The increasing power consumption in the existing computation architecture presents grand challenges for the performance and reliability of very‐large‐scale integrated circuits. Inspired by the characteristics of the human brain for processing complicated tasks with low power, neuromorphic computing is intensively investigated for decreasing power consumption and enriching computation functions. Hardware implementation of neuromorphic computing with emerging devices substantially reduces power consumption down to a few mW cm−2, compared with the central processing unit based on conventional Si complementary metal–oxide semiconductor (CMOS) technologies (50–100 W cm−2). Herein, a brief introduction on the characteristics of neuromorphic computing is provided. Then, emerging devices for low‐power neuromorphic computing are overviewed, e.g., resistive random access memory with low power consumption (< pJ) per synaptic event. A few computation models for artificial neural networks (NNs), including spiking neural network (SNN) and deep neural network (DNN), which boost power efficiency by simplifying the computing procedure and minimizing memory access are discussed. A few examples for system‐level demonstration are described, such as mixed synchronous–asynchronous and reconfigurable convolution neuron network (CNN)–recurrent NN (RNN) for low‐power computing.
现有计算架构中不断增加的功耗对超大规模集成电路的性能和可靠性提出了巨大的挑战。受人脑处理低功耗复杂任务的特点的启发，神经形态计算在降低功耗和丰富计算功能方面得到了广泛的研究。与基于传统Si互补金属氧化物半导体(CMOS)技术(50-100 W cm - 2)的中央处理单元相比，新兴器件的神经形态计算硬件实现大大降低了功耗，功耗低至几mW cm - 2。本文简要介绍了神经形态计算的特点。然后，概述了用于低功耗神经形态计算的新兴器件，例如，每个突触事件低功耗(< pJ)的电阻式随机存取存储器。讨论了几种人工神经网络(NNs)的计算模型，包括尖峰神经网络(SNN)和深度神经网络(DNN)，它们通过简化计算过程和最小化内存访问来提高功率效率。本文描述了一些用于系统级演示的例子，例如用于低功耗计算的混合同步-异步和可重构卷积神经元网络(CNN) -循环神经网络(RNN)。
{"title":"Low‐Power Computing with Neuromorphic Engineering","authors":"Dingbang Liu, Hao Yu, Y. Chai","doi":"10.1002/aisy.202000150","DOIUrl":"https://doi.org/10.1002/aisy.202000150","url":null,"abstract":"The increasing power consumption in the existing computation architecture presents grand challenges for the performance and reliability of very‐large‐scale integrated circuits. Inspired by the characteristics of the human brain for processing complicated tasks with low power, neuromorphic computing is intensively investigated for decreasing power consumption and enriching computation functions. Hardware implementation of neuromorphic computing with emerging devices substantially reduces power consumption down to a few mW cm−2, compared with the central processing unit based on conventional Si complementary metal–oxide semiconductor (CMOS) technologies (50–100 W cm−2). Herein, a brief introduction on the characteristics of neuromorphic computing is provided. Then, emerging devices for low‐power neuromorphic computing are overviewed, e.g., resistive random access memory with low power consumption (< pJ) per synaptic event. A few computation models for artificial neural networks (NNs), including spiking neural network (SNN) and deep neural network (DNN), which boost power efficiency by simplifying the computing procedure and minimizing memory access are discussed. A few examples for system‐level demonstration are described, such as mixed synchronous–asynchronous and reconfigurable convolution neuron network (CNN)–recurrent NN (RNN) for low‐power computing.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88412006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minimally invasive endovascular interventions have become the cornerstone of medical practice in the treatment of a variety of vascular diseases. Tools developed for these interventions have also opened new avenues for targeted delivery of therapeutics, such as chemotherapy or radiation therapy, using the vessels as highways into remote lesions. A more ambitious move toward an all‐endovascular approach to acute or chronic conditions, however, is fundamentally hindered by a variety of challenges, such as the complexity of the vascular anatomy, access to smaller vessels, the fragility of diseased vessels, emergency procedure requirements, prolonged exposure to ionizing X‐ray radiation, and patient‐specific factors including coagulopathy. These shortcomings necessitate new advances to the current practice. Smart soft‐body robots that fit the smallest vessels with high‐precision wireless control and autonomous capabilities have the potential to set the future standards of minimally invasive endovascular therapies. Herein, the current state of the small‐scale robotics from the viewpoint of endovascular applications is discussed, and their potential advantages to the existing tethered clinical devices are compared. Then, technical challenges and the clinical requirements toward realistic applications of small‐scale untethered robots inside the vasculature are discussed.
