Youngheon Yun, Dongchan Lee, Soyeon Lee, Salvador Pané, Josep Puigmartí‐Luis, Sungwoo Chun, Bumjin Jang
The research addresses the limitations inherent in conventional Hall effect‐based tactile sensors, particularly their restricted sensitivity by introducing an innovative metastructure. Through meticulous finite element analysis optimization, the Hall effect‐based auxetic tactile sensor (HEATS), featuring a rotating square plate configuration as the most effective auxetic pattern to enhance sensitivity, is developed. Experimental validation demonstrates significant sensitivity enhancements across a wide sensing range. HEATS exhibits a remarkable 20‐fold and 10‐fold improvement at tensile rates of 0.9% and 30%, respectively, compared to non‐auxetic sensors. Furthermore, comprehensive testing demonstrates HEATS’ exceptional precision in detecting various tactile stimuli, including muscle movements and joint angles. With its unparalleled accuracy and adaptability, HEATS offers vast potential applications in human–machine and human–robot interaction, where subtle tactile communication is a prerequisite.
{"title":"Enhancing Sensitivity across Scales with Highly Sensitive Hall Effect‐Based Auxetic Tactile Sensors","authors":"Youngheon Yun, Dongchan Lee, Soyeon Lee, Salvador Pané, Josep Puigmartí‐Luis, Sungwoo Chun, Bumjin Jang","doi":"10.1002/aisy.202400337","DOIUrl":"https://doi.org/10.1002/aisy.202400337","url":null,"abstract":"The research addresses the limitations inherent in conventional Hall effect‐based tactile sensors, particularly their restricted sensitivity by introducing an innovative metastructure. Through meticulous finite element analysis optimization, the Hall effect‐based auxetic tactile sensor (HEATS), featuring a rotating square plate configuration as the most effective auxetic pattern to enhance sensitivity, is developed. Experimental validation demonstrates significant sensitivity enhancements across a wide sensing range. HEATS exhibits a remarkable 20‐fold and 10‐fold improvement at tensile rates of 0.9% and 30%, respectively, compared to non‐auxetic sensors. Furthermore, comprehensive testing demonstrates HEATS’ exceptional precision in detecting various tactile stimuli, including muscle movements and joint angles. With its unparalleled accuracy and adaptability, HEATS offers vast potential applications in human–machine and human–robot interaction, where subtle tactile communication is a prerequisite.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"36 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645384","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}
Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall N. Niles, Ken Pathak, Joe Tom
Onboard image analysis enables real‐time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision‐making critical for time‐sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time‐sensitive inference. This article introduces the widened attention‐enhanced atrous convolution‐based efficient network (WACEfNet), a new convolutional neural network designed specifically for real‐time visual classification challenges using resource‐constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width‐wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive overhead. Extensive benchmarking confirms state‐of‐the‐art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high‐fidelity real‐time analytics across a variety of embedded perception paradigms.
{"title":"Widened Attention‐Enhanced Atrous Convolutional Network for Efficient Embedded Vision Applications under Resource Constraints","authors":"Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall N. Niles, Ken Pathak, Joe Tom","doi":"10.1002/aisy.202300480","DOIUrl":"https://doi.org/10.1002/aisy.202300480","url":null,"abstract":"Onboard image analysis enables real‐time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision‐making critical for time‐sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time‐sensitive inference. This article introduces the widened attention‐enhanced atrous convolution‐based efficient network (WACEfNet), a new convolutional neural network designed specifically for real‐time visual classification challenges using resource‐constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width‐wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive overhead. Extensive benchmarking confirms state‐of‐the‐art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high‐fidelity real‐time analytics across a variety of embedded perception paradigms.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"8 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141683437","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}
Longitudinal analysis of the gut microbiota is crucial for understanding its relationship with gastrointestinal (GI) diseases and advancing diagnostics and treatments. Most ingestible sampling devices move passively within the GI tract, rely on physiological factors, and fail at multipoint sampling. This study proposes a multiple‐sampling capsule robot capable of collecting gut microbiota from various locations within the GI tract with minimal cross‐contamination. The proposed capsule comprises a body, a driving unit, six sampling tools, a central rod, and two heads. Electromagnetic field control facilitates control of the orientation and position of the capsule, particularly to align the channel of the capsule where the sample is collected facing downward. The capsule can collect six gut microbiota samples preventing contamination before and after sampling. The active locomotion and multiple sampling performance of the capsule are evaluated through basic performance tests (propulsion direction precision: 0.76 ± 0.52°, channel alignment precision: 0.84 ± 0.55°), phantom tests (average amount per sample: 10.3 ± 2.4 mg, cross‐contamination: 0.6 ± 0.4%), and ex‐vivo tests (average amount per sample: 9.9 ± 1.7 mg). The possibility of integration and clinical application of the capsule is confirmed through preclinical tests using a porcine model.
