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suspension bridge 3

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color gamut 2

construction 2

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Perspectives on cross-domain visual analysis of cyber-physical-social big data Perspective

Wei Chen, Tianye Zhang, Haiyang Zhu, Xumeng Wang, Yunhai Wang,cloudseawang@gmail.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12, Pages 1551-1684 doi: 10.1631/FITEE.2100553

Abstract: The domain of cyber-physical-social (CPS) big data is generally defined as the set consisting of all the elements in its defined domain, including domains of data, objects, tasks, application scenarios, and subjects. Visual analytics is an emerging human-in-the-loop big data analytics paradigm that can exploit human perception to enhance human cognitive efficiency. In this paper, we explore the perspectives on cross-domain visual analysis of CPS big data. We also highlight new challenges brought by the cross-domain nature of CPS big data—data, subject, and task domains—and propose a novel visual analytics model and a suite of approaches to address these challenges.

Keywords：可视分析；三元空间；大数据；跨域

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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 learning domain-invariant representations. Recent approaches achieve this by directly matching the marginal distributions of these two domains. Most of them, however, ignore exploration of the dynamic trade-off between and learning, thus rendering them susceptible to the problems of negative transfer and outlier samples. To address these issues, we introduce the dynamic parameterized learning framework. First, by exploring domain-level semantic knowledge, the dynamic alignment parameter is proposed, to adaptively adjust the of and learning. Besides, for obtaining semantic-discriminative and domain-invariant representations, we propose to align training trajectories on both source and target domains. Comprehensive experiments are conducted to validate the effectiveness of the proposed methods, and extensive comparisons are conducted on seven datasets of three visual tasks to demonstrate their practicability.

Keywords： Unsupervised domain adaptation Optimization steps Domain alignment Semantic discrimination

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Development and Key Technologies of Maritime Unmanned Systems

Qiu Zhiming, Meng Xiangyao, Ma Yan, Chen Yi, Feng Wei

Strategic Study of CAE 2023, Volume 25, Issue 3, Pages 74-83 doi: 10.15302/J-SSCAE-2023.03.005

Abstract:

Maritime unmanned system are crucial for future intelligent and unmanned warfare. They have become a new height of competition in the maritime domain and will play an increasingly important role in national defense security. Considering the development demand for national intelligence and unmanned strategies, the study analyzes the development status of maritime unmanned systems and corresponding technologies in China and abroad from the aspects of strategic planning and conceptual guidance, technological research and equipment development, and system demonstration and capability verification. The challenges and trends for the development of maritime unmanned systems are summarized and key technical challenges are proposed. Moreover, development pathways of maritime unmanned systems in key directions are explored and countermeasures to promote the sustained, steady, and rapid development of maritime unmanned systems are recommended from the perspectives of overall thinking, system composition, equipment development, and technology breakthroughs.

Keywords： maritime unmanned systems cluster intelligent cross-domain collaboration key technologies

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Construction of Cross-regional Cooperative Governance System in Qinba Mountain Area

Yuan Xiaojun, Wen Na, Zhang Jinle

Strategic Study of CAE 2020, Volume 22, Issue 1, Pages 50-55 doi: 10.15302/J-SSCAE-2020.01.005

Abstract:

The solution of regional problems depends on the cooperation among local governments within the same region. Currently,cooperation among local governments in the Qinba Mountain area is inadequate; it lacks systematic planning and overall ideas and has a narrow scope; and overlap in governance and lack of policy coordination between the local governments have markedly constrained the sustainable development of the economy and ecology in the Qinba Mountain Area. To solve these problems, this study proposes a three-level cooperative governance system based on the theory of inter-organizational cooperation networks; analyzes the successful experiences of co-governance in the Yangtze River Basin; and proposes some suggestions, including strengthening overall planning,establishing a specialized agency for coordination at the national level, giving full play to the leading and practicing role of regional key construction projects in cooperative governance, and promoting personnel exchanges among local governments within this area.This study is hoped to provide references for establishing a coordinated governance mechanism among local governments in the Qinba Mountain Area and meanwhile improving their governance effectiveness.

