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Remote sensing images have been widely used in military, national defense, disaster emergency response, ecological environment monitoring, among other applications. However, fog always causes definition of remote sensing images to decrease. The performance of traditional image defogging methods relies on the fog-related prior knowledge, but they cannot always accurately obtain the scene depth information used in the defogging process. Existing deep learning-based image defogging methods often perform well, but they mainly focus on defogging ordinary outdoor foggy images rather than remote sensing images. Due to the different imaging mechanisms used in ordinary outdoor images and remote sensing images, fog residue may exist in the defogged remote sensing images obtained by existing deep learning-based image defogging methods. Therefore, this paper proposes remote sensing image defogging networks based on dual self-attention boost residual octave convolution (DOC). Residual octave convolution (residual OctConv) is used to decompose a source image into high- and low-frequency components. During the extraction of feature maps, high- and low-frequency components are processed by convolution operations, respectively. The entire network structure is mainly composed of encoding and decoding stages. The feature maps of each network layer in the encoding stage are passed to the corresponding network layer in the decoding stage. The dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage, thereby obtaining the refined feature maps. The strengthen-operate-subtract (SOS) boosted module is used to fuse the refined feature maps of each network layer with the upsampling feature maps from the corresponding decoding stage. Compared with existing image defogging methods, comparative experimental results confirm the proposed method improves both visual effects and objective indicators to varying degrees and effectively enhances the definition of foggy remote sensing images.
Zhiqin Zhu; Yaqin Luo; Guanqiu Qi; Jun Meng; Yong Li; Neal Mazur. Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution. Remote Sensing 2021, 13, 3104 .
AMA StyleZhiqin Zhu, Yaqin Luo, Guanqiu Qi, Jun Meng, Yong Li, Neal Mazur. Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution. Remote Sensing. 2021; 13 (16):3104.
Chicago/Turabian StyleZhiqin Zhu; Yaqin Luo; Guanqiu Qi; Jun Meng; Yong Li; Neal Mazur. 2021. "Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution." Remote Sensing 13, no. 16: 3104.
Object detection is one of the main tasks in computer vision and has made great progress in recent years. However, the performance of target detectors is significantly dropped by the differences between existing datasets and application scenarios, leading to the so-called domain shift problem. To address such an issue, a novel co-teaching based pseudo label refinery framework for cross-domain object detection is developed, which cooperates with two models to select data from target domain for each other. This strategy can effectively purify the predicted pseudo labels and resist noisy labels. Specifically, the framework consists of two encoders (i.e. structure encoder and global encoder), two classifiers and one discriminator, in which structure encoder is used to extract structural features that are not disturbed by colour, and the global encoder is used to extract the complete discriminant features. The two encoders are each followed by a classifier. In training, the structure and global encoder with labelled source samples are first trained, so that it has the initial recognition ability. Then the samples assigned are used with pseudo labels by the classifier following the structure encoder to fine-tune the global encoder which pre-trained on the labelled source domain and obtain the refined labels for the target data. With the refined labels, the structure encoder is further optimised on the target domain. During this process, the proposal is to cross use the two classifiers to promote the mutual transfer of complementary capabilities of the two encoders. Moreover, a novel residual channel attention block (RCA) embedded with salient features is designed to pay more attention to the target regions. Extensive experiments demonstrate that the developed framework can generate clean labels for unlabelled target data and boost the performance of cross domain object detection. The code is available at http://www.msp-lab.cn:1436/msp/cbplr-master.
Kunpeng Wang; Jingxiang Cai; Juan Yao; Peng Liu; Zhiqin Zhu. Co‐teaching based pseudo label refinery for cross‐domain object detection. IET Image Processing 2021, 1 .
AMA StyleKunpeng Wang, Jingxiang Cai, Juan Yao, Peng Liu, Zhiqin Zhu. Co‐teaching based pseudo label refinery for cross‐domain object detection. IET Image Processing. 2021; ():1.
Chicago/Turabian StyleKunpeng Wang; Jingxiang Cai; Juan Yao; Peng Liu; Zhiqin Zhu. 2021. "Co‐teaching based pseudo label refinery for cross‐domain object detection." IET Image Processing , no. : 1.
Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.
Zhiqin Zhu; Yaqin Luo; Hongyan Wei; Yong Li; Guanqiu Qi; Neal Mazur; Yuanyuan Li; Penglong Li. Atmospheric Light Estimation Based Remote Sensing Image Dehazing. Remote Sensing 2021, 13, 2432 .
AMA StyleZhiqin Zhu, Yaqin Luo, Hongyan Wei, Yong Li, Guanqiu Qi, Neal Mazur, Yuanyuan Li, Penglong Li. Atmospheric Light Estimation Based Remote Sensing Image Dehazing. Remote Sensing. 2021; 13 (13):2432.
Chicago/Turabian StyleZhiqin Zhu; Yaqin Luo; Hongyan Wei; Yong Li; Guanqiu Qi; Neal Mazur; Yuanyuan Li; Penglong Li. 2021. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13: 2432.
Machine learning related research in transient control has drawn considerable attention with the rapid increase in data measurement from power grids. Under limited cyber-physical resources, control algorithms and control system structures work together to determine control performance. So, this paper proposes a sparse neural network based reinforcement learning scheme to optimize the control system structure for the transient stability enhancement with energy storage systems (ESS). One adjustable group sparse weight matrix is introduced to formulate both control system structure and actor-critic networks. This strategy enables the proposed scheme to simultaneously schedule the control system structure and design the control laws by online learning without solving any combinatorial optimization problems or requiring any linearized analytical models. The sufficient conditions of learning stability, control stability, and group sparsity are thoroughly studied by mathematical analysis. The proposed scheme is simulated on an IEEE 118-bus test system to confirm the feasibility, advantages, and adaptability.
Jian Sun; Guanqiu Qi; Neal Mazur; Zhiqin Zhu. Structural Scheduling of Transient Control under Energy Storage Systems by Sparse-Promoting Reinforcement Learning. IEEE Transactions on Industrial Informatics 2021, PP, 1 -1.
AMA StyleJian Sun, Guanqiu Qi, Neal Mazur, Zhiqin Zhu. Structural Scheduling of Transient Control under Energy Storage Systems by Sparse-Promoting Reinforcement Learning. IEEE Transactions on Industrial Informatics. 2021; PP (99):1-1.
Chicago/Turabian StyleJian Sun; Guanqiu Qi; Neal Mazur; Zhiqin Zhu. 2021. "Structural Scheduling of Transient Control under Energy Storage Systems by Sparse-Promoting Reinforcement Learning." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.
As a crucial task in surveillance and security, person re-identification (re-ID) aims to identify the targeted pedestrians across multiple images captured by non-overlapping cameras. However, existing person re-ID solutions have two main challenges: the lack of pedestrian identification labels in the captured images, and domain shift issue between different domains. A generative adversarial networks (GAN)-based self-training framework with progressive augmentation (SPA) is proposed to obtain the robust features of the unlabeled data from the target domain, according to the preknowledge of the labeled data from the source domain. Specifically, the proposed framework consists of two stages: the style transfer stage (STrans), and self-training stage (STrain). First, the targeted data is complemented by a camera style transfer algorithm in the STrans stage, in which CycleGAN and Siamese Network are integrated to preserve the unsupervised self-similarity (the similarity of the same image between before and after transformation) and domain dissimilarity (the dissimilarity between a transferred source image and the targeted image). Second, clustering and classification are alternately applied to enhance the model performance progressively in the STrain stage, in which both global and local features of the target-domain images are obtained. Compared with the state-of-the-art methods, the proposed method achieves the competitive accuracy on two existing datasets.
Yuanyuan Li; Sixin Chen; Guanqiu Qi; Zhiqin Zhu; Matthew Haner; Ruihua Cai. A GAN-Based Self-Training Framework for Unsupervised Domain Adaptive Person Re-Identification. Journal of Imaging 2021, 7, 62 .
AMA StyleYuanyuan Li, Sixin Chen, Guanqiu Qi, Zhiqin Zhu, Matthew Haner, Ruihua Cai. A GAN-Based Self-Training Framework for Unsupervised Domain Adaptive Person Re-Identification. Journal of Imaging. 2021; 7 (4):62.
