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arXiv 2024年

作者： Manan, Malik Abdul Jinchao, Feng Yaqub, Muhammad Ahmed, Shahzad Imran, Syed Muhammad Ali Chuhan, Imran Shabir Khan, Haroon Ahmed Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of computer science and information technology Superior University Lahore Pakistan Interdisciplinary Research Institute Faculty of Science Beijing University of Technology Beijing China Islamabad Pakistan

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer. © 2024, CC BY.

关键词： Semantic Segmentation

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Multimodel Predictive Control for Wastewater Treatment Process with Multirate Sampling Intervals

Multimodel Predictive Control for Wastewater Treatment Proce...

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IEEE International Conference on Cyber Technology in Automation, Control, and intelligent systems

作者： Shijia Fu Haoyuan Sun Zheng Liu Honggui Han Yan Zhang Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing University of Technology Beijing China China Academy of Information and Communications Technology Beijing China

Influenced by the biochemical reactions and sensor characteristics, the wastewater treatment process (WWTP) has been regarded as a multirate sampling system. To deal with the asynchronous control problem caused by multirate sampling, a multimodel predictive control (MMPC) scheme is proposed to improve the operation performance of WWTPs. First, a multimodel predictive control structure is constructed such that the controlled variables with multirate sampling intervals of WWTPs are able to be tracked synchronously at each sampling instant. Second, the fuzzy neural networks (FNNs) are adopted as the carrier for multimodel to compute the synchronous prediction outputs of all controlled variables at each sampling instant. Moreover, an adaptive cost function is excogitated to obtain the optimal control action in which the adaptive parameter is to minimize the impact of forecast errors on the tracking effect. Ultimately, the experiment results on the benchmark simulation model 1 (BSM1) have verified the efficiency of the proposed MMPC.

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Anammox Granule Destruction and Reconstruction in a Partial Nitrification/Anammox system Under Hydroxylamine Stress

SSRN

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SSRN 2023年

作者： Feng, Wanyi Qiao, Junfei Li, Jialin Zhang, Fangzhai Zhang, Qiong Li, Xiyao Peng, Yongzhen National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology Engineering Research Center of Beijing Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

Nitrite oxidizing bacteria (NOB) outcompeting anammox bacteria (AnAOB) poses a challenge to the practical implementation of the partial nitrification/anammox (PN/A) process for municipal wastewater. A granules-based PN/A bioreactor was operated for 260 d with hydroxylamine (NH2OH)added halfway through. qPCR results detected the different amounts of NOB among granules and flocs and the dynamic succession during operation. CLSM images revealed a unique layered structure of granules that NOB located inside led to the inhibition effect of NH2OH delayed. Besides, the physical and morphological characteristics revealed that anammox granules experienced destruction. AnAOB took the broken granules as an initial biofilm aggregate to reconstruct new granules. RT-qPCR and high throughput sequencing results suggested that functional gene expression and community structure were regulated for the AnAOB metabolism process. Correspondingly, the rapid proliferation (0.52→1.99 %) of AnAOB was realized, and the nitrogen removal rate achieved a nearly quadruple improvement (0.21→0.83 kg-N/m3·d). This study revealed that anammox granules can self-reconstruct in the PN/A system when granules are disintegrated under NH2OH stress, broadening the feasibility of applying PN/A process. © 2023, The Authors. All rights reserved.

关键词： Wastewater treatment

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An Actor-Critic Framework Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling Problem

An Actor-Critic Framework Deep Reinforcement Learning Approa...

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Digital Twins and Parallel intelligence (DTPI), IEEE International Conference on

作者： Zhilong Tian Runmei Li Gang Xiong Fenghua Zhu School of Automation and Intelligence Beijing Jiaotong University Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

High-speed trains are inevitably affected by emergencies in their daily operation, which may cause the trains to fail to run according to the original planned timetable. Therefore, how to adjust the timetable of subsequent trains in emergencies is crucial for railway operations. This paper introduces a deep reinforcement learning algorithm based on an actor-critic network, which minimizes the total delay time by defining the departure order as an action. Numerical experiments are performed on the timetable of beijing-Shanghai high-speed railway line.

关键词： Optimization models Deep reinforcement learning Rail transportation Dispatching Digital twins Delays Optimization

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MRI guided wearable near infrared spectral tomography: Simulation study 14

MRI guided wearable near infrared spectral tomography: Simul...

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Optical Tomography and Spectroscopy of Tissue XIV 2021

作者： Feng, Jinchao Wei, Chengpu Jiang, Shudong Zhou, Mingwei Cao, Xu Pogue, Brian W. Paulsen, Keith D. Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Thayer School of Engineering Dartmouth College HanoverNH03755 United States

ISBN: (数字)9781510641143

ISBN: (纸本)9781510641136

A new low-cost imaging system has been developed for MRI-guided Near-Infrared Spectral Tomography (MRI-NIRST) for breast cancer detection. In this new system, 8 flexible sensing strips with 4 source fibers and 4 photodetectors will be applied directly to the breast, from nipple towards chest-wall, to cover the entire breast. For each source illumination, data will be collected at 32 detector locations. The feasibility of this new imaging system was demonstrated using 3D simulation breast phantoms that created from real breast MR images. Our results indicated that total hemoglobin in a tumor smaller than 1 mm could be recovered with this system. © 2021 SPIE

关键词： Magnetic resonance imaging

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Design of echo state network with multi-objective optimization algorithm and l1 regularization

Design of echo state network with multi-objective optimizati...

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Chinese Control and Decision Conference, CCDC

作者： Zhanhong Wu Cuili Yang Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing University of Technology Beijing

ISBN: (纸本)9781665478977

In this paper, a new approach for optimizing the structure and prediction error of echo state network (ESN) is proposed. ESN is a kind of recurrent neural network with simple training and strong generalization ability. Reservoir is an important structure of ESN, which determine network performance. Thus, multi-objective optimization algorithm is used to optimize network structure and training error simultaneously. Moreover, a local search algorithm based on l 1 regularization is used to accelerate convergence. The experiment results of time series prediction and standard classification show that MESN can improve the network prediction performance while sparse network structure.

关键词： Training Recurrent neural networks Time series analysis Benchmark testing Prediction algorithms Reservoirs Classification algorithms

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Text Semantic Analysis and intelligent Interaction in Parallel Dispatching systems for Technical Station

Text Semantic Analysis and Intelligent Interaction in Parall...

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Digital Twins and Parallel intelligence (DTPI), IEEE International Conference on

作者： Yuantao Jiao Jian Wang Runmei Li Gang Xiong Shichao Chen School of Automation and Intelligence Beijing Jiaotong University Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, and many scenarios cannot be applied in the real system. Therefore, this paper utilizes the data-driven form to establish a parallel dispatching system for technical stations, semantically analyzes text data rich in dispatcher experience, completes the Computational Experiments on the textual semantic data in the artificial dispatching system, and synchronizes the Parallel Execution of data interaction with the real dispatching system.

关键词： Training Analytical models Semantics Dispatching Digital twins Synchronization Feeds

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Few-shot RUL Prediction with A Hypernetwork Structure Incorporating Uncertainty Quantification and Calibration

Few-shot RUL Prediction with A Hypernetwork Structure Incorp...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Ying Wang Fangyu Li Di Wang Department of Industrial Engineering and Management School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education and Beijing Laboratory for Urban Mass Transit Beijing University of Technology Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Deep learning (DL) has emerged as a powerful tool for predicting the remaining useful lifetime (RUL) of components and systems. However, there are two challenges. First, DL-based methods require a sufficient number of labelled samples, while the number of run-to-failure units in practical is often small due to the high cost and time-consuming life test. Second, it is desirable to provide the prediction uncertainty of RUL for maintenance decision-making. To tackle above issues, we propose a novel few-shot RUL prediction model with a hypernetwork structure incorporating uncertainty quantification and calibration (Hyper-UC). The proposed Hyper-UC uses a shared feature embedding network and a unit-specific weight to model the mapping from sensor signals to RUL, and a generative network is designed to learn the meta-knowledge of generating distributions over the unit-specific weights. To accurately provide the predictive uncertainty, the Hyper-UC systematically models two types of uncertainty: epistemic uncertainty and aleatoric uncertainty. In the online phase, the unit-specific weights of in-service units are obtained through calibration, and subsequently the predictive distribution of the RUL can be obtained. The proposed method is evaluated to have superior model performance than the benchmark methods in a case study using the C-MAPSS dataset.

关键词： Deep learning Uncertainty Costs Computer aided software engineering Decision making Predictive models Benchmark testing Calibration Maintenance Prognostics and health management

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Steady Conformal Support Structures for 3D Printing*

Steady Conformal Support Structures for 3D Printing*

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Dong Wang Zhen Shen Qihang Fang Dong-Ming Yan Weixing Wang Qing Wang Gang Xiong School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Beijing TenDimensions Technology Co. Ltd. Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In vat photopolymerization 3D printing, cylindrical supports are commonly utilized due to their versatility. However, these supports, which vary in length and possess independent structures, are susceptible to deformation when exposed to external forces exerted by the 3D printer’s scraper during its movement. This deformation can potentially compromise the accuracy of the entire printing process. We propose a conformal support structure that adapts to the shape of the bottom contour of the model. By employing the Delaunay triangulation algorithm to approximate the bottom contour points of the model, the conformal support surface significantly reduces the length of the top support bars and integrates all the top support rods into a unified structure, thereby enhancing the stability of the entire support system. Additionally, the bottom ribs of the support structure can be reoriented to withstand forces in the X or Y direction, which are generated by the 3D printer’s scraper during its movement. We have developed a support region finding algorithm based on the Union-Find Disjoint Sets data structure, which calculates the region requiring support in the 3D model to be printed and generates the top support rods quickly. Finite element simulations have been conducted, and the results demonstrate that the conformal support structure exhibits a 96% reduction in deformation compared to the cylindrical support, indicating that our proposed structures are steadier.

关键词： Deformable models Solid modeling Adaptation models Three-dimensional displays Deformation Simulation Three-dimensional printing Stability analysis Finite element analysis Surface treatment

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Data-Driven Optimal Tracking Control for the Wastewater Treatment Plant via Generalized GDHP

Data-Driven Optimal Tracking Control for the Wastewater Trea...

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Chinese Control and Decision Conference, CCDC

作者： Xin Li Ding Wang Mingming Zhao Lingzhi Hu Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Institute of Artificial Intelligence Beijing China

ISBN: (纸本)9781665478977

In order to implement the tracking control design of dissolved oxygen and nitrate nitrogen concentrations in a nonaffine wastewater treatment system, a globalized dual heuristic dynamic programming (GDHP) algorithm is established. First, the algorithm is initialized with a positive definite function to ensure that the sequence of cost functions is monotonically nonincreasing. Then, the updating rules of the cost function, the costate function, and the tracking control law are provided in the framework of generalized value iteration. Moreover, the monotonicity and the admissibility analysis of the generalized iteration algorithm are given. The model network, the critic network, and the action network are introduced to facilitate the implementation of the algorithm. Finally, the evolving policies in the iterative process are adopted to adjust the concentrations of dissolved oxygen and nitrate nitrogen in the experimental simulation, which verifies that the proposed generalized GDHP-based optimal tracking control method is applicable online.

关键词： Adaptation models Adaptive systems Control design Heuristic algorithms Process control Cost function Dynamic programming

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