{"title":"Multiple Sampling Capsule Robot for Studying Gut Microbiome","authors":"Sanghyeon Park, M. Hoang, Jayoung Kim, Sukho Park","doi":"10.1002/aisy.202300625","DOIUrl":"https://doi.org/10.1002/aisy.202300625","url":null,"abstract":"Longitudinal analysis of the gut microbiota is crucial for understanding its relationship with gastrointestinal (GI) diseases and advancing diagnostics and treatments. Most ingestible sampling devices move passively within the GI tract, rely on physiological factors, and fail at multipoint sampling. This study proposes a multiple‐sampling capsule robot capable of collecting gut microbiota from various locations within the GI tract with minimal cross‐contamination. The proposed capsule comprises a body, a driving unit, six sampling tools, a central rod, and two heads. Electromagnetic field control facilitates control of the orientation and position of the capsule, particularly to align the channel of the capsule where the sample is collected facing downward. The capsule can collect six gut microbiota samples preventing contamination before and after sampling. The active locomotion and multiple sampling performance of the capsule are evaluated through basic performance tests (propulsion direction precision: 0.76 ± 0.52°, channel alignment precision: 0.84 ± 0.55°), phantom tests (average amount per sample: 10.3 ± 2.4 mg, cross‐contamination: 0.6 ± 0.4%), and ex‐vivo tests (average amount per sample: 9.9 ± 1.7 mg). The possibility of integration and clinical application of the capsule is confirmed through preclinical tests using a porcine model.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141273145","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}
Adolescent psychiatric disorders arise from intricate interactions of clinical histories and disruptions in brain development. While connections between psychopathology and brain functional connectivity are studied, the use of deep learning to elucidate overlapping neural mechanisms through multimodal brain images remains nascent. Utilizing two adolescent datasets—the Philadelphia Neurodevelopmental Cohort (PNC, n = 1100) and the Adolescent Brain Cognitive Development (ABCD, n = 7536)—this study employs interpretable neural networks and demonstrates that incorporating brain morphology, along with functional and structural networks, augments traditional clinical characteristics (age, gender, race, parental education, medical history, and trauma exposure). Predictive accuracy reaches 0.37–0.464 between real and predicted general psychopathology and four psychopathology dimensions (externalizing, psychosis, anxiety, and fear). The brain morphology and connectivities within the frontoparietal, default mode network, and visual associate networks are recurrent across general psychopathology and four psychopathology dimensions. Unique structural and functional pathways originating from the cerebellum, amygdala, and visual‐sensorimotor cortex are linked with these individual dimensions. Consistent findings across both PNC and ABCD affirm the generalizability. The results underscore the potential of diverse sensory inputs in steering executive processes tied to psychopathology dimensions in adolescents, hinting at neural avenues for targeted therapeutic interventions and preventive strategies.
