Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer‐aided design model into a physical object in a layer‐by‐layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade‐off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the temporal domain (e.g., higher speed in serial processes) or in the spatial domain (e.g., more tools in parallel processes). To improve the resolution without sacrificing efficiency, a data‐driven mask image planning method based on subpixel shifting in a split second by tuning the process in both temporal and spatial domains is presented. The method is based on the optimized pixel blending principle and a fast error diffusion‐based optimization model. Various simulation and experimental tests are carried out to verify the developed subpixel shifting method. The experimental results demonstrate the data‐driven‐based mask image calibration and planning techniques significantly improve the fabricated part quality without compromising the process efficiency. The presented spatiotemporal strategy may shed light for future research on the projection‐based AM processes.
{"title":"Spatiotemporal Projection‐Based Additive Manufacturing: A Data‐Driven Image Planning Method for Subpixel Shifting in a Split Second","authors":"Chi Zhou, Han Xu, Yong Chen","doi":"10.1002/aisy.202100079","DOIUrl":"https://doi.org/10.1002/aisy.202100079","url":null,"abstract":"Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer‐aided design model into a physical object in a layer‐by‐layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade‐off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the temporal domain (e.g., higher speed in serial processes) or in the spatial domain (e.g., more tools in parallel processes). To improve the resolution without sacrificing efficiency, a data‐driven mask image planning method based on subpixel shifting in a split second by tuning the process in both temporal and spatial domains is presented. The method is based on the optimized pixel blending principle and a fast error diffusion‐based optimization model. Various simulation and experimental tests are carried out to verify the developed subpixel shifting method. The experimental results demonstrate the data‐driven‐based mask image calibration and planning techniques significantly improve the fabricated part quality without compromising the process efficiency. The presented spatiotemporal strategy may shed light for future research on the projection‐based AM processes.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79330118","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}
Chengjun Wang, Min Cai, Zengming Hao, Shuang Nie, Changying Liu, Honggen Du, Jian Wang, Wei-qiu Chen, Jizhou Song
The concurrent collection of surface electromyography (sEMG) and strain signals is important for many applications, such as human–machine interaction, sign language recognition, and clinical evaluation of muscle function. Nevertheless, the conventional sensor systems made of rigid, bulky components cannot provide a reliable, conformal interface for accurate, continuous measurements of the epidermal physiological signals. Herein, a skin‐interfaced, multifunctional epidermal sensor patch with characteristics of mechanical softness, large stretchability, and wearable conformability for multimodal measurements of sEMG signals and associated skin deformations from various muscle activities and joint motions is reported. The sensor patch features two pairs of stretchable sEMG electrodes and two thin, miniaturized strain sensors, which are connected by stretchable filamentary serpentine interconnects in an open‐meshed structure. Experimental and computational studies reveal the design and operation of the sensor patch, which exhibit stable and repetitive performance even under a 30% stretching strain level. Demonstrations of the sensor patch on the wrist for simple sign language recognition and on the lower back for the flexion‐relaxation phenomenon illustrate its potential for the comprehensive assessment of the muscle activities and related motions of muscle joints.
