Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on industrial informatics Ročník 14; číslo 7; s. 3235 - 3243
Hlavní autori:	Yuan, Xiaofeng, Huang, Biao, Wang, Yalin, Yang, Chunhua, Gui, Weihua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Correlation analysis Data mining Deep learning Feature extraction Feature recognition Industrial applications Informatics Machine learning Monitoring Multilayers Neural networks output prediction Pattern recognition Process control Representations Sensors soft sensor stacked autoencoder (SAE) Training variable-wise weighted SAE (VW-SAE)
ISSN:	1551-3203, 1941-0050
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract feature extraction in pattern recognition areas, which also have great potential for soft sensing applications. Hence, deep stacked autoencoder (SAE) is introduced for soft sensor in this paper. As for output prediction purpose, traditional deep learning algorithms cannot extract high-level output-related features. Thus, a novel variable-wise weighted stacked autoencoder (VW-SAE) is proposed for hierarchical output-related feature representation layer by layer. By correlation analysis with the output variable, important variables are identified from other ones in the input layer of each autoencoder. The variables are assigned with different weights accordingly. Then, variable-wise weighted autoencoders are designed and stacked to form deep networks. An industrial application shows that the proposed VW-SAE can give better prediction performance than the traditional multilayer neural networks and SAE.
AbstractList	In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract feature extraction in pattern recognition areas, which also have great potential for soft sensing applications. Hence, deep stacked autoencoder (SAE) is introduced for soft sensor in this paper. As for output prediction purpose, traditional deep learning algorithms cannot extract high-level output-related features. Thus, a novel variable-wise weighted stacked autoencoder (VW-SAE) is proposed for hierarchical output-related feature representation layer by layer. By correlation analysis with the output variable, important variables are identified from other ones in the input layer of each autoencoder. The variables are assigned with different weights accordingly. Then, variable-wise weighted autoencoders are designed and stacked to form deep networks. An industrial application shows that the proposed VW-SAE can give better prediction performance than the traditional multilayer neural networks and SAE.
Author	Gui, Weihua Yuan, Xiaofeng Wang, Yalin Yang, Chunhua Huang, Biao
Author_xml	– sequence: 1 givenname: Xiaofeng orcidid: 0000-0002-9072-7179 surname: Yuan fullname: Yuan, Xiaofeng email: yuanxf@csu.edu.cn organization: College of Information Science and Engineering, Central South University, Changsha, China – sequence: 2 givenname: Biao orcidid: 0000-0001-9082-2216 surname: Huang fullname: Huang, Biao email: biao.huang@ualberta.ca organization: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada – sequence: 3 givenname: Yalin orcidid: 0000-0002-1876-7707 surname: Wang fullname: Wang, Yalin email: ylwang@csu.edu.cn organization: College of Information Science and Engineering, Central South University, Changsha, China – sequence: 4 givenname: Chunhua orcidid: 0000-0003-2550-1509 surname: Yang fullname: Yang, Chunhua email: ychh@csu.edu.cn organization: College of Information Science and Engineering, Central South University, Changsha, China – sequence: 5 givenname: Weihua orcidid: 0000-0003-0312-436X surname: Gui fullname: Gui, Weihua email: gwh@csu.edu.cn organization: College of Information Science and Engineering, Central South University, Changsha, China