{"title":"Unraveling Multimodal Brain Signatures: Deciphering Transdiagnostic Dimensions of Psychopathology in Adolescents","authors":"Jing Xia, Nanguang Chen, Anqi Qiu","doi":"10.1002/aisy.202300577","DOIUrl":"https://doi.org/10.1002/aisy.202300577","url":null,"abstract":"Adolescent psychiatric disorders arise from intricate interactions of clinical histories and disruptions in brain development. While connections between psychopathology and brain functional connectivity are studied, the use of deep learning to elucidate overlapping neural mechanisms through multimodal brain images remains nascent. Utilizing two adolescent datasets—the Philadelphia Neurodevelopmental Cohort (PNC, n = 1100) and the Adolescent Brain Cognitive Development (ABCD, n = 7536)—this study employs interpretable neural networks and demonstrates that incorporating brain morphology, along with functional and structural networks, augments traditional clinical characteristics (age, gender, race, parental education, medical history, and trauma exposure). Predictive accuracy reaches 0.37–0.464 between real and predicted general psychopathology and four psychopathology dimensions (externalizing, psychosis, anxiety, and fear). The brain morphology and connectivities within the frontoparietal, default mode network, and visual associate networks are recurrent across general psychopathology and four psychopathology dimensions. Unique structural and functional pathways originating from the cerebellum, amygdala, and visual‐sensorimotor cortex are linked with these individual dimensions. Consistent findings across both PNC and ABCD affirm the generalizability. The results underscore the potential of diverse sensory inputs in steering executive processes tied to psychopathology dimensions in adolescents, hinting at neural avenues for targeted therapeutic interventions and preventive strategies.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"47 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103467","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}
Decision‐making in unmanned combat aerial vehicles (UCAVs) presents a multifaceted challenge because of the complexity and dynamics of the flight environment, which leads to hurdles in training convergence, low decision validity, and the dimensionality catastrophe for decision‐making neural networks. A novel framework is proposed to address breaking down the complicated decision issues, which combines the strengths of graph convolutional networks in relation extraction with the ability of hierarchical reinforcement learning. To solve the problem of decision validity under high‐dimensional inputs, the joint framework is applied to the Maneuver Intent's decision, and a maneuver library‐based state space design method is suggested. The joint framework executes adaptable strategies and flight maneuvers to address the issue of training non‐convergence or task failure due to difficult‐to‐obtain reward signals across various scenarios. Then, the recurrent curriculum training and cross‐entropy rewards are designed to train decisions on different sub‐strategies. The experimental evaluation demonstrated more flexibility and adaptability in decision‐making problems under complex tasks compared to rule‐based and reinforcement learning baseline methods. The method proposed in this article provides a novel approach to resolving intricate decision problems, and which has certain theoretical significance and reference value for engineering applications.
{"title":"Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions","authors":"Ruihai Chen, Hao Li, Guanwei Yan, Haojie Peng, Qian Zhang","doi":"10.1002/aisy.202300574","DOIUrl":"https://doi.org/10.1002/aisy.202300574","url":null,"abstract":"Decision‐making in unmanned combat aerial vehicles (UCAVs) presents a multifaceted challenge because of the complexity and dynamics of the flight environment, which leads to hurdles in training convergence, low decision validity, and the dimensionality catastrophe for decision‐making neural networks. A novel framework is proposed to address breaking down the complicated decision issues, which combines the strengths of graph convolutional networks in relation extraction with the ability of hierarchical reinforcement learning. To solve the problem of decision validity under high‐dimensional inputs, the joint framework is applied to the Maneuver Intent's decision, and a maneuver library‐based state space design method is suggested. The joint framework executes adaptable strategies and flight maneuvers to address the issue of training non‐convergence or task failure due to difficult‐to‐obtain reward signals across various scenarios. Then, the recurrent curriculum training and cross‐entropy rewards are designed to train decisions on different sub‐strategies. The experimental evaluation demonstrated more flexibility and adaptability in decision‐making problems under complex tasks compared to rule‐based and reinforcement learning baseline methods. The method proposed in this article provides a novel approach to resolving intricate decision problems, and which has certain theoretical significance and reference value for engineering applications.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"103 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678886","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}
Geonwoo Hwang, Jongseok Nam, Minki Kim, David Santiago Diaz Cortes, Ki-Uk Kyung
Human–robot collaboration (HRC) is effective to improve productivity in industrial fields, based on the robot's fast and precise work and the human's flexible skill. To facilitate the HRC system, the first priority is to ensure safety in the event of accidents, such as collisions between robots and humans. Therefore, a protective and collision-sensitive robot skin, named Gel-Skin is proposed to guarantee the safety in HRC. The Gel-Skin is composed of polyvinyl chloride (PVC) gel, which is a functional material with piezoresistive characteristics and impact absorption capability. In particular, the PVC gel has a distinctive piezoresistive property that the relation between mechanical pressure and electrical resistance is tunable depending on an applied voltage. When a voltage is applied to the PVC gel, the electrical charges are accumulated around the anode and it shows increased piezoresistive sensitivity. In this study, it is verified for the PVC gel to exhibit the 4.78 times higher sensitivity by simply applying a voltage. This novel physical phenomenon enables the Gel-Skin to detect the collision rapidly. Finally, the Gel-Skin is applicated to a real robot system and it is verified that the Gel-Skin can detect a collision and absorb impact to ensure safety.
