Magnetic mobile microrobots navigating biofluids with both upstream and downstream locomotion provide a promising solution to targeted drug delivery for precision medicine. However, the biofluid environment in blood vessels is complicated due to variations in flow rate and direction. It is still unknown how to make magnetic microrobots resist the variable flow rate in biofluids with both upstream and downstream locomotion. Herein, magnetic microrobots with various shapes and sizes have been controlled to navigate diverse biofluids under different flow rates and directions. Simulation and experimental studies have been conducted to analyze the influences of microrobot size and shape on translational velocity in confined microchannels filled with biofluids. A strategy is proposed to choose the optimized parameters of rotating magnetic field actuation for precise delivery of microrobots in a microfluidic chip, which contains a complex biofluid environment with variable flow rate and direction. The results are validated using various microrobots navigating the microfluidic chip and the yolks of zebrafish larvae in vivo. This work provides a guideline for selecting desirable microrobot dimensions and magnetic field actuation parameters for controllable navigation of magnetic mobile microrobots in complex biofluid flows.
{"title":"Magnetic Mobile Microrobots for Upstream and Downstream Navigation in Biofluids with Variable Flow Rate","authors":"Zehao Wu, Yuting Zhang, Nana Ai, Haoran Chen, Wei Ge, Qingsong Xu","doi":"10.1002/aisy.202100266","DOIUrl":"https://doi.org/10.1002/aisy.202100266","url":null,"abstract":"Magnetic mobile microrobots navigating biofluids with both upstream and downstream locomotion provide a promising solution to targeted drug delivery for precision medicine. However, the biofluid environment in blood vessels is complicated due to variations in flow rate and direction. It is still unknown how to make magnetic microrobots resist the variable flow rate in biofluids with both upstream and downstream locomotion. Herein, magnetic microrobots with various shapes and sizes have been controlled to navigate diverse biofluids under different flow rates and directions. Simulation and experimental studies have been conducted to analyze the influences of microrobot size and shape on translational velocity in confined microchannels filled with biofluids. A strategy is proposed to choose the optimized parameters of rotating magnetic field actuation for precise delivery of microrobots in a microfluidic chip, which contains a complex biofluid environment with variable flow rate and direction. The results are validated using various microrobots navigating the microfluidic chip and the yolks of zebrafish larvae in vivo. This work provides a guideline for selecting desirable microrobot dimensions and magnetic field actuation parameters for controllable navigation of magnetic mobile microrobots in complex biofluid flows.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"217 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75379851","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}
Zhiping Chai, L. Lyu, Menghao Pu, Xianwen Chen, Jiaqi Zhu, Huageng Liang, Han Ding, Zhigang Wu
Being minimally invasive and highly effective, radiofrequency ablation (RFA) is widely used for small‐sized malignant tumor treatment. However, in clinical practice, a large number of tumors are found in irregular shape, while the current RFA devices are hard to control the morphologic appearance of RFA lesions on demand, which usually ends up with unnecessarily excessive tissue ablation and subsequently often brings irreversible damage to the organs’ functions. Herein, active cannulas for each of the individually controlled subelectrodes to achieve an on‐demand shape morphing and thus conformal RFA lesion are introduced. The target shape as well as the length of inserted subelectrodes can be precisely controlled by tuning the active stylets and cannulas. What's more, owing to independent movement and energy control of each subelectrodes, the electrode is shown to be not only efficient enough to accomplish accurate trajectory control to target tissue in a single insertion, but also adaptive enough to ablate target tissues with diverse morphologic appearances and locations. On‐demand conformal ablation of target tissue is demonstrated as well under the guidance of ultrasound imaging with the device. Potentially, the RFA electrode is a promising minimally invasive treatment of malignant tumors in future clinical practice. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.164019293.38729522.
