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Chinese Control Conference (CCC)

作者： Bingbing Li Chengdong Wu Jianning Chi Xiaosheng Yu Gang Wang College of Information Science and Engineering Northeastern University Shenyang College of Robot Science and Engineering Northeastern University Shenyang Shanxi Xinhua Chemical Co. Ltd. Taiyuan

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Automated segmentation of brain tumor is of great importance for the diagnosis and treatment. Although manual segmentation could achieve high accuracy, it is time and labor consuming, and not available batch processing. Recently, deep neural networks represented by U-Net have been successfully applied on medical image segmentation and achieved state-of-the-art performance. However, there is no standard segmentation method that can produce desirable results for all images, various segmentation methods are optimized to deal with specific areas and imaging modes, which have strong pertinence. In this paper, we propose a U-Net like by integrating residual blocks, convolutions with dense connection and auxiliary outputs for multi-class brain tumor segmentation. Firstly, residual blocks are used in the contracting and expanding path to extract deep semantic information of images. Secondly, densely connected convolutions are applied in the last layer of contracting path to strengthen feature propagation and feature reuse. Three auxiliary outputs are then employed as deep supervision before each upper sampling to decrease the probabilities of vanishing gradient and avoid overfitting. Finally, we use weighted cross entropy and generalized dice loss as a fused loss function to address the class imbalance problem in brain tumor data. The proposed model is evaluated on the BraTS 2018 training dataset and achieves state-of-the-art performance, with average dice scores of 0.884, 0.806, 0.702 for the whole tumor, tumor core and enhancing tumor core, respectively.

关键词： Tumors Image segmentation Feature extraction Training Convolutional neural networks Data mining Entropy

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A robust laser-inertial odometry and mapping method for large-scale highway environments

arXiv

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arXiv 2020年

作者： Zhao, Shibo Fang, Zheng Li, HaoLai Scherer, Sebastian Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

In this paper, we propose a novel laser-inertial odometry and mapping method to achieve real-time, low-drift and robust pose estimation in large-scale highway environments. The proposed method is mainly composed of four sequential modules, namely scan pre-processing module, dynamic object detection module, laser-inertial odometry module and laser mapping module. Scan pre-processing module uses inertial measurements to compensate the motion distortion of each laser scan. Then, the dynamic object detection module is used to detect and remove dynamic objects from each laser scan by applying CNN segmentation network. After obtaining the undistorted point cloud without moving objects, the laser-inertial odometry module uses an Error State Kalman Filter to fuse the data of laser and IMU and output the coarse pose estimation at high frequency. Finally, the laser mapping module performs a fine processing step and the "Frame-to-Model" scan matching strategy is used to create a static global map. We compare the performance of our method with two state-of-the-art methods, LOAM and SuMa, using KITTI dataset and real highway scene dataset. Experiment results show that our method performs better than the state-of-the-art methods in real highway environments and achieves competitive accuracy on the KITTI dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

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EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble...

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2020 IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Yanmin Wu Yunzhou Zhang Delong Zhu Yonghui Feng Sonya Coleman Dermot Kerr Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China The Chinese University of Hong Kong China School of Computing and Intelligent Systems Ulster University N. Ireland UK

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on https://***/yanmin-wu/EAO-SLAM.

关键词： Simultaneous localization and mapping Navigation Pose estimation Semantics Robustness Nonparametric statistics Intelligent robots

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Nanocarrier drug resistant tumor interactions: novel approaches to fight drug resistance in cancer

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Cancer Drug Resistance 2021年第2期4卷 264-297页

作者： Aleksandra Benko David Medina-Cruz Ada Vernet-Crua Catherine P.O'Connell MałgorzataŚwiętek Hamed Barabadi Muthupandian Saravanan Thomas J.Webster AGH University of Science and Technology Faculty of Materials Science and CeramicsKrakow 30059Poland Department of Chemical Engineering Northeastern UniversityBostonMA 02115USA Institute of Macromolecular Chemistry Czech Academy of SciencesPrague 16206Czech Republic Department of Pharmaceutical Biotechnology School of PharmacyShahid Beheshti University of Medical SciencesTehran 19919-53381Iran Department of Medical Microbiology and Immunology Division of Biomedical SciencesSchool of MedicineCollege of Health SciencesMekelle UniversityMekelle 231Ethiopia.