{"title":"Protective and Collision-Sensitive Gel-Skin: Visco-Elastomeric Polyvinyl Chloride Gel Rapidly Detects Robot Collision by Breaking Electrical Charge Accumulation Stability","authors":"Geonwoo Hwang, Jongseok Nam, Minki Kim, David Santiago Diaz Cortes, Ki-Uk Kyung","doi":"10.1002/aisy.202300583","DOIUrl":"https://doi.org/10.1002/aisy.202300583","url":null,"abstract":"Human–robot collaboration (HRC) is effective to improve productivity in industrial fields, based on the robot's fast and precise work and the human's flexible skill. To facilitate the HRC system, the first priority is to ensure safety in the event of accidents, such as collisions between robots and humans. Therefore, a protective and collision-sensitive robot skin, named Gel-Skin is proposed to guarantee the safety in HRC. The Gel-Skin is composed of polyvinyl chloride (PVC) gel, which is a functional material with piezoresistive characteristics and impact absorption capability. In particular, the PVC gel has a distinctive piezoresistive property that the relation between mechanical pressure and electrical resistance is tunable depending on an applied voltage. When a voltage is applied to the PVC gel, the electrical charges are accumulated around the anode and it shows increased piezoresistive sensitivity. In this study, it is verified for the PVC gel to exhibit the 4.78 times higher sensitivity by simply applying a voltage. This novel physical phenomenon enables the Gel-Skin to detect the collision rapidly. Finally, the Gel-Skin is applicated to a real robot system and it is verified that the Gel-Skin can detect a collision and absorb impact to ensure safety.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569526","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}
Jieun Park, Minho Kim, Jinhyung Park, Myungrae Hong, Sunghoon Im, Damin Choi, Eunyoung Kim, Dohyeon Gong, Seokhaeng Huh, Seung-Un Jo, ChangHwan Kim, Je-Sung Koh, Seungyong Han, Daeshik Kang
Rat whiskers are an exceptional sensing system, extracting information from their surrounding environment. Inspired by this concept, active whisker sensors measure various physical and geometric properties through contact with objects. However, previous research has focused on measuring the object geometry, often overlooking the potential for broader applications of the sensors. Herein, an active whisker sensor that enables simple measurement of the surface properties such as surface hardness and adhesiveness is reported. Composed of motor-, wire-, and crack-based mechanosensor, the active whisker sensor implements a tapping process inspired by the movement of a rat's whiskers to quickly evaluate the object surface. One area of potential application is the food industry. The active whisker sensors offer a new approach to measuring surface properties of viscoelastic and inelastic food that are difficult to measure with traditional bulky systems. Herein, it is validated that the tapping process can be used to measure the surface properties of a various foods. With the aid of machine learning algorithms, sensor can also recognize differences in the surface properties of bananas at different ripeness stages and classify them with 99% accuracy. In this report, the possibilities for applications of active whisker sensors, including food industry, robotics, and medical devices, are opened up.