BookMark	eNp9UU1LAzEUDKLg513wEvC89SVxv461fhUqgq32uGQ3L23Kml2TFPHfm7LiwYOnNzxm5jHzjsm-7SwScs5gxBiUV4vpdMSBFSNeQJkL2CNHrLxmCUAK-xGnKUsEB3FIjr3fAIgcRHlEPm8RezpD6ayxq-RGelT0HmXYOqQv2Dv0aIMMprNUWkWnwdNx37emGXa6c3Te6UDnaH3ET53CNjrRpQlr-iadkXWLydJ4pEs0q3WI_vPx3Sk50LL1ePYzT8jr_d1i8pjMnh-mk_EsaXjJQtIwXbOCYwl5lrGGa54zhKIuUuRCNLWSecpQ17rQtVClUAp0ncZojeKZElKckMvBt3fdxxZ9qDbd1tl4suKQXQsGLBWRlQ2sxnXeO9RVY4bQwUnTVgyqXclVLLnalVz9lByF8EfYO_Mu3dd_kotBYhDxl14I4PFd4htzLInk
CODEN	ITIICH
CitedBy_id	crossref_primary_10_1002_cjce_25483 crossref_primary_10_1109_TCYB_2019_2923756 crossref_primary_10_1016_j_jprocont_2019_11_012 crossref_primary_10_1109_JSEN_2024_3398032 crossref_primary_10_1016_j_trac_2020_115901 crossref_primary_10_1002_cjce_70051 crossref_primary_10_1016_j_chemolab_2019_01_008 crossref_primary_10_1007_s13198_022_01744_9 crossref_primary_10_3390_s20216350 crossref_primary_10_1109_LRA_2021_3091012 crossref_primary_10_1007_s10845_023_02254_6 crossref_primary_10_1109_TII_2021_3065377 crossref_primary_10_1016_j_ifacol_2020_12_703 crossref_primary_10_1016_j_rineng_2025_107127 crossref_primary_10_1007_s11042_021_11043_3 crossref_primary_10_1016_j_jprocont_2019_11_007 crossref_primary_10_1109_TIE_2020_2984443 crossref_primary_10_1109_JSEN_2021_3102586 crossref_primary_10_1016_j_compchemeng_2020_106964 crossref_primary_10_1016_j_aej_2025_05_078 crossref_primary_10_1016_j_jprocont_2023_04_002 crossref_primary_10_1109_TASE_2024_3506835 crossref_primary_10_1109_TIM_2024_3400358 crossref_primary_10_1016_j_conengprac_2020_104458 crossref_primary_10_1109_TIE_2020_3014574 crossref_primary_10_1002_cite_202100134 crossref_primary_10_1109_JSEN_2022_3219253 crossref_primary_10_1109_TII_2021_3053128 crossref_primary_10_1016_j_ifacol_2021_08_217 crossref_primary_10_1109_JSEN_2025_3563586 crossref_primary_10_1109_JSTARS_2024_3491294 crossref_primary_10_1109_ACCESS_2021_3053045 crossref_primary_10_1016_j_neucom_2018_11_107 crossref_primary_10_1109_ACCESS_2020_2965623 crossref_primary_10_1016_j_ins_2021_12_106 crossref_primary_10_1109_JSEN_2021_3056210 crossref_primary_10_1016_j_jwpe_2023_104654 crossref_primary_10_1109_TIM_2020_2992873 crossref_primary_10_1002_apj_70048 crossref_primary_10_1016_j_ifacol_2018_09_443 crossref_primary_10_1109_TII_2021_3078414 crossref_primary_10_1016_j_renene_2025_123680 crossref_primary_10_1109_ACCESS_2024_3409899 crossref_primary_10_1016_j_vibspec_2022_103450 crossref_primary_10_1109_JSTARS_2023_3342986 crossref_primary_10_1109_TCYB_2024_3365068 crossref_primary_10_1080_14498596_2024_2305119 crossref_primary_10_3390_math11081777 crossref_primary_10_1109_JSEN_2018_2882201 crossref_primary_10_3390_pr12030495 crossref_primary_10_1016_j_eswa_2022_119208 crossref_primary_10_1016_j_isatra_2021_01_002 crossref_primary_10_1109_ACCESS_2019_2907328 crossref_primary_10_1016_j_asoc_2022_108898 crossref_primary_10_1002_cjce_25145 crossref_primary_10_1016_j_ins_2020_03_018 crossref_primary_10_1016_j_jprocont_2020_07_009 crossref_primary_10_1109_TCYB_2020_3010331 crossref_primary_10_1109_JSEN_2024_3375072 crossref_primary_10_1109_TII_2022_3202909 crossref_primary_10_1016_j_cjche_2025_02_011 crossref_primary_10_1109_TII_2024_3424510 crossref_primary_10_1155_2022_2806183 crossref_primary_10_1016_j_conengprac_2021_104784 crossref_primary_10_1109_TIM_2022_3222506 crossref_primary_10_1109_TITS_2021_3072743 crossref_primary_10_1109_ACCESS_2023_3334012 crossref_primary_10_1002_cjce_25676 crossref_primary_10_1007_s11869_020_00795_w crossref_primary_10_1109_JAS_2024_124227 crossref_primary_10_1177_09596518221082857 crossref_primary_10_1007_s12596_023_01187_w crossref_primary_10_1109_TII_2019_2941750 crossref_primary_10_1109_TC_2021_3129342 crossref_primary_10_3390_s23229175 crossref_primary_10_1109_TII_2019_2893125 crossref_primary_10_3390_diagnostics10010027 crossref_primary_10_1016_j_mineng_2023_108179 crossref_primary_10_1109_TIM_2020_2977793 crossref_primary_10_1109_TCYB_2022_3152107 crossref_primary_10_1002_cjce_25447 crossref_primary_10_1016_j_ress_2021_108082 