Chicago/Turabian StyleYuanyuan Li; Sixin Chen; Guanqiu Qi; Zhiqin Zhu; Matthew Haner; Ruihua Cai. 2021. "A GAN-Based Self-Training Framework for Unsupervised Domain Adaptive Person Re-Identification." Journal of Imaging 7, no. 4: 62.
Due to the complex structure of a distributed energy resources system (DES) and a large amount of sensor data, local computers cannot provide enough computing resources to process the related data in a short time. Moreover, network integration causes a power system vulnerable to denial of service (DoS) attacks. DoS attacks result in the loss of partial sensor data, which affects the control performance of local computers on a power system. Therefore, this paper proposes a power system structure optimization strategy based on both sparse constraint optimization and cloud computing to solve the lack of computing power from local computers and prevent DoS attacks. Cloud computing is introduced to provide powerful computing resources for processing the related data in the proposed solution. The blocking probability of sensor data caused by DoS attacks is reduced by optimizing the sensor layout of a power system and reducing the transmission of sensor data. This paper also proposes a control strategy based on actor–critic reinforcement learning (RL) to maintain the stability of a power system during the structure optimization process. Three IEEE bus test systems are used to verify the effectiveness of the proposed structure optimization method and control strategy. The experimental results confirm that the proposed structure optimization method and control strategy can maintain the stability of a power system under DoS attacks.
Zhiqin Zhu; Fancheng Zeng; Guanqiu Qi; Yuanyuan Li; Hou Jie; Neal Mazur. Power system structure optimization based on reinforcement learning and sparse constraints under DoS attacks in cloud environments. Simulation Modelling Practice and Theory 2021, 110, 102272 .
AMA StyleZhiqin Zhu, Fancheng Zeng, Guanqiu Qi, Yuanyuan Li, Hou Jie, Neal Mazur. Power system structure optimization based on reinforcement learning and sparse constraints under DoS attacks in cloud environments. Simulation Modelling Practice and Theory. 2021; 110 ():102272.
Chicago/Turabian StyleZhiqin Zhu; Fancheng Zeng; Guanqiu Qi; Yuanyuan Li; Hou Jie; Neal Mazur. 2021. "Power system structure optimization based on reinforcement learning and sparse constraints under DoS attacks in cloud environments." Simulation Modelling Practice and Theory 110, no. : 102272.
Person re-identification (Re-ID) is challenging due to host of factors: the variety of human positions, difficulties in aligning bounding boxes, and complex backgrounds, among other factors. This paper proposes a new framework called EXAM (EXtreme And Moderate feature embeddings) for Re-ID tasks. This is done using discriminative feature learning, requiring attention-based guidance during training. Here “Extreme” refers to salient human features and “Moderate” refers to common human features. In this framework, these types of embeddings are calculated by global max-pooling and average-pooling operations respectively; and then, jointly supervised by multiple triplet and cross-entropy loss functions. The processes of deducing attention from learned embeddings and discriminative feature learning are incorporated, and benefit from each other in this end-to-end framework. From the comparative experiments and ablation studies, it is shown that the proposed EXAM is effective, and its learned feature representation reaches state-of-the-art performance.
Guanqiu Qi; Gang Hu; Xiaofei Wang; Neal Mazur; Zhiqin Zhu; Matthew Haner. EXAM: A Framework of Learning Extreme and Moderate Embeddings for Person Re-ID. Journal of Imaging 2021, 7, 6 .
AMA StyleGuanqiu Qi, Gang Hu, Xiaofei Wang, Neal Mazur, Zhiqin Zhu, Matthew Haner. EXAM: A Framework of Learning Extreme and Moderate Embeddings for Person Re-ID. Journal of Imaging. 2021; 7 (1):6.
Chicago/Turabian StyleGuanqiu Qi; Gang Hu; Xiaofei Wang; Neal Mazur; Zhiqin Zhu; Matthew Haner. 2021. "EXAM: A Framework of Learning Extreme and Moderate Embeddings for Person Re-ID." Journal of Imaging 7, no. 1: 6.