{"title":"Active Whisker-Inspired Food Material Surface Property Measurement Using Deep-Learned Mechanosensor","authors":"Jieun Park, Minho Kim, Jinhyung Park, Myungrae Hong, Sunghoon Im, Damin Choi, Eunyoung Kim, Dohyeon Gong, Seokhaeng Huh, Seung-Un Jo, ChangHwan Kim, Je-Sung Koh, Seungyong Han, Daeshik Kang","doi":"10.1002/aisy.202300660","DOIUrl":"https://doi.org/10.1002/aisy.202300660","url":null,"abstract":"Rat whiskers are an exceptional sensing system, extracting information from their surrounding environment. Inspired by this concept, active whisker sensors measure various physical and geometric properties through contact with objects. However, previous research has focused on measuring the object geometry, often overlooking the potential for broader applications of the sensors. Herein, an active whisker sensor that enables simple measurement of the surface properties such as surface hardness and adhesiveness is reported. Composed of motor-, wire-, and crack-based mechanosensor, the active whisker sensor implements a tapping process inspired by the movement of a rat's whiskers to quickly evaluate the object surface. One area of potential application is the food industry. The active whisker sensors offer a new approach to measuring surface properties of viscoelastic and inelastic food that are difficult to measure with traditional bulky systems. Herein, it is validated that the tapping process can be used to measure the surface properties of a various foods. With the aid of machine learning algorithms, sensor can also recognize differences in the surface properties of bananas at different ripeness stages and classify them with 99% accuracy. In this report, the possibilities for applications of active whisker sensors, including food industry, robotics, and medical devices, are opened up.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760053","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}
Ngoc Hung Phi, Huu Nguyen Bui, Seong-Yoen Moon, Jong‐Wook Lee
Nonreciprocity plays a fundamental role in governing direction‐dependent asymmetric wave propagation. Previous approaches to nonreciprocity involve ferrite‐based devices with bulky systems. Herein, the controlled synthesis of a space–time modulation (STM) metamaterial for enhanced nonreciprocity using machine learning (ML) is investigated. The design of STM metamaterial poses great challenges due to the nonlinear nature of time‐periodic Floquet harmonics, which are inefficiently handled in traditional methods such as numerical optimization. To deal with the challenge, an ML approach is proposed that learns from the accumulated training data using the guided objective function and generates high‐quality designs by leveraging the learned features. This approach first trains a residual neural network (ResNet) to learn the nonlinear relationships between the STM parameters and nonreciprocal responses. The trained ResNet achieves a high testing accuracy, with 96.7% of the 9000 instances having a mean square error less than 0.6 × 10−4. For the synthesis of STM metamaterial, a customized Wasserstein generative adversarial network (WGAN) is proposed, which leverages the discovered nonlinearity and synthesizes large‐scale datasets using small computational costs. The histogram obtained using 90 000 data samples shows that WGAN generates designs with an average normalized nonreciprocity of 0.83, four times higher than the measured data.
{"title":"Controlled Synthesis of Space–Time Modulated Metamaterial for Enhanced Nonreciprocity by Machine Learning","authors":"Ngoc Hung Phi, Huu Nguyen Bui, Seong-Yoen Moon, Jong‐Wook Lee","doi":"10.1002/aisy.202300565","DOIUrl":"https://doi.org/10.1002/aisy.202300565","url":null,"abstract":"Nonreciprocity plays a fundamental role in governing direction‐dependent asymmetric wave propagation. Previous approaches to nonreciprocity involve ferrite‐based devices with bulky systems. Herein, the controlled synthesis of a space–time modulation (STM) metamaterial for enhanced nonreciprocity using machine learning (ML) is investigated. The design of STM metamaterial poses great challenges due to the nonlinear nature of time‐periodic Floquet harmonics, which are inefficiently handled in traditional methods such as numerical optimization. To deal with the challenge, an ML approach is proposed that learns from the accumulated training data using the guided objective function and generates high‐quality designs by leveraging the learned features. This approach first trains a residual neural network (ResNet) to learn the nonlinear relationships between the STM parameters and nonreciprocal responses. The trained ResNet achieves a high testing accuracy, with 96.7% of the 9000 instances having a mean square error less than 0.6 × 10−4. For the synthesis of STM metamaterial, a customized Wasserstein generative adversarial network (WGAN) is proposed, which leverages the discovered nonlinearity and synthesizes large‐scale datasets using small computational costs. The histogram obtained using 90 000 data samples shows that WGAN generates designs with an average normalized nonreciprocity of 0.83, four times higher than the measured data.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"21 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139861213","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}
Jungmin Hamm, Seonghyeon Lim, Jiae Park, Jiwon Kang, Injun Lee, Yoongeun Lee, Jiseok Kang, Youngjun Jo, Jaejin Lee, Seoyeong Lee, M. C. Ratri, A. I. Brilian, Seungyeon Lee, Seokhwan Jeong, Kwanwoo Shin
Robotic arms are now commonplace in diverse settings and are poised to play a crucial role in automating laboratory tasks. However, biological experiments remain challenging for automation due to their dependence on human factors, such as researchers’ skills and experience. This article introduces robotic automation and remote control for both general and biological research tasks through a modularized platform comprising a robotic arm, auxiliary tools, and software. This platform facilitates fully automated or remote execution of key experiments in chemistry and biology, including liquid handling, mixing, cell seeding, culturing, and genetic manipulation. The robot interfaces seamlessly with standard laboratory equipment and operates remotely in real time through an online program. Integration of a vision system via robotic arm webcams ensures precise positioning and object localization, enhancing accuracy. This modularized robotic platform signifies a substantial advancement in lab automation, promising enhanced efficiency, reproducibility, and scientific progress compared to human‐led experiments.