crossref_primary_10_1109_TII_2021_3090753 crossref_primary_10_1007_s00521_023_09198_1 crossref_primary_10_1016_j_compchemeng_2024_108707 crossref_primary_10_1109_TIM_2022_3219482 crossref_primary_10_1016_j_conengprac_2019_07_016 crossref_primary_10_1016_j_chemolab_2023_104878 crossref_primary_10_1109_TIM_2021_3122187 crossref_primary_10_1016_j_asoc_2024_111551 crossref_primary_10_1016_j_jprocont_2022_11_001 crossref_primary_10_1016_j_conengprac_2018_05_002 crossref_primary_10_1109_TII_2022_3182343 crossref_primary_10_1016_j_conengprac_2019_104187 crossref_primary_10_3390_electronics9111837 crossref_primary_10_1016_j_jprocont_2021_10_011 crossref_primary_10_1088_1361_6501_ad6684 crossref_primary_10_1109_TGRS_2020_3005431 crossref_primary_10_1002_cjce_70101 crossref_primary_10_12688_f1000research_73108_1 crossref_primary_10_1109_TIM_2021_3058367 crossref_primary_10_3390_pr10112408 crossref_primary_10_1007_s00202_024_02638_8 crossref_primary_10_12688_f1000research_73108_2 crossref_primary_10_1088_1361_6501_ab5f1b crossref_primary_10_1016_j_conengprac_2019_104198 crossref_primary_10_1016_j_conengprac_2018_11_006 crossref_primary_10_1002_cjce_24790 crossref_primary_10_3390_pr8091079 crossref_primary_10_1088_1361_6501_ac5280 crossref_primary_10_1007_s10845_020_01581_2 crossref_primary_10_1109_TNNLS_2019_2951708 crossref_primary_10_1007_s00170_023_12678_2 crossref_primary_10_1016_j_engappai_2021_104341 crossref_primary_10_1109_TIM_2023_3324003 crossref_primary_10_1016_j_eswa_2022_119193 crossref_primary_10_1109_TASE_2019_2935314 crossref_primary_10_3390_pr13030706 crossref_primary_10_1109_TII_2018_2875149 crossref_primary_10_1016_j_conengprac_2024_105955 crossref_primary_10_1109_TIM_2025_3551563 crossref_primary_10_1109_TIM_2023_3284017 crossref_primary_10_1109_TIM_2024_3502771 crossref_primary_10_1109_TSMC_2021_3130232 crossref_primary_10_3390_s21216971 crossref_primary_10_1016_j_knosys_2025_113495 crossref_primary_10_1109_TII_2023_3295428 crossref_primary_10_1109_ACCESS_2019_2926448 crossref_primary_10_1109_TII_2020_2969709 crossref_primary_10_1109_TII_2023_3333921 crossref_primary_10_1177_09544070241275724 crossref_primary_10_1016_j_cej_2021_130298 crossref_primary_10_1109_ACCESS_2018_2877447 crossref_primary_10_1016_j_jprocont_2020_01_009 crossref_primary_10_1016_j_measurement_2019_06_029 crossref_primary_10_1016_j_ins_2022_02_049 crossref_primary_10_1016_j_engappai_2025_111681 crossref_primary_10_1109_TIE_2019_2898619 crossref_primary_10_1109_TIM_2024_3502784 crossref_primary_10_1109_TII_2018_2880968 crossref_primary_10_1016_j_compchemeng_2023_108324 crossref_primary_10_3390_s21123991 crossref_primary_10_1002_cem_3185 crossref_primary_10_1016_j_asoc_2023_110608 crossref_primary_10_1109_JSEN_2023_3336789 crossref_primary_10_1016_j_jtice_2023_105117 crossref_primary_10_1371_journal_pone_0266373 crossref_primary_10_1016_j_jprocont_2022_04_018 crossref_primary_10_1007_s10586_025_05319_8 crossref_primary_10_1016_j_bej_2021_108048 crossref_primary_10_1016_j_measurement_2020_108158 crossref_primary_10_1016_j_ces_2023_118958 crossref_primary_10_1016_j_ces_2020_115509 crossref_primary_10_1109_TASE_2023_3302794 crossref_primary_10_1016_j_ins_2021_03_026 crossref_primary_10_1109_TIM_2022_3180405 crossref_primary_10_1016_j_jtice_2024_105666 crossref_primary_10_1109_TCYB_2020_3038832 crossref_primary_10_3390_s21051846 crossref_primary_10_1109_ACCESS_2020_3009562 crossref_primary_10_1109_TII_2019_2901934 crossref_primary_10_1109_ACCESS_2019_2894764 crossref_primary_10_1109_TASE_2019_2957232 crossref_primary_10_1109_TNNLS_2019_2957366 crossref_primary_10_1016_j_eswa_2023_121428 crossref_primary_10_1016_j_compchemeng_2023_108463 crossref_primary_10_1088_1361_6501_ad66f7 crossref_primary_10_1002_cem_3040 crossref_primary_10_1016_j_measurement_2025_116761 crossref_primary_10_1109_TII_2021_3116528 crossref_primary_10_1109_TII_2024_3431087 crossref_primary_10_1109_TII_2018_2886048 