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Authors: Zhang, Jun

Article Type: Research Article

Abstract: OBJECTIVE: This study aimed to further clarify the underlying pathomechanism of non-union skeletal fractures. METHODS: Gene expression profile dataset GSE494 obtained from six non-union skeletal fracture and six normal samples was downloaded from the Gene Expression Omnibus database. Overlapping genes in at least two platforms were analyzed, and differentially expressed genes (DEGs) between normal and disease groups were screened. Transcriptional regulatory relationships and differentially regulated modules of various transcription factors (TFs) were determined. Differentially regulated modules with unknown functions were subjected to functional enrichment analysis. RESULTS: Overall, 4,252 overlapping genes in at least two platforms and 77 DEGs, including 31 …up and 46 downregulated genes, were obtained. Overall, 64,623 transcriptional regulatory relationships, including 49 TFs and 3,900 target genes, and 9 significant modules for differential regulation were identified. Three modules with unknown functions regulated by TFs, including zinc finger, ZZ-type containing 3 (ZZZ3), nuclear TF Y, alpha (NFYA), and POU class 2 homeobox 2 (POU2F2), were identified. Enriched GO-BP terms of NFYA and POU2F2 modules included cell adhesion and related terms and those of ZZ3 included cell cycle, cell proliferation, and associated terms. CONCLUSION: Three TFs, including ZZZ3, POU2F2, and NFYA, and their regulated modules may have important effects on non-union skeletal fractures. Cell proliferation may be related with ZZZ3; cell adhesion and its similar process may be related with POU2F2 and NFYA. Show more

Keywords: Non-union skeletal fractures, differentially expressed genes, transcription factors

DOI: 10.3233/BMR-169596

Citation: Journal of Back and Musculoskeletal Rehabilitation, vol. 31, no. 4, pp. 623-628, 2018

Authors: Zhang, Cong | Zhang, Jun

Article Type: Research Article

Abstract: We discuss the interpolation strategies related to Richardson extrapolation and repeated Richardson extrapolation. We emphasize that, in most computations, the interest is in obtaining accurate solution on the current computational grid, not that on the coarse level grids on which the extrapolated solutions reside. We tackle the interpolation issue that has largely been overlooked in Richardson extrapolation related applications. We present numerical experiments to support our analysis.

Keywords: Computational grid, finite difference scheme, Richardson extrapolation, interpolation, high order solution

DOI: 10.3233/JIFS-169177

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 2935-2939, 2016

Authors: Zhang, Jun | Liu, Junjun

Article Type: Research Article

Abstract: Remote sensing is an indispensable technical way for monitoring earth resources and environmental changes. However, optical remote sensing images often contain a large number of cloud, especially in tropical rain forest areas, make it difficult to obtain completely cloud-free remote sensing images. Therefore, accurate cloud detection is of great research value for optical remote sensing applications. In this paper, we propose a saliency model-oriented convolution neural network for cloud detection in remote sensing images. Firstly, we adopt Kernel Principal Component Analysis (KCPA) to unsupervised pre-training the network. Secondly, small labeled samples are used to fine-tune the network structure. And, remote …sensing images are performed with super-pixel approach before cloud detection to eliminate the irrelevant backgrounds and non-clouds object. Thirdly, the image blocks are input into the trained convolutional neural network (CNN) for cloud detection. Meanwhile, the segmented image will be recovered. Fourth, we fuse the detected result with the saliency map of raw image to further improve the accuracy of detection result. Experiments show that the proposed method can accurately detect cloud. Compared to other state-of-the-art cloud detection method, the new method has better robustness. Show more

Keywords: Cloud detection, remote sensing, convolutional neural network, kernel principal component analysis, saliency model, super-pixel segment

DOI: 10.3233/MGS-210352

Citation: Multiagent and Grid Systems, vol. 17, no. 3, pp. 235-247, 2021

Authors: Zhang, Jun | Li, Dandan | Chen, Wei

Article Type: Research Article

Abstract:  This paper presents some continuous dependence theorems on solutions of uncertain differential equations based on uncertain measure. We first introduce some properties on solution of uncertain differential equation. And then, we provide a continuous dependence theorem, and a continuity theorem to the initial value. In the proposed continuity theorem, the solution is regarded as a ternary function of initial values. Furthermore, we discuss how the solution continuously depends on initial value and parameter, and propose two theorems, namely, continuous dependence theorem on parameter, and continuity theorem on parameter to the initial value.

