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Efficient Dynamic Locomotion of Quadruped Robot via Adaptive Diagonal Gait

Journal of Bionic engineering 2024年第1期21卷 126-136页

作者： Jian Bi Teng Chen Xuewen Rong Guoteng Zhang Guanglin Lu Jingxuan Cao Han Jiang Yibin Li The Center for Robotics School of Control Science and EngineeringShandong UniversityJi Nan 250000China

Quadruped animals in the nature realize high energy efficiency locomotion by automatically changing their gait at different *** by this character,an efficient adaptive diagonal gait locomotion controller is designed for quadruped robot.A unique gait planning method is proposed in this *** the speed of robot varies,the gait cycle time and the proportion of stance and swing phase of each leg are adjusted to form a variety of *** optimal joint torque is calculated by the controller combined with Virtual Model Control(VMC)and Whole-Body Control(WBC)to realize the desired *** gait and step frequency of the robot can automatically adapt to the change of *** experiments are done with a quadruped robot made by our laboratory to verify that the gait can change automatically from slow-trotting to flying-trot during the period when speed is from 0 to 4 m/*** ratio of swing phase is from less than 0.5 to more than 0.5 to realize the running motion with four feet off the *** have shown that the controller can indeed consume less energy when robot runs at a wide range of speeds comparing to the basic controller.

关键词： Quadruped robot Gait transition Adaptive gait Energy efficiency

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Transfer Learning Based Yogic Posture Recognition System Using Deep Pre-trained Features

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SN computer science 2024年第6期5卷 746页

作者： Rajendran, Arun Kumar Sethuraman, Sibi Chakkaravarthy School of Computer Science and Engineering VIT-AP University School of Computer Science & amp Engineering and Center of Excellence Artificial Intelligence and Robotics (AIR) VIT-AP University

Yoga is a healthy exercise that focuses on physical, psychological, and divine connections. However, engaging in yoga while adopting poor postures might result in health issues like muscle discomfort and sprains. Discovering the specific posture amid an activity is still a very challenging and complicated task in artificial intelligence. With the aid of transfer learning, a model which can identify the yogic posture from images has been constructed in this work. In this approach, sixteen postures of yogic data are collected, and then pre-processing techniques are used to resize the images. This work employs the transfer learning method to extract the features from popular convolutional neural network architectures such as VGG16, VGG19, InceptionV3, Xception, MobileNet, and MobileNetV2;all are pre-trained on the ImageNet database. Extracted features are fed to machine learning models like Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for classification. The linear SVM-based classifier, trained on features extracted by MobileNet, performs better than its rivals with an accuracy of 99.92%. In total, six transfer learning models are exploited in this yogic posture classification approach and yield encouraging results in terms of prediction accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： InceptionV3 MobileNet MobileNetV2 NB RF SVM Transfer learning VGG16 VGG19 Xception XGBoost Yoga posture classification

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MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

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computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

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MDCN:Modified Dense Convolution Network Based Disease Classification in Mango Leaves

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computers, Materials & Continua 2024年第2期78卷 2511-2533页

作者： Chirag Chandrashekar K.P.Vijayakumar K.Pradeep A.Balasundaram School of Computer Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)ChennaiIndia

The most widely farmed fruit in the world is *** the production and quality of the mangoes are hampered by many *** diseases need to be effectively controlled and ***,a quick and accurate diagnosis of the disorders is *** convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification ***,it requires large training datasets and several parameters that need careful *** proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango *** model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning *** data loader also builds mini-batches for training the models to reduce training ***,optimization approaches help increase the overall model’s efficiency and lower computing *** employed on the MangoLeafBD Dataset consists of a total of 4,000 *** the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.

关键词： Leaf disease detection deep convolutional neural networks transfer learning optimization MangoLeafBD Dataset

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Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

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computers, Materials & Continua 2023年第8期76卷 1721-1744页

作者： Seshu Bhavani Mallampati Seetha Hari School of Computer Science and Engineering VIT-AP UniversityAndhra PradeshIndia Center of Excellence AIand RoboticsVIT-AP UniversityAndhra PradeshIndia

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous *** Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and *** extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still ***,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm ***,to improve training data quality,various preprocessing techniques are *** Adaptive Synthetic oversampling technique generates synthetic samples for minority *** the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHAP)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its *** by simple plurality voting,the most optimal features are *** the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the *** experiments were carried out on the original imbalanced data and balanced *** outcomes demonstrate that the balanced data scenario knocked out the imbalanced ***,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laborat

关键词： Cyber security feature ranking imbalance preprocessing IDS SHAP

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Highly transparent anti-reflection coating enhances the underwater efficiency and stability of perovskite solar modules