crossref_primary_10_1109_TII_2022_3209238 crossref_primary_10_1109_TIM_2024_3450080 crossref_primary_10_1016_j_apenergy_2022_119608 crossref_primary_10_1109_TII_2020_3031497 crossref_primary_10_1109_TIM_2020_2993980 crossref_primary_10_1109_TII_2021_3129888 crossref_primary_10_1109_JIOT_2024_3434732 crossref_primary_10_3390_e23030339 crossref_primary_10_1007_s11042_020_09762_0 crossref_primary_10_1111_cote_12609 crossref_primary_10_1109_TII_2019_2952429 crossref_primary_10_1109_TII_2020_3008223 crossref_primary_10_1109_TBDATA_2022_3160477 crossref_primary_10_1109_TII_2023_3329684 crossref_primary_10_1109_TIM_2018_2884450 crossref_primary_10_1109_TNNLS_2022_3144162 crossref_primary_10_1016_j_conengprac_2022_105109 crossref_primary_10_1109_TIM_2021_3135537 crossref_primary_10_1002_ese3_392 crossref_primary_10_1109_TII_2023_3240923 crossref_primary_10_1002_cjce_23665 crossref_primary_10_1049_ipr2_13108 crossref_primary_10_1109_JAS_2025_125204 crossref_primary_10_1016_j_compind_2021_103489 crossref_primary_10_1088_1361_6501_addf65 crossref_primary_10_1016_j_conengprac_2023_105613 crossref_primary_10_1088_1361_6501_ad7483 crossref_primary_10_1109_TIM_2021_3075754 crossref_primary_10_1109_TII_2021_3127204 crossref_primary_10_1080_24725854_2023_2242434 crossref_primary_10_1109_TNNLS_2024_3415072 crossref_primary_10_1016_j_jprocont_2024_103300 crossref_primary_10_1088_1361_6501_ad7a14 crossref_primary_10_1002_cjce_24886 crossref_primary_10_1002_cjce_24404 crossref_primary_10_1016_j_jprocont_2024_103301 crossref_primary_10_1109_TII_2018_2869899 crossref_primary_10_1109_TII_2018_2868687 crossref_primary_10_1109_TITS_2021_3076607 crossref_primary_10_1155_2021_1682163 crossref_primary_10_1016_j_phycom_2022_101940 crossref_primary_10_1177_1477153520926200 crossref_primary_10_1007_s00521_021_05776_3 crossref_primary_10_1007_s10489_025_06368_7 crossref_primary_10_1109_TAI_2022_3145758 crossref_primary_10_1109_TIM_2020_3026456 crossref_primary_10_1109_TII_2019_2950272 crossref_primary_10_3390_s19245368 crossref_primary_10_1002_cjce_25745 crossref_primary_10_1109_TII_2021_3104111 crossref_primary_10_1016_j_chemolab_2020_104050 crossref_primary_10_1016_j_energy_2024_132477 crossref_primary_10_1109_JSEN_2023_3274156 crossref_primary_10_1109_TIM_2020_3039612 crossref_primary_10_1155_2022_5450473 crossref_primary_10_1109_TII_2018_2841658 crossref_primary_10_1109_TIM_2024_3394496 crossref_primary_10_1016_j_ifacsc_2021_100150 crossref_primary_10_1016_j_eswa_2021_115389 crossref_primary_10_1016_j_compchemeng_2023_108182 crossref_primary_10_1109_TIM_2022_3200088 crossref_primary_10_1002_cjce_23739 crossref_primary_10_1109_TII_2018_2873492 crossref_primary_10_1016_j_jprocont_2025_103401 crossref_primary_10_1109_TII_2022_3227731 crossref_primary_10_1109_TII_2023_3248059 crossref_primary_10_1016_j_chemolab_2020_104062 crossref_primary_10_1016_j_jprocont_2021_04_003 crossref_primary_10_1016_j_jprocont_2023_01_010 crossref_primary_10_1109_TII_2020_3008021 crossref_primary_10_1016_j_jprocont_2018_05_010 crossref_primary_10_3390_s25144303 crossref_primary_10_3390_s22155861 crossref_primary_10_1002_cem_3225 crossref_primary_10_1016_j_psep_2021_08_008 crossref_primary_10_1016_j_isatra_2025_05_044 crossref_primary_10_1016_j_aei_2023_102220 crossref_primary_10_1109_TASE_2019_2956087 crossref_primary_10_1016_j_jprocont_2020_11_012 crossref_primary_10_1109_TII_2020_2983749 crossref_primary_10_1109_TII_2021_3093386 crossref_primary_10_1109_TII_2019_2959784 crossref_primary_10_1007_s00521_024_10539_x crossref_primary_10_1016_j_measurement_2020_108109 crossref_primary_10_1109_TII_2024_3371990 crossref_primary_10_3390_app11094113 crossref_primary_10_1049_el_2020_1857 crossref_primary_10_1109_TIM_2022_3205679 crossref_primary_10_1088_1757_899X_1049_1_012004 crossref_primary_10_1109_TAI_2021_3134186 crossref_primary_10_1109_TII_2020_3000204 