Keywords： Qinba Mountain Area government governance coordination and cooperation inter-organizational cooperation network

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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 during training. On the unlabeled target domain, the target model needs to transfer supervision knowledge with the collaboration of source models, while the domain gap will lead to limited adaptation performance from source models. On the labeled source domain, the source model tends to overfit its domain data in the scenario, which leads to the problem. For these challenges, we propose dual collaboration for decentralized by training and aggregating the local source models and local target model in collaboration with each other. On the target domain, we train the local target model by distilling supervision knowledge and fully using the unlabeled target domain data to alleviate the problem with the collaboration of local source models. On the source domain, we regularize the local source models in collaboration with the local target model to overcome the problem. This forms a dual collaboration between the decentralized source domains and target domain, which improves the domain adaptation performance under the scenario. Extensive experiments indicate that our method outperforms the state-of-the-art methods by a large margin on standard datasets.

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

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Preference transfer model in collaborative filtering for implicit data Project supported by the National Basic Research Program (973) of China (No. 2012CB316400) and the National Natural Science Foundation of China (No. 61571393) Article

Bin JU,Yun-tao QIAN,Min-chao YE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 6, Pages 489-500 doi: 10.1631/FITEE.1500313

Abstract: Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users’ buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized. Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then, two factor-user matrices can be used to construct a so-called ‘preference dictionary’ that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.

Keywords： Recommender systems Collaborative filtering Preference transfer model Cross domain Implicit data

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Determination and Division of Radiative Color Gamut in Primary-Color Method for Temperature Measurement

Wang Anquan,Cheng Xiaofang,Lu Shaosong

Strategic Study of CAE 2002, Volume 4, Issue 8, Pages 54-57

Abstract:

The principle of primary-color method for temperature measurement is firstly introduced in this article. Based on the lineal model of spectral emissivity in the range of visible spectrum, the formulation of radiative chrominance of real bodies is put forward. Afterwards, it's made clear that two temperature solutions can be derived from one specific chrominance at most. The radiative color gamut is determined and divided into two components, the single-solution gamut and the dual-solution gamut.

Keywords： temperature measurement primary-color method color gamut emissivity

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Displacement characteristics of long span three-tower suspension bridge

Chen Ce, Yin Haihua

Strategic Study of CAE 2010, Volume 12, Issue 8, Pages 79-82

Abstract:

Taizhou Yangtze Highway Bridge adopts three-tower suspension bridge scheme which has two 1 080 m main span in its primary design. This paper studies the different displacement traits between three-tower suspension bridge and two-tower suspension bridge. It also analyzes the influence of various parameters, such as the rigidity and height of the tower, the height of the main girder, the sag-to-span ratio and side span's length to middle span's ratio, on displacement feature of three-tower suspension bridge.

Keywords： three-tower suspension bridge Taizhou Bridge displacement characteristics influencing parameters

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Theoretical Study of Color Gamut of Higher Plant Leaves

Cheng Xiaofang,Dong Jinyi,Fan Xueliang,Ding Jinlei

Strategic Study of CAE 2006, Volume 8, Issue 12, Pages 66-69

Abstract:

The chlorophylls and cartotenoids are the main pigments in leaves of the higher plants, which giveing the color of leaves. Based on the CIE1931 chromaticity coordinates of the chlorophylls and cartotenoids obtained from their classical absorption spectrum and the law of additive color mixing, the theoretical color gamut of higher plant leaves is determined. The theoretical prediction agrees well with the experimental results.

Keywords： higher plant pigment absorption spectra chromaticity coordinate color gamut

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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: Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities. However, conventional deep networks assume that the training and test data are sampled from the same distribution, and this assumption is often violated in real-world scenarios. To address the domain shift or data bias problems, we introduce layer-wise domain correction (LDC), a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network. Through the additive layers, the representations of source and target domains can be perfectly aligned. The corrections that are trained via maximum mean discrepancy, adapt to the target domain while increasing the representational capacity of the network. LDC 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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Dynamic behavior analysis of Taizhou Bridge

Ruan Jing,Ma Rujin

Strategic Study of CAE 2010, Volume 12, Issue 8, Pages 83-87

Abstract:

The dynamic behaviors have critical influences on the design of structure dynamics, health monitoring and maintenances. The three- pylon two-span suspension bridge does have different static and dynamic behaviors comparing with traditional one-span suspension bridge. This paper analyzes the dynamic behavior of Taizhou Bridge under construction stage and operation stage based on ANSYS. Some meaningful results have been obtained and can provide references for engineers in the further.

