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A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 8,   Pages 617-633 doi: 10.1631/FITEE.1400263

Abstract: local discriminant regularized soft -means (LDRSKM) method with Bayesian inference is proposed for multimode

Keywords： Multimode process monitoring     Local discriminant regularized soft k-means clustering     Kernel support    

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A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Engineering 2021, Volume 7, Issue 9,   Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028

Abstract:

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the
collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.

Keywords： Process monitoring     Multimode process     Dictionary learning     Transfer learning    

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Ten-channel mode-division-multiplexed silicon photonic integrated circuit with sharp bends Special Feature on Optoelectronic Devices and Inte

Chen-lei LI, Xiao-hui JIANG, Yung HSU, Guan-hong CHEN, Chi-wai CHOW, Dao-xin DAI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 498-506 doi: 10.1631/FITEE.1800386

Abstract:

A multimode silicon photonic integrated circuit (PIC) comprising a pair of on-chip mode (de)multiplexerswith 10-mode channels and a multimode bus waveguide with sharp bends is demonstrated to enable multi-channelThe core width of the multimode bus waveguide is chosen such that it can support 10 guided modes, ofThis multimode bus waveguide comprises sharp bends based on modified Euler curves.

Keywords： Silicon     Multimode     Waveguide     Euler-bends    

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A multistandard and resource-efficient Viterbi decoder for a multimode communication system None

Yi-qi XIE, Zhi-guo YU, Yang FENG, Lin-na ZHAO, Xiao-feng GU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 536-543 doi: 10.1631/FITEE.1601596

Abstract: We present a novel standard convolutional symbols generator (SCSG) block for a multi-parameter reconfigurable Viterbi decoder to optimize resource consumption and adaption of multiple parameters. The SCSG block generates all the states and calculates all the possible standard convolutional symbols corresponding to the states using an iterative approach. The architecture of the Viterbi decoder based on the SCSG reduces resource consumption for recalculating the branch metrics and rearranging the correspondence between branch metrics and transition paths. The proposed architecture supports constraint lengths from 3 to 9, code rates of 1/2, 1/3, and 1/4, and fully optional polynomials. The proposed Viterbi decoder has been implemented on the Xilinx XC7VX485T device with a high throughput of about 200 Mbps and a low resource consumption of 162k logic gates.

Keywords： Reconfigurable Viterbi decoder     Multi-parameter     Low resource consumption     Standard convolutional symbols generator (SCSG)     Fully optional polynomials    

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Title Author Date Type Operation

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Shi-jin REN,Yin LIANG,Xiang-jun ZHAO,Mao-yun YANG

Journal Article

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Journal Article

Ten-channel mode-division-multiplexed silicon photonic integrated circuit with sharp bends

Chen-lei LI, Xiao-hui JIANG, Yung HSU, Guan-hong CHEN, Chi-wai CHOW, Dao-xin DAI

Journal Article

A multistandard and resource-efficient Viterbi decoder for a multimode communication system

Yi-qi XIE, Zhi-guo YU, Yang FENG, Lin-na ZHAO, Xiao-feng GU

Journal Article