crossref_primary_10_1007_s11581_024_05868_9 crossref_primary_10_1109_TIM_2020_2968162 crossref_primary_10_1002_cjce_23753 crossref_primary_10_1016_j_isatra_2019_07_001 crossref_primary_10_1109_TIM_2025_3578182 crossref_primary_10_1109_TII_2018_2885363 crossref_primary_10_3390_s21103430 crossref_primary_10_1109_TII_2023_3297663 crossref_primary_10_1080_00986445_2019_1641488 crossref_primary_10_1109_TII_2021_3130411 crossref_primary_10_1016_j_measurement_2020_108200 crossref_primary_10_1016_j_measurement_2020_108566 crossref_primary_10_3390_s20133743 crossref_primary_10_1109_TSM_2021_3088855 crossref_primary_10_1002_srin_202400872 crossref_primary_10_1016_j_conengprac_2025_106322 crossref_primary_10_1109_TII_2018_2889750 crossref_primary_10_3390_act13010038 crossref_primary_10_1016_j_neucom_2019_04_090 crossref_primary_10_1016_j_isatra_2019_08_023 crossref_primary_10_1109_ACCESS_2020_2968033 crossref_primary_10_1109_TII_2019_2952931 crossref_primary_10_1109_TII_2024_3475419 crossref_primary_10_3390_foods12122322 crossref_primary_10_1002_cjce_23934 crossref_primary_10_1016_j_neunet_2020_11_006 crossref_primary_10_1016_j_neucom_2021_07_086 crossref_primary_10_1016_j_chemolab_2020_104086 crossref_primary_10_1109_JSEN_2024_3351431 crossref_primary_10_1016_j_ifacol_2020_12_114 crossref_primary_10_1109_TASE_2023_3290352 crossref_primary_10_1109_TIM_2023_3264046 crossref_primary_10_1007_s11042_022_12502_1 crossref_primary_10_1109_ACCESS_2019_2961414 crossref_primary_10_1109_TNNLS_2023_3276593 crossref_primary_10_1631_FITEE_2000426 crossref_primary_10_1109_TIM_2020_2985614 crossref_primary_10_1109_JSEN_2021_3096215 crossref_primary_10_1016_j_compchemeng_2022_107857 crossref_primary_10_1109_TNNLS_2023_3321691 crossref_primary_10_1088_1361_6501_ad7a93 crossref_primary_10_1088_1361_6501_addc87 crossref_primary_10_1016_j_chemolab_2019_103921 crossref_primary_10_1016_j_measurement_2023_113477 crossref_primary_10_3390_rs13071368 crossref_primary_10_1016_j_psep_2021_09_024 crossref_primary_10_1109_TII_2019_2951622 crossref_primary_10_1016_j_ifacol_2020_12_126 crossref_primary_10_1002_cjce_24808 crossref_primary_10_1016_j_ins_2021_09_056 crossref_primary_10_1002_cem_3134 crossref_primary_10_1016_j_conengprac_2024_106181 crossref_primary_10_1088_1361_6501_ac7b6b crossref_primary_10_1016_j_compchemeng_2021_107418 crossref_primary_10_1016_j_chemolab_2025_105535 crossref_primary_10_1109_TIM_2020_3011251 crossref_primary_10_1088_1361_6501_acf665 crossref_primary_10_3389_frai_2020_00041 crossref_primary_10_1109_TII_2019_2945411 crossref_primary_10_1109_TNNLS_2024_3453288 crossref_primary_10_1109_TCYB_2019_2948202 crossref_primary_10_1109_TIM_2025_3595248 crossref_primary_10_1109_JSEN_2020_3003826 crossref_primary_10_1109_TASE_2025_3561754 crossref_primary_10_1016_j_ifacol_2022_07_549 crossref_primary_10_1016_j_jfranklin_2020_04_024 crossref_primary_10_1016_j_engappai_2024_108361 crossref_primary_10_1109_TII_2021_3110507 crossref_primary_10_1109_ACCESS_2021_3052217 crossref_primary_10_1088_1361_6501_aba6b9 crossref_primary_10_1002_cjce_23968 crossref_primary_10_1016_j_measurement_2023_113491 crossref_primary_10_1016_j_eswa_2023_120097 crossref_primary_10_1016_j_isatra_2021_07_030 crossref_primary_10_1109_TII_2022_3161990 crossref_primary_10_1109_JSEN_2023_3279203 crossref_primary_10_3390_pr13051526 crossref_primary_10_3390_pr12040663 crossref_primary_10_1007_s00500_022_07022_x crossref_primary_10_1109_TII_2020_3046278 crossref_primary_10_1109_ACCESS_2018_2825451 crossref_primary_10_1016_j_neucom_2023_01_010 crossref_primary_10_1109_TIM_2024_3427786 crossref_primary_10_1109_ACCESS_2025_3607733 crossref_primary_10_1016_j_cie_2020_106284 crossref_primary_10_1109_TII_2024_3399932 crossref_primary_10_3390_pr8050518 crossref_primary_10_1109_TII_2019_2902129 crossref_primary_10_1109_TIM_2020_3009354 crossref_primary_10_1109_TIM_2020_3031186 crossref_primary_10_1109_ACCESS_2020_2994913 