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Spatial Pyramid Pooling With 3D Convolution Improves Lung Cancer Detection

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 1165-1172页

作者： Causey, Jason Li, Keyu Chen, Xianghao Dong, Wei Walker, Karl Qualls, Jake Stubblefield, Jonathan Moore, Jason H. Guan, Yuanfang Huang, Xiuzhen Arkansas State Univ Ctr Boundary Thinking CNBT Dept Comp Sci Jonesboro AR 72467 USA Arkansas State Univ Ctr Boundary Thinking CNBT Mol Bioscicneces Program Jonesboro AR 72467 USA Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA Ann Arbor Algorithm Ann Arbor MI 48103 USA Univ Arkansas Pine Bluff Dept Math & Comp Sci Pine Bluff AR 55455 USA Univ Penn Inst Biomed Informat Philadelphia PA 19104 USA

Lung cancer is the leading cause of cancer deaths. Low-dose computed tomography (CT)screening has been shown to significantly reduce lung cancer mortality but suffers from a high false positive rate that leads to unnecessary diagnostic procedures. The development of deep learning techniques has the potential to help improve lung cancer screening technology. Here we present the algorithm, deepScreener, which can predict a patient's cancer status from a volumetric lung CT scan. deepScreener is based on our model of Spatial Pyramid Pooling, which ranked 16th of 1972 teams (top 1 percent)in the Data Science Bowl 2017 competition (DSB2017), evaluated with the challenge datasets. Here we test the algorithm with an independent set of 1449 low-dose CT scans of the National Lung Screening Trial (NLST)cohort, and we find that deepScreener has consistent performance of high accuracy. Furthermore, by combining Spatial Pyramid Pooling and 3D Convolution, it achieves an AUC of 0.892, surpassing the previous state-of-the-art algorithms using only 3D convolution. The advancement of deep learning algorithms can potentially help improve lung cancer detection with low-dose CT scans.

关键词： Cancer Computed tomography Lung Biological system modeling Training Testing Machine learning Lung cancer screening low-dose CT scan deep learning algorithm convolutional neural network (CNN) medical imaging

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Design of a Miniaturized Wide-Angle Fisheye Lens Based on deep learning and Optimization Techniques

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MICROMACHINES 2022年第9期13卷 1409-1409页

作者： Tien, Chuen-Lin Chiang, Chun-Yu Sun, Wen-Shing Feng Chia Univ Dept Elect Engn Taichung 40724 Taiwan Feng Chia Univ PhD Program Elect & Commun Engn Taichung 40724 Taiwan Natl Cent Univ Dept Opt & Photon Chungli 32001 Taiwan

This paper presents the optimization design of a miniaturized five-element wide-angle fisheye lens using a deep learning algorithm. Zemax optical design software was used to simulate and optimize the wide-angle fisheye lens. A deep learning algorithm helped to find the best combination of different lens materials. We first used six lens elements as an initial configuration to design miniaturized wide-angle fisheye lenses using the optimization process. The optical system components were gradually decreased to five lens elements. Both OKP4HT and polymethyl methacrylate (PMMA) plastic aspheric lenses were selected to replace the second spherical glass lens in the original design. We propose two types of wide-angle fisheye lens designs with four spherical lenses and one aspheric lens. The results for these designs indicated a viewing angle of 174 degrees, a total length of less than 15 mm, a spot size of less than 6 mu m, lateral color within +/- 1 mu m, field curvature within +/- 0.02 mm, and F-theta distortion of +/- 3.5%. In addition, the MTF value was larger than 0.4 at the spatial frequency of 100 cycles/mm.

关键词： wide-angle fisheye lens deep learning algorithm optimization design modulation transfer function (MTF)

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Object Detection for Cargo Unloading System Based on Fuzzy C Means

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Computers, Materials & Continua 2022年第5期71卷 4167-4181页

作者： Sunwoo Hwang Jaemin Park Jongun Won Yongjang Kwon Youngmin Kim Department of Systems Engineering Ajou UniversitySuwon16499Korea New Transportation Innovative Research Center Korea Railroad Research InstituteUiwang16105Korea Innovative Transportation and Logistics Research Center Korea Railroad Research InstituteUiwang16105Korea

With the recent increase in the utilization of logistics and courier services,it is time for research on logistics systems fused with the fourth industry *** studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field,but so far,algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further *** this study,the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study,and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification *** fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once)and Mask R-CNN(Regions with Convolutional Neuron Networks).Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second)and a recognition rate of 95%.In addition,there were significant improvements in the range of actual box *** results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices.

关键词： deep learning algorithm YOLOv2 Mask R-CNN Fuzzy C Means unloading system

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An Enhanced Predictive Cruise Control System Design With Data-Driven Traffic Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8170-8183页

作者： Jia, Dongyao Chen, Haibo Zheng, Zuduo Watling, David Connors, Richard Gao, Jianbing Li, Ying Univ Queensland Sch Civil Engn Brisbane Qld 4072 Australia Univ Leeds Inst Transport Studies Leeds LS2 9JT W Yorkshire England Dynnoteq Ltd London HR5 3DJ England

The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and computational capabilities available on-hoard, the conventional PCC system can only obtain a sub-optimal speed trajectory based on a shorter prediction horizon. The recently emerging information and communication technologies such as vehicular communication, cloud computing, and Internet of Things provide huge potentials to improve the traditional PCC system. In this paper, we propose a general framework for the enhanced cloud-based PCC system which integrates a data-driven traffic predictive model and the instantaneous control algorithms. Specifically, we introduce a novel multi-view CNN deep learning algorithm to predict traffic situation based on the historical and real-time traffic data collected from fields, and the time-varying adaptive model predictive control (MPC) to calculate the instantaneous optimal speed profile with the aim of minimizing energy consumption. We verified our approach via simulations in which the impact of various traffic condition on the PCC-enabled HDV has been fully evaluated.

关键词： Predictive cruise control cloud-based system model predictive control traffic prediction deep learning algorithm

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Data-driven state estimation for light-emitting diode underwater optical communication 22

Data-driven state estimation for light-emitting diode underw...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Li, Yingquan Liang, Zhenwen N'Doye, Ibrahima Zhang, Xiangliang Alouini, Mohamed-Slim Laleg-Kirati, Taous-Meriem KAUST Comp Elect & Math Sci & Engn Div CEMSE Thuwal 239556900 Saudi Arabia Univ Notre Dame Notre Dame IN 46556 USA INRIA Paris France

ISBN: (纸本)9781713872344

Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for underwater robots. However, maintaining a controlled line of sight link between transmitter and receiver is challenging due to the constant movement of the underlying optical platform caused by the dynamic uncertainties of the LED model and vibration effects. Additionally, the alignment angle required for tracking is not directly measured and has to be estimated. Besides, the light scattering propagates beam pulse in water temporally, resulting in nonlinearities and time-varying underwater optical links with interference and introducing challenges in the estimation problem. In this paper, we address the state estimation problem by designing a Luenberger observer for the LED communication system that provides the angular position and velocity state information involved in the challenges of maintaining a controlled LOS optical wireless communication. In this line, we leverage the power of deep learning-based observer design to estimate the state of the LED communication model online. Simulation results are presented to illustrate the performance of the data-driven LED state estimation. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC- ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Light-emitting diode underwater optical wireless communication online estimation observer design nonlinear systems deep learning algorithm neural networks.

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deep learning-based noise robust flexible piezoelectric acoustic sensors for speech processing

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NANO ENERGY 2022年第0期101卷

作者： Jung, Young Hoon Pham, Trung Xuan Issa, Dias Wang, Hee Seung Lee, Jae Hee Chung, Mingi Lee, Bo-Yeon Kim, Gwangsu Yoo, Chang D. Lee, Keon Jae Korea Adv Inst Sci & Technol KAIST Dept Mat Sci & Engn 291 Daehak ro Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KAIST Dept Elect Engn 291 Daehak ro Daejeon 34141 South Korea Korea Inst Machinery & Mat KIMM Dept Nat Inspired Syst & Applicat 156 Gajeongbuk Ro Daejeon 34103 South Korea

Flexible piezoelectric acoustic sensors (f-PAS) have attracted significant attention as a promising component for voice user interfaces (VUI) in the era of artificial intelligence of things (AIoT). The signal distortion issue of highly sensitive biomimetic f-PAS is one of the most challenging obstacle for real-life application, due to the fundamental difference compared with the conventional microphones. Here, a noise-robust flexible piezoelectric acoustic sensor (NPAS) is demonstrated by designing the multi-resonant bands outside the noise dominant frequency range. Broad voice coverage up to 8 kHz is achieved by adopting an advanced piezoelectric membrane (Nb-doped PZT;PNZT) with the optimized polymer ratio. deep learning-based speech processing of multichannel NPAS is demonstrated to show the outstanding improvement in speaker recognition and speech enhancement compared to a commercial microphone. Finally, the NPAS filtered the crowd condition noises, showing independent speaker's speeches can be identified and digitalized simultaneously.

关键词： Flexible piezoelectric Acoustic sensor deep learning algorithm Noise -robust speaker recognition Speech enhancement

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Research on the Identification Method of Dangerous Goods in Security Inspection Images Based on deep learning 22

Research on the Identification Method of Dangerous Goods in ...