A gray-box parsimonious subspace identification method using sampled dynamical and steady-state data is proposed for block-oriented Hammerstein-type systems. Compared with the conventional subspace identification methods based on over-parameterized models, the proposed method assumes parsimonious models, where the number of parameters is as minimal as possible to ensure the model accuracy, especially for highly nonlinear models. Generally, the available dynamical data do not contain adequate information on the low-frequency characteristics of the system. To improve the accuracy of mode identification, the steady-state information of the system is taken into account, and a multi-regularization method is developed, where the whole model parameters are estimated in a hierarchical, iterative manner from two sets of data: the dynamical input-output data and the steady state input-output data. The use of parsimonious models and hierarchical estimation can significantly reduce the size the associated parameter estimation error covariance matrix, thus improving the model accuracy and decreasing the variance of the estimated parameters, compared with the over-parametrization methods only using dynamica data. The effectiveness and merits are demonstrated by a simulation example and a real-word example.
Jie Hou; Fengwei Chen; Penghua Li; Zhiqin Zhu. Gray-Box Parsimonious Subspace Identification of Hammerstein-Type Systems. IEEE Transactions on Industrial Electronics 2020, 68, 9941 -9951.
AMA StyleJie Hou, Fengwei Chen, Penghua Li, Zhiqin Zhu. Gray-Box Parsimonious Subspace Identification of Hammerstein-Type Systems. IEEE Transactions on Industrial Electronics. 2020; 68 (10):9941-9951.
Chicago/Turabian StyleJie Hou; Fengwei Chen; Penghua Li; Zhiqin Zhu. 2020. "Gray-Box Parsimonious Subspace Identification of Hammerstein-Type Systems." IEEE Transactions on Industrial Electronics 68, no. 10: 9941-9951.
Poor weather conditions, such as fog, haze, and mist, cause visibility degradation in captured images. Existing imaging devices lack the ability to effectively and efficiently mitigate the visibility degradation caused by poor weather conditions in real time. Image depth information is used to eliminate hazy effects by using existing physical model-based approaches. However, the imprecise depth information always affects dehazing performance. This paper proposes an image fusion-based algorithm to enhance the performance and robustness of image dehazing. Based on a set of gamma-corrected underexposed images, pixelwise weight maps are constructed by analyzing both global and local exposedness to guide the fusion process. The spatial-dependence of luminance of the fused image is reduced, and its color saturation is balanced in the dehazing process. The performance of the proposed solution is confirmed in both theoretical analysis and comparative experiments.
Zhiqin Zhu; Hongyan Wei; Gang Hu; Yuanyuan Li; Guanqiu Qi; Neal Mazur. A Novel Fast Single Image Dehazing Algorithm Based on Artificial Multiexposure Image Fusion. IEEE Transactions on Instrumentation and Measurement 2020, 70, 1 -23.
AMA StyleZhiqin Zhu, Hongyan Wei, Gang Hu, Yuanyuan Li, Guanqiu Qi, Neal Mazur. A Novel Fast Single Image Dehazing Algorithm Based on Artificial Multiexposure Image Fusion. IEEE Transactions on Instrumentation and Measurement. 2020; 70 (99):1-23.
Chicago/Turabian StyleZhiqin Zhu; Hongyan Wei; Gang Hu; Yuanyuan Li; Guanqiu Qi; Neal Mazur. 2020. "A Novel Fast Single Image Dehazing Algorithm Based on Artificial Multiexposure Image Fusion." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-23.
Near-infrared (NIR) spectroscopy techniques have been widely used to classify tobacco cultivation regions. NIR spectroscopy of tobacco leaves involves a large number of correlated features, so it is difficult to find the connection between spectral data and tobacco cultivation regions. This paper proposes a novel classification model of tobacco cultivation regions that integrates residual network (ResNet) and NIR spectroscopy techniques. As the number of neural network layers increases, the network may have issues such as network degradation, gradient disappearance, and the reduction of sample recognition rate. The proposed model applies a residual module to a neural network which effectively solves or alleviates the vanishing gradient issues caused by the increase of network depth. This paper also adds balance and suppression factors to the loss function to solve the issues caused by uneven sizes of tobacco samples collected from different cultivation regions in the training process. In the proposed method, tobacco samples are marked as internal and external samples respectively during the training process. Internal samples are collected from the corresponding cultivation regions in the north, northeast, and northwest of Guizhou Province, China. External samples are collected from other cultivation regions. The weight distributions of internal and external samples can be adjusted by experimental results to improve the identification accuracy of the proposed solution. The size of training samples determines the generalization ability of the network and affects the experimental results. A parametric rectified linear unit (PReLU) function is integrated into the network, in which the parameters of a linear unit are adaptively learned to further improve the identification accuracy of the proposed solution. Compared with current mainstream methods, the experimental results confirm that the proposed model is superior in accurately identifying different cultivation regions of tobacco leaves.
