Advanced Intelligent Systems最新文献_第9页

Ink‐Based Additive Nanomanufacturing of Functional Materials for Human‐Integrated Smart Wearables 用于人体集成智能可穿戴设备的功能材料的油墨纳米增材制造

Advanced Intelligent Systems Pub Date : 2020-07-28 DOI: 10.1002/aisy.202000117

Shujia Xu, Wenzhuo Wu

{"title":"Ink‐Based Additive Nanomanufacturing of Functional Materials for Human‐Integrated Smart Wearables","authors":"Shujia Xu, Wenzhuo Wu","doi":"10.1002/aisy.202000117","DOIUrl":"https://doi.org/10.1002/aisy.202000117","url":null,"abstract":"The economical, agile, customizable manufacturing, and integration of multifunctional device modules into networked systems with mechanical compliance and robustness enable unprecedented human‐integrated smart wearables and usher in exciting opportunities in emerging technologies. The additive manufacturing (AM) processes have emerged as potential candidates for rapid prototyping printed devices with diversified functionalities, e.g., energy harvesting/storage, sensing, actuation, and computation. However, there are few review reports about the ink‐based additive nanomanufacturing of functional materials for human‐integrated smart wearables. To fill this gap, herein, the recent progress in ink‐based additive nanomanufacturing technologies, focusing on their capability and potential for producing wearable human‐integrated devices, is reviewed. The manufacturing process integration, functional materials, device implementation, and application performance issues in designing and implementing the ink‐based additively nanomanufactured wearable systems are thoroughly discussed. The recent printed devices focusing on the processing conditions and performance metrics are comprehensively reviewed. Finally, the vision and outlook for the challenges and opportunities associated with related topics are provided. The rapid progress achieved in related disciplines enables more capable smart human‐integrated wearable systems that can be fully printed with rapid, agile, reconfigurable, and smart AM platforms.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85659195","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}

引用次数: 12

Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries 各种几何形状注射成型工艺条件推荐的人工神经网络系统的开发

Advanced Intelligent Systems Pub Date : 2020-07-23 DOI: 10.1002/aisy.202000037

Chihun Lee, Juwon Na, Kyongho Park, Hye-jeong Yu, Jongsun Kim, Kwon-Il Choi, D. Park, Seongjin Park, J. Rho, Seungchul Lee

{"title":"Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries","authors":"Chihun Lee, Juwon Na, Kyongho Park, Hye-jeong Yu, Jongsun Kim, Kwon-Il Choi, D. Park, Seongjin Park, J. Rho, Seungchul Lee","doi":"10.1002/aisy.202000037","DOIUrl":"https://doi.org/10.1002/aisy.202000037","url":null,"abstract":"This study combines an artificial neural network (ANN) and a random search to develop a system to recommend process conditions for injection molding. Both simulation and experimental results are collected using a mixed sampling method that combines Taguchi and random sampling. The dataset consists of 3600 simulations and 476 experiments from 36 different molds. Each datum has five process and 15 geometry features as input and one weight feature as output. Hyper‐parameter tuning is conducted to find the optimal ANN model. Then, transfer learning is introduced, which allows the use of simultaneous experimental and simulation data to reduce the error. The final prediction model has a root mean‐square error of 0.846. To develop a recommender system, random search is conducted using the trained ANN forward model. As a result, the weight‐prediction model based on simulated data has a relative error (RE) of 0.73%, and the weight prediction using the transfer model has an RE of 0.662%. A user interface system is also developed, which can be used directly with the injection‐molding machine. This method enables the setting of process conditions that yield parts having weights close to the target, by considering only the geometry and target weight.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75161616","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}

引用次数: 13

Lighter and Stronger: Cofabricated Electrodes and Variable Stiffness Elements in Dielectric Actuators 更轻、更强:电介质致动器中的共制电极和变刚度元件

Advanced Intelligent Systems Pub Date : 2020-07-23 DOI: 10.1002/aisy.202000069

Yegor Piskarev, J. Shintake, V. Ramachandran, Neil Baugh, M. Dickey, D. Floreano

引用次数: 16

Engineering Intelligent Nanosystems for Enhanced Medical Imaging 工程智能纳米系统增强医学成像

Advanced Intelligent Systems Pub Date : 2020-07-21 DOI: 10.1002/aisy.202000087

Guido T. van Moolenbroek, Tania Patiño, J. Llop, S. Sánchez

{"title":"Engineering Intelligent Nanosystems for Enhanced Medical Imaging","authors":"Guido T. van Moolenbroek, Tania Patiño, J. Llop, S. Sánchez","doi":"10.1002/aisy.202000087","DOIUrl":"https://doi.org/10.1002/aisy.202000087","url":null,"abstract":"Medical imaging serves to obtain anatomical and physiological data, supporting medical diagnostics as well as providing therapeutic evaluation and guidance. A variety of contrast agents have been developed to enhance the recorded signals and to provide molecular imaging. However, fast clearance from the body or nonspecific biodistribution often limit their efficiency, constituting challenges that need to be overcome. Nanoparticle‐based systems are currently emerging as versatile and highly integrated platforms providing improved circulating times, tissue specificity, high loading capacity for signaling moieties, and multimodal imaging features. Furthermore, nanoengineered devices can be tuned for specific applications and the development of responsive behaviors. Responses include in situ modulation of nanoparticle size, increased intratissue mobility through active propulsion of motorized particles, and active modulation of the particle surroundings such as the extracellular matrix for an improved penetration and retention at the desired locations. Once accumulated in the targeted tissue, smart nanoparticle‐based contrast agents can provide molecular sensing of biomarkers or characteristics of the tissue microenvironment. In this case, the signal or contrast provided by the nanosystem is responsive to the presence or concentration of an analyte. Herein, recent developments of intelligent nanosystems to improve medical imaging are presented.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80807782","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}