crossref_primary_10_1109_JSEN_2020_2965988 crossref_primary_10_1109_TAI_2022_3217028 crossref_primary_10_1109_ACCESS_2019_2903906 crossref_primary_10_3390_pr12040676 crossref_primary_10_1515_htmp_2024_0067 crossref_primary_10_1016_j_conengprac_2019_05_012 crossref_primary_10_1109_TII_2023_3281835 crossref_primary_10_1515_cppm_2018_0066 crossref_primary_10_1007_s13762_022_03940_3 crossref_primary_10_1016_j_ins_2020_06_062 crossref_primary_10_1109_JSEN_2024_3367909 crossref_primary_10_1016_j_conengprac_2024_106020 crossref_primary_10_1109_TASE_2019_2896205 crossref_primary_10_1109_TII_2019_2938890 crossref_primary_10_1016_j_chemolab_2022_104679 crossref_primary_10_1088_1361_6501_ad8be6 crossref_primary_10_1016_j_conengprac_2021_104914 crossref_primary_10_1109_TASE_2023_3259165 crossref_primary_10_1016_j_jprocont_2021_11_001 crossref_primary_10_3390_polym14051018 crossref_primary_10_1007_s40194_021_01211_2 crossref_primary_10_1109_ACCESS_2020_3006729 crossref_primary_10_1016_j_jprocont_2020_05_015 crossref_primary_10_1016_j_jprocont_2020_05_012 crossref_primary_10_1109_JSEN_2023_3266104 crossref_primary_10_1109_TII_2022_3181692 crossref_primary_10_1109_TII_2023_3268740 crossref_primary_10_1109_ACCESS_2021_3057382 crossref_primary_10_1109_TIE_2019_2891449 crossref_primary_10_1016_j_aei_2020_101136 crossref_primary_10_1016_j_aei_2021_101434 crossref_primary_10_1049_smt2_12092 crossref_primary_10_1109_TII_2021_3086798 crossref_primary_10_1109_TII_2021_3131471 crossref_primary_10_1016_j_conengprac_2018_10_005 crossref_primary_10_1016_j_conengprac_2020_104626 crossref_primary_10_1016_j_ins_2019_11_039 crossref_primary_10_1109_TII_2020_3027746 crossref_primary_10_1109_TIM_2019_2943824 crossref_primary_10_1016_j_ces_2021_116560 crossref_primary_10_1109_TIE_2023_3279576 crossref_primary_10_1016_j_jprocont_2021_07_013 crossref_primary_10_1016_j_jprocont_2025_103373 crossref_primary_10_1016_j_jtice_2022_104483 crossref_primary_10_1109_ACCESS_2021_3079184 crossref_primary_10_1109_TII_2020_3000194 crossref_primary_10_1080_13504851_2025_2491726 crossref_primary_10_1002_srin_202200680 crossref_primary_10_1016_j_engappai_2024_109711 crossref_primary_10_1016_j_measurement_2020_108782 crossref_primary_10_1109_TIM_2024_3493884 crossref_primary_10_1109_TIM_2024_3472794 crossref_primary_10_1016_j_sna_2021_112803 crossref_primary_10_1109_ACCESS_2020_2966260 crossref_primary_10_1109_TII_2019_2902560 crossref_primary_10_1016_j_asoc_2020_107072 crossref_primary_10_1088_1361_6501_ac86e6 crossref_primary_10_1109_ACCESS_2021_3110510 crossref_primary_10_1109_TCSS_2023_3272330 crossref_primary_10_1007_s11356_023_27630_w crossref_primary_10_3390_pr12091807 crossref_primary_10_1109_TNNLS_2020_2985223 crossref_primary_10_1016_j_chemolab_2018_09_009 crossref_primary_10_1145_3420038 crossref_primary_10_1088_1361_6501_ad5612 crossref_primary_10_1109_TIM_2021_3094834 crossref_primary_10_1016_j_apenergy_2021_116847 crossref_primary_10_1016_j_bspc_2023_105039 crossref_primary_10_1016_j_dche_2021_100008 crossref_primary_10_1016_j_cherd_2024_11_003 crossref_primary_10_1109_TSMC_2023_3322195 crossref_primary_10_1109_TII_2025_3547031 crossref_primary_10_3390_s21124043 crossref_primary_10_1109_JSTARS_2021_3103176 crossref_primary_10_1007_s11277_024_10958_y crossref_primary_10_1109_TII_2024_3353921 crossref_primary_10_1016_j_knosys_2022_108510 crossref_primary_10_1109_TIE_2018_2868316 crossref_primary_10_1016_j_conengprac_2019_104235 crossref_primary_10_1109_TIM_2024_3373085 crossref_primary_10_1016_j_chemolab_2025_105499 crossref_primary_10_1016_j_arcontrol_2022_09_005 crossref_primary_10_1016_j_chemolab_2022_104624 crossref_primary_10_1016_j_ins_2020_04_013 crossref_primary_10_1088_1361_6501_aceb82 crossref_primary_10_1016_j_knosys_2021_107350 crossref_primary_10_1016_j_energy_2023_127846 crossref_primary_10_1016_j_ins_2020_11_039 crossref_primary_10_1109_TCYB_2021_3067786 crossref_primary_10_1109_TIM_2023_3278292 crossref_primary_10_1109_TIM_2024_3428604 crossref_primary_10_1371_journal_pone_0321559 crossref_primary_10_1109_TII_2022_3220857 crossref_primary_10_1016_j_ins_2021_12_131 crossref_primary_10_1016_j_isatra_2020_03_011
