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Learning physical characteristics like animals for legged robots

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National Science Review 2023年第5期10卷 22-37页

作者： Peng Xu Liang Ding Zhengyang Li Huaiguang Yang Zhikai Wang Haibo Gao Ruyi Zhou Yang Su Zongquan Deng Yanlong Huang Key Laboratory of Robotics and Systems Harbin Institute of Technology School of computing University of Leeds

Physical characteristics of terrains,such as softness and friction,provide essential information for legged robots to avoid non-geometric obstacles,like mires and slippery stones,in the wild.The perception of such characteristics often relies on tactile perception and vision prediction.Although tactile perception is more accurate,it is limited to close-range us e;by contrast,establishing a supervised or self-supervised contactless prediction system using computer vision re quires adequate labeled data and lacks the ability to adapt to the dynamic environment.In this paper,we simulate the behavior of animals and propose an unsupervised learning framework for legged robots to learn the physical characteristics of terrains,which is the first report to manage it online,incrementally and with the ability to solve cognitive conflicts.The proposed scheme allows robots to interact with the environment and adjust their cognition in real time,therefore endowing robots with the adaptation ability.Indoor and outdoor experiments on a hexapod robot are carried out to show that the robot can extract tactile and visual features of terrains to create cognitive networks independently;an associative layer between visual and tactile features is created during the robot’s exploration;with the layer,the robot can autonomously generate a physical segmentation model of terrains and solve cognitive conflicts in an ever-changing environment,facilitating its safe navigation.

关键词： legged robot unsupervised learning environmental perception cognitive learning

The Vibrant Field of Parallel and Distributed computing--Scan the Special Issue in Honor of Professor Kai Hwang’s 80th Birthday

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Journal of Computer Science & technology 2023年第1期38卷 1-2页

作者：李国杰 Institute of Computing Technology Chinese Academy of SciencesBeijing

It is my great pleasure to write this editorial for the special issue in honor of Professor Kai Hwang’s 80th birthday.The articles are written by Professor Hwang’s academic descendants and colleagues,to pay tribute ... 详细信息

关键词： wan honor editorial

Near-ground trajectory planning for UAVs via multi-resolution hybrid voxel-surfel map

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Science China(Technological Sciences) 2023年第5期66卷 1245-1254页

作者： GAO TianYu WENG Rui WU Tong ZHANG RuiXian HAN ChengZhe JI XiaoYu LIU Ming School of Astronautics Harbin Institute of TechnologyHarbin 150000China Faculty of computing Harbin Institute of TechnologyHarbin 150000China

This paper is concerned with trajectory planning problems for UAVs operating near ground.Most existing studies focus on solving the problem of collision-free trajectory planning between pre-defined path points,but ignore the need of navigation method for UAVs working on specific operating surfaces in near-ground space.In this paper,a novel near-ground trajectory planning framework is proposed,where the hybrid voxel-surfel map is developed to model the environment with special attention to the uneven operating surface.To improve the frequency of updates,a probability-based surfel fusion method and a resolution adaptive adjustment method based on the fusion result are proposed in this paper.By using possibility information in the map,a path search method is established to generate the initial trajectory.The trajectory is then further optimized based on map gradient information to generate a final trajectory that tracks the specified operating surface according to the task requirements.Compared with existing methods,the multi-resolution hybrid voxel-surfel map proposed in this paper has advantages in terms of operating efficiency.A series of experiments in simulated and real scenarios validate the effectiveness of the proposed trajectory planning framework.

关键词： near-ground trajectory planning hybrid voxel-surfel map probability-based surfel fusion operating surface tracking

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Survey on leveraging pre-trained generative adversarial networks for image editing and restoration

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Science China(Information Sciences) 2023年第5期66卷 28-55页

作者： Ming LIU Yuxiang WEI Xiaohe WU Wangmeng ZUO Lei ZHANG School of Computer Science and technology Harbin Institute of Technology Department of computing Hong Kong Polytechnic University

Generative adversarial networks(GANs) have drawn enormous attention due to their simple yet efective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolution(e.g., 1024 × 1024) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore, many recent studies show emerging interest to take advantage of pre-trained GAN models by exploiting the well-disentangled latent space and the learned GAN priors. In this study, we briefly review recent progress on leveraging pre-trained large-scale GAN models from three aspects, i.e.,(1) the training of large-scale generative adversarial networks,(2) exploring and understanding the pre-trained GAN models, and(3) leveraging these models for subsequent tasks like image restoration and editing.