{"title":"An Individually Controlled Multitined Expandable Electrode Using Active Cannula‐Based Shape Morphing for On‐Demand Conformal Radiofrequency Ablation Lesions","authors":"Zhiping Chai, L. Lyu, Menghao Pu, Xianwen Chen, Jiaqi Zhu, Huageng Liang, Han Ding, Zhigang Wu","doi":"10.1002/aisy.202100262","DOIUrl":"https://doi.org/10.1002/aisy.202100262","url":null,"abstract":"Being minimally invasive and highly effective, radiofrequency ablation (RFA) is widely used for small‐sized malignant tumor treatment. However, in clinical practice, a large number of tumors are found in irregular shape, while the current RFA devices are hard to control the morphologic appearance of RFA lesions on demand, which usually ends up with unnecessarily excessive tissue ablation and subsequently often brings irreversible damage to the organs’ functions. Herein, active cannulas for each of the individually controlled subelectrodes to achieve an on‐demand shape morphing and thus conformal RFA lesion are introduced. The target shape as well as the length of inserted subelectrodes can be precisely controlled by tuning the active stylets and cannulas. What's more, owing to independent movement and energy control of each subelectrodes, the electrode is shown to be not only efficient enough to accomplish accurate trajectory control to target tissue in a single insertion, but also adaptive enough to ablate target tissues with diverse morphologic appearances and locations. On‐demand conformal ablation of target tissue is demonstrated as well under the guidance of ultrasound imaging with the device. Potentially, the RFA electrode is a promising minimally invasive treatment of malignant tumors in future clinical practice. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.164019293.38729522.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85302016","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}
Additive manufacturing (AM) has matured in parallel with advances in computation. This is not a coincidence as taking advantage of the structural freedom afforded by AM requires detailed calculations and an ability to design and process complex structures in three dimensions. However, the ability to program AM systems is not the only way in which computation, and more recently machine learning, have impacted AM as a field. In fact, recent years have seen a number of innovations in AM that have endowed the process with varying degrees of ‘intelligence’ in distinct ways. While many of these are connected, several of these approaches to smart AM are wholly distinct in that they advance different aspects of the state-of-the-art. Our goal in this editorial is to highlight three such dimensions of intelligence in AM and connect them to articles in this special issue of Advanced Intelligent Systems that discuss innovations along these dimensions. These dimensions include advances in the materials and structures produced by AM to make them smarter or more functional, advances in processing to produce better and more reliable products, and advances in using AM as an ecosystem that is more agile and capable than traditional manufacturing (Figure 1).
{"title":"Dimensions of Smart Additive Manufacturing","authors":"Keith A. Brown, Grace X. Gu","doi":"10.1002/aisy.202100240","DOIUrl":"https://doi.org/10.1002/aisy.202100240","url":null,"abstract":"Additive manufacturing (AM) has matured in parallel with advances in computation. This is not a coincidence as taking advantage of the structural freedom afforded by AM requires detailed calculations and an ability to design and process complex structures in three dimensions. However, the ability to program AM systems is not the only way in which computation, and more recently machine learning, have impacted AM as a field. In fact, recent years have seen a number of innovations in AM that have endowed the process with varying degrees of ‘intelligence’ in distinct ways. While many of these are connected, several of these approaches to smart AM are wholly distinct in that they advance different aspects of the state-of-the-art. Our goal in this editorial is to highlight three such dimensions of intelligence in AM and connect them to articles in this special issue of Advanced Intelligent Systems that discuss innovations along these dimensions. These dimensions include advances in the materials and structures produced by AM to make them smarter or more functional, advances in processing to produce better and more reliable products, and advances in using AM as an ecosystem that is more agile and capable than traditional manufacturing (Figure 1).","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84102294","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 additive manufacturing (AM) industry is rapidly developing and expanding, thereby becoming an important and integral component of the digital revolution in manufacturing practices. While the engineering aspects of AM are under intensive research, there still remain many chances to strengthen the sustainability of additive manufacturing (SAM). Cogently increasing the AM community's attention to SAM is vital for developing the AM industry sustainably from the bottom up. The digital nature of AM provides new opportunities for acquiring, storing, and utilizing data to strengthen SAM through data‐driven approaches. Herein, spotlight on SAM is shone upon and it is placed on a more concrete footing. The corresponding advances in data‐driven methods that can strengthen SAM are featured, such as optimizing designs for AM, reducing material waste, and developing databases. How the AM workforce can be developed and grown as a collaboration between the industry, government, and academia to extensively harness the full potential of AM as well as mitigate its adversarial social impact is discussed. Finally, several critical digital techniques that have the potential to further strengthen SAM in the factory of the future, including hybrid manufacturing, Internet of Things, and machine learning and artificial intelligence, are highlighted.