Daiyu Jiang; Guanqiu Qi; Gang Hu; Neal Mazur; Zhiqin Zhu; Di Wang. A residual neural network based method for the classification of tobacco cultivation regions using near-infrared spectroscopy sensors. Infrared Physics & Technology 2020, 111, 103494 .
AMA StyleDaiyu Jiang, Guanqiu Qi, Gang Hu, Neal Mazur, Zhiqin Zhu, Di Wang. A residual neural network based method for the classification of tobacco cultivation regions using near-infrared spectroscopy sensors. Infrared Physics & Technology. 2020; 111 ():103494.
Chicago/Turabian StyleDaiyu Jiang; Guanqiu Qi; Gang Hu; Neal Mazur; Zhiqin Zhu; Di Wang. 2020. "A residual neural network based method for the classification of tobacco cultivation regions using near-infrared spectroscopy sensors." Infrared Physics & Technology 111, no. : 103494.
Haze can seriously affect the visible and visual quality of outdoor images. As a challenge in practice, image dehazing techniques are always used to remove haze from the captured images. Existing image dehazing algorithms focus on enhancing both global image contrast and saturation, but ignore the local enhancement. So the dehazed images do not often have good performance in the visual quality of local details. This paper proposes a new single-image dehazing solution based on the adaptive structure decomposition integrated multi-exposure image fusion (PADMEF). A set of underexposed image sequences are extracted from a single blurred image first by a series of gamma correction and the spatial linear adjustment of saturation. Then different exposure-level images are fused into a haze-free image by applying a multi-exposure image fusion (MEF) scheme based adaptive structure decomposition to each image patch. The proposed image dehazing scheme can effectively eliminate the visual degradation caused by haze without the physical model inversion of haze formation. Both apriori estimation of scene depth and the expensive refinement process of depth mapping can be avoided. The entropy of image texture named as texture energy is used to measure the image energy and obtain the information size contained in an image. Meanwhile, a texture energy based method is presented to adaptively select the corresponding patch size for the decomposition of image structure. In addition, this paper verifies that the dehazed images obtained by the patch based MEF algorithm always meet the requirements of intensity decrease. The comparative experiment results are evaluated in both qualitative and quantitative aspects, which confirm the effectiveness of the proposed solution in haze removal.
Mingyao Zheng; Guanqiu Qi; Zhiqin Zhu; Yuanyuan Li; Hongyan Wei; Yu Liu. Image Dehazing by an Artificial Image Fusion Method Based on Adaptive Structure Decomposition. IEEE Sensors Journal 2020, 20, 8062 -8072.
AMA StyleMingyao Zheng, Guanqiu Qi, Zhiqin Zhu, Yuanyuan Li, Hongyan Wei, Yu Liu. Image Dehazing by an Artificial Image Fusion Method Based on Adaptive Structure Decomposition. IEEE Sensors Journal. 2020; 20 (14):8062-8072.
Chicago/Turabian StyleMingyao Zheng; Guanqiu Qi; Zhiqin Zhu; Yuanyuan Li; Hongyan Wei; Yu Liu. 2020. "Image Dehazing by an Artificial Image Fusion Method Based on Adaptive Structure Decomposition." IEEE Sensors Journal 20, no. 14: 8062-8072.
Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.
Guanqiu Qi; Liang Chang; Yaqin Luo; Yinong Chen; Zhiqin Zhu; Shujuan Wang. A Precise Multi-Exposure Image Fusion Method Based on Low-level Features. Sensors 2020, 20, 1597 .
AMA StyleGuanqiu Qi, Liang Chang, Yaqin Luo, Yinong Chen, Zhiqin Zhu, Shujuan Wang. A Precise Multi-Exposure Image Fusion Method Based on Low-level Features. Sensors. 2020; 20 (6):1597.
Chicago/Turabian StyleGuanqiu Qi; Liang Chang; Yaqin Luo; Yinong Chen; Zhiqin Zhu; Shujuan Wang. 2020. "A Precise Multi-Exposure Image Fusion Method Based on Low-level Features." Sensors 20, no. 6: 1597.
Near-infrared (NIR) spectral sensors can deliver the spectral response of light absorbed by materials. Data analysis technology based on NIR sensors has been a useful tool for quality identification. In this paper, an improved deep convolutional neural network (CNN) with batch normalization and MSRA (Microsoft Research Asia) initialization is proposed to discriminate the tobacco cultivation regions using data collected from NIR sensors. The network structure is created with six convolutional layers and three full connection layers, and the learning rate is controlled by exponential attenuation method. One-dimensional kernel is applied as the convolution kernel to extract features. Meanwhile, the methods of L2 regularization and dropout are used to avoid the overfitting problem, which improve the generalization ability of the network. Experimental results show that the proposed deep network structure can effectively extract the complex characteristics inside the spectrum, which proves that it has excellent recognition performance on tobacco cultivation region discrimination, and it also demonstrates that the deep CNN is more suitable for information mining and analysis of big data.
Di Wang; Fengchun Tian; Simon X. Yang; Zhiqin Zhu; Daiyu Jiang; Bin Cai. Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors. Sensors 2020, 20, 874 .
AMA StyleDi Wang, Fengchun Tian, Simon X. Yang, Zhiqin Zhu, Daiyu Jiang, Bin Cai. Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors. Sensors. 2020; 20 (3):874.
Chicago/Turabian StyleDi Wang; Fengchun Tian; Simon X. Yang; Zhiqin Zhu; Daiyu Jiang; Bin Cai. 2020. "Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors." Sensors 20, no. 3: 874.
As a popular application of cloud computing, cloud storage has been widely used. In distributed computing system, blockchain as a secure mechanism is often used to store data, such as account information, trading record, and others. Once any data is saved in blockchain, it is sealed and cannot be changed any more. All the data copies must be consistent in blockchain. In cloud computing, data is always duplicated to prevent the data loss. However, the consensus issue of different data copies may appear in various data operations. Sometimes the results of data operations may be seriously affected. As a useful technique, blockchain can effectively maintain the data consistency in a distributed computing system, including the cloud-based applications, such as cloud storage. Based on a specific type of blockchain called practical Byzantine fault tolerance (PBFT), this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains the data consistency, but also has much higher efficiency than other general blockchain algorithms. In a small cloud environment, SBFT is simulated to compare with both Byzantine fault tolerance (BFT) and PBFT. The experiment results demonstrate the better performance of SBFT in data consistency, efficiency, and reliability.
Zhiqin Zhu; Guanqiu Qi; Mingyao Zheng; Jian Sun; Yi Chai. Blockchain based consensus checking in decentralized cloud storage. Simulation Modelling Practice and Theory 2019, 102, 101987 .
AMA StyleZhiqin Zhu, Guanqiu Qi, Mingyao Zheng, Jian Sun, Yi Chai. Blockchain based consensus checking in decentralized cloud storage. Simulation Modelling Practice and Theory. 2019; 102 ():101987.
Chicago/Turabian StyleZhiqin Zhu; Guanqiu Qi; Mingyao Zheng; Jian Sun; Yi Chai. 2019. "Blockchain based consensus checking in decentralized cloud storage." Simulation Modelling Practice and Theory 102, no. : 101987.
In this paper, a prior-knowledge-based subspace identification method (SIM) is proposed for batch processes subject to repeatable disturbances. The proposed method is a two-step procedure for state-space model identification: in the first step, the extended observability and triangular Toeplitz matrices are estimated simultaneously from a parity space of the experimental data and, based on which, the corresponding system matrices are retrieved in the second step. More specifically, A and C are retrieved from the estimated extended observability matrix, while B and D are retrieved from the estimated triangular Toeplitz matrix. The proposed method shows several superiorities in the following aspects. Firstly, it is able to provide unbiased parameter estimation in the presence of repeatable disturbances, thanks to the proposed difference operator which eliminates the disturbance effect. Secondly, it shows better robustness to measurement noise compared with the existing SIMs using parity space, due to the inherent instrumental variable mechanism and the new technique to build the instrument, which greatly enhance the estimated model efficiency/accuracy. Lastly, by taking the auxiliary static-gain information into account in the identification procedure, the variance properties of the parameters can be improved, especially for the system matrices B and D. All the above-mentioned developments are analyzed with strict mathematical proofs, along with two illustrative examples to confirm the effectiveness and merits.
Jie Hou; Fengwei Chen; Penghua Li; Zhiqin Zhu. Prior-knowledge-based subspace identification for batch processes. Journal of Process Control 2019, 82, 22 -30.
AMA StyleJie Hou, Fengwei Chen, Penghua Li, Zhiqin Zhu. Prior-knowledge-based subspace identification for batch processes. Journal of Process Control. 2019; 82 ():22-30.
Chicago/Turabian StyleJie Hou; Fengwei Chen; Penghua Li; Zhiqin Zhu. 2019. "Prior-knowledge-based subspace identification for batch processes." Journal of Process Control 82, no. : 22-30.
Image fusion techniques are applied to the synthesis of two or more images captured in the same scene to obtain a high-quality image. However, most of the existing fusion algorithms are aimed at single-mode images. To improve the fusion quality of multi-modal images, a novel multi-sensor image fusion framework based on non-subsampled shearlet transform (NSST) is proposed. First, the proposed solution uses NSST to decompose source images into high- and low-frequency components. Then, an improved pulse coupled neural network (PCNN) is proposed to process high-frequency components. Thus, the feature extraction effect of the high-frequency component is meliorated. After that, a sparse representation (SR) based measure, including compact dictionary learning and Max-L1 fusion rule, is designed to enhance the detailed features of the low-frequency component. Finally, the final image is obtained by the reconstruction of high- and low-frequency components via NSST inverse transformation. The proposed method is compared with several existing fusion methods. The experiment results show that the proposed algorithm outperforms other algorithms in both subjective and objective evaluation.
Li Yin; Mingyao Zheng; Guanqiu Qi; Zhiqin Zhu; Fu Jin; Jaesung Sim. A Novel Image Fusion Framework Based on Sparse Representation and Pulse Coupled Neural Network. IEEE Access 2019, 7, 98290 -98305.
AMA StyleLi Yin, Mingyao Zheng, Guanqiu Qi, Zhiqin Zhu, Fu Jin, Jaesung Sim. A Novel Image Fusion Framework Based on Sparse Representation and Pulse Coupled Neural Network. IEEE Access. 2019; 7 ():98290-98305.
Chicago/Turabian StyleLi Yin; Mingyao Zheng; Guanqiu Qi; Zhiqin Zhu; Fu Jin; Jaesung Sim. 2019. "A Novel Image Fusion Framework Based on Sparse Representation and Pulse Coupled Neural Network." IEEE Access 7, no. : 98290-98305.
With the rapid growth of distributed energy sources, power grid has become a flexible and complex networked control system. However, it increases the chances of being a denial-of-service attack, which degrades the performance of the power grid, even causing cascading failures. To mitigate negative effects from denial-of-service attack and enhance the reliability of the power grid, we propose a networked control system structure based optimization scheme that is derived from a Stackelberg game model for the frequency regulation of a power grid with distributed energy sources. In the proposed game model, both denial-of-service attacker and control system designer as a defender are considered without using any analytical model. For defenders, we propose a sparse neural network based DES control and system structure design scheme. The neural network is used to approximate the desired control output and reinforce signals for the improvements of short- and long-term performance. It also introduces the sparse regulation of column grouping in the neural network learning process to explore the structure of control system that involves the placement of sensor, distributed energy sources actuator, and communication topology. For denial-of-service attackers, the related attack constraints and attack rewards are established. The solution of game equilibrium is considered as an optimal solution for both denial-of-service attack strategy and control structure. An offline optimization algorithm is proposed to solve the game equilibrium. The effectiveness of proposed scheme is verified by two cases, which illustrate the optimal solutions of both control structure and denial-of-service attack strategy.