{"title":"Robotic Devices for Minimally Invasive Endovascular Interventions: A New Dawn for Interventional Radiology","authors":"S. Gunduz, H. Albadawi, R. Oklu","doi":"10.1002/aisy.202000181","DOIUrl":"https://doi.org/10.1002/aisy.202000181","url":null,"abstract":"Minimally invasive endovascular interventions have become the cornerstone of medical practice in the treatment of a variety of vascular diseases. Tools developed for these interventions have also opened new avenues for targeted delivery of therapeutics, such as chemotherapy or radiation therapy, using the vessels as highways into remote lesions. A more ambitious move toward an all‐endovascular approach to acute or chronic conditions, however, is fundamentally hindered by a variety of challenges, such as the complexity of the vascular anatomy, access to smaller vessels, the fragility of diseased vessels, emergency procedure requirements, prolonged exposure to ionizing X‐ray radiation, and patient‐specific factors including coagulopathy. These shortcomings necessitate new advances to the current practice. Smart soft‐body robots that fit the smallest vessels with high‐precision wireless control and autonomous capabilities have the potential to set the future standards of minimally invasive endovascular therapies. Herein, the current state of the small‐scale robotics from the viewpoint of endovascular applications is discussed, and their potential advantages to the existing tethered clinical devices are compared. Then, technical challenges and the clinical requirements toward realistic applications of small‐scale untethered robots inside the vasculature are discussed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79697482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a lack of reliable prognostic biomarkers for hypoxic‐ischemic (HI) brain injury in preterm infants. Herein, spectrally detailed wavelet scalograms (WSs), derived from the 1024 Hz sampled electroencephalograms (EEG) of preterm fetal sheep after HI (n = 7), are infused into a high‐performance deep convolutional neural network (CNN) pattern classifier to identify high‐frequency spike transient biomarkers. The deep WS‐CNN pattern classifier identifies EEG spikes with remarkable accuracy of 99.81 = 0.15% (area under curve, AUC = 1.000), cross‐validated across 5010 EEG waveforms, during the first 6 h post‐HI (42 h total), an important clinical period for diagnosis of HI brain injury. Further, a feature‐fusion strategy is introduced to extract the spectrally dominant features of the raw EEG epochs to form robust 3D input matrix sets to be infused into the deep 2D‐CNNs for pattern classification. The results show that the proposed WS‐CNN approach is less sensitive to the potential morphological variations of spikes across all subjects compared to other deep CNNs and spectral‐fuzzy classifiers, allowing the user to flexibly choose an approach depending on their computational requirements. Collectively, the data provide a reliable framework that could help support well‐timed diagnosis of at‐risk neonates in clinical practice.
{"title":"Advanced Deep Learning Spectroscopy of Scalogram Infused CNN Classifiers for Robust Identification of Post‐Hypoxic Epileptiform EEG Spikes","authors":"H. Abbasi, A. Gunn, C. Unsworth, L. Bennet","doi":"10.1002/aisy.202000198","DOIUrl":"https://doi.org/10.1002/aisy.202000198","url":null,"abstract":"There is a lack of reliable prognostic biomarkers for hypoxic‐ischemic (HI) brain injury in preterm infants. Herein, spectrally detailed wavelet scalograms (WSs), derived from the 1024 Hz sampled electroencephalograms (EEG) of preterm fetal sheep after HI (n = 7), are infused into a high‐performance deep convolutional neural network (CNN) pattern classifier to identify high‐frequency spike transient biomarkers. The deep WS‐CNN pattern classifier identifies EEG spikes with remarkable accuracy of 99.81 = 0.15% (area under curve, AUC = 1.000), cross‐validated across 5010 EEG waveforms, during the first 6 h post‐HI (42 h total), an important clinical period for diagnosis of HI brain injury. Further, a feature‐fusion strategy is introduced to extract the spectrally dominant features of the raw EEG epochs to form robust 3D input matrix sets to be infused into the deep 2D‐CNNs for pattern classification. The results show that the proposed WS‐CNN approach is less sensitive to the potential morphological variations of spikes across all subjects compared to other deep CNNs and spectral‐fuzzy classifiers, allowing the user to flexibly choose an approach depending on their computational requirements. Collectively, the data provide a reliable framework that could help support well‐timed diagnosis of at‐risk neonates in clinical practice.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77987668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leonardo Dominguez Rubio, M. Potomkin, R. Baker, Ayusman Sen, L. Berlyand, I. Aranson
Active particles consume energy stored in the environment and convert it into mechanical motion. Many potential applications of these systems involve their flowing, extrusion, and deposition through channels and nozzles, such as targeted drug delivery and out‐of‐equilibrium self‐assembly. However, understanding their fundamental interactions with flow and boundaries remain incomplete. Herein, experimental and theoretical studies of hydrogen peroxide (H2O2) powered self‐propelled gold–platinum nanorods in parallel channels and nozzles are conducted. The behaviors of active (self‐propelled) and passive rods are systematically compared. It is found that most active rods self‐align with the flow streamlines in areas with high shear and exhibit rheotaxis (swimming against the flow). In contrast, passive rods continue moving unaffected until the flow rate is very high, at which point they also start showing some alignment. The experimental results are rationalized by computational modeling delineating activity and rod‐flow interactions. The obtained results provide insight into the manipulation and control of active particle flow and extrusion in complex geometries.
