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Article ; Online: A Condition Knowledge Representation and Feedback Learning Framework for Dynamic Optimization of Integrated Energy Systems.

Wang, Tianyu / Zhao, Jun / Leung, Henry / Wang, Wei

IEEE transactions on cybernetics

2024 Volume 54, Issue 5, Page(s) 2880–2890

Abstract: An optimal energy scheduling strategy for integrated energy systems (IESs) can effectively improve the energy utilization efficiency and reduce carbon emissions. Due to the large-scale state space of IES caused by uncertain factors, it would be ... ...

Abstract	An optimal energy scheduling strategy for integrated energy systems (IESs) can effectively improve the energy utilization efficiency and reduce carbon emissions. Due to the large-scale state space of IES caused by uncertain factors, it would be beneficial for the model training process to formulate a reasonable state-space representation. Thus, a condition knowledge representation and feedback learning framework based on contrastive reinforcement learning is designed in this study. Considering that different state conditions would bring inconsistent daily economic costs, a dynamic optimization model based on deterministic deep policy gradient is established, so that the condition samples can be partitioned according to the preoptimized daily costs. In order to represent the overall conditions on a daily basis and constrain the uncertain states in the IES environment, the state-space representation is constructed by a contrastive network considering the time dependence of variables. A Monte-Carlo policy gradient-based learning architecture is further proposed to optimize the condition partition and improve the policy learning performance. To verify the effectiveness of the proposed method, typical load operation scenarios of an IES are used in our simulations. The human experience strategies and state-of-the-art approaches are selected for comparisons. The results validate the advantages of the proposed approach in terms of cost effectiveness and ability to adapt in uncertain environments.
Language	English
Publishing date	2024-04-16
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2023.3234077
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Spatio-Temporal Network for Low SNR Cryo-EM Movie Frame Enhancement.

Chong, Xiaoya / Leung, Howard / Li, Qing / Yao, Jianhua / Zhou, Niyun

IEEE/ACM transactions on computational biology and bioinformatics

2024 Volume PP

Abstract: Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused by ... ...

Abstract	Cryo-EM in single particle analysis is known to have low SNR and requires to utilize several frames of the same particle sample to restore one high-quality image for visualizing that particle. However, the low SNR of cryo-EM movie and motion caused by beam striking make the task very challenging. Video enhancement algorithms in computer vision shed new light on tackling such tasks by utilizing deep neural networks. However, they are designed for natural images with high SNR. Meanwhile, the lack of ground truth in cryo-EM movie seems to be one major limiting factor of the progress. Hence, we present a synthetic cryo-EM movie generation pipeline, which can produce realistic diverse cryo-EM movie datasets with low-SNR movie frames and multiple ground truth values. Then we propose a deep spatio-temporal network (DST-Net) for cryo-EM movie frame enhancement trained on our synthetic data. Spatial and temporal features are first extracted from each frame. Spatio-temporal fusion and high-resolution re-constructor are designed to obtain the enhanced output. For evaluation, we train our model on seven synthetic cryo-EM movie datasets and infer on real cryo-EM data. The experimental results show that DST-Net can achieve better enhancement performance both quantitatively and qualitatively compared with others.
Language	English
Publishing date	2024-03-25
Publishing country	United States
Document type	Journal Article
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2024.3380410
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: An Adaptive Kernels Layer for Deep Neural Networks Based on Spectral Analysis for Image Applications.

Al Shoura, Tariq / Leung, Henry / Balaji, Bhashyam

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 3

Abstract: As the pixel resolution of imaging equipment has grown larger, the images' sizes and the number of pixels used to represent objects in images have increased accordingly, exposing an issue when dealing with larger images using the traditional deep ... ...

Abstract	As the pixel resolution of imaging equipment has grown larger, the images' sizes and the number of pixels used to represent objects in images have increased accordingly, exposing an issue when dealing with larger images using the traditional deep learning models and methods, as they typically employ mechanisms such as increasing the models' depth, which, while suitable for applications that have to be spatially invariant, such as image classification, causes issues for applications that relies on the location of the different features within the images such as object localization and change detection. This paper proposes an adaptive convolutional kernels layer (
Language	English
Publishing date	2023-01-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23031527
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: 3D Object Detection Using Multiple-Frame Proposal Features Fusion.

Huang, Minyuan / Leung, Henry / Hou, Ming

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 22

Abstract: Object detection is important in many applications, such as autonomous driving. While 2D images lack depth information and are sensitive to environmental conditions, 3D point clouds can provide accurate depth information and a more descriptive ... ...

Abstract	Object detection is important in many applications, such as autonomous driving. While 2D images lack depth information and are sensitive to environmental conditions, 3D point clouds can provide accurate depth information and a more descriptive environment. However, sparsity is always a challenge in single-frame point cloud object detection. This paper introduces a two-stage proposal-based feature fusion method for object detection using multiple frames. The proposed method, called proposal features fusion (PFF), utilizes a cosine-similarity approach to associate proposals from multiple frames and employs an attention weighted fusion (AWF) module to merge features from these proposals. It allows for feature fusion specific to individual objects and offers lower computational complexity while achieving higher precision. The experimental results on the nuScenes dataset demonstrate the effectiveness of our approach, achieving an mAP of 46.7%, which is 1.3% higher than the state-of-the-art 3D object detection method.
Language	English
Publishing date	2023-11-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23229162
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: AR-UNet: A Deformable Image Registration Network with Cyclic Training.