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Proceedings of the 2022 5th International Conference on Sensors, Signal and Image Processing

作者： Yuanwei Li Guangdong Industry Polytechnic China

ISBN: (纸本)9781450397124

This paper explored the application of deep learning target detection methods in the field of X-ray security screening. Faster R-CNN is a fully supervised deep learning method that uses only abnormal images containing dangerous goods as the training set, thus making it difficult to learn the features of normal images. It results in its high false detection rate when detecting normal images. In view of the above problems, combined with the characteristics of most of the X-ray security images are normal images, the author proposed a pre-classified head X-ray security image recognition method to reduce the false detection rate, while improving the performance and efficiency of dangerous goods detection, and more suitable for real X-ray security application scenarios.

关键词： Dangerous foods identification deep learning algorithm Target detection

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deep-learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators

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ENERGIES 2022年第9期15卷 3136页

作者： Roh, Chan In Je Univ Dept Energy Engn 197 Inje Ro Gimhaesi 50834 South Korea

The pitch controller of a floating offshore wind power system has an important influence on the power generation and movement of the floating body. It drives the turbine blade pitch using a hydraulic actuator, whose inherent characteristics cause a delay in response, which increases with the system capacity. As a result, the power generation is reduced, and the pitch motion of the floating body is increased. This paper proposes an advanced pitch controller designed to compensate for the delay in the hydraulic actuator response. The proposed pitch controller applies an artificial-intelligence-based deep learning algorithm to predict the delay time in the hydraulic actuator. This delay is compensated for by preferentially predicting the blade pitch control angle even if a delay occurs in the hydraulic actuator. The performance of the proposed pitch controller was analyzed using the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) v8 model developed by the US National Renewable Energy Laboratory and was compared against that of the ideal pitch controller and the pitch controller that reflects the response delay. Compared with the latter, the proposed method increased the average power generation by approximately 5% and reduced the standard deviation of the floating body's pitch motion by approximately 50%.

关键词： floating offshore wind turbine pitch controller hydraulic actuator time delay deep learning algorithm long short-term memory Fatigue Aerodynamics Structures and Turbulence (FAST)

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Modeling lithium-ion Battery in Grid Energy Storage Systems: A Big Data and Artificial Intelligence Approach 6

Modeling lithium-ion Battery in Grid Energy Storage Systems:...

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IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Miao, Yong He, Xinyuan Gu, Chenghong Shanghai Elect Power Transmiss & Distribut Grp Shanghai Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 7AY England

ISBN: (纸本)9798350311259

Grid energy storage system (GESS) has been widely used in smart homes and grids, but its safety problem has impacted its application. Battery is one of the key components that affect the performance of GESS. Its performance and working conditions directly affect the safety and reliability of the power grid. With the development of data analytics and machine learning, the accuracy and adaptability of the battery state estimation model can be greatly improved. This paper proposes a new method to model battery, with low-quality data. First, it designs a data cleaning method for GESS battery operating data, including missing data filling and outlier data repair. Then, the repaired data is used to model battery. A battery mathematical model is proposed based on a deep learning algorithm to realize accurate GESS state estimation. The performance of the developed deep learning method is compared with conventional BP neural network and generalized regression neural network to highlight the technical merits. Results derived in this paper provide a solid basis for high-efficiency GESS operation and energy management.

关键词： Grid energy storage system Big data Battery modeling Battery state estimation deep learning algorithm Artificial intelligence

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Pedestrian tracking and counting based on YOLOv4 and deepSORT in Subway Stations 35

Pedestrian tracking and counting based on YOLOv4 and DeepSOR...

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35th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Hui Xu, Jie Zhang, Xiaowen Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China

ISBN: (纸本)9798350334722

Real walking parameters of pedestrians are the basis of research on pedestrian dynamics. The detection and tracking technology provides the possibility of accurate walking parameters. To tackle the problem of lower detection accuracy of small-scale pedestrians, the YOLOv4 algorithm has been improved in two aspects. Then, the improved YOLOv4_1 model acts as the detector of deepSORT algorithm for pedestrian tracking and counting. After the improvement, the YOLOv4_I algorithm performs well in both the public and private datasets. The mAP is 90.61% in the private dataset, raising about 3.42%. Detection speeds reach 28.2 and 18.2 fps for processing images and video, respectively. Based on the YOLOv4_1-deepSORT algorithm, trajectories and walking parameters of pedestrians can he obtained. Results show the average speed of pedestrians on stairs in subway stations follows the normal distribution, the mean value is 1.27 m/s, and the standard deviation is 0.33 m(2)/s(2).

关键词： deep learning algorithm pedestrian detection pedestrian tracking trajectories walking parameters

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