{"title":"Self‐Propulsion and Shear Flow Align Active Particles in Nozzles and Channels","authors":"Leonardo Dominguez Rubio, M. Potomkin, R. Baker, Ayusman Sen, L. Berlyand, I. Aranson","doi":"10.1002/aisy.202000178","DOIUrl":"https://doi.org/10.1002/aisy.202000178","url":null,"abstract":"Active particles consume energy stored in the environment and convert it into mechanical motion. Many potential applications of these systems involve their flowing, extrusion, and deposition through channels and nozzles, such as targeted drug delivery and out‐of‐equilibrium self‐assembly. However, understanding their fundamental interactions with flow and boundaries remain incomplete. Herein, experimental and theoretical studies of hydrogen peroxide (H2O2) powered self‐propelled gold–platinum nanorods in parallel channels and nozzles are conducted. The behaviors of active (self‐propelled) and passive rods are systematically compared. It is found that most active rods self‐align with the flow streamlines in areas with high shear and exhibit rheotaxis (swimming against the flow). In contrast, passive rods continue moving unaffected until the flow rate is very high, at which point they also start showing some alignment. The experimental results are rationalized by computational modeling delineating activity and rod‐flow interactions. The obtained results provide insight into the manipulation and control of active particle flow and extrusion in complex geometries.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77674263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chao-Chiun Liang, Hailin Jiang, Shaopeng Lin, Huashan Li, Biao Wang
Intelligent generation of time‐variant control series remains the critical challenge for acquiring the desired system evolution, due to the difficulties in perceiving temporal correlation and conducting appropriate feedback propagation. A machine learning (ML) algorithm named time‐series generative adversarial network (TSGAN) is developed to overcome the difficulties, by incorporating a long short‐term memory (LSTM) kernel for recognizing multirange temporal patterns beyond the Markovian approximation and an adversarial training mechanism for efficient optimization. A variety of time series are examined by temperature‐control experiments, and the results demonstrate an exceptional accuracy (>95%, 35% higher than prevalent ML methods) as well as strong transferability and stability of the TSGAN algorithm. The dependence of generation performance on underlying statistical mechanisms associated with different ML algorithms, including the deep neural network (DNN), hidden Markov model (HMM), LSTM, and TSGAN, is elucidated by analyzing the generation quality of characteristic temporal patterns. The capability of generating arbitrarily complex response series opens an opportunity for inverse design of time‐variant functionals as strenuously pursued in material science and modern technology.
{"title":"Intelligent Generation of Evolutionary Series in a Time‐Variant Physical System via Series Pattern Recognition","authors":"Chao-Chiun Liang, Hailin Jiang, Shaopeng Lin, Huashan Li, Biao Wang","doi":"10.1002/aisy.202000172","DOIUrl":"https://doi.org/10.1002/aisy.202000172","url":null,"abstract":"Intelligent generation of time‐variant control series remains the critical challenge for acquiring the desired system evolution, due to the difficulties in perceiving temporal correlation and conducting appropriate feedback propagation. A machine learning (ML) algorithm named time‐series generative adversarial network (TSGAN) is developed to overcome the difficulties, by incorporating a long short‐term memory (LSTM) kernel for recognizing multirange temporal patterns beyond the Markovian approximation and an adversarial training mechanism for efficient optimization. A variety of time series are examined by temperature‐control experiments, and the results demonstrate an exceptional accuracy (>95%, 35% higher than prevalent ML methods) as well as strong transferability and stability of the TSGAN algorithm. The dependence of generation performance on underlying statistical mechanisms associated with different ML algorithms, including the deep neural network (DNN), hidden Markov model (HMM), LSTM, and TSGAN, is elucidated by analyzing the generation quality of characteristic temporal patterns. The capability of generating arbitrarily complex response series opens an opportunity for inverse design of time‐variant functionals as strenuously pursued in material science and modern technology.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79646364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gungun Lin, Yuan Liu, Guan Huang, Yinghui Chen, D. Makarov, Jun Lin, Z. Quan, D. Jin
Utilizing the magnetic interactions between microparticle building blocks allows creating long‐range ordered structures and constructing smart multifunctional systems at different scales. The elaborate control over the inter‐particle magnetic coupling interaction is entailed to unlock new magnetoactuation functionalities. Herein, dimer‐type microstructures consisting of a pair of magnetic emulsions with tailorable dimension and magnetic coupling strength are fabricated using a microfluidic emulsion‐templated assembly approach. The magnetite nanoparticles dispersed in vinylbenzene monomers are partitioned into a pair of emulsions with conserved volume, which are wrapped by an aqueous hydrogel shell and finally polymerized to form discrete structures. Tunable synchronous–asynchronous rotation over 60 dB is unlocked in magnetic dimers, which is shown to be dependent on the magnetic moments induced. This leads to a new class of magnetic actuators for the parallelized assay of distinctive virus DNAs and the dynamic optical evaluation of 3D cell cultures. The work suggests a new perspective to design smart multifunctional microstructures and devices by exploring their natural variance in magnetic coupling.