Cites_doi	10.1002/aic.14299 10.1109/TIE.2017.2733448 10.1016/j.jprocont.2013.05.007 10.1109/TIM.2017.2658158 10.1002/aic.14663 10.1038/nature14539 10.1016/j.compchemeng.2007.07.005 10.1109/TPAMI.2016.2577031 10.1016/j.ultras.2016.09.011 10.1109/TIE.2017.2739691 10.1109/TIM.2006.887331 10.1109/TII.2012.2214394 10.1109/TIE.2017.2733443 10.1109/TII.2016.2612640 10.1109/TII.2016.2610839 10.1016/j.compchemeng.2008.12.012 10.1109/TNNLS.2016.2599820 10.1021/ie4041252 10.1109/TPAMI.2013.50 10.1016/j.jprocont.2014.01.012 10.1109/TIE.2016.2622668 10.1109/SMC.2017.8122666 10.1162/neco.2006.18.7.1527 10.1109/TII.2009.2025124 10.1109/TII.2013.2283147 10.1016/j.conengprac.2004.04.013
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D
DOI	10.1109/TII.2018.2809730
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1941-0050
EndPage	3243
ExternalDocumentID	10_1109_TII_2018_2809730 8302941
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) grantid: 61703440 funderid: 10.13039/501100001809 – fundername: National Natural Science Foundation of China; NSFC grantid: 61621062 funderid: 10.13039/501100001809 – fundername: National Natural Science Foundation of China; NSFC grantid: 61590921; B17048 funderid: 10.13039/501100001809
GroupedDBID	0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D RIG
ID	FETCH-LOGICAL-c291t-c1fb182e907661c2f271e08b85e233cbda751efbf8fb3d93dd0fb5703cd26d3a3
IEDL.DBID	RIE
ISICitedReferencesCount	307
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000437883400042&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1551-3203
IngestDate	Mon Jun 30 10:19:59 EDT 2025 Tue Nov 18 22:34:16 EST 2025 Sat Nov 29 04:40:23 EST 2025 Wed Aug 27 08:33:13 EDT 2025
IsPeerReviewed	false
IsScholarly	true
Issue	7
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c291t-c1fb182e907661c2f271e08b85e233cbda751efbf8fb3d93dd0fb5703cd26d3a3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-0312-436X 0000-0002-1876-7707 0000-0003-2550-1509 0000-0002-9072-7179 0000-0001-9082-2216
PQID	2064310153
PQPubID	85507
PageCount	9
ParticipantIDs	proquest_journals_2064310153 crossref_citationtrail_10_1109_TII_2018_2809730 crossref_primary_10_1109_TII_2018_2809730 ieee_primary_8302941
PublicationCentury	2000
PublicationDate	2018-07-01
PublicationDateYYYYMMDD	2018-07-01
PublicationDate_xml	– month: 07 year: 2018 text: 2018-07-01 day: 01
PublicationDecade	2010
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE transactions on industrial informatics
PublicationTitleAbbrev	TII
PublicationYear	2018
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 schölkopf (ref28) 0 ref14 ref11 ref10 ref2 ref1 ref17 ref16 ref19 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref27 ref29 ref8 fortuna (ref30) 2007 ref7 lecun (ref18) 2015; 521 ref4 huang (ref9) 2012 ref3 ref6 rosipal (ref15) 2002; 2 ref5
References_xml	– ident: ref13 doi: 10.1002/aic.14299 – ident: ref27 doi: 10.1109/TIE.2017.2733448 – ident: ref1 doi: 10.1016/j.jprocont.2013.05.007 – ident: ref10 doi: 10.1109/TIM.2017.2658158 – ident: ref11 doi: 10.1002/aic.14663 – year: 2012 ident: ref9 publication-title: Dynamic Modelling and Predictive Control in Solid Oxide Fuel Cells First Principle and Data-Based Approaches – volume: 521 start-page: 436 year: 2015 ident: ref18 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: ref3 doi: 10.1016/j.compchemeng.2007.07.005 – ident: ref24 doi: 10.1109/TPAMI.2016.2577031 – ident: ref19 doi: 10.1016/j.ultras.2016.09.011 – ident: ref21 doi: 10.1109/TIE.2017.2739691 – ident: ref12 doi: 10.1109/TIM.2006.887331 – ident: ref5 doi: 10.1109/TII.2012.2214394 – ident: ref8 