Your search for: 'author:("Zhang, Jun")' has returned 19 results. (0.025s) Save search

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: 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: 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

Authors: You, Fang | Li, Yaru | Fu, Qianwen | Zhang, Jun

Article Type: Research Article

Abstract: With the increasing levels of intelligence and automation, the relationship between humans and vehicles has evolved from a utilitarian perspective to a partnership. Among the crucial factors for enhancing user experiences are the analysis of driving tasks, the construction of user needs models, and the design of intelligent interfaces. Based on this background, this paper proposes a cognitive task analysis model using intelligent steering wheel information interaction design as the vehicle. The model aims to extract key design elements to assist designers in making design decisions, thereby improving the human-machine cooperation performance of intelligent automobiles and enhancing user perceptual experiences. …Firstly, within the context of human-machine cooperation systems, a cognitive task analysis method integrating the SRK model is proposed. By analyzing the behavioral decision characteristics between the vehicle and the user, a framework for the human-machine interface (HMI) logic of the steering wheel and a dynamic layout prototype are established. Secondly, the design of the steering wheel’s HMI interaction is based on an analysis of users’ affective needs and rational physiological characteristics. This paper integrates the analysis of users’ affective needs to identify design elements that align with a high level of user satisfaction. Lastly, the design methodology model is applied to a navigation scenario, resulting in the creation of a steering wheel HMI prototype within a human-machine cooperation system. The prototype is then subjected to a combined subjective and objective experimental analysis, thereby validating the superiority of the steering wheel HMI’s detection indicators over those of the central control HMI and establishing the design pattern for the steering wheel HMI. Show more

Keywords: Intelligent cockpit, steering wheel, cognitive task analysis, human-machine interaction interface

DOI: 10.3233/JIFS-233500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6439-6464, 2024

Authors: Zhang, Jun | Geng, Xingguang | Zhang, Yitao | Yao, Fei | Wang, Yunfeng | Zhang, Haiying

Article Type: Research Article

Abstract: BACKGROUND: The wrist pulse wave under the optimal pulse pressure plays an important role in detecting human body’s physiological and pathological information. Wavelet threshold filtering is a common method for pulse wave de-noising. However, traditional filtering methods cannot smoothen the whole pulse wave well and highlight the details. OBJECTIVE: In view of this, an attempt is made in this paper to propose the pulse wave denoising algorithm for pulse wave under optimal pulse pressure according to the translation invariant wavelet transform (TIWT) and the new threshold function. METHODS: Firstly, by using hyperbolic tangent curve and combining the advantages of soft …threshold function and hard threshold function, the new threshold function is derived. Secondly, based on the TIWT, pseudo-Gibbs phenomenon gets suppressed. RESULTS: The experiments show that in comparison to the traditional wavelet filtering algorithm, the novel algorithm can better maintain the pulse wave geometric characteristics and has a higher signal to noise ratio (SNR). CONCLUSION: The TIWT with improved new threshold compensates the shortcomings of the traditional wavelet threshold denoising methods in a better way. It lays a foundation for extracting time-domain characteristics of pulse wave. Show more

Keywords: Pulse wave, denoising method, a new threshold function, translation invariant wavelet transform

DOI: 10.3233/THC-236049

Citation: Technology and Health Care, vol. 31, no. S1, pp. 551-563, 2023

Authors: Zhang, Jun | Qin, Yanping | Zhang, Xinyu | Che, Gen | Sun, Xuan | Duo, Huaqiong

Article Type: Research Article

Abstract: Non-equidistant GM(1,1) (abbreviated as NEGM) model is widely used in building settlement prediction because of its high accuracy and outstanding adaptability. To improve the building settlement prediction accuracy of the NEGM model, the fractional-order non-equidistant GM(1,1) model (abbreviated as FNEGM) is established in this study. In the modeling process of the FNEGM model, the fractional-order accumulated generating sequence is extended based on the first-order accumulated generating sequence, and the optimal parameters that increase the prediction precision of the model are obtained by using the whale optimization algorithm. The FNEGM model and the other two grey prediction models are applied to …three cases, and five prediction performance indexes are used to evaluate the prediction precision of the three models. The results show that the FNEGM model is more suitable for predicting the settlement of buildings than the other two grey prediction models. Show more

Keywords: Non-equidistant GM(1, 1) model, fractional-order accumulation, grey prediction model

DOI: 10.3233/JIFS-210936

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1559-1573, 2022

Authors: Li, Yansheng | Zhang, Jun | Chen, Jinbo | Chang, Sheng-Hung | Lam, Anthony | Zhao, Qiong

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. The retraction notice is available at https://doi.org/10.3233/JIFS-219326 .

DOI: 10.3233/JIFS-179476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1147-1157, 2020

Authors: Zhou, Xinyi | Dong, Chunshan | Zhang, Jun | Sun, Peng | Lu, Qiang | Ma, Xiang

Article Type: Research Article

Abstract: BACKGROUND: Lung-protective ventilation (LPV) strategies have been considered as best practice in the care of critically patients. OBJECTIVE: This study aimed to investigate the effects individualized perioperative LPV with a positive end-expiratory pressure (PEEP) and low tidal volumes (V T ) based on a target airway plateau pressure (Pplat) in patients during and after an operation compared with conventional ventilation in elderly patients during abdominal surgery. METHODS: Sixty-one elderly patients with American Society of Anesthesiologists (ASA) I to III undergoing open abdominal surgery received either conventional ventilation (8 ml/kg - 1 V T ; CV group) or LPV (V T …was adjusted to a target Pplat [⩽ 20 cm H 2 O]) in the volume-controlled mode with PEEP (9 cm H 2 O; LPV group) ventilation. RESULTS: Patients in the LPV group showed significantly lower pH values (7.30 ± 0.07 vs. 7.38 ± 0.05, P < 0.001) and respiratory indexes than that of CV group (0.806 ± 0.339 vs. 0.919 ± 0.300, P = 0.043) at the end of surgery. Compared with the CV group, the dynamic lung compliance (33.39 ± 3.163 vs. 30.15 ± 2.572, P < 0.001) was significantly higher, and the body temperature remained significantly more favorable in the LPV group (35.9 ± 0.3 vs. 35.1 ± 0.4, P < 0.001). Patients in the LPV group had significantly faster postoperative recovery than that of the CV group (P < 0.001). CONCLUSIONS: The study showed that LPV could be beneficial for ventilation, core body temperature, and postoperative recovery in elderly patients with healthy lungs. Show more

Keywords: Tidal volume, positive end-expiratory pressure, airway plateau pressure, lung function, postoperative recovery

DOI: 10.3233/THC-220144

Citation: Technology and Health Care, vol. 31, no. 2, pp. 539-551, 2023

Authors: Chen, Zheng-Lin | Ma, Ying-Yu | Mou, Xiao-Zhou | Zhang, Jun-Gang

Article Type: Research Article

Abstract: OBJECTIVE: MED subunits have been reported to be associated with various types of tumors, however, the potential role of MED7 in hepatocellular carcinoma (HCC) was still unclear. The aim of the study was to explore the role of MED7 in HCC. METHODS: In this study, MED7 mRNA expression levels between HCC and adjacent normal tissues were first analyzed by several public datasets. Then we utilized a tissue microarray (TMA) to investigate the clinical role of MED7 in HCC by immunohistochemistry (IHC). Meanwhile, the potential mechanisms of MED7 based on gene-gene correlation analyses were also explored. RESULTS: High mRNA level of …MED7 correlated with advanced stage and worse grade of differentiation. IHC results showed that MED7 protein level was upregulated in HCC and associated with Edmondson grade and Microvascular invasion in 330 cases of HCC. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed that MED7 co-expressed genes participate primarily in ribonucleoprotein complex biogenesis, protein targeting, mRNA processing and nucleoside triphosphate metabolic process et cetera. Further analysis also revealed that MED7 mRNA level has significant correlation with immune cells infiltration levels. CONCLUSION: MED7 was upregulated in HCC and correlated with progression of HCC. Meanwhile, MED7 may promote HCC through participating in multiple gene networks to influence tumorigenesis as well as immune response in HCC microenvironment. Show more

Keywords: MED7, hepatocellular carcinoma, tumorigenesis, progression

DOI: 10.3233/CBM-220439

Citation: Cancer Biomarkers, vol. 38, no. 4, pp. 603-611, 2023

Authors: Zhang, Jun | Chen, Zongren | Wu, Weimei | Shao, Liuyang | Deng, Kaihuan | Gao, Shixiong

Article Type: Research Article

Abstract: Fault detection for photovoltaic power generation system is a challenging problem in condition monitoring and troubleshooting, which aims to maintain the safe operation of equipment and improve the benefit of photovoltaic industry. Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault detection approach for photovoltaic power generation system via bidirectional long/short memory deep auto-encoder which combines the auto-encoder in deep learning with the Bi-directional Long Short-Term Memory (BiLSTM). Specifically, We first take the statistical feature enhanced as the input of an …auto-encoder based on BiLSTM. Then, we build a simulation model of Grid-connected PV system. Finally, we use the operation results under normal conditions to train the fault detection model to obtain the reconstruction error and determine the fault detection threshold, so as to judge the anomalies of the photovoltaic power generation system. We simulate the shadow occlusion fault and verify the effectiveness of the proposed method, and the fault detection accuracy of 0.95 is achieved. Compare with other models, the results show that it could set up better dependence on multi-dimensional data in time sequences, effectively testing solar panel failures and solving insufficient data labels problems. Show more

Keywords: Fault detection, deep learning, photovoltaic power generation system, deep auto-encoder, bidirectional long/short memory

DOI: 10.3233/JCM-237070

Citation: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 2, pp. 849-861, 2024