{"title":"Strengthening the Sustainability of Additive Manufacturing through Data‐Driven Approaches and Workforce Development","authors":"Tianjiao Li, J. Yeo","doi":"10.1002/aisy.202100069","DOIUrl":"https://doi.org/10.1002/aisy.202100069","url":null,"abstract":"The additive manufacturing (AM) industry is rapidly developing and expanding, thereby becoming an important and integral component of the digital revolution in manufacturing practices. While the engineering aspects of AM are under intensive research, there still remain many chances to strengthen the sustainability of additive manufacturing (SAM). Cogently increasing the AM community's attention to SAM is vital for developing the AM industry sustainably from the bottom up. The digital nature of AM provides new opportunities for acquiring, storing, and utilizing data to strengthen SAM through data‐driven approaches. Herein, spotlight on SAM is shone upon and it is placed on a more concrete footing. The corresponding advances in data‐driven methods that can strengthen SAM are featured, such as optimizing designs for AM, reducing material waste, and developing databases. How the AM workforce can be developed and grown as a collaboration between the industry, government, and academia to extensively harness the full potential of AM as well as mitigate its adversarial social impact is discussed. Finally, several critical digital techniques that have the potential to further strengthen SAM in the factory of the future, including hybrid manufacturing, Internet of Things, and machine learning and artificial intelligence, are highlighted.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73133455","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}
Y. Lim, Chee Koon Ng, U. S. Vaitesswar, K. Hippalgaonkar
{"title":"Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models","authors":"Y. Lim, Chee Koon Ng, U. S. Vaitesswar, K. Hippalgaonkar","doi":"10.1002/aisy.202170077","DOIUrl":"https://doi.org/10.1002/aisy.202170077","url":null,"abstract":"","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80326485","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}
Seyyed Hadi Seifi, A. Yadollahi, Wenmeng Tian, H. Doude, V. H. Hammond, L. Bian
The presence of process‐induced internal defects (i.e., pores, microcracks, and lack‐of‐fusions) significantly deteriorates the structural durability of parts fabricated by additive manufacturing. However, traditional defects characterization techniques, such as X‐ray CT and ultrasonic scanning, are costly and time‐consuming. There is a research gap in the nondestructive evaluation of fatigue performance directly from the process signature of laser‐based additive manufacturing processes. Herein, a novel two‐phase modeling methodology is proposed for fatigue life prediction based on in situ monitoring of thermal history. Phase (I) includes a convolutional neural network designed to detect the relative size of the defects (i.e., small gas pores and large lack‐of‐fusions) by leveraging processed thermal images. Subsequently, a fatigue‐life prediction model is trained in Phase (II) by incorporating the defect characteristics extracted from Phase (I) to evaluate the fatigue performance. Estimating defect characteristics from the in situ thermal history facilitates the fatigue predicting process.