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Nano Research 2024年第9期17卷 8126-8133页

作者： Feng Qian Shihao Yuan Ting Zhang Lei Wang Xiaobo Li Hualin Zheng Qien Xu Zhi David Chen Shibin Li School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Department of Electrical and Computer Engineering and Center for Nanoscale Science and Engineering University of KentuckyLexingtonKentucky 40506USA

Perovskite solar cells have shown great potential in the field of underwater solar cells due to their excellent optoelectronic properties;however,their underwater performance and stability still hinder their practical *** this research,a 1H,1H,2H,2H-heptadecafluorodecyl acrylate(HFDA)anti-reflection coating(ARC)was introduced as a high-transparent material for encapsulating perovskite solar modules(PSMs).Optical characterization results revealed that HFDA can effectively reduce reflection of light below 800 nm,aiding in the absorption of light within this wavelength range by underwater solar ***,a remarkable efficiency of 14.65%was achieved even at a water depth of 50 ***,the concentration of Pb^(2+)for HFDA-encapsulated film is significantly reduced from 186 to 16.5 ppb after being immersed in water for 347 ***,the encapsulated PSMs still remained above 80%of their initial efficiency after continuous underwater illumination for 400 ***,being exposed to air,the encapsulated PSMs maintained 94%of their original efficiency after 1000 h light *** highly transparent ARC shows great potentials in enhancing the stability of perovskite devices,applicable not only to underwater cells but also extendable to land-based photovoltaic devices.

关键词： underwater photovoltaics perovskite solar modules anti-reflection coating underwater stability

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Mode Competition of Low Voltage Backward Wave Oscillator near 500 GHz with Parallel Multi-Beam

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Chinese Journal of Electronics 2024年第2期33卷 488-495页

作者： Xiaoyan ZHAO Jincheng HU Haoran ZHANG Sidou GUO Yuming FENG Lin TANG Kaichun ZHANG Diwei LIU THz Center School of Electronic Science and Engineering University of Electronic Science and Technology of China School of Computer Science Southwest Petroleum University

A backward wave oscillator with parallel multiple beams and multi-pin slow-wave structure(SWS)operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studio simulation results reveal that there are obvious mode competition problems in this kind of terahertz *** that the structure of the multi-pin SWS is similar to that of two-dimensional photonic crystals, we introduce the defects of photonic crystal with the property of filtering into the SWS to suppress high-order ***, a detailed study of the effect of suppressing higher-order modes is carried out in the process of changing location and arrangement pattern of the point defects. The stable, single-mode operation of the terahertz source is realized. The simulation results show that the ratio of the output peak power of the higher-order modes to that of the fundamental mode is less than 1.9%. Also, the source can provide the output peak power of 44.8 m W at the frequency of 502.2 GHz in the case of low beam voltage of 4.7 kV.

关键词： Mode competition Backward wave oscillator Multi-pin slow-wave structure Parallel multiple beams Defects

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Robust video question answering via contrastive cross-modality representation learning

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science China(Information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of Information Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Computer Science and Information Engineering Hefei University of Technology Institutes of Physical Science and Information Technology Anhui University School of Computer Science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

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CLIP-Flow:Decoding images encoded in CLIP space

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Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

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Design and developing a robot-assisted cell batch microinjection system for zebrafish embryo

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Microsystems & Nanoengineering 2025年第1期11卷 403-415页

作者： Xiangyu Guo Antian Zhao Youchao Zhang Huanyu Jiang Longhua Tang Bo Lu Yibin Ying Mingchuan Zhou Robotic Micro-nano Manipulation Lab College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina College of Optical Science and Engineering Zhejiang UniversityHangzhouChina Robotics and Microsystems Center School of Mechanical and Electric EngineeringSoochow UniversitySuzhouChina

The microinjection of Zebrafish embryos is significant to life science and biomedical *** this article,a novel automated system is developed for cell microinjection.A sophisticated microfluidic chip is designed to transport,hold,and inject cells *** the first time,a microinjector with microforce perception is proposed and integrated within the enclosed microfluidic chip to judge whether cells have been successfully *** deep learning model is employed to detect the yolk center of zebrafish embryos and locate the position of the injection needle within the yolk,which enables enhancing the precision of cell injection.A prototype is fabricated to achieve automatic batch *** results demonstrated that the injection efficiency is about 20 seconds per *** puncture success rate and cell survival rate are 100%and 84%,*** to manual operation,this proposed system improves cell operation efficiency and cell survival *** proposed microinjection system has the potential to greatly reduce the workload of the experimenters and shorten the relevant study period.

关键词： deep learning model cell microinjectiona detect yolk center microfluidic chip microforce perception cell batch microinjection zebrafish embryo automated system

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