引用次数: 23

Stent Deployment Detection Using Radio Frequency‐Based Sensor and Convolutional Neural Networks 基于射频传感器和卷积神经网络的支架部署检测

Advanced Intelligent Systems Pub Date : 2020-07-20 DOI: 10.1002/aisy.202000092

Mengya Xu, Seenivasan Lalithkumar, L. Yeo, Hongliang Ren

{"title":"Stent Deployment Detection Using Radio Frequency‐Based Sensor and Convolutional Neural Networks","authors":"Mengya Xu, Seenivasan Lalithkumar, L. Yeo, Hongliang Ren","doi":"10.1002/aisy.202000092","DOIUrl":"https://doi.org/10.1002/aisy.202000092","url":null,"abstract":"A lack of sensory feedback often hinders minimally invasive operations. Although endoscopy has addressed this limitation to an extent, endovascular procedures such as angioplasty or stenting still face significant challenges. Sensors that rely on a clear line of sight cannot be used because it is unable to gather feedback in blood environments. During the stent deployment procedure, feedback on the deployed stent's state is critical because a partially open stent can affect the blood flow. Despite this, no robust and noninvasive clinical solutions that allow real‐time monitoring of the stent deployment exists. In recent years, radio frequency (RF)‐based sensors can detect the shape and material of an object that is hidden from the direct line of sight. Herein, the use of a 3D RF‐based imaging sensor and a novel Convolutional Neural Network (CNN) called StentNet is proposed for detecting the stent's state without a need for a clear line of sight. The StentNet achieves an overall accuracy of 90% in detecting the state of an occluded stent in the test dataset. Compared with an existing CNN model, the StentNet significantly outperforms the 3D LeNet in the evaluation metrics such as accuracy, precision, recall, and F1‐score.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"178 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73656083","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}

引用次数: 2

Programmable Photoelectric Memristor Gates for In Situ Image Compression 用于原位图像压缩的可编程光电忆阻门

Advanced Intelligent Systems Pub Date : 2020-07-14 DOI: 10.1002/aisy.202000079

D. Berco, D. Ang, P. S. Kalaga

{"title":"Programmable Photoelectric Memristor Gates for In Situ Image Compression","authors":"D. Berco, D. Ang, P. S. Kalaga","doi":"10.1002/aisy.202000079","DOIUrl":"https://doi.org/10.1002/aisy.202000079","url":null,"abstract":"Integrated circuits designed to perform mathematical operations, such as Fourier transforms and matrix multiplications, in artificial visual perception and intelligent image processing are mainly constructed of conventional logic gates. However, Boolean logic is probably not the most optimal approach for brain‐inspired computing due to the fuzzy nature of biologic neural networks. This work demonstrates an application based on programmable fuzzy‐logic gates capable of combined photoelectric computations. Such an apparatus may be used to perform image compression immediately upon acquisition without having the need to rely on interaction between separate processor and sensor modules. It is based on resistive memory devices capable of state transitions in response to both electronic and light stimulations. Material nonimplication and logical true operations are first presented. A more complex functionality for material nonimplication of a logic conjunction is then demonstrated. These gates are then used as building blocks in the design and simulation of a configurable matrix multiplication unit that effectively implements in situ image compression. A membership function (FUZZIFY) that may be used to map strict logic levels to incremental fuzzy analog ones is also shown. Finally, an approach for integrating conventional logic with a fuzzy computation is discussed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80576656","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}

引用次数: 7

3D Manipulation of Magnetic Liquid Metals 磁性液态金属的三维操作

Advanced Intelligent Systems Pub Date : 2020-07-12 DOI: 10.1002/aisy.201900170

Wenqing Zhou, Qingxuan Liang, Tianning Chen

引用次数: 13

Robot Audition and Computational Auditory Scene Analysis 机器人试听和计算听觉场景分析

Advanced Intelligent Systems Pub Date : 2020-07-08 DOI: 10.1002/aisy.202000050

K. Nakadai, Hiroshi G. Okuno

引用次数: 7

Pyroelectric Tweezers for Handling Liquid Unit Volumes 热释电镊子处理液体单位体积

Advanced Intelligent Systems Pub Date : 2020-07-01 DOI: 10.1002/aisy.202000044

G. Nasti, S. Coppola, V. Vespini, S. Grilli, A. Vettoliere, C. Granata, P. Ferraro

{"title":"Pyroelectric Tweezers for Handling Liquid Unit Volumes","authors":"G. Nasti, S. Coppola, V. Vespini, S. Grilli, A. Vettoliere, C. Granata, P. Ferraro","doi":"10.1002/aisy.202000044","DOIUrl":"https://doi.org/10.1002/aisy.202000044","url":null,"abstract":"Liquids are the primary environments in which chemical, physical, and biological processes occur. Considering a liquid bridge as liquid unit volume (LUV) element, it is highly desirable to develop reliable tools for handling such volumes. Herein, a sort of intelligent microfluidic platform based on the pyroelectric‐electrohydrodynamics (EHD) is shown for manipulating liquid bridges and thus performing multiple functions in a flexible and simple way. Several basic operations with liquid bridges using an EHD‐pin matrix based on the pyroelectric effect engineered in ferroelectric crystals are demonstrated. By activating pyro‐EHD effect in predetermined positions (pins of the array), the locomotion and handling of single or multiple LUVs simultaneously are controlled. In particular, multiple operations such as lift, displacement, mixing, stretching, and carrying vector for microparticles, are shown. These tweezers based on a pyro