Keywords: Uncertain variable, numerical series, positive numerical series, convergence in measure

DOI: 10.3233/JIFS-182815

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6455-6465, 2019

Authors: Zhang, Jun | He, Meiqi | Zeng, Gongxian | Yiu, Siu-Ming

Article Type: Research Article

Abstract: With the popularity of cloud computing, an increasing number of institutions outsource their data to a third-party cloud system which could be untrusted. The institutions encrypt their data before outsourcing to protect data privacy. On the other hand, data mining techniques are used widely but computationally intensive, especially for large datasets. Combining data from different institutions for a big and varied training set helps enhance data mining performance. Therefore, it is important to make the cloud system which has powerful computing abilities run data mining algorithms on the encrypted data from multiple institutions. Two challenges need attention – how to …compute on encrypted data under multiple keys and how to verify the correctness of the result. There are no existing methods that solve the two challenges at the same time. Elastic net is a useful linear regression tool to find genomic biomarkers. In this paper, we propose the first privacy-preserving verifiable elastic net protocol based on reduction to support vector machine using two non-colluding servers. We construct a homomorphic cryptosystem that supports one multiply operation and multiple add operations under both single key and different keys. We allow the involved institutions to verify the correctness of the final result. The collaboration between multiple institutions is made possible without jeopardizing the privacy of data records. We formally prove that our protocol is secure and implement the protocol. The experimental results show that our protocol runs reasonably fast, and thus can be applied in practice. Show more

Keywords: Privacy-preserving elastic net, support vector machine, verifiable computation, multiple encryption keys

DOI: 10.3233/JCS-171107

Citation: Journal of Computer Security, vol. 26, no. 6, pp. 791-815, 2018

Authors: Chen, Wei | Wang, Yun | Zhang, Jun | Lu, Shan

Article Type: Research Article

Abstract: In the mean-variance-skewness-kurtosis framework, this paper discusses an uncertain higher-order moment portfolio selection problem when security returns are given by experts’ evaluations. Based on uncertainty theory and the assumption that the security returns are zigzag uncertain variables, an uncertain multi-objective portfolio optimization model is proposed by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk and kurtosis of portfolio return. Subsequently, the proposed model is transformed into a single-objective programming model by using fuzzy programming approach, in which investor preferences for high moments are incorporated. Furthermore, a modified flower pollination algorithm …(MFPA) is developed for solution, in which PSO in local update strategy (PSOLUS) and dynamic switching probability strategy (DSPS) are employed to enhance the local searching and global searching abilities. Finally, a numerical example is presented to illustrate the application of the proposed model and solution comparisons are also given to demonstrate the effectiveness of the designed algorithm. Show more

Keywords: Portfolio selection, uncertain variable, high-order moments, flower pollination algorithm, uncertain programming

DOI: 10.3233/JIFS-17369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 3, pp. 1397-1411, 2017

Authors: Yang, Haodong | Zhang, Jun | Li, Shuohao | Luo, Tingjin

Article Type: Research Article

Abstract: Human action recognition in naturalistic videos is an important task with a broad range of applications. Recently, the encoder-decoder framework based on attention mechanism has been applied to action recognition. Although such conventional methods reach state-of-the-art, they always face a bottleneck of distinguishing similar actions. To solve this problem, we propose a novel recurrent attention convolutional neural network (RACNN), which incorporates convolutional neural networks (CNNs), long short-term memory (LSTM) and attention mechanism. Inspired by the composition of the action, the pre-action and the result of action might be important parts of an action, we introduce bi-direction LSTM with hierarchical structure. …Additionally, the separated spatial-temporal attention is employed into our method. Furthermore, we find that incorporating spatio-temporal features extracted from three-dimensional CNNs (3DCNNs) and RGB features can enhance the relationship mined in each frame. Our comprehensive experimental results on two benchmark datasets, i.e., HMDB51 and UCF101, verify the effectiveness of our proposed methods and show that our proposals can significantly outperform the current state-of-the-art methods. Show more

Keywords: Action recognition, bi-direction hierarchical LSTM, spatial-temporal attention

DOI: 10.3233/JIFS-18209

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 775-786, 2019

Authors: An, Qing | Tang, Ruoli | Su, Hongfeng | Zhang, Jun | Li, Xin

Article Type: Research Article

Abstract: Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system …based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy. Show more

Keywords: Building integrated photovoltaic system, maximum power point tracking, PV configuration, intelligent control, extreme learning machine

DOI: 10.3233/JIFS-210424

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12283-12300, 2021

Authors: Han, Hao | Zhang, Zhi-Fang | Zhang, Jun-Fei | Zhang, Bin

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTION”. The retraction notice is available at http://doi.org/10.3233/MGC-220954 .

DOI: 10.3233/MGC-180682

Citation: Main Group Chemistry, vol. 18, no. 2, pp. 71-79, 2019

Authors: Xu, Chenguang | Zhang, Chao | Ma, Mingxi | Zhang, Jun

Article Type: Research Article

Abstract:  Blind image deconvolution has attracted growing attention in image processing and computer vision. The total variation (TV) regularization can effectively preserve image edges. However, due to lack of self-adaptability, it does not perform very well on restoring images with complex structures. In this paper, we propose a new blind image deconvolution model using an adaptive weighted TV regularization. This model can better handle local features of image. Numerically, we design an effective alternating direction method of multipliers (ADMM) to solve this non-smooth model. Experimental results illustrate the superiority of the proposed method compared with other related blind deconvolution methods.

Keywords: Blind deconvolution, Total variation regularization, Adaptive weighted matrix, ADMM

DOI: 10.3233/JIFS-223828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 6497-6511, 2023