关键词： survey generative adversarial networks pre-trained models image editing image restoration

Artificial intelligence for science-bridging data to wisdom

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The Innovation 2023年第6期4卷 21-22页

作者： Yongjun Xu Fei Wang Zhulin An Qi Wang Zhao Zhang Institute of Computing Technology Chinese Academy of SciencesBejjing 100190China

INTRODUCTION.Throughout the scientific discoveries of human history,research paradigms have undergone profound changes.'As shown in Figure 1,from the empirical paradigm,which relies on experimental observation,to the theoretical paradigm,which is based on theoretical deduction,and further to the computational para-digm,which is associated with simulation and emulation,and finally to the data-driven paradigm,which is grounded in human-machine object integration,every transformation of scientific paradigm has brought about a major leap in science and technology.

关键词： technology. Figure bridging

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

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Computer Systems Science & Engineering 2023年第8期46卷 1811-1826页

作者： M.Uvaneshwari M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of computing Technologies School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia

The Brain Tumor(BT)is created by an uncontrollable rise of anomalous cells in brain tissue,and it consists of 2 types of cancers they are malignant and benign tumors.The benevolent BT does not affect the neighbouring healthy and normal tissue;however,the malignant could affect the adjacent brain tissues,which results in death.Initial recognition of BT is highly significant to protecting the patient’s life.Generally,the BT can be identified through the magnetic resonance imaging(MRI)scanning technique.But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape of the tumor in the brain.Recently,ML has prevailed against standard image processing techniques.Several studies denote the superiority of machine learning(ML)techniques over standard techniques.Therefore,this study develops novel brain tumor detection and classification model using met heuristic optimization with machine learning(BTDC-MOML)model.To accomplish the detection of brain tumor effectively,a Computer-Aided Design(CAD)model using Machine Learning(ML)technique is proposed in this research manuscript.Initially,the input image pre-processing is performed using Gaborfiltering(GF)based noise removal,contrast enhancement,and skull stripping.Next,mayfly optimization with the Kapur’s thresholding based segmentation process takes place.For feature extraction proposes,local diagonal extreme patterns(LDEP)are exploited.At last,the Extreme Gradient Boosting(XGBoost)model can be used for the BT classification process.The accuracy analysis is performed in terms of Learning accuracy,and the validation accuracy is performed to determine the efficiency of the proposed research work.The experimental validation of the proposed model demonstrates its promising performance over other existing methods.

关键词： Brain tumor machine learning segmentation computer-aided diagnosis skull stripping

A Note on Maximizing Regularized Submodular Functions Under Streaming

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Tsinghua Science and technology 2023年第6期28卷 1023-1029页

作者： Qinqin Gong Kaiqiao Meng Ruiqi Yang Zhenning Zhang Beijing institute for Scientific and Engineering computing Beijing University of TechnologyBeijing 100124China

Recent progress in maximizing submodular functions with a cardinality constraint through centralized and streaming modes has demonstrated a wide range of applications and also developed comprehensive theoretical guarantees.The submodularity was investigated to capture the diversity and representativeness of the utilities,and the monotonicity has the advantage of improving the coverage.Regularized submodular optimization models were developed in the latest studies(such as a house on fire),which aimed to sieve subsets with constraints to optimize regularized utilities.This study is motivated by the setting in which the input stream is partitioned into several disjoint parts,and each part has a limited size constraint.A first threshold-based bicriteria(1/3,2/3/)-approximation for the problem is provided.

关键词： submodular optimization regular model streaming algorithms threshold technique

Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks.To address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different metrics.The rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other features.However,the methods suffer from mitigating intrusion attacks at a higher rate.This article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these deficiencies.The method involves analyzing service growth,network growth,and quality of service growth.The process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user requests.The proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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Computer Systems Science & Engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various problems.The same can be adapted to the problem of brain tumor classification.However,several deep learning models are presented earlier,but they need better classification accuracy.An efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this issue.The method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple levels.The noise-removed image has been equalized for its quality by using histogram equalization.Further,the features like white mass,grey mass,texture,and shape are extracted from the images.Extracted features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality reduction.The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution layer.The neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images.Based on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor