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13th Asian Control Conference, ASCC 2022

作者： Xu, Zhichao Wu, Jundong Wang, Yawu China University of Geosciences School of Automation Wuhan China

ISBN: (纸本)9788993215236

To achieve the tracking control objective of the dielectric elastomer actuator (DEA), this paper proposes the fractional order modeling method and the fractional order internal model control method. Firstly, a fractional order transfer function (FOTF) model of the DEA is built to depict its complex "memory"property. Then, to achieve the computer control, an integer order approximation model (IOAM) of the FOTF model is built by using the Oustaloup filter. Considering that the order of the IOAM is too high, a reduced integer order approximation model is established by using the Ramer-Douglas-Peucker line simplification algorithm to facilitate the system controller design. Next, a fractional order internal model controller (FOIMC) is designed, and a reduced integer order approximation controller of the FOIMC is developed. Finally, some experiments are exerted to demonstrate the effectiveness of the proposed control method. © 2022 ACA.

关键词： Digital control Computational modeling Random access memory Transfer functions filtering algorithms Dielectric elastomers Approximation algorithms

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Combination of Elman Neural Network and Kalman Network for Modeling of Batch Distillation Process 13

Combination of Elman Neural Network and Kalman Network for M...

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13th Asian Control Conference, ASCC 2022

作者： Putri, Adi Novitarini Machbub, Carmadi Idris Hidayat, Egi Muhammad Institut Teknologi Bandung School of Electrical Engineering and Informatics 40132 Indonesia

ISBN: (纸本)9788993215236

Batch distillation is one of the nonlinear systems in a real-world application. Before designing and implementing a control system, a model is needed to capture its dynamics. The batch distillation process could be represented in a state-space model. Nonetheless, the sensor is limited so it could not access its states directly from the sensor. Then, the state estimation method is needed to estimate its state trajectories. On the other hand, state estimation is one of the fundamental problems in signal processing. Kalman Filter (KF) is an optimal filtering algorithm and the most widely used in signal processing. Meanwhile, deep learning is the most active and present topic nowadays on modeling and estimation. We use a recurrent neural network scheme to develop the Nonlinear State Space (NLSS) model. In this research, we use KalmanNet to overcome the state estimation problem if the noise statistic matrices are unknown. This idea has been done using a Long Short-Term Memory (LSTM) scheme which produces the Kalman Gain value. In this study, we compare conventional Kalman gain value and KalmanNet to find which one is better to solve the state estimation problem. From the experiment, the combination of the Elman Neural Network (ENN) model and KalmanNet which have the best performance from its Mean-Squared Error (MSE) value, 1.30E03. © 2022 ACA.

关键词： Recurrent neural networks Distillation equipment Signal processing filtering algorithms Control systems Trajectory State-space methods

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Improved Rabin-Karp Algorithm Using Bloom Filter 27

Improved Rabin-Karp Algorithm Using Bloom Filter

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27th International Computer Conference, Computer Society of Iran, CSICC 2022

作者： Moeini, Masoumeh Shahhoseini, Hadi Shahriar Iran University Of Science And Technology School Of Electrical Engineering Tehran Iran

ISBN: (纸本)9781665480277

String matching algorithms are used widely in computer science that is a very important issue in text processing. Also, string matching algorithms are used as basic components in the practical software in operating systems. A lot of research is performed in this field that hash based matching algorithms like Rabin-Karp is one of their fastest, but due to the rapid growth of data volumes, traditional algorithms are not sufficient. To solve this problem, combining Bloom filter features with string matching algorithms, makes it possible to reduce the execution time. In present paper, a modified version of the Rabin-Karp algorithm is presented by using a Bloom filter as preprocessing phase that can early detect pattern absence. In addition, this approach maximize speed up and reduce the number of patterns needed for the exact Rabin-Karp matching phase. Accordingly, the proposed results as well as the Rabin-Karp implementations have been compared to verify the impact of the proposed method. © 2022 IEEE.

关键词： Hash functions Matched filters Runtime Operating systems Software algorithms filtering algorithms Software

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Minimax Design of Graph Filter Using Chebyshev Polynomial Approximation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第5期68卷 1630-1634页

作者： Tseng, Chien-Cheng Lee, Su-Ling Natl Kaohsiung Univ Sci & Technol Dept Comp & Commun Engn Kaohsiung 824005 Taiwan Chang Jung Christian Univ Dept Comp Sci & Informat Engn Tainan 711301 Taiwan

In this brief, the minimax design problem of graph filter using Chebyshev polynomial approximation (CPA) is studied. First, conventional CPA graph filter design is investigated to show that it does not provide the minimax design, so the peak error of the spectral response of the designed filter is not minimized. Then, a spectral transformation is used to convert the minimax design problem of CPA graph filter into the one of the type-I linear-phase FIR digital filter such that the Parks-McClellan method can be employed to obtain the optimal filter coefficients of minimax design. Next, using the recurrence relation of Chebyshev polynomials, an implementation structure of CPA graph filter with low computational complexity is presented. Finally, a graph signal denoising application example is illustrated to show the usefulness of the proposed CPA graph filter.

关键词： Finite impulse response filters IIR filters Optimized production technology filtering algorithms Filter banks Chebyshev approximation Circuits and systems Chebyshev polynomial digital filter graph filter minimax design signal denoising

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LCL Filter based Grid-tied Converter with Single Current Sensor Approach 2

LCL Filter based Grid-tied Converter with Single Current Sen...

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2nd International Conference on Power, Control and Computing Technologies, ICPC2T 2022

作者： Kumari, Khushboo Jain, Amit Kumar Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi India

ISBN: (纸本)9781665458580

This work is dedicated to implementing a LCL filter-based grid-tied converter with a reduced sensor approach. A conventional system requires two grid-side current sensors, two capacitor-side voltage sensors, and two inverter-side current sensors. This paper has proposed a novel method that leads to a substantial reduction of the sensor. In this method, a single grid-side current sensor is required to perform the control methodology. In this technique, the grid current reference in the d-q axis is used for estimation of grid current in ß axis. As an outcome, the size, weight, cost, and complexity of the system are reduced. A comparative assessment is performed between the proposed system and the conventional system. The system offers a cost-efficient solution for grid integration. The proposed system is validated by simulation results obtained from Matlab/Simulink platform. © 2022 IEEE.

关键词： Costs Simulation Estimation Prototypes filtering algorithms Inverters Power system reliability

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Bilateral False Contour Elimination Filter-Based Image Bit-Depth Enhancement

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1585-1589页

作者： Peng, Changmeng Xia, Maohan Fu, Zhizhong Xu, Jin Li, Xiaofeng Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

Bit-depth enhancement is of significant research interest, and its main challenge lies in eliminating false contours in low-bit images. Existing algorithms either fail to effectively suppress false contours or rely on a vast number of samples and high-performance graphics cards, resulting in poor practicality. To solve these problems, we propose a bilateral false contour elimination filter-based bit-depth enhancement algorithm: BEF-BDE, which contains three innovative modules: Bilateral false contour Elimination Filter (BEF), iterative false contour elimination, and content-adaptive iteration number selection. Experiments on three datasets show that our method can achieve superior subjective and objective results.

关键词： Signal processing algorithms Image edge detection Tablet computers Measurement filtering algorithms Training Quantization (signal) Bit-depth enhancement false contour elimination bilateral filter image processing

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Multidimensional Blind Deconvolution Method Based on Cross-Sparse filtering for Weak Fault Diagnosis

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IEEE ACCESS 2020年 8卷 209415-209427页

作者： Wang, Shan Zhang, Zongzhen Wang, Jinrui Han, Baokun Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

Fault diagnosis and condition monitoring of rotating machinery has drawn considerable attention. The complex structure of rotating machinery and poor working conditions cause two challenges: weak signature detection (WSD) and weak compound fault separation (WCFS). A superior method should realize these two functions simultaneously. This paper proposed a multidimensional blind deconvolution method based on cross-sparse filtering (Cr-SF) for WSD and WCFS, which can enhance the weak signature and decompose the different components from compound fault adaptively without any preprocessing and priori knowledge. Cross kurtosis pursuit (CKP), a novel filter selection technology, is proposed for determining the final filters. The experimental and simulated signals verified the performance of the proposed algorithm. The robustness is also investigated using the success rate of repeated experiments. The results indicate that Cr-SF can handle different fault compounding modes under the strong noise environment and perform strong robustness and noise adaptability.

关键词： Fault diagnosis Handwriting recognition filtering algorithms Robustness Compounds Machinery Optimization Unsupervised learning cross-sparse filtering blind deconvolution weak signature detection compound fault separation

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Data Distribution Service Based on Symbol Bloom Filter for Large-Scale Distributed Computing 7

Data Distribution Service Based on Symbol Bloom Filter for L...

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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Liu, Zhexu Zhao, Zhen College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (纸本)9781665487115

Data distribution service (DDS) has been widely used for the data communication of distributed computing system. But the existing DDS is based on the Simple Discovery Protocol (SDP). Its high network data transmission cannot meet the real-time requirement of the large-scale distributed computing. For this problem, this paper proposes a lightweight automatic discovery algorithm for DDS. In this algorithm, a new Bloom Filter with symbol is presented to compress the information transmitted between the distributed computing nodes, which is called Symbol Bloom Filter (SBF). Then it is combined with the SDP to reduce the consumption of the automatic discovery process in DDS. Experiments show that the proposed SDP_SBF can present a lower fault positive ratio while reducing data transmission, and therefore improves the real-time performance of DDS in the large-scale distributed computing. © 2022 IEEE.

关键词： Cloud computing Protocols Symbols Distributed databases filtering algorithms Information filters Real-time systems

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Research on Escaping the Big-Data Traps in O2O Service Recommendation Strategy

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IEEE TRANSACTIONS ON BIG DATA 2021年第1期7卷 199-213页

作者： Xiao Xue Shuai Huangfu Zhang, Lejun Wang, Shufang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454010 Henan Peoples R China Henan Polytech Univ Jiaozuo 454010 Henan Peoples R China Yangzhou Univ Sch Comp Sci & Technol Yangzhou Jiangsu Peoples R China Tianjin Normal Univ Tianjin Peoples R China

Internet business can be divided into two categories: pure online business and Online to Offline (O2O) business. Currently, the recommendation technology for online business is maturing, such as news, movies, products, and so forth. However, traditional recommendation technology can easily cause the overcrowding at some O2O services because of the big data traps. In the end, the users' experience with the O2O service recommendation is useless or very poor because they have to wait for a long time and can't enjoy the service immediately. Hence, how to improve the performance of O2O service recommendation has become a vital problem. To solve the problem, this paper proposes a research framework based on the continuous feedback learning mechanism between cyber layer and social layer. Then, the continuous feedback ideas are implemented in the design of the O2O service recommendation strategy step by step. Furthermore, the computational experiment system is constructed to perform performance analysis of these service strategies. The results show that our research framework is conductive to help O2O service recommendation to escape the big-data traps and to improve user experience.

关键词： Big Data Business Prediction algorithms Clustering algorithms Learning systems Collaboration filtering algorithms Online to offline continuous feedback learning service recommendation strategy computational experiment

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Accelerated 4PCS Algorithm for Point Cloud Registration 27

Accelerated 4PCS Algorithm for Point Cloud Registration

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27th International Computer Conference, Computer Society of Iran, CSICC 2022

作者： Pazoki, Fatemeh Mousavinia, Amir Abdoni, Fatemeh K. N. Toosi University Of Technology Electrical Engineering Department Tehran Iran

ISBN: (纸本)9781665480277

Point cloud registration is one of the most vital steps in 3D machine vision and photogrammetry. Most registration methods have low accuracy and speed, and 4PCS (Four Points Congruent Sets), one of the primary methods, is no exception. That is due to the vastness of the search space for the corresponding points, which usually yields a large number of candidate points. By down-sampling the input point cloud and then adding a new feature in the search step, this article presents a modified 4PCS algorithm that is much faster without reducing the accuracy. Simulation results show at least a 60-percentage increase in speed. © 2022 IEEE.

关键词： Point cloud compression Three-dimensional displays Simulation Object segmentation filtering algorithms Labeling

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