{"title":"In Situ Nondestructive Fatigue‐Life Prediction of Additive Manufactured Parts by Establishing a Process–Defect–Property Relationship","authors":"Seyyed Hadi Seifi, A. Yadollahi, Wenmeng Tian, H. Doude, V. H. Hammond, L. Bian","doi":"10.1002/aisy.202000268","DOIUrl":"https://doi.org/10.1002/aisy.202000268","url":null,"abstract":"The presence of process‐induced internal defects (i.e., pores, microcracks, and lack‐of‐fusions) significantly deteriorates the structural durability of parts fabricated by additive manufacturing. However, traditional defects characterization techniques, such as X‐ray CT and ultrasonic scanning, are costly and time‐consuming. There is a research gap in the nondestructive evaluation of fatigue performance directly from the process signature of laser‐based additive manufacturing processes. Herein, a novel two‐phase modeling methodology is proposed for fatigue life prediction based on in situ monitoring of thermal history. Phase (I) includes a convolutional neural network designed to detect the relative size of the defects (i.e., small gas pores and large lack‐of‐fusions) by leveraging processed thermal images. Subsequently, a fatigue‐life prediction model is trained in Phase (II) by incorporating the defect characteristics extracted from Phase (I) to evaluate the fatigue performance. Estimating defect characteristics from the in situ thermal history facilitates the fatigue predicting process.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73853259","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}
Local or national crises, such as natural disasters, major infrastructure failures, and pandemics, pose dire threats to manufacturing. The concept of a rideshare‐like distributed network of consumer‐type 3D printers is proposed to address the limited ability of the industrial base to quickly respond to abrupt changes in critical product demand or to disruptions in manufacturing and supply‐chain capacity. The technical challenges that prevent the implementation of such a network are discussed, including 1) remote qualification of 3D printers, 2) dynamic routing algorithms with reactive and predictive components, which take advantage of real‐time information about current events that may affect the network, and 3) performance evaluation of the network. Furthermore, a cyber‐infrastructure that enables autonomous operation and reconfiguration of the network to render it “crisis‐proof” by minimizing human involvement is introduced. The concept of a distributed network of consumer‐type 3D printers allows anyone with a 3D printer and access to the internet to manufacture critical supplies, triggered by actual and predicted customer demand. Beyond crisis relief, distributed networks of manufacturing assets have broad relevance, and they can establish a virtual marketplace to exchange manufacturing capacity. Thus, this future manufacturing platform has the potential to transform how to manufacture for the masses.
{"title":"Manufacturing for the Masses: A Novel Concept for Consumer 3D Printer Networks in the Context of Crisis Relief","authors":"B. Raeymaekers, K. Leang, M. Porfiri, Shenghan Xu","doi":"10.1002/aisy.202100121","DOIUrl":"https://doi.org/10.1002/aisy.202100121","url":null,"abstract":"Local or national crises, such as natural disasters, major infrastructure failures, and pandemics, pose dire threats to manufacturing. The concept of a rideshare‐like distributed network of consumer‐type 3D printers is proposed to address the limited ability of the industrial base to quickly respond to abrupt changes in critical product demand or to disruptions in manufacturing and supply‐chain capacity. The technical challenges that prevent the implementation of such a network are discussed, including 1) remote qualification of 3D printers, 2) dynamic routing algorithms with reactive and predictive components, which take advantage of real‐time information about current events that may affect the network, and 3) performance evaluation of the network. Furthermore, a cyber‐infrastructure that enables autonomous operation and reconfiguration of the network to render it “crisis‐proof” by minimizing human involvement is introduced. The concept of a distributed network of consumer‐type 3D printers allows anyone with a 3D printer and access to the internet to manufacture critical supplies, triggered by actual and predicted customer demand. Beyond crisis relief, distributed networks of manufacturing assets have broad relevance, and they can establish a virtual marketplace to exchange manufacturing capacity. Thus, this future manufacturing platform has the potential to transform how to manufacture for the masses.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74523941","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}
Multimaterial 3D printing in electronics is expanding due to the ability to realize geometrically complex systems with simplified processes compared with conventional printed circuit board. Herein, the feasibility of using a copper‐based filament to realize 3D circuits with planar and vertical interconnections is presented. The resistivity of the tracks (1–3 mm wide) is studied with reference to printing parameters and orientation. Using lateral infill for 1 mm tracks offers lower resistance compared with longitudinal infill (≈75%). For wider tracks, the effect of infill orientation on resistance diminishes. The evaluation of tracks embedded in polylactic acid shows a drop in maximum current (to ≈11 mA) compared with exposed tracks (≈16 mA). There is no observed correlation between electrical performance and number of embedding layers. However, a significant correlation is observed between the tracks’ resistance and the amount of time the filament remains in the heated nozzle. This in‐depth study leads to optimum resolution to realize conductive tracks of 0.67 mm thickness and the first integration of fused deposition modeling (FDM)‐printed conductive traces with small‐outline integrated circuits to open a pathway for higher‐density 3D printed circuits. Finally, the transmission of digital data by a 3D printed circuit is demonstrated.
{"title":"Fused Deposition Modeling‐Based 3D‐Printed Electrical Interconnects and Circuits","authors":"Habib Nassar, R. Dahiya","doi":"10.1002/aisy.202100102","DOIUrl":"https://doi.org/10.1002/aisy.202100102","url":null,"abstract":"Multimaterial 3D printing in electronics is expanding due to the ability to realize geometrically complex systems with simplified processes compared with conventional printed circuit board. Herein, the feasibility of using a copper‐based filament to realize 3D circuits with planar and vertical interconnections is presented. The resistivity of the tracks (1–3 mm wide) is studied with reference to printing parameters and orientation. Using lateral infill for 1 mm tracks offers lower resistance compared with longitudinal infill (≈75%). For wider tracks, the effect of infill orientation on resistance diminishes. The evaluation of tracks embedded in polylactic acid shows a drop in maximum current (to ≈11 mA) compared with exposed tracks (≈16 mA). There is no observed correlation between electrical performance and number of embedding layers. However, a significant correlation is observed between the tracks’ resistance and the amount of time the filament remains in the heated nozzle. This in‐depth study leads to optimum resolution to realize conductive tracks of 0.67 mm thickness and the first integration of fused deposition modeling (FDM)‐printed conductive traces with small‐outline integrated circuits to open a pathway for higher‐density 3D printed circuits. Finally, the transmission of digital data by a 3D printed circuit is demonstrated.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84565067","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}
Inspired by competitive rules of the nature, competitive learning contributes to the specialization of the human brain and the general creativity of mankind. However, the construction of hardware competitive learning neural network still faces great challenges due to the lack of an accurate distance computation method and a self‐adaptive in situ training scheme. Herein, a fully memristive Euclidean distance (ED) engine based on analog multiply‐accumulate operation in a 32 × 32 TiN/TaO x /HfO x /TiN 1T1R array is demonstrated. The dual‐layer devices perform multilevel modulation under the target‐aware programming method with excellent read linearity in a dynamic range of 10–100 μS. The ED calculation is verified experimentally on a test board with an O(1) temporal complexity. Furthermore, in situ training and offline inference schemes for competitive learning, based on the ED engine, are developed and the simulated results show comparable success rates with those obtained by the CPU‐based software. Compared with a state‐of‐the‐art RTX6000 GPU (0.5 TOPS W−1), the energy efficiency of competitive learning models on ED engines can yield 100× improvements by utilizing optimized memristive devices.
竞争性学习受自然界竞争规律的启发，有助于人类大脑的专业化和人类的普遍创造力。然而，由于缺乏精确的距离计算方法和自适应的原位训练方案，硬件竞争学习神经网络的构建仍然面临着很大的挑战。本文演示了一种基于32 × 32 TiN/TaO x /HfO x /TiN 1T1R阵列模拟乘法累加运算的全记忆性欧氏距离(ED)引擎。该双层器件在目标感知编程方法下进行多电平调制，在10-100 μS的动态范围内具有良好的读取线性度。在时间复杂度为0(1)的测试板上进行了实验验证。此外，基于ED引擎开发了用于竞争学习的现场训练和离线推理方案，仿真结果显示与基于CPU的软件获得的成功率相当。与最先进的RTX6000 GPU (0.5 TOPS W−1)相比，利用优化的记忆器件，ED发动机上的竞争学习模型的能源效率可以提高100倍。
{"title":"Energy‐Efficient Memristive Euclidean Distance Engine for Brain‐Inspired Competitive Learning","authors":"Houji Zhou, Jia Chen, Yinan Wang, Sen Liu, Yi Li, Qingjiang Li, Qi Liu, Zhongrui Wang, Yuhui He, Hui Xu, X. Miao","doi":"10.1002/aisy.202100114","DOIUrl":"https://doi.org/10.1002/aisy.202100114","url":null,"abstract":"Inspired by competitive rules of the nature, competitive learning contributes to the specialization of the human brain and the general creativity of mankind. However, the construction of hardware competitive learning neural network still faces great challenges due to the lack of an accurate distance computation method and a self‐adaptive in situ training scheme. Herein, a fully memristive Euclidean distance (ED) engine based on analog multiply‐accumulate operation in a 32 × 32 TiN/TaO x /HfO x /TiN 1T1R array is demonstrated. The dual‐layer devices perform multilevel modulation under the target‐aware programming method with excellent read linearity in a dynamic range of 10–100 μS. The ED calculation is verified experimentally on a test board with an O(1) temporal complexity. Furthermore, in situ training and offline inference schemes for competitive learning, based on the ED engine, are developed and the simulated results show comparable success rates with those obtained by the CPU‐based software. Compared with a state‐of‐the‐art RTX6000 GPU (0.5 TOPS W−1), the energy efficiency of competitive learning models on ED engines can yield 100× improvements by utilizing optimized memristive devices.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77154710","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}
Untethered, magnetically driven microrobots have great potential in practical applications such as minimally invasive surgery. Microrods, also known as “nanowires,” are the most commonly used type of structure for microrobots due to the easy fabrication and promising functions. Driven by a uniform rotating magnetic field, microrods can perform a 2D movement with the assistance of a boundary surface, which severely limits the application of microrods in 3D spaces. Herein, an asymmetric structural design is proposed to construct rod‐shaped micropropellers that can achieve a surface‐free 3D propulsion. A theoretical model is formulated based on resistive force theory to investigate the dynamics of micropropellers. It is theoretically demonstrated and experimentally verified that the magnetic micropropeller can realize not only a 3D propulsion, but also multimodal locomotion to adapt to the environment. The work provides guidance for the design and optimization of autonomous micropropellers.
{"title":"3D Propulsions of Rod‐Shaped Micropropellers","authors":"Yuan Zhang, Xiangkui Tan, Xiying Li, Pengyu Lv, Tian-Yun Huang, Jianying Yang, Huiling Duan","doi":"10.1002/aisy.202100083","DOIUrl":"https://doi.org/10.1002/aisy.202100083","url":null,"abstract":"Untethered, magnetically driven microrobots have great potential in practical applications such as minimally invasive surgery. Microrods, also known as “nanowires,” are the most commonly used type of structure for microrobots due to the easy fabrication and promising functions. Driven by a uniform rotating magnetic field, microrods can perform a 2D movement with the assistance of a boundary surface, which severely limits the application of microrods in 3D spaces. Herein, an asymmetric structural design is proposed to construct rod‐shaped micropropellers that can achieve a surface‐free 3D propulsion. A theoretical model is formulated based on resistive force theory to investigate the dynamics of micropropellers. It is theoretically demonstrated and experimentally verified that the magnetic micropropeller can realize not only a 3D propulsion, but also multimodal locomotion to adapt to the environment. The work provides guidance for the design and optimization of autonomous micropropellers.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"164 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80389027","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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