Zhou, Hanchong / Leung, Henry / Balaji, Bhashyam

IEEE/ACM transactions on computational biology and bioinformatics

2023 Volume PP

Abstract: Deformable image registration is a process to determine the non-linear spatial correspondence among deformed image pairs. Generative registration network is a novel structure involving a generative registration network and a discriminative network that ... ...

Abstract	Deformable image registration is a process to determine the non-linear spatial correspondence among deformed image pairs. Generative registration network is a novel structure involving a generative registration network and a discriminative network that encourages the former to generate better results. We propose an Attention Residual UNet (AR-UNet) to estimate the complicated deformation field. The model is trained using perceptual cyclic constraints. As an unsupervised method, we require labelling for training and use virtual data augmentation to improve the robustness of the proposed model. We also introduce comprehensive metrics for image registration comparison. Experimental results show quantitative evidence that the proposed method can predict reliable deformation field at a reasonable speed and outperform conventional learning based and non-learning based deformable image registration methods.
Language	English
Publishing date	2023-06-08
Publishing country	United States
Document type	Journal Article
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2023.3284215
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Psychological well-being of healthcare workers during COVID-19 in a mental health institution.

Leung, HoiTing / Lim, Madeline / Lim, Wee Onn / Lee, Sara-Ann / Lee, Jimmy

PloS one

2024 Volume 19, Issue 3, Page(s) e0300329

Abstract: Introduction: This study examined the psychological wellbeing of Healthcare Workers (HCWs) during COVID-19 in a mental health setting, associations of psychosocial wellbeing with coping style, and ways that organisations can mitigate the psychosocial ... ...

Abstract	Introduction: This study examined the psychological wellbeing of Healthcare Workers (HCWs) during COVID-19 in a mental health setting, associations of psychosocial wellbeing with coping style, and ways that organisations can mitigate the psychosocial burden on HCWs. Methods: Thirty-seven Mental HCWs (MHCWs) from infected and non-infected wards (control group), were recruited and assessed at three timepoints. Psychological wellbeing, perceived cohesion, and coping style (Brief-COPE) were assessed. Reports on individual coping and feedback on the organisation were collected through in-depth interview. Comparison between infected and non-infected wards, as well as comparison of psychosocial measures and perceived cohesion, across the three timepoints were made. As there were no significant changes in coping styles across the timepoints, Timepoint 1 (T1) coping style was used to correlate with the psychosocial measures across all timepoints. Thematic analysis was used for qualitative data. Results: MHCWs from infected wards reported significantly higher levels of stress, χ2(1) = 6.74, p = 0.009, effect size: medium (ε2 = 0.198), and more severe sleep disturbance (PSQI), χ2(1) = 6.20, p = 0.013, effect size: medium (ε2 = 0.182), as compared to the control group at T2. They also engaged in more problem-focused coping (T2 and T3) and emotion-focused coping (T2). As expected, negative coping style was correlated with negative outcomes except problem-focused coping that was correlated with both negative (sleep disturbance and anxiety symptoms) and positive outcomes (wellbeing). Emotion-focused coping was moderately correlated (Tb = 0.348, p<0.017) with higher levels of wellbeing at T2. Thematic analyses revealed MHCWs felt supported by the responsiveness of the institution, emotional and informational support, and the availability from direct leaders, presence of team and hospital leaders on the ground, helped build trust and confidence in the leadership. Conclusions: MHCWs experienced significantly higher levels of stress and sleep disturbance during COVID-19. The ways that organizations can offset the psychological burden of pandemics on MHCWs are discussed.
MeSH term(s)	Humans ; COVID-19/epidemiology ; Mental Health ; Adaptation, Psychological ; Psychological Well-Being ; Stress, Psychological/psychology ; Health Personnel/psychology
Language	English
Publishing date	2024-03-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0300329
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: M-Denoiser: Unsupervised image denoising for real-world optical and electron microscopy data.

Chong, Xiaoya / Cheng, Min / Fan, Wenqi / Li, Qing / Leung, Howard

Computers in biology and medicine

2023 Volume 164, Page(s) 107308

Abstract: Real-world microscopy data have a large amount of noise due to the limited light/electron that can be used to capture images. The noise of microscopy data is composed of signal-dependent shot noise and signal-independent read noise, and the Poisson- ... ...

Abstract	Real-world microscopy data have a large amount of noise due to the limited light/electron that can be used to capture images. The noise of microscopy data is composed of signal-dependent shot noise and signal-independent read noise, and the Poisson-Gaussian noise model is usually used to describe the noise distribution. Meanwhile, the noise is spatially correlated because of the data acquisition process. Due to the lack of clean ground truth, unsupervised and self-supervised denoising algorithms in computer vision shed new light on tackling such tasks by utilizing paired noisy images or one single noisy image. However, they usually make the assumption that the noise is signal-independent or pixel-wise independent, which contradicts with the actual case. Hence, we propose M-Denoiser for denoising real-world microscopy data in an unsupervised manner. Firstly, the shatter module is used to break the dependency and correlation before denoising. Secondly, a novelly designed unsupervised training loss based on a pair of noisy images is proposed for real-world microscopy data. For evaluation, we train our model on optical and electron microscopy datasets. The experimental results show that M-Denoiser achieves the best performance both quantitatively and qualitatively compared with all the baselines.
MeSH term(s)	Signal-To-Noise Ratio ; Algorithms ; Microscopy, Electron ; Normal Distribution ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-07-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107308
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Consensus-Based Labeled Multi-Bernoulli Filter for Multitarget Tracking in Distributed Sensor Network.

Shen, Kai / Dong, Peng / Jing, Zhongliang / Leung, Henry

IEEE transactions on cybernetics

2022 Volume 52, Issue 12, Page(s) 12722–12733

Abstract: This article introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multitarget tracking (MTT) in a distributed sensor network (DSN), whose sensor nodes have limited and different fields of view (FoVs). Although consensus-based ...

Abstract	This article introduces a novel consensus-based labeled multi-Bernoulli (LMB) filter to tackle multitarget tracking (MTT) in a distributed sensor network (DSN), whose sensor nodes have limited and different fields of view (FoVs). Although consensus-based algorithms are effective for distributed fusion and MTT, it may be problematic when distributed sensor nodes have different FoVs. To deal with this issue, the proposed method constructs an extended label space mapping to overcome the "label space mismatching" phenomenon; after that, the model of the undetected multitargets is established so that the tracks can be initialized outside the FoV of local sensors; finally and most important, weight selection and evolution mechanism are proposed such that the fusion weights are automatically tuned for each track at each time step and consensus step. The efficiency and robustness of the proposed algorithm are demonstrated in a distributed MTT scenario via numerical simulations.
MeSH term(s)	Consensus ; Algorithms
Language	English
Publishing date	2022-11-18
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2021.3087521
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Variational Learning Data Fusion With Unknown Correlation.

Zhang, Wanying / Liang, Yan / Leung, Henry / Yang, Feng

IEEE transactions on cybernetics

2022 Volume 52, Issue 8, Page(s) 7814–7824

Abstract: This article proposes the problem of joint state estimation and correlation identification for data fusion with unknown and time-varying correlation under the Bayesian learning framework. The considered data correlation is represented by the randomly ... ...

Abstract	This article proposes the problem of joint state estimation and correlation identification for data fusion with unknown and time-varying correlation under the Bayesian learning framework. The considered data correlation is represented by the randomly weighted sum of positive semi-definite matrices, where the random weights depict at least three kinds of unknown correlation across single-sensor measurement components, multisensor measurements, and local estimates. Based on the variational Bayesian mechanism, the joint posterior distribution of the state and weights is derived in a closed-form iterative manner, through minimizing the Kullback-Leibler divergence. The three-case simulation shows the superiority of the proposed method in the root-mean-square error of estimation and identification.
Language	English
Publishing date	2022-07-19
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2021.3049769
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: PERFORMANCE ASSESSMENT OF OBJECT DETECTION FROM MULTI SATELLITES AND AERIAL IMAGES

Ahmed, M. / El-Sheimy, N. / Leung, H. / Kamel, A. M. / Moussa, A.

eISSN: 2194-9050

2023

Abstract: Object detection in remote sensing imagery plays an important role in many applications, such as tracking and change detection. With the development of deep learning algorithms and advancement in hardware systems, improved accuracies have been achieved ... ...

Abstract	Object detection in remote sensing imagery plays an important role in many applications, such as tracking and change detection. With the development of deep learning algorithms and advancement in hardware systems, improved accuracies have been achieved in the detection of various objects from remote sensing images. However, object detection across heterogeneous remote sensing imagery remains an important issue, particularly for satellite and aerial imagery. The colour variation for the same ground objects, variable resolutions, different platform heights, the parallax effect, and image distortion brought on by diverse shooting angles are the biggest hurdles in satellite-aerial detection applications. The research aims to obtain successful model for detecting aircrafts from satellite and aerial images and reduce cost and the gap of revisit time between sensors. The networks were tested using aerial, GF-2, Jilin-1 (JL-1) and Pleiades satellites test sets after being trained individually using the RGB high-resolution aerial set and panchromatic low-resolution GF-2 satellite set to validate the efficiency of the trained models. Also, the aerial-trained model and GF-2 satellite-trained model as dedicated models were compared with each other, and model trained by all dataset for Object Detection in Aerial Images (DOTA). It is observed that the anchor sizes and augmentation methods can enhance the performance of detection models. k-means algorithm and data augmentation were applied to produce better anchor box selection and avoid overfitting, atmospheric conditions problems, respectively. The accuracy assessment results demonstrate that the aerial-trained model outperforms the GF-2 satellite-trained model. In addition, the results of two dedicated detection models show improved accuracy compared to the DOTA-trained model.
Subject code	710
Language	English
Publishing date	2023-12-04
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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