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Dynamic parameterized learning for unsupervised domain adaptation Research Article

Runhua JIANG, Yahong HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11, Pages 1616-1632 doi: 10.1631/FITEE.2200631

Abstract: enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learningdomain-invariant representations.First, by exploring domain-level semantic knowledge, the dynamic alignment parameter is proposed, toBesides, for obtaining semantic-discriminative and domain-invariant representations, we propose to align

Keywords： Unsupervised domain adaptation Optimization steps Domain alignment Semantic discrimination

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Layer-wise domain correction for unsupervised domain adaptation Article

Shuang LI, Shi-ji SONG, Cheng WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 91-103 doi: 10.1631/FITEE.1700774

Abstract: To address the domain shift or data bias problems, we introduce layer-wise domain correction (LDC), anew unsupervised domain adaptation algorithm which adapts an existing deep network through additiveThe corrections that are trained via maximum mean discrepancy, adapt to the target domain while increasingLDC requires no target labels, achieves state-of-the-art performance across several adaptation benchmarks, and requires significantly less training time than existing adaptation methods.

Keywords： Unsupervised domain adaptation Maximum mean discrepancy Residual network Deep learning

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Dual collaboration for decentralized multi-source domain adaptation Research Article

Yikang WEI, Yahong HAN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12, Pages 1780-1794 doi: 10.1631/FITEE.2200284

Abstract: The goal of decentralized is to conduct unsupervised in a scenario.The challenge of is that the source domains and target domain lack cross-domain collaboration duringof source models, while the domain gap will lead to limited adaptation performance from source modelsOn the labeled source domain, the source model tends to overfit its domain data in the scenario, whichthe domain adaptation performance under the scenario.

Keywords： Multi-source domain adaptation Data decentralization Domain shift Negative transfer

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Automatically building large-scale named entity recognition corpora from Chinese Wikipedia

Jie ZHOU,Bi-cheng LI,Gang CHEN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11, Pages 940-956 doi: 10.1631/FITEE.1500067

Abstract: Named entity recognition (NER) is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotations in different languages and domains. This paper presents a method for automatically building silver-standard NER corpora from Chinese Wikipedia. We refine novel and language-dependent features by exploiting the text and structure of Chinese Wikipedia. To reduce tagging errors caused by entity classification, we design four types of heuristic rules based on the characteristics of Chinese Wikipedia and train a supervised NE classifier, and a combined method is used to improve the precision and coverage. Then, we realize type identification of implicit mention by using boundary information of outgoing links. By selecting the sentences related with the domains of test data, we can train better NER models. In the experiments, large-scale NER corpora containing 2.3 million sentences are built from Chinese Wikipedia. The results show the effectiveness of automatically annotated corpora, and the trained NER models achieve the best performance when combining our silver-standard corpora with gold-standard corpora.

Keywords： NER corpora Chinese Wikipedia Entity classification Domain adaptation Corpus selection

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Speech emotion recognitionwith unsupervised feature learning

Zheng-wei HUANG,Wen-tao XUE,Qi-rong MAO

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 5, Pages 358-366 doi: 10.1631/FITEE.1400323

Abstract: In this paper, we apply several unsupervised feature learning algorithms (including -means clustering

Keywords： Speech emotion recognition Unsupervised feature learning Neural network Affect computing

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Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications

Y. G. LI, P. PILIDIS,

Frontiers in Energy 2009, Volume 3, Issue 4, Pages 446-455 doi: 10.1007/s11708-009-0042-9

Abstract: method and the other using a Genetic Algorithm-based adaptation approach.The advantages and disadvantages of the two adaptation methods have been compared with each other.The two adaptation approaches have been applied to a model gas turbine engine.The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is fasterand more accurate, while the genetic algorithm-based adaptation method is more robust but slower.