{"title":"3D Rotation‐Trackable and Differentiable Micromachines with Dimer‐Type Structures for Dynamic Bioanalysis","authors":"Gungun Lin, Yuan Liu, Guan Huang, Yinghui Chen, D. Makarov, Jun Lin, Z. Quan, D. Jin","doi":"10.1002/aisy.202000205","DOIUrl":"https://doi.org/10.1002/aisy.202000205","url":null,"abstract":"Utilizing the magnetic interactions between microparticle building blocks allows creating long‐range ordered structures and constructing smart multifunctional systems at different scales. The elaborate control over the inter‐particle magnetic coupling interaction is entailed to unlock new magnetoactuation functionalities. Herein, dimer‐type microstructures consisting of a pair of magnetic emulsions with tailorable dimension and magnetic coupling strength are fabricated using a microfluidic emulsion‐templated assembly approach. The magnetite nanoparticles dispersed in vinylbenzene monomers are partitioned into a pair of emulsions with conserved volume, which are wrapped by an aqueous hydrogel shell and finally polymerized to form discrete structures. Tunable synchronous–asynchronous rotation over 60 dB is unlocked in magnetic dimers, which is shown to be dependent on the magnetic moments induced. This leads to a new class of magnetic actuators for the parallelized assay of distinctive virus DNAs and the dynamic optical evaluation of 3D cell cultures. The work suggests a new perspective to design smart multifunctional microstructures and devices by exploring their natural variance in magnetic coupling.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87190742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The recent development of human–machine interface (HMI) involves advances in wearable devices that safely interact with the human body while providing high mechanical compliance. Various cutting‐edge technologies such as highly stretchable electronics, multiple sensor fusion, and wearable exoskeletons have enabled a higher level of interactivity. Notably, recent developments using machine intelligence have achieved unprecedented performance and solved various challenges. Herein, the recent progresses in stretchable HMI including stretchable sensors, stretchable actuating systems, and machine intelligence‐aided stretchable devices are presented, and their principles and working mechanisms are discussed.
{"title":"Smart Stretchable Electronics for Advanced Human–Machine Interface","authors":"K. Kim, Y. Suh, S. Ko","doi":"10.1002/aisy.202000157","DOIUrl":"https://doi.org/10.1002/aisy.202000157","url":null,"abstract":"The recent development of human–machine interface (HMI) involves advances in wearable devices that safely interact with the human body while providing high mechanical compliance. Various cutting‐edge technologies such as highly stretchable electronics, multiple sensor fusion, and wearable exoskeletons have enabled a higher level of interactivity. Notably, recent developments using machine intelligence have achieved unprecedented performance and solved various challenges. Herein, the recent progresses in stretchable HMI including stretchable sensors, stretchable actuating systems, and machine intelligence‐aided stretchable devices are presented, and their principles and working mechanisms are discussed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"437 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75798400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. M. Nasab, Siavash Sharifi, Shuai Chen, Yang Jiao, W. Shan
Materials with tunable properties, especially dynamically tunable stiffness, have been of great interest for the field of soft robotics. Herein, a novel design concept of robust three‐component elastomer–particle–fiber composite system with tunable mechanical stiffness and electrical conductivity is introduced. These smart materials are capable of changing their mechanical stiffness rapidly and reversibly when powered with electrical current. One implementation of the composite system demonstrated here is composed of a polydimethylsiloxane (PDMS) matrix, Field's metal (FM) particles, and nickel‐coated carbon fibers (NCCF). It is demonstrated that the mechanical stiffness and the electrical conductivity of the composite are highly tunable and dependent on the volume fraction of the three components and the temperature, and can be reasonably estimated using effective medium theory. Due to its superior electrical conductivity, Joule heating can be used as the activation mechanism to realize ≈20× mechanical stiffness changes in seconds. The performance of the composites is thermally and mechanically robust. The shape memory effect of these composites is also demonstrated. The combination of tunable mechanical and electrical properties makes these composites promising candidates for sensing and actuation applications for soft robotics.