Cancer is one of the biggest healthcare concerns in our century,a disease whose treatment has become even more difficult following reports of drug-resistant *** this happens,chemotherapy treatments fail or decrease in efficiency,leading to catastrophic consequences to the *** discovery,along with the fact that drug resistance limits the efficacy of current treatments,has led to a new wave of discovery for new methods of *** use of nanomedicine has been widely studied in current years as a way to effectively fight drug resistance in *** in the area of cancer nanotechnology over the past decades has led to tremendous advancement in the synthesis of tailored nanoparticles with targeting ligands that can successfully attach to chemotherapy-resistant cancer by preferentially accumulating within the tumor region through means of active and passive ***,these approaches can reduce the off-target accumulation of their payload and lead to reduced cytotoxicity and better *** review explores some categories of nanocarriers that have been used in the treatment of drug-resistant cancers,including polymeric,viral,lipid-based,metal-based,carbon-based,and magnetic nanocarriers,opening the door for an exciting field of discovery that holds tremendous promise in the treatment of these tumors.

关键词： Drug-resistance cancer nanotechnology nanocarriers drug delivery

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Control Method Based on Deep Reinforcement Learning for robotic Follower with Monocular Vision

Control Method Based on Deep Reinforcement Learning for Robo...

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2019 3rd International Conference on Machine Vision and Information Technology (CMVIT 2019)

作者： Dongdong Wang Feng Qiu Xiaobo Liu Faculty of Robot Science and Engineering Northeastern University

robotic follower is receiving attention widely in recent years. Aiming at the problems of low sample collection efficiency, high training cost and difficult design of reward function in the real world, we propose a control method based on deep reinforcement learning. Different depth layers are adopted to attain the end-to-end control of the robotic follower through pre-trained. Then, we design a reward function mechanism to judge whether the robot follower follow falsely. Then the appropriate pre-trained network is transferred to reinforcement learning, and a deep reinforcement learning system for monocular vision robot following tasks is established. According to the experimental results, the proposed deep reinforcement learning method can efficiently collect a large number of data sets, shorten the training period and reduce the number of times that the robot follower loses its target.

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A Modified Multi-Pedestrian Tracking System

A Modified Multi-Pedestrian Tracking System

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第三十八届中国控制会议

作者： Yuan Gong Jianning Chi Xiaosheng Yu Chengdong Wu Zixi Jia Faculty of Robot Science and Engineering Northeastern University

The intelligent vehicle driverless technology has become a very hot topic in the past two decades. To solve the road safety problem, the multi-target tracking system has attracted more and more attention in recent study since it can detect and track multiple objects in traffic scene so as to help the whole driving system plan the safe route. In this work, a novel multipedestrian tracking system based on deep neural network is proposed to improve the tracking efficiency while providing high recognition accuracy. The proposed tracking system consists of two parts: pedestrian detector and target tracker. We refer to the structure of SSD detection network to design our own detection network, replacing the traditional pooling layers by image convolution operation implemented in complex frequency domain. Then, a deep appearance model is trained to track the detected targets by their similarity between the continuous frames. To evaluate the performance of our proposed system, we implement the system on M OT16 benchmark. The detector can improve tracking accuracy by up to 9.3% and the tracker updates at a rate of 138 Hz, our extensions reducing the number of identity switches by 16.2%. The experimental results show that the proposed multi-pedestrian tracking system provides better real-time performance and accuracy than the state-of-the-art methods, which can provide more accurate object information for the following auto-driving system with higher safety.

关键词： multi-pedestrian tracking system pedestrian detector target tracker real-time performance tracking accuracy

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Method and Application of Homomorphic Subtraction of the Paillier Cryptosystem in Secure Multi-party Computational Geometry 9th

Method and Application of Homomorphic Subtraction of the Pai...