Jian Sun; Guanqiu Qi; Zhiqin Zhu. A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid. Applied Sciences 2019, 9, 2217 .
AMA StyleJian Sun, Guanqiu Qi, Zhiqin Zhu. A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid. Applied Sciences. 2019; 9 (11):2217.
Chicago/Turabian StyleJian Sun; Guanqiu Qi; Zhiqin Zhu. 2019. "A Sparse Neural Network Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid." Applied Sciences 9, no. 11: 2217.
As the electronically-interfaced distributed energy resources (DERs) grow rapidly in power grid, power demand satisfaction and frequency regulation are two main challenges in control area. However, it is difficult to model the analysis of a large-scale grid as well as design a stable and optimal control scheme. With the support of DERs, this paper proposes an actor-critic neural network that integrates a distributed reinforcement learning control scheme to compensate frequency regulation of power grid. The short-term performance and stability is improved by a deterministic learning algorithm that is used to obtain the approximation of desired control output. Meanwhile, a long-term strategic utility function is estimated by the integrated actor-critic neural network. The mapping from system state and control output to the strategic utility function value is identified by neural network, as well as utilized in sub-optimal control learning for further improvement of long-term system performance. Theoretical analysis guarantees the stability. Frequency deviation, tie-line power flow, and long-term cost are coincident with uniform ultimate boundness (UUB). In addition, the upper bound of long-term system cost is also reckoned. The effectiveness and advantages of proposed scheme are illustrated in two case studies. The simulation results indicate that the proposed scheme has better performance under certain condition, compared with some actor-critic network control schemes in frequency regulation of power grid.
Jian Sun; Zhiqin Zhu; Huaqing Li; Yi Chai; Guanqiu Qi; Huiwei Wang; Yu Hen Hu. An integrated critic-actor neural network for reinforcement learning with application of DERs control in grid frequency regulation. International Journal of Electrical Power & Energy Systems 2019, 111, 286 -299.
AMA StyleJian Sun, Zhiqin Zhu, Huaqing Li, Yi Chai, Guanqiu Qi, Huiwei Wang, Yu Hen Hu. An integrated critic-actor neural network for reinforcement learning with application of DERs control in grid frequency regulation. International Journal of Electrical Power & Energy Systems. 2019; 111 ():286-299.
Chicago/Turabian StyleJian Sun; Zhiqin Zhu; Huaqing Li; Yi Chai; Guanqiu Qi; Huiwei Wang; Yu Hen Hu. 2019. "An integrated critic-actor neural network for reinforcement learning with application of DERs control in grid frequency regulation." International Journal of Electrical Power & Energy Systems 111, no. : 286-299.
Zhiqin Zhu; Mingyao Zheng; Guanqiu Qi; Di Wang; Yan Xiang. A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain. IEEE Access 2019, 7, 20811 -20824.
AMA StyleZhiqin Zhu, Mingyao Zheng, Guanqiu Qi, Di Wang, Yan Xiang. A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain. IEEE Access. 2019; 7 ():20811-20824.
Chicago/Turabian StyleZhiqin Zhu; Mingyao Zheng; Guanqiu Qi; Di Wang; Yan Xiang. 2019. "A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain." IEEE Access 7, no. : 20811-20824.
Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Specifically, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the final fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.
Yuanyuan Li; Yanjing Sun; Mingyao Zheng; Xinghua Huang; Guanqiu Qi; HeXu Hu; Zhiqin Zhu. A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure. Entropy 2018, 20, 935 .
AMA StyleYuanyuan Li, Yanjing Sun, Mingyao Zheng, Xinghua Huang, Guanqiu Qi, HeXu Hu, Zhiqin Zhu. A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure. Entropy. 2018; 20 (12):935.
Chicago/Turabian StyleYuanyuan Li; Yanjing Sun; Mingyao Zheng; Xinghua Huang; Guanqiu Qi; HeXu Hu; Zhiqin Zhu. 2018. "A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure." Entropy 20, no. 12: 935.