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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1622-3

Abstract:

● A novel deep learning framework for short-term water demand forecasting

Keywords： Short-term water demand forecasting Long-short term memory neural network Convolutional Neural Network

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Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaosneural network model

LI Xiaodong, ZENG Guangming, HUANG Guohe, LI Jianbing, JIANG Ru

Frontiers of Environmental Science & Engineering 2007, Volume 1, Issue 3, Pages 334-338 doi: 10.1007/s11783-007-0057-6

Abstract: Based on this, a short-term forecasting chaos neural network model of WWTP influent quantity was built

Keywords： nonlinear reconstruction WWTP influent characteristic Reasonable forecasting

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Short-term Load Forecasting Using Neural Network

Luo Mei

Strategic Study of CAE 2007, Volume 9, Issue 5, Pages 77-80

Abstract: three BP ANN models, namely SDBP, LMBP and BRBP Model, are established to carry out the short-termminimizing the optimized function, an optimized L-M algorithm, which can accelerate the training of neuralnetwork and improve the stability of the convergence, should be applied to forecast to reduce

Keywords： short-term load forecasting(STLF) ANN Levenberg-Marquardt Bayesian regularization optimized algorithms

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Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting

Chen Shouyu,Guo Yu,Wang Dagang

Strategic Study of CAE 2006, Volume 8, Issue 7, Pages 30-35

Abstract:

Intelligent calculating tools such as fuzzy optimization approaches, BP neural network and geneticapproaches, and then, in this paper, the author organically synthesizes fuzzy optimal selection, BP neuralnetwork and genetic algorithm and establishes intelligent forecasting mode and method.When illustrating the method by an application to forecast mid and long term hydrological process ofnetwork to train link-weights, and finally, uses gained link-weight values to verify forecasting.

Keywords： fuzzy optimal selection BP neural network genetic algorithm intelligent forecasting mode mid and longterm intelligent hydrological forecasting

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NIPAD: a non-invasive power-based anomaly detection scheme for programmable logic controllers Article

Yu-jun XIAO, Wen-yuan XU, Zhen-hua JIA, Zhuo-ran MA, Dong-lian QI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4, Pages 519-534 doi: 10.1631/FITEE.1601540

Abstract: power measurements, we extract a discriminative feature set from the power trace, and then train a longshort-term memory (LSTM) neural network with the features of normal samples to predict the next time

Keywords： Industrial control system Programmable logic controller Side-channel Anomaly detection Long short-termmemory neural networks

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Anlotinib as third- or further-line therapy for short-term relapsed small-cell lung cancer: subgroup

Frontiers of Medicine 2022, Volume 16, Issue 5, Pages 766-772 doi: 10.1007/s11684-021-0916-8

Abstract: investigate the efficacy and safety of anlotinib as third- or further-line therapy in patients with short-termPatients with short-term relapsed SCLC (disease progression within 3 months after completing ≥ two linesFor patients with short-term relapsed SCLC, third- or further-line anlotinib treatment was associated

Keywords： anlotinib chemotherapy short-term relapsed small-cell lung cancer

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Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China Article

Yuanyuan Man, Qinli Yang, Junming Shao, Guoqing Wang, Linlong Bai, Yunhong Xue

Engineering 2023, Volume 24, Issue 5, Pages 230-239 doi: 10.1016/j.eng.2021.12.022

Abstract: To address this issue, this study proposes an enhanced long short-term memory (LSTM) model for runoffWater Balance Model (AWBM), Sacramento, SimHyd and Tank Model) and the data-driven models (artificial neuralnetwork (ANN), support vector regression (SVR), and gated recurrent units (GRU)).