doi: 10.1109/TIE.2017.2733443 – volume: 2 start-page: 97 year: 2002 ident: ref15 article-title: Kernel partial least squares regression in reproducing kernel hilbert space publication-title: J Mach Learn Res – ident: ref22 doi: 10.1109/TII.2016.2612640 – ident: ref4 doi: 10.1109/TII.2016.2610839 – ident: ref2 doi: 10.1016/j.compchemeng.2008.12.012 – ident: ref23 doi: 10.1109/TNNLS.2016.2599820 – ident: ref14 doi: 10.1021/ie4041252 – ident: ref17 doi: 10.1109/TPAMI.2013.50 – start-page: 153 year: 0 ident: ref28 article-title: Greedy layer-wise training of deep networks publication-title: Proc Int Conf Neural Inf Process – year: 2007 ident: ref30 publication-title: Soft Sensors for Monitoring and Control of Industrial Processes – ident: ref25 doi: 10.1016/j.jprocont.2014.01.012 – ident: ref26 doi: 10.1109/TIE.2016.2622668 – ident: ref20 doi: 10.1109/SMC.2017.8122666 – ident: ref16 doi: 10.1162/neco.2006.18.7.1527 – ident: ref7 doi: 10.1109/TII.2009.2025124 – ident: ref6 doi: 10.1109/TII.2013.2283147 – ident: ref29 doi: 10.1016/j.conengprac.2004.04.013
SSID	ssj0037039
Score	2.6439185
Snippet	In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	3235
SubjectTerms	Correlation analysis Data mining Deep learning Feature extraction Feature recognition Industrial applications Informatics Machine learning Monitoring Multilayers Neural networks output prediction Pattern recognition Process control Representations Sensors soft sensor stacked autoencoder (SAE) Training variable-wise weighted SAE (VW-SAE)
Title	Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE
URI	https://ieeexplore.ieee.org/document/8302941 https://www.proquest.com/docview/2064310153
Volume	14
WOSCitedRecordID	wos000437883400042&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0050 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0037039 issn: 1551-3203 databaseCode: RIE dateStart: 20050101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5VPOjBt1itkoMXwdjdpOsmx_rCgohYtd6WbDLRgqzSbvXvO0m3VVEEbzkkYdkvmUdm5htC9mKLRjwXjhkdJayVCJSDMjEMjQ8ndSvlJgTa7y_Tqyv58KCua-RgWgsDACH5DA79MMTy7YsZ-aeypueqUr5KfSZNj8a1WhOpK_DkqsCNmsRM8EhMQpKRat52Oj6HSx5y6clpom8qKPRU-SGIg3Y5X_rfdy2TxcqKpO0x7CukBsUqWfjCLbhG3k8BXmlFn_rIjlFbWeoNvtEA6E3If63KjgqqC0s75ZC2P6PZFI1Z2kUZTbvo6OLYN03zpeu01y-f6D262L7oivX6Q6C98L6K-3fbZ-vk7vzs9uSCVV0WmOEqLpmJXY5OBqCXjLracMfTGCKZywS4ECa3Ok1icLmTLhdWCWsjl3veLmP5kRVabJDZ4qWATUKVy0GCzYEr3bK6pXEWCA2p4dZXS9VJc_LjM1NRkPtOGM9ZcEUilSFUmYcqq6Cqk_3pitcx_cYfc9c8NNN5FSp10phgm1X3c4jr0BJDaZSIrd9XbZN5v_c4MbdBZsvBCHbInHkr-8PBbjh6H_f21sQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwED9NAwl4YMCYKNvAD7wg4TWxE2I_dl9aRVdNtKx7ixz7DJVQNrUp-_d3dtOOCYTEmx9sJ8o59-G73-8APqSOnHghPbcmyXmWS9KDKrecnA-vTFYIGxPtl4NiOFRXV_piAz6tsTCIGIvP8CAMYy7fXdtFuCrrBq4qHVDqj_IsE8kSrbXSu5LOro7sqHnKpUjkKimZ6O643w9VXOpAqEBPkzwwQrGryh-qONqX063_e7MX8Lz1I1lvKfiXsIH1K3j2G7vgNtweI96wlkD1Oz8ke-VYcPkWM2RfYwVsCzyqmakd6zdz1rvPZzNyZ9mItDQbUahL49A2LYDX2WTa_GCXFGQH2BWfTOfIJvGGlfYf9U5ew7fTk_HRGW_7LHArdNpwm_qKwgykOJmstRVeFCkmqlI5Cilt5UyRp-grr3wlnZbOJb4KzF3Wic9OGrkDm_V1jW-AaV-hQleh0CZzJjM0C6XBwgoX8FId6K4-fGlbEvLQC-NnGYORRJckqjKIqmxF1YGP6xU3SwKOf8zdDqJZz2ul0oG9lWzL9g-d0zryxUgf5fLt31e9hydn4_NBOegPv-zC0_CcZZnuHmw2swXuw2P7q5nOZ-_iMbwDSRfaCw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+Learning-Based+Feature+Representation+and+Its+Application+for+Soft+Sensor+Modeling+With+Variable-Wise+Weighted+SAE&rft.jtitle=IEEE+transactions+on+industrial+informatics&rft.au=Yuan%2C+Xiaofeng&rft.au=Huang%2C+Biao&rft.au=Wang%2C+Yalin&rft.au=Yang%2C+Chunhua&rft.date=2018-07-01&rft.pub=IEEE&rft.issn=1551-3203&rft.volume=14&rft.issue=7&rft.spage=3235&rft.epage=3243&rft_id=info:doi/10.1109%2FTII.2018.2809730&rft.externalDocID=8302941
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1551-3203&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1551-3203&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1551-3203&client=summon