Keywords： three-pylon two-span suspension bridge dynamic property self-vibration frequency vibration shape

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Robust cross-modal retrieval with alignment refurbishment Research Article

Jinyi GUO, Jieyu DING,jinyi_g@njust.edu.cn,djy@qdu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10, Pages 1403-1415 doi: 10.1631/FITEE.2200514

Abstract: tries to achieve mutual retrieval between modalities by establishing consistent alignment for different modal data. Currently, many methods have been proposed and have achieved excellent results; however, these are trained with clean cross-modal pairs, which are semantically matched but costly, compared with easily available data with noise alignment (i.e., paired but mismatched in semantics). When training these methods with noise-aligned data, the performance degrades dramatically. Therefore, we propose a robust with alignment refurbishment (RCAR), which significantly reduces the impact of noise on the model. Specifically, RCAR first conducts multi-task learning to slow down the overfitting to the noise to make data separable. Then, RCAR uses a two-component to divide them into clean and noise alignments and refurbishes the label according to the posterior probability of the noise-alignment component. In addition, we define partial and complete noises in the noise-alignment paradigm. Experimental results show that, compared with the popular methods, RCAR achieves more robust performance with both types of noise.

Keywords： Cross-modal retrieval Robust learning Alignment correction Beta-mixture model

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Words alignment based on association rules for cross-domain sentiment classification None

Xi-bin JIA, Ya JIN, Ning LI, Xing SU, Barry CARDIFF, Bir BHANU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2, Pages 260-272 doi: 10.1631/FITEE.1601679

Abstract: Automatic classification of sentiment data (e.g., reviews, blogs) has many applications in enterprise user management systems, and can help us understand people’s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules (WAAR) for cross-domain sentiment classification, which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on AmazonR datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.

Keywords： Sentiment classification Cross-domain Association rules

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Research on bolt-cable reducing span support technology in large span roadway of fully-mechanized coal faces

Zhu Yongjian,Luo Yixin,Zhang Daobing

Strategic Study of CAE 2010, Volume 12, Issue 3, Pages 51-55

Abstract:

Aiming at the locale facts of difficult roof controlling for large span gateway of fully-mechanized coal faces, based on analysis of the influence factors of monolayer strata stability, the numerical simulation soft UDEC3.0 is employed to deeply research influence factors and changing rules of roof stability of the gateway which is composed by some monolayer strata. A new supporting idea of reducing span supporting function by bolts cooperating with anchor cables is brought forward by disposing anchor cables at the appropriate locations, the traditional supporting theory such as suspensory and compounding beam (arch) are still operated at the same time. A good result is achieved after the new supporting idea is applied in the large span gateway of Bulianta Colliery in Shendong diggings.

Keywords： large span roadway roof bolt—cable reducing span support

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High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction Research Article

Kaili QI, Minqing ZHANG, Fuqiang DI, Yongjun KONG,1804480181@qq.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8, Pages 1156-1168 doi: 10.1631/FITEE.2200501

Abstract: To improve the embedding capacity of , a new RDH-EI scheme is proposed based on and most significant bit (MSB) prediction. First, according to the smoothness of the image, the image is partitioned into blocks based on , and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images. In the data embedding stage, the adaptive MSB prediction method proposed by Wang and He (2022) is improved by taking the upper-left pixel in the block as the target pixel, to predict other pixels to free up more embedding space. To the best of our knowledge, quadtree partitioning is first applied to RDH-EI. Simulation results show that the proposed method is reversible and separable, and that its average embedding capacity is improved. For gray images with a size of 512×512, the average embedding capacity is increased by 25 565 bits. For all smooth images with improved embedding capacity, the average embedding capacity is increased by about 35 530 bits.

Keywords： Adaptive quadtree partitioning Adaptive most significant bit (MSB) prediction Reversible data hiding in encrypted images (RDH-EI) High embedding capacity