{"title":"A Modular Robotic Platform for Biological Research: Cell Culture Automation and Remote Experimentation","authors":"Jungmin Hamm, Seonghyeon Lim, Jiae Park, Jiwon Kang, Injun Lee, Yoongeun Lee, Jiseok Kang, Youngjun Jo, Jaejin Lee, Seoyeong Lee, M. C. Ratri, A. I. Brilian, Seungyeon Lee, Seokhwan Jeong, Kwanwoo Shin","doi":"10.1002/aisy.202300566","DOIUrl":"https://doi.org/10.1002/aisy.202300566","url":null,"abstract":"Robotic arms are now commonplace in diverse settings and are poised to play a crucial role in automating laboratory tasks. However, biological experiments remain challenging for automation due to their dependence on human factors, such as researchers’ skills and experience. This article introduces robotic automation and remote control for both general and biological research tasks through a modularized platform comprising a robotic arm, auxiliary tools, and software. This platform facilitates fully automated or remote execution of key experiments in chemistry and biology, including liquid handling, mixing, cell seeding, culturing, and genetic manipulation. The robot interfaces seamlessly with standard laboratory equipment and operates remotely in real time through an online program. Integration of a vision system via robotic arm webcams ensures precise positioning and object localization, enhancing accuracy. This modularized robotic platform signifies a substantial advancement in lab automation, promising enhanced efficiency, reproducibility, and scientific progress compared to human‐led experiments.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139814864","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}
Brittan T. Wilcox, John Joyce, Michael D. Bartlett
Biological organisms are extraordinary in their ability to change physical form to perform different functions. Mimicking these capabilities in engineered systems has the potential to create multifunctional robots that adapt form and function on-demand for search and rescue, environmental monitoring, and transportation. Organisms are able to navigate such unstructured environments with the ability to rapidly change shape, move swiftly in multiple locomotion modes, and do this efficiently and reversibly without external power sources, feats which are difficult for robots. Herein, a bio-inspired latch-mediated, spring-actuated (LaMSA) morphing mechanism is harnessed to near-instantaneously and reversibly reconfigure a multifunctional robot to achieve driving and flying configurations. This shape change coupled with a combined propeller/wheel leverages the same motors and electronics for both flying and driving, providing efficiency of morphing and locomotion for completely untethered operation. The adaptive robotic vehicle can move through confined spaces and rough terrain which are difficult to pass by driving or flying alone, and expands the potential range through power savings in the driving mode. This work provides a powerful scheme for LaMSA in robots, in which controlled, small-scale LaMSA systems can be integrated as individual components to robots of all sizes to enable new functionalities and enhance performance.
{"title":"Rapid and Reversible Morphing to Enable Multifunctionality in Robots","authors":"Brittan T. Wilcox, John Joyce, Michael D. Bartlett","doi":"10.1002/aisy.202300694","DOIUrl":"https://doi.org/10.1002/aisy.202300694","url":null,"abstract":"Biological organisms are extraordinary in their ability to change physical form to perform different functions. Mimicking these capabilities in engineered systems has the potential to create multifunctional robots that adapt form and function on-demand for search and rescue, environmental monitoring, and transportation. Organisms are able to navigate such unstructured environments with the ability to rapidly change shape, move swiftly in multiple locomotion modes, and do this efficiently and reversibly without external power sources, feats which are difficult for robots. Herein, a bio-inspired latch-mediated, spring-actuated (LaMSA) morphing mechanism is harnessed to near-instantaneously and reversibly reconfigure a multifunctional robot to achieve driving and flying configurations. This shape change coupled with a combined propeller/wheel leverages the same motors and electronics for both flying and driving, providing efficiency of morphing and locomotion for completely untethered operation. The adaptive robotic vehicle can move through confined spaces and rough terrain which are difficult to pass by driving or flying alone, and expands the potential range through power savings in the driving mode. This work provides a powerful scheme for LaMSA in robots, in which controlled, small-scale LaMSA systems can be integrated as individual components to robots of all sizes to enable new functionalities and enhance performance.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139558178","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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