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9th EAI International Conference on Cloud Computing, CloudComp 2019 and the 4th EAI International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Liu, Meng Luo, Yun Yang, Chi Xu, Dongliang Wu, Taoran School of Mechanical Electrical and Information Engineering Shandong University Weihai China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China College of Professional Studies Northeastern University Boston United States

ISBN: (纸本)9783030485122

A secure two-party computation protocol for the problem of the distance between two private points is important and can be used as the building block for some secure multi-party computation (SMC) problems in the field of geometry. Li’s solution to this problem is inefficient based on oblivious transfer protocol and some drawbacks still remain while applied to compute the relationship between a private circle and a private point. Two protocols are also proposed based on the Paillier cryptosystem by Luo et al. and more efficient than Li’s solution, but there also remain some drawbacks. In this paper, we propose an idea to improve the efficiency of secure protocol by using its homomorphic subtraction based on the Paillier cryptosystem. Then we apply it to solve the secure two-party computation problem for the distance between two private points. Using our solution, the SMC protocol to the relationship between a private point and a private circle area is more efficient and private than Li’s solution. In addition, we also find that our solution is also more efficient than the BGN-based solution and much better while the plaintext can be in some large range. © 2020, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Computational geometry

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A Fast Hybrid-kernel Correlation Filter Tracking Algorithm

A Fast Hybrid-kernel Correlation Filter Tracking Algorithm

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第三十八届中国控制会议

作者： Na Gao Jianning Chi Chengdong Wu Xiaosheng Yu Zixi Jia Faculty of Robot Science and Engineering Northeastern University

With the development of computer vision and imaging technology, high frame rate cameras improve tracking performance by higher quality videos. However, recent tracking algorithms are mostly proposed for ordinary cameras, of which the processing speed is far from enough to keep along with the acquisition speed in high frame rate cameras. In this paper, a fast tracking algorithm for high frame rate cameras was proposed. The algorithm improves the kernel correlation filter method in the kernel function and feature description. For the kernel function, a hybrid-kernel function was employed by fusing local and global kernel. Fo r feature description and dimensionality reduction, the deformable part model without image pyramid is adopted for image description due to the improvement of video quality. The proposed fast hybrid-kernel correlation filter tracking algorithm performs better for real-time target tracking in the video of high frame rate camera than other methods. The experimental results on 50-segment videos of the OTB2015 and Need for speed（240 fps） database verify the effectiveness. In OTB2015, the accuracy is 74.3% with 320 fps in CPU, and the accuracy and frame rate are 70.6% and 273 fps, respectively in Need for Speed.

关键词： Fast Visual Tracking High Frame Rate Cameras Hybrid-kernel Function Circulant Matrix

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Short-Term Load Forecasting with TDCI: Temporal Decomposition and Channel Independence

Short-Term Load Forecasting with TDCI: Temporal Decompositio...

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2023 7th International Conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Wenxue Li Chi Cheng Peihua Xu Renfeng Liu Yun Zhang Huabing Zhou College of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Meteorological Service Center Wuhan China Faculty of Artificial Intelligence Education School of Educational Information Technology Central China Normal University Wuhan China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models.

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Monocular adaptive inverse depth filtering algorithm based on Gaussian model

Monocular adaptive inverse depth filtering algorithm based o...

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第32届中国控制与决策会议

作者： Chenglong Xu Chengdong Wu Daokui Qu Haibo Sun Jilai Song Xiaofeng Wang Faculty of Robot Science and Engineering Northeastern University Shenyang SIASUN Robot & Automation Co. LTD. State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

This paper presents an adaptive filtering algorithm for monocular depth *** is a geometric calculation method under the assumption that the inverse depth conforms to the Gaussian *** the inverse depth update,the average value of the historical stable points is introduced to smooth the output,and the depth uncertainty of the unit pixel offset is calculated to eliminate the estimation *** the similarity search along the epipolar line,the normalized cross-correlation and gradient are used as joint ***,the effectiveness of the algorithm is verified through experiments,and it can get a better trade-off between performance and time.

关键词： Monocular depth estimation Inverse depth filtering Gaussian model Normalized cross-correlation

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