{"title":"Robust Three‐Component Elastomer–Particle–Fiber Composites with Tunable Properties for Soft Robotics","authors":"A. M. Nasab, Siavash Sharifi, Shuai Chen, Yang Jiao, W. Shan","doi":"10.1002/aisy.202000166","DOIUrl":"https://doi.org/10.1002/aisy.202000166","url":null,"abstract":"Materials with tunable properties, especially dynamically tunable stiffness, have been of great interest for the field of soft robotics. Herein, a novel design concept of robust three‐component elastomer–particle–fiber composite system with tunable mechanical stiffness and electrical conductivity is introduced. These smart materials are capable of changing their mechanical stiffness rapidly and reversibly when powered with electrical current. One implementation of the composite system demonstrated here is composed of a polydimethylsiloxane (PDMS) matrix, Field's metal (FM) particles, and nickel‐coated carbon fibers (NCCF). It is demonstrated that the mechanical stiffness and the electrical conductivity of the composite are highly tunable and dependent on the volume fraction of the three components and the temperature, and can be reasonably estimated using effective medium theory. Due to its superior electrical conductivity, Joule heating can be used as the activation mechanism to realize ≈20× mechanical stiffness changes in seconds. The performance of the composites is thermally and mechanically robust. The shape memory effect of these composites is also demonstrated. The combination of tunable mechanical and electrical properties makes these composites promising candidates for sensing and actuation applications for soft robotics.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84830056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kazutoshi Tanaka, Shihao Yang, Yuji Tokudome, Yuna Minami, Yuyao Lu, T. Arie, S. Akita, K. Takei, K. Nakajima
Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings.
{"title":"Flapping‐Wing Dynamics as a Natural Detector of Wind Direction","authors":"Kazutoshi Tanaka, Shihao Yang, Yuji Tokudome, Yuna Minami, Yuyao Lu, T. Arie, S. Akita, K. Takei, K. Nakajima","doi":"10.1002/aisy.202000174","DOIUrl":"https://doi.org/10.1002/aisy.202000174","url":null,"abstract":"Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89257793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many soft robots are composed of soft fluidic actuators that are fabricated from silicone rubbers and use hydraulic or pneumatic actuation. The strong nonlinearities and complex geometries of soft actuators hinder the development of analytical models to describe their motion. Finite element modeling provides an effective solution to this issue and allows the user to predict performance and optimize soft actuator designs. Herein, the literature on a finite element analysis of soft actuators is reviewed. First, the required nonlinear elasticity concepts are introduced with a focus on the relevant models for soft robotics. In particular, the procedure for determining material constants for the hyperelastic models from material testing and curve fitting is explored. Then, a comprehensive review of constitutive model parameters for the most widely used silicone rubbers in the literature is provided. An overview of the procedure is provided for three commercially available software packages (Abaqus, Ansys, and COMSOL). The combination of modeling procedures, material properties, and design guidelines presented in this article can be used as a starting point for soft robotic actuator design.
{"title":"Finite Element Modeling of Soft Fluidic Actuators: Overview and Recent Developments","authors":"Matheus S. Xavier, A. Fleming, Y. Yong","doi":"10.1002/aisy.202000187","DOIUrl":"https://doi.org/10.1002/aisy.202000187","url":null,"abstract":"Many soft robots are composed of soft fluidic actuators that are fabricated from silicone rubbers and use hydraulic or pneumatic actuation. The strong nonlinearities and complex geometries of soft actuators hinder the development of analytical models to describe their motion. Finite element modeling provides an effective solution to this issue and allows the user to predict performance and optimize soft actuator designs. Herein, the literature on a finite element analysis of soft actuators is reviewed. First, the required nonlinear elasticity concepts are introduced with a focus on the relevant models for soft robotics. In particular, the procedure for determining material constants for the hyperelastic models from material testing and curve fitting is explored. Then, a comprehensive review of constitutive model parameters for the most widely used silicone rubbers in the literature is provided. An overview of the procedure is provided for three commercially available software packages (Abaqus, Ansys, and COMSOL). The combination of modeling procedures, material properties, and design guidelines presented in this article can be used as a starting point for soft robotic actuator design.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77900762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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