Keywords： Runoff prediction Long short-term memory Upper Huai River Basin Extreme runoff Loss function

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Frontier of continuous structural health monitoring system for short & medium span bridges and condition

Ayaho MIYAMOTO, Risto KIVILUOMA, Akito YABE

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3, Pages 569-604 doi: 10.1007/s11709-018-0498-y

Abstract: It is becoming an important social problem to make maintenance and rehabilitation of existing short andmedium span(10-20 m) bridges because there are a huge amount of short and medium span bridges in servicestructural health monitoring systems are described to review and analyse the potential of utilizing the longterm health monitoring in safety assessment and management issues for short and medium span bridge.The second is a long term health monitoring method by using the public buses as part of a public transit

Keywords： condition assessment short & medium span bridge structural health monitoring(SHM) long-term data collection

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Response of bacterial communities to short-term pyrene exposure in red soil

Jingjing PENG, Hong LI, Jianqiang SU, Qiufang ZHANG, Junpeng RUI, Chao CAI

Frontiers of Environmental Science & Engineering 2013, Volume 7, Issue 4, Pages 559-567 doi: 10.1007/s11783-013-0501-8

Abstract: However, long-term exposure studies did not detect any significant effects of pyrene on soil microorganismIn this study, short-term microcosm experiments were conducted to identify the immediate effect of pyreneShort-term exposure to pyrene resulted in dominance of Proteobacteria in soil, followed by Acidobacteria

Keywords： pyrene bacterial communities terminal restriction fragment length polymorphism short-term exposure rank-abundance

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Dynamic time prediction for electric vehicle charging based on charging pattern recognition Research Article

Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2, Pages 299-313 doi: 10.1631/FITEE.2200212

Abstract: Overcharging is an important safety issue in the charging process of electric vehicle power batteries, and can easily lead to accelerated battery aging and serious safety accidents. It is necessary to accurately predict the vehicle's to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various s, the traditional prediction method often encounters modeling difficulties and low accuracy. In response to the above problems, data drivers and machine learning theories are applied. On the basis of fully considering the different electric vehicle battery management system (BMS) s, a prediction method with recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and fusion is proposed to classify vehicle s. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance prediction method is constructed by fully integrating and a strong tracking filter. Finally, the data run by the actual engineering system are verified for the proposed prediction algorithm. Experimental results show that the new method can effectively distinguish the s of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.

Keywords： Charging mode Charging time Random forest Long short-term memory (LSTM) Simplified particle swarm

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Insights into simultaneous anammox and denitrification system with short-term pyridine exposure: Process

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 6, doi: 10.1007/s11783-021-1433-3

Abstract:

• Short-term effect of the pyridine exposure on the SAD process was

Keywords： Anammox Inhibition Metabolic pathway Microbial community Pyridine SAD

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Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis Article

Yesheng Xu, Ming Kong, Wenjia Xie, Runping Duan, Zhengqing Fang, Yuxiao Lin, Qiang Zhu, Siliang Tang, Fei Wu, Yu-Feng Yao

Engineering 2021, Volume 7, Issue 7, Pages 1002-1010 doi: 10.1016/j.eng.2020.04.012

Abstract:

Infectious keratitis is the most common condition of corneal diseases in which a pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues. Infectious keratitis is a medical emergency for which a rapid and accurate diagnosis is needed to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage; otherwise, it may develop a sight threatening and even eye-globe-threatening condition. In this paper, we propose a sequentiallevel deep model to effectively discriminate infectious corneal disease via the classification of clinical images. In this approach, we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis. In a comparison, the performance of the proposed sequential-level deep model achieved 80% diagnostic accuracy, far better than the 49.27% ± 11.5% diagnostic accuracy achieved by 421 ophthalmologists over 120 test images.

Keywords： Deep learning Corneal disease Sequential features Machine learning Long short-term memory

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distal pancreatectomy versus conventional laparoscopic distal pancreatectomy: a comparative study for short-term

Eric C. H. Lai,Chung Ngai Tang

Frontiers of Medicine 2015, Volume 9, Issue 3, Pages 356-360 doi: 10.1007/s11684-015-0404-0

Abstract:

Robotic system has been increasingly used in pancreatectomy. However, the effectiveness of this method remains uncertain. This study compared the surgical outcomes between robot-assisted laparoscopic distal pancreatectomy and conventional laparoscopic distal pancreatectomy. During a 15-year period, 35 patients underwent minimally invasive approach of distal pancreatectomy in our center. Seventeen of these patients had robot-assisted laparoscopic approach, and the remaining 18 had conventional laparoscopic approach. Their operative parameters and perioperative outcomes were analyzed retrospectively in a prospective database. The mean operating time in the robotic group (221.4 min) was significantly longer than that in the laparoscopic group (173.6 min) (P = 0.026). Both robotic and conventional laparoscopic groups presented no significant difference in spleen-preservation rate (52.9% vs. 38.9%) (P = 0.505), operative blood loss (100.3 ml vs. 268.3 ml) (P = 0.29), overall morbidity rate (47.1% vs. 38.9%) (P = 0.73), and post-operative hospital stay (11.4 days vs. 14.2 days) (P = 0.46). Both groups also showed no perioperative mortality. Similar outcomes were observed in robotic distal pancreatectomy and conventional laparoscopic approach. However, robotic approach tended to have the advantages of less blood loss and shorter hospital stay. Further studies are necessary to determine the clinical position of robotic distal pancreatectomy.

Keywords： distal pancreatectomy pancreatic neoplasm robotic surgery

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Stabilization-based soil remediation should consider long-term challenges

Zhengtao Shen, Zhen Li, Daniel S. Alessi

Frontiers of Environmental Science & Engineering 2018, Volume 12, Issue 2, doi: 10.1007/s11783-018-1028-9

Abstract: However, there are emerging challenges regarding the long-term performance of the technology, which maystem from a research gap regarding the development of accurate, quantitative laboratory simulations of long-termTherefore, field trials coupled with long-term monitoring are critical to further verify conditions underSustainability is also an important factor affecting the long-term stability of site remediation.

Keywords： Stabilization Soil remediation Long-term Trace metals

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Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11, Pages 930-939 doi: 10.1631/FITEE.1500125

Abstract: In this paper we propose a novel method to estimate head pose based on a deep convolutional neural networkThen two convolutional neural networks are set up to train the head pose classifier and then comparedBefore training the network, two reasonable strategies including shift and zoom are executed to prepare

Keywords： Head pose estimation Deep convolutional neural network Multiclass classification

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Title Author Date Type Operation

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

Journal Article

Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaosneural network model

LI Xiaodong, ZENG Guangming, HUANG Guohe, LI Jianbing, JIANG Ru

Journal Article

Short-term Load Forecasting Using Neural Network

Luo Mei

Journal Article

Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting

Chen Shouyu,Guo Yu,Wang Dagang

Journal Article

NIPAD: a non-invasive power-based anomaly detection scheme for programmable logic controllers

Yu-jun XIAO, Wen-yuan XU, Zhen-hua JIA, Zhuo-ran MA, Dong-lian QI

Journal Article

Anlotinib as third- or further-line therapy for short-term relapsed small-cell lung cancer: subgroup

Journal Article

Enhanced LSTM Model for Daily Runoff Prediction in the Upper Huai River Basin, China

Yuanyuan Man, Qinli Yang, Junming Shao, Guoqing Wang, Linlong Bai, Yunhong Xue

Journal Article

Frontier of continuous structural health monitoring system for short & medium span bridges and condition

Ayaho MIYAMOTO, Risto KIVILUOMA, Akito YABE

Journal Article

Response of bacterial communities to short-term pyrene exposure in red soil

Jingjing PENG, Hong LI, Jianqiang SU, Qiufang ZHANG, Junpeng RUI, Chao CAI

Journal Article

Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Chunxi LI, Yingying FU, Xiangke CUI, Quanbo GE

Journal Article

Insights into simultaneous anammox and denitrification system with short-term pyridine exposure: Process

Journal Article

Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis

Yesheng Xu, Ming Kong, Wenjia Xie, Runping Duan, Zhengqing Fang, Yuxiao Lin, Qiang Zhu, Siliang Tang, Fei Wu, Yu-Feng Yao

Journal Article

distal pancreatectomy versus conventional laparoscopic distal pancreatectomy: a comparative study for short-term

Eric C. H. Lai,Chung Ngai Tang

Journal Article

Stabilization-based soil remediation should consider long-term challenges

Zhengtao Shen, Zhen Li, Daniel S. Alessi

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

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