Keywords： gas turbine engine performance adaptation performance matching design-point performance simulation influence

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Time-domain and frequency-domain approaches to identification of bridge flutter derivatives

Zhengqing CHEN

Frontiers of Structural and Civil Engineering 2009, Volume 3, Issue 2, Pages 173-179 doi: 10.1007/s11709-009-0034-1

Abstract: method and a frequency-domain method.It was shown that all the flutter derivatives of the thin-plate model identified with the frequency-domainmethod and time-domain method, respectively, agree very well.More precisely, the frequency-domain method usually results in smooth curves of the flutter derivativesThe formulation of time-domain method makes the identification results of flutter derivatives relatively

Keywords： long-span bridges wind-induced vibration flutter derivatives forced vibration test time-domain method frequency-domain method

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Entity and relation extraction with rule-guided dictionary as domain knowledge

Frontiers of Engineering Management Pages 610-622 doi: 10.1007/s42524-022-0226-0

Abstract: Entity and relation extraction is an indispensable part of domain knowledge graph construction, whichcan serve relevant knowledge needs in a specific domain, such as providing support for product research, sales, risk control, and domain hotspot analysis.However, the performance of these methods degrades when they face domain-specific datasets.To address the problems above, this paper first introduced prior knowledge composed of domain dictionaries

Keywords： entity extraction relation extraction prior knowledge domain rule

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Damage identification in connections of moment frames using time domain responses and an optimization

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4, Pages 851-866 doi: 10.1007/s11709-021-0739-3

Abstract: study, an optimization-based method for joint damage identification of moment frames using the time-domain

Keywords： damage identification beam-to-column connection time-domain response optimization

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Unsupervised feature selection via joint local learning and group sparse regression Regular Papers

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4, Pages 538-553 doi: 10.1631/FITEE.1700804

Abstract: Because label information is expensive to obtain, unsupervised feature selection methods are more widelyThe key to unsupervised feature selection is to find features that effectively reflect the underlyingTo address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning

Keywords： Unsupervised Local learning Group sparse regression Feature selection

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Terahertz time-domain spectroscopy of high-pressure flames

Jason BASSI, Mark STRINGER, Bob MILES, Yang ZHANG

Frontiers in Energy 2009, Volume 3, Issue 2, Pages 123-133 doi: 10.1007/s11708-009-0033-x

Abstract: Laser spectroscopy in the visible and near infrared is widely used as a diagnostic tool for combustion devices, but this approach is difficult at high pressures within a sooty flame itself. High soot concentrations render flames opaque to visible light, but they remain transparent to far-infrared or terahertz (THz) radiation. The first far-infrared absorption spectra, to the best of our knowledge, of sooty, non-premixed, ethylene high-pressure flames covering the region of 0.2-2.5 THz is presented. A specially designed high-pressure burner which is optically accessible to THz radiation has been built allowing flame transmission measurements up to pressures of 1.6 MPa. Calculations of the theoretical combustion species absorption spectra in the 0.2-3 THz range have shown that almost all the observable features arise from H O. A few OH (1.84 and 2.51 THz), CH (2.58 THz), and NH (1.77 and 2.95 THz) absorption lines are also observable in principle. A large number of H O absorption lines are observed in the ground vibrational in a laminar non-premixed, sooty flame (ethylene) at pressures up to 1.6 MPa.

Keywords： terahertz time-domain spectroscopy high-pressure flames H₂O absorption lines

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framework for underground structures in layered ground under inclined P-SV waves using stiffness matrix and domain

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 1, Pages 10-24 doi: 10.1007/s11709-022-0904-3

Abstract: Then, the domain reduction method was employed to reproduce the wavefield in the numerical model of the

Keywords： underground structures seismic response stiffness matrix method domain reduction method P-SV waves

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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 1, Pages 1-16 doi: 10.1007/s11709-014-0277-3

Abstract: A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

Keywords： structural health monitoring early-damage detection principal component analysis symbolic data symbolic dissimilarity measures cluster analysis numerical model damage simulations

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Comments on “Adaptation of Chinese and German maize-based food-feed-energy systems to limited phosphate

Antje SCHWALB

Frontiers of Agricultural Science and Engineering 2019, Volume 6, Issue 4, Pages 443-444 doi: 10.15302/J-FASE-2019288

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BUEES: a bottom-up event extraction system

Xiao DING,Bing QIN,Ting LIU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 7, Pages 541-552 doi: 10.1631/FITEE.1400405

Abstract: When transferring to a new domain, we have to build a new event type paradigm and annotate a new corpuspresent BUEES, a bottom-up event extraction system, which extracts events from the web in a completely unsupervised

Keywords： Event extraction Unsupervised learning Bottom-up