{"title":"Stretchable, Multifunctional Epidermal Sensor Patch for Surface Electromyography and Strain Measurements","authors":"Chengjun Wang, Min Cai, Zengming Hao, Shuang Nie, Changying Liu, Honggen Du, Jian Wang, Wei-qiu Chen, Jizhou Song","doi":"10.1002/aisy.202100031","DOIUrl":"https://doi.org/10.1002/aisy.202100031","url":null,"abstract":"The concurrent collection of surface electromyography (sEMG) and strain signals is important for many applications, such as human–machine interaction, sign language recognition, and clinical evaluation of muscle function. Nevertheless, the conventional sensor systems made of rigid, bulky components cannot provide a reliable, conformal interface for accurate, continuous measurements of the epidermal physiological signals. Herein, a skin‐interfaced, multifunctional epidermal sensor patch with characteristics of mechanical softness, large stretchability, and wearable conformability for multimodal measurements of sEMG signals and associated skin deformations from various muscle activities and joint motions is reported. The sensor patch features two pairs of stretchable sEMG electrodes and two thin, miniaturized strain sensors, which are connected by stretchable filamentary serpentine interconnects in an open‐meshed structure. Experimental and computational studies reveal the design and operation of the sensor patch, which exhibit stable and repetitive performance even under a 30% stretching strain level. Demonstrations of the sensor patch on the wrist for simple sign language recognition and on the lower back for the flexion‐relaxation phenomenon illustrate its potential for the comprehensive assessment of the muscle activities and related motions of muscle joints.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"2004 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88332761","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}
P. Dong, Weizhong Xu, Zhongwen Kuang, Youxing Yao, Zhiqin Zhang, D. Guo, Huaping Wu, T. Zhao, Aiping Liu
Smart hydrogel actuators with programmable anisotropic structures present fascinating prospects considering their distinctive shape transformation and controllable environmental responsiveness under external stimuli. However, the design of anisotropic hydrogels with simple and universal fabrication and programmable functionality is challenging for their valuable applications in smart actuators and soft robots. Herein, a simple, green, and devisable strategy is proposed to construct a heterogeneous porous hydrogel system by the different liquid diffusion (such as amyl alcohol, water, and ethanol) into a monomeric precursor solution of thermosensitive hydrogels. The well‐defined micro/nanoporous gradient and patterned structures related to selective liquid stratification and interfacial diffusion favor the fast response and accurate programmable deformation of hydrogels under temperature stimuli. Inspiringly, this simple diffusion‐driven tactic can be perfectly applicable for different responsive hydrogels with programmable multifunctionality by adding functional nanomaterials into the diffusible liquid. This green, general, and facile diffusion‐driven strategy provides significant guidance for fabricating environmentally responsive hydrogels with tailorable functionality for their multipurpose applications in drug delivery, bioengineering, smart actuators, and soft robots.
{"title":"Liquid Stratification and Diffusion‐Induced Anisotropic Hydrogel Actuators with Excellent Thermosensitivity and Programmable Functionality","authors":"P. Dong, Weizhong Xu, Zhongwen Kuang, Youxing Yao, Zhiqin Zhang, D. Guo, Huaping Wu, T. Zhao, Aiping Liu","doi":"10.1002/aisy.202100030","DOIUrl":"https://doi.org/10.1002/aisy.202100030","url":null,"abstract":"Smart hydrogel actuators with programmable anisotropic structures present fascinating prospects considering their distinctive shape transformation and controllable environmental responsiveness under external stimuli. However, the design of anisotropic hydrogels with simple and universal fabrication and programmable functionality is challenging for their valuable applications in smart actuators and soft robots. Herein, a simple, green, and devisable strategy is proposed to construct a heterogeneous porous hydrogel system by the different liquid diffusion (such as amyl alcohol, water, and ethanol) into a monomeric precursor solution of thermosensitive hydrogels. The well‐defined micro/nanoporous gradient and patterned structures related to selective liquid stratification and interfacial diffusion favor the fast response and accurate programmable deformation of hydrogels under temperature stimuli. Inspiringly, this simple diffusion‐driven tactic can be perfectly applicable for different responsive hydrogels with programmable multifunctionality by adding functional nanomaterials into the diffusible liquid. This green, general, and facile diffusion‐driven strategy provides significant guidance for fabricating environmentally responsive hydrogels with tailorable functionality for their multipurpose applications in drug delivery, bioengineering, smart actuators, and soft robots.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88372066","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}
Andrew Y. Chen, E. Pegg, Ailin Chen, Zeqing Jin, Grace X. Gu
In recent years, the intersection of 3D printing and “smart” stimuli‐responsive materials has led to the development of 4D printing, an emerging field that is a subset of current additive manufacturing research. By integrating existing printing processes with novel materials, 4D printing enables the direct fabrication of sensors, controllable structures, and other functional devices. Compared to traditional manufacturing processes for smart materials, 4D printing permits a high degree of design freedom and flexibility in terms of printable geometry. An important branch of 4D printing concerns electroactive materials, which form the backbone of printable devices with practical applications throughout biology, engineering, and chemistry. Herein, the recent progress in the 4D printing of electroactive materials using several widely studied printing processes is reviewed. In particular, constituent materials and mechanisms for their preparation and printing are discussed, and functional electroactive devices fabricated using 4D printing are highlighted. Current challenges are also described and some of the many data‐driven opportunities for advancement in this promising field are presented.
{"title":"4D Printing of Electroactive Materials","authors":"Andrew Y. Chen, E. Pegg, Ailin Chen, Zeqing Jin, Grace X. Gu","doi":"10.1002/aisy.202100019","DOIUrl":"https://doi.org/10.1002/aisy.202100019","url":null,"abstract":"In recent years, the intersection of 3D printing and “smart” stimuli‐responsive materials has led to the development of 4D printing, an emerging field that is a subset of current additive manufacturing research. By integrating existing printing processes with novel materials, 4D printing enables the direct fabrication of sensors, controllable structures, and other functional devices. Compared to traditional manufacturing processes for smart materials, 4D printing permits a high degree of design freedom and flexibility in terms of printable geometry. An important branch of 4D printing concerns electroactive materials, which form the backbone of printable devices with practical applications throughout biology, engineering, and chemistry. Herein, the recent progress in the 4D printing of electroactive materials using several widely studied printing processes is reviewed. In particular, constituent materials and mechanisms for their preparation and printing are discussed, and functional electroactive devices fabricated using 4D printing are highlighted. Current challenges are also described and some of the many data‐driven opportunities for advancement in this promising field are presented.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89969182","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}
Microrobots with simultaneously improved degradability and mechanical strength are highly demanded in performing in vivo delivery tasks in clinical applications. The properties of degradability and mechanical strength are contradictory for many materials used to make microrobots. This article proposes a new design that can result in 3D cell culture microrobots with improved degradability and mechanical strength from the following perspectives. First, the mechanical strength of a microrobot is improved using triangle patterns to replace hexagon pattern in the microrobot structure, which can provide more supporting grids to obtain increased mechanical strength. Second, the relationship between structural design and material composition in relation to the mechanical strength of microrobot is investigated. The study reveals that triangle‐patterned microrobots have increased mechanical strength compared with hexagon‐patterned microrobots, thereby allowing high composition of degradable material that leads to the fast degradation of the microrobot. It is also shown that the triangle‐patterned microrobots can maintain the same structural integrity and cell capacity as hexagon‐patterned microrobots. Finally, the demonstration shows that the triangle‐patterned microrobot can be precisely navigated in microfluidic channels. This article successfully demonstrates that the degradability and mechanical strength can be improved simultaneously through the microrobot structural design.
{"title":"Development of a Cell‐Loading Microrobot with Simultaneously Improved Degradability and Mechanical Strength for Performing In Vivo Delivery Tasks","authors":"Tanyong Wei, Junyang Li, Liushuai Zheng, Cheng Wang, Feng Li, Hua Tian, Dong Sun","doi":"10.1002/aisy.202100052","DOIUrl":"https://doi.org/10.1002/aisy.202100052","url":null,"abstract":"Microrobots with simultaneously improved degradability and mechanical strength are highly demanded in performing in vivo delivery tasks in clinical applications. The properties of degradability and mechanical strength are contradictory for many materials used to make microrobots. This article proposes a new design that can result in 3D cell culture microrobots with improved degradability and mechanical strength from the following perspectives. First, the mechanical strength of a microrobot is improved using triangle patterns to replace hexagon pattern in the microrobot structure, which can provide more supporting grids to obtain increased mechanical strength. Second, the relationship between structural design and material composition in relation to the mechanical strength of microrobot is investigated. The study reveals that triangle‐patterned microrobots have increased mechanical strength compared with hexagon‐patterned microrobots, thereby allowing high composition of degradable material that leads to the fast degradation of the microrobot. It is also shown that the triangle‐patterned microrobots can maintain the same structural integrity and cell capacity as hexagon‐patterned microrobots. Finally, the demonstration shows that the triangle‐patterned microrobot can be precisely navigated in microfluidic channels. This article successfully demonstrates that the degradability and mechanical strength can be improved simultaneously through the microrobot structural design.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88223192","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}
Hyungyo Kim, Joon Hwang, D. Kwon, Jangsaeng Kim, Min-Kyu Park, Ji-Young Im, Byung-Gook Park, Jong-Ho Lee
On‐chip training of neural networks (NNs) is regarded as a promising training method for neuromorphic systems with analog synaptic devices. Herein, a novel on‐chip training method called direct gradient calculation (DGC) is proposed to substitute conventional backpropagation (BP). In this method, the gradients of a cost function with respect to the weights are calculated directly by sequentially applying a small temporal change to each weight and then measuring the change in cost value. DGC achieves a similar accuracy to that of BP while performing a handwritten digit classification task, validating its training feasibility. In particular, DGC can be applied to analog hardware‐based convolutional NNs (CNNs), which is considered to be a challenging task, enabling appropriate on‐chip training. A hybrid method is also proposed that efficiently combines DGC and BP for training CNNs, and the method achieves a similar accuracy to that of BP and DGC while enhancing the training speed. Furthermore, networks utilizing DGC maintain a higher level of accuracy than those using BP in the presence of variations in hardware (such as synaptic device conductance and neuron circuit component variations) while requiring fewer circuit components.
{"title":"Direct Gradient Calculation: Simple and Variation‐Tolerant On‐Chip Training Method for Neural Networks","authors":"Hyungyo Kim, Joon Hwang, D. Kwon, Jangsaeng Kim, Min-Kyu Park, Ji-Young Im, Byung-Gook Park, Jong-Ho Lee","doi":"10.1002/aisy.202100064","DOIUrl":"https://doi.org/10.1002/aisy.202100064","url":null,"abstract":"On‐chip training of neural networks (NNs) is regarded as a promising training method for neuromorphic systems with analog synaptic devices. Herein, a novel on‐chip training method called direct gradient calculation (DGC) is proposed to substitute conventional backpropagation (BP). In this method, the gradients of a cost function with respect to the weights are calculated directly by sequentially applying a small temporal change to each weight and then measuring the change in cost value. DGC achieves a similar accuracy to that of BP while performing a handwritten digit classification task, validating its training feasibility. In particular, DGC can be applied to analog hardware‐based convolutional NNs (CNNs), which is considered to be a challenging task, enabling appropriate on‐chip training. A hybrid method is also proposed that efficiently combines DGC and BP for training CNNs, and the method achieves a similar accuracy to that of BP and DGC while enhancing the training speed. Furthermore, networks utilizing DGC maintain a higher level of accuracy than those using BP in the presence of variations in hardware (such as synaptic device conductance and neuron circuit component variations) while requiring fewer circuit components.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89587468","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}
V. Ramachandran, Fabian Schilling, A. Wu, D. Floreano
People learn motor activities best when they are conscious of their errors and make a concerted effort to correct them. While haptic interfaces can facilitate motor training, existing interfaces are often bulky and do not always ensure post‐training skill retention. Herein, a programmable haptic sleeve composed of textile‐based electroadhesive clutches for skill acquisition and retention is described. Its functionality in a motor learning study where users control a drone's movement using elbow joint rotation is shown. Haptic feedback is used to restrain elbow motion and make users aware of their errors. This helps users consciously learn to avoid errors from occurring. While all subjects exhibited similar performance during the baseline phase of motor learning, those subjects who received haptic feedback from the haptic sleeve committed 23.5% fewer errors than subjects in the control group during the evaluation phase. The results show that the sleeve helps users retain and transfer motor skills better than visual feedback alone. This work shows the potential for fabric‐based haptic interfaces as a training aid for motor tasks in the fields of rehabilitation and teleoperation.
{"title":"Smart Textiles that Teach: Fabric‐Based Haptic Device Improves the Rate of Motor Learning","authors":"V. Ramachandran, Fabian Schilling, A. Wu, D. Floreano","doi":"10.1002/aisy.202100043","DOIUrl":"https://doi.org/10.1002/aisy.202100043","url":null,"abstract":"People learn motor activities best when they are conscious of their errors and make a concerted effort to correct them. While haptic interfaces can facilitate motor training, existing interfaces are often bulky and do not always ensure post‐training skill retention. Herein, a programmable haptic sleeve composed of textile‐based electroadhesive clutches for skill acquisition and retention is described. Its functionality in a motor learning study where users control a drone's movement using elbow joint rotation is shown. Haptic feedback is used to restrain elbow motion and make users aware of their errors. This helps users consciously learn to avoid errors from occurring. While all subjects exhibited similar performance during the baseline phase of motor learning, those subjects who received haptic feedback from the haptic sleeve committed 23.5% fewer errors than subjects in the control group during the evaluation phase. The results show that the sleeve helps users retain and transfer motor skills better than visual feedback alone. This work shows the potential for fabric‐based haptic interfaces as a training aid for motor tasks in the fields of rehabilitation and teleoperation.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84281360","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 sustainability of ever more sophisticated artificial intelligence relies on the continual development of highly energy‐efficient and compact computing hardware that mimics the biological neural networks. Recently, the neural firing properties have been widely explored in various spiking neuron devices, which could emerge as the fundamental building blocks of future neuromorphic/in‐memory computing hardware. By leveraging the intrinsic device characteristics, the device‐based spiking neuron has the potential advantage of a compact circuit area for implementing neural networks with high density and high parallelism. However, a comprehensive benchmark that considers not only the device but also the peripheral circuit necessary for realizing complete neural functions is still lacking. Herein, the recent progress of emerging spiking neuron devices and circuits is reviewed. By implementing peripheral analog circuits for supporting various spiking neuron devices in the in‐memory computing architecture, the advantages and challenges in area and energy efficiency are discussed by benchmarking various technologies. A small or even no membrane capacitor, a self‐reset property, and a high spiking frequency are highly desirable.
{"title":"Progress and Benchmark of Spiking Neuron Devices and Circuits","authors":"Fu-Xiang Liang, I-Ting Wang, T. Hou","doi":"10.1002/aisy.202100007","DOIUrl":"https://doi.org/10.1002/aisy.202100007","url":null,"abstract":"The sustainability of ever more sophisticated artificial intelligence relies on the continual development of highly energy‐efficient and compact computing hardware that mimics the biological neural networks. Recently, the neural firing properties have been widely explored in various spiking neuron devices, which could emerge as the fundamental building blocks of future neuromorphic/in‐memory computing hardware. By leveraging the intrinsic device characteristics, the device‐based spiking neuron has the potential advantage of a compact circuit area for implementing neural networks with high density and high parallelism. However, a comprehensive benchmark that considers not only the device but also the peripheral circuit necessary for realizing complete neural functions is still lacking. Herein, the recent progress of emerging spiking neuron devices and circuits is reviewed. By implementing peripheral analog circuits for supporting various spiking neuron devices in the in‐memory computing architecture, the advantages and challenges in area and energy efficiency are discussed by benchmarking various technologies. A small or even no membrane capacitor, a self‐reset property, and a high spiking frequency are highly desirable.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73181130","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}
Reliable image perception is critical for living organisms. Biologic sensory organs and nervous systems evolved interdependently to allow apprehension of visual information regardless of spatial orientation. By contrast, convolutional neural networks usually have limited tolerance to rotational transformations. There are software‐based approaches used to address this issue, such as artificial rotation of training data or preliminary image processing. However, these workarounds require a large computational effort and are mostly done offline. This work presents a bioinspired, robotic vision system with inherent rotation‐invariant properties that may be taught either offline or in real time by feeding back error indications. It is successfully trained to counter the move of a human player in a game of Paper Scissors Stone. The architecture and operation principles are first discussed alongside the experimental setup. This is followed by performance analysis of pattern recognition under misaligned and rotated conditions. Finally, the process of online, supervised learning is demonstrated and analyzed.
{"title":"Bioinspired Robotic Vision with Online Learning Capability and Rotation‐Invariant Properties","authors":"D. Berco, D. Ang","doi":"10.1002/aisy.202100025","DOIUrl":"https://doi.org/10.1002/aisy.202100025","url":null,"abstract":"Reliable image perception is critical for living organisms. Biologic sensory organs and nervous systems evolved interdependently to allow apprehension of visual information regardless of spatial orientation. By contrast, convolutional neural networks usually have limited tolerance to rotational transformations. There are software‐based approaches used to address this issue, such as artificial rotation of training data or preliminary image processing. However, these workarounds require a large computational effort and are mostly done offline. This work presents a bioinspired, robotic vision system with inherent rotation‐invariant properties that may be taught either offline or in real time by feeding back error indications. It is successfully trained to counter the move of a human player in a game of Paper Scissors Stone. The architecture and operation principles are first discussed alongside the experimental setup. This is followed by performance analysis of pattern recognition under misaligned and rotated conditions. Finally, the process of online, supervised learning is demonstrated and analyzed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"140 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86590398","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}
Yuyang Ji, Congcong Luan, Xinhua Yao, Jianzhong Fu, Yong He
Recently, considerable achievements have been made with the advancements of smart structures, which are known for their controlled deformation, self‐repair, and sensing characteristics. Such capabilities have significant potential in the field of bionics. 3D printing methods have revolutionized the high‐resolution integrated manufacturing of complex smart structures, resulting in new types of soft robots, actuators, wearable flexible electronics, and biomedical equipment. There is therefore a need for academia and industry to receive an update on the status of these tools. For this reason, herein, a comprehensive overview of the latest progress in printing methods, materials, and applications of various smart structures is provided. Temperature‐ and electromagnetic‐responsive smart structures are highlighted, in addition to self‐healing and smart‐sensing devices. Current exigencies and future development trends of 3D printing methods and smart structures are also summarized.
{"title":"Recent Progress in 3D Printing of Smart Structures: Classification, Challenges, and Trends","authors":"Yuyang Ji, Congcong Luan, Xinhua Yao, Jianzhong Fu, Yong He","doi":"10.1002/aisy.202000271","DOIUrl":"https://doi.org/10.1002/aisy.202000271","url":null,"abstract":"Recently, considerable achievements have been made with the advancements of smart structures, which are known for their controlled deformation, self‐repair, and sensing characteristics. Such capabilities have significant potential in the field of bionics. 3D printing methods have revolutionized the high‐resolution integrated manufacturing of complex smart structures, resulting in new types of soft robots, actuators, wearable flexible electronics, and biomedical equipment. There is therefore a need for academia and industry to receive an update on the status of these tools. For this reason, herein, a comprehensive overview of the latest progress in printing methods, materials, and applications of various smart structures is provided. Temperature‐ and electromagnetic‐responsive smart structures are highlighted, in addition to self‐healing and smart‐sensing devices. Current exigencies and future development trends of 3D printing methods and smart structures are also summarized.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90978156","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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