 929-938页

作者： Zhang, Lei Li, Sheng Zhang, Enze Chen, Qingwei Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Yangzhou Univ Coll Informat Engn Yangzhou 225009 Jiangsu Peoples R China

In this study, a novel robust measure of non-linearity-based cubature Kalman filter (RMoNCKF) is proposed to obtain good performance with lower computational burden. The proposed filter inherits the virtues of high accuracy of the high-degree filter and computation efficiency of the low-degree one. When the measure of non-linearity (MoN) is evaluated and compared with the threshold in the dynamic system, the cubature rules nested in the RMoNCKF can be switched autonomously to decrease the computation complexity in the low non-linear condition. Furthermore, the robust estimation technology can help to improve the value of MoN for the non-Gaussian distributed case. Simulation results of target tracking and integrated navigation system demonstrate that the RMoNCKF can have a close performance to the fifth-degree CKF with less computation time. In the circumstances of the time-varying noise and contaminated Gaussian distributed noise, the RMoNCKF outperforms the UKF, the third-degree CKF and fifth-degree CKF.

关键词： Kalman filters computational complexity estimation theory target tracking navigation Gaussian noise nonlinearity-based cubature Kalman filter RMoNCKF measure of nonlinearity evaluation MoN evaluation computation complexity robust estimation technology nonGaussian distributed case target tracking integrated navigation system time-varying noise contaminated Gaussian distributed noise third-degree CKF fifth-degree CKF UKF

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Analysis of black phosphorus double gate MOSFET using hybrid method for analogue/RF application

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IET CIRCUITS DEVICES & SYSTEMS 2020年第8期14卷 1167-1172页

作者： Rathinam, Ramesh Pon, Adhithan Carmel, Santhia Bhattacharyya, Arkaprava SASTRA Deemed Univ Sch Elect & Elect Engn Device Modeling Lab Thanjavur 613401 Tamil Nadu India

In this work, the authors study the performance of black phosphorus double gate MOSFET (BP-DGMOSFET) within the ballistic limit. A hybrid simulation technique involving both atomistic and technology computer-aided design (TCAD) tool has been used for the first time to simulate the device characteristics. First, the density functional theory has been used to simulate the electrical characteristics of single and fewlayer (five layers) phosphorene (including armchair and zigzag directions). The parameters such as bandgap and effective mass obtained using an atomistic simulator tool are exported into Sentaurus TCAD to simulate the BP-DGMOSFET characteristics. They have used the kinetic velocity model and quantum model to account for the ballistic mobility and quantum effects in the device. The current drain characteristics are calibrated with non-equilibrium Greens function (NEGF) simulation and radio frequency (RF) characteristics with compact model values and found that their results agree with them. Secondly, the other RF figure of merits, such as maximum frequency of oscillation, transconductance, output conductance and stability has also been simulated. It is found that their proposed method shows results comparable to NEGF with reduced computation time and complexity.

关键词： technology CAD (electronics) phosphorus Green's function methods ballistic transport semiconductor device models density functional theory MOSFET electronic engineering computing ballistic limit hybrid simulation technique device characteristics density functional theory electrical characteristics atomistic simulator tool BP-DGMOSFET characteristics kinetic velocity model quantum model ballistic mobility quantum effects current drain characteristics black phosphorus double gate MOSFET analysis analogue-RF application technology computer-aided design tool phosphorene effective mass bandgap Sentaurus TCAD nonequilibrium Greens function simulation NEGF simulation reduced computation time computation complexity

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computation-efficient multicast key distribution

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2008年第5期19卷 577-586页

作者： Xu, Lihao Huang, Cheng Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Microsoft Res Labs Redmond WA 98052 USA

Efficient key distribution is an important problem for secure group communications. The communication and storage complexity of multicast key distribution problem has been studied extensively. In this paper, we propose a new multicast key distribution scheme whose computation complexity is significantly reduced. Instead of using conventional encryption algorithms, the scheme employs MDS codes, a class of error control codes, to distribute multicast key dynamically. This scheme drastically reduces the computation load of each group member compared to existing schemes employing traditional encryption algorithms. Such a scheme is desirable for many wireless applications where portable devices or sensors need to reduce their computation as much as possible due to battery power limitations. Easily combined with any key-tree-based schemes, this scheme provides much lower computation complexity while maintaining low and balanced communication complexity and storage complexity for secure dynamic multicast key distribution.

关键词： key distribution multicast MDS codes erasure decoding computation complexity

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An MPC Approximation Approach for Adaptive Cruise Control With Reduced computational complexity and Low Memory Footprint

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3154-3167页

作者： Nguyen, Duc Giap Park, Suyong Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok Kyungpook Natl Univ Grad Sch Mech Engn Daegu 41566 South Korea Hyundai Motor Co Electrified Syst Control Res Lab Hwaseong 18280 South Korea

This work demonstrates the application of deep neural networks (DNN) to alleviate the computational complexity associated with Model Predictive Control (MPC), which has always been an obstacle hindering the practical adoption of MPC. This challenge is particularly critical in applications for autonomous vehicles where achieving multiple objectives while enforcing a certain number of system constraints is essential. We first revisit and design a control algorithm tailored to the Adaptive Cruise Control (ACC) problem. The developed algorithm consists of two distinct implicit MPCs, each addressing a specific control mode, namely velocity and space control. Multiple control objectives and constraints are integrated into the algorithm synthesis to ensure satisfactory control performance. We further adopt supervised learning with deep neural networks to reduce the computational cost of MPC, thereby making MPC more accessible for practical use. Simulation results affirm that the DNN-based approximated policy can match the control performance in terms of both tracking precision and constraint satisfaction of state-of-the-art solvers dedicated to optimization problems. Remarkably, the execution time of the approximated policy is approximately one order of magnitude lower than that of implicit MPCs, while its memory footprint is significantly lower than those of its counterparts, thereby emphasizing its distinct advantages.

关键词： Approximation algorithms Optimization Cruise control computational complexity Aerospace electronics Supervised learning Predictive control Adaptive cruise control autonomous vehicle approximated MPC computation complexity deep neural networks model predictive control

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Image filtering method using trimmed statistics and edge preserving

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IET IMAGE PROCESSING 2018年第7期12卷 1086-1094页

作者： Cai, Weiling Yang, Ming Song, Fengyi Nanjing Normal Univ Dept Comp Sci & Technol Nanjing 210097 Jiangsu Peoples R China

Image filtering is to retain the details of the image as much as possible and meanwhile suppress the noise pollution to great extent. This study presents an image filtering using the truncated statistics and edge preserving. In the first step of our method, the alpha-trimmed filter is utilized to remove a variety of types of noises;in the second step, taking the image after alpha-trimmed filtering as a guide image, the local linear model between the guide image and the target image is established;in the third step, the obtained local linear model is further simplified to reduce the time complexity;and finally, using the relationship between image local variance and the global variance, the local linear model is modified to enhance the details of the image and meanwhile remove halo phenomenon. This method has three advantages: (i) it is flexible to deal with the images stained by various types of high-intensity noise;(ii) it is effective to keep the image details and profile information, and remove the halo phenomenon;and (iii) it runs in time linear in the image size, thus its computation complexity is low. Experimental results show that the proposed filter is robust and efficient.

关键词： image filtering image denoising computational complexity image filtering method trimmed statistics edge preserving noise pollution suppression subsequent image processing reliability truncated statistics alpha-trimmed filter noise removal guide image local linear model target image time complexity reduction image local variance global variance halo phenomenon removal high-intensity noise computation complexity

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Histogram-based cost aggregation strategy with joint bilateral filtering for stereo matching

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IET COMPUTER VISION 2016年第3期10卷 173-181页

作者： Fu, Limei Peng, Guohua Song, Weijie Northwestern Polytech Univ Sch Sci Xian 710072 Peoples R China

The edge-aware bilateral filter has been demonstrated to be effective for preserving depth edges, and disparity maps obtained from Fast Bilateral Stereo (FBS) have enhanced the efficiency of algorithm and the robustness to noise. However, they also lead to a non-perfect localisation of discontinuities. To overcome this issue, a new bilateral filtering based cost aggregation utilising colour statistical classification and similarity measurement within annular blocks is proposed in this study. We have adopted the similarity of histograms evaluated by Earth Mover Distance (EMD) to obtain the raw matching cost in the raised annular block, since histograms are very effective and efficient in capturing the distribution characteristics of visual features. For the weights aggregation, the spatial weight is assumed to be a constant. The colour weight is calculated by using a cluster-mean-value strategy, which is implemented by the local colour histogram. It improves the accuracy in the discontinuous areas. computation redundancy is reduced by disparity candidate selection using the local minimal relevancy in the corresponding annular blocks. We use the efficiency and accuracy to demonstrate the performance of our proposed method. Experimental results have shown that the proposed method reduces the mismatch at depth discontinuous and the computation complexity significantly.

关键词： stereo image processing filtering theory image enhancement image matching statistical analysis image colour analysis histogram-based cost aggregation strategy stereo matching edge-aware bilateral filter bilateral filtering based cost aggregation strategy colour histogram statistical classification similarity measurement Earth mover distance cluster-mean-value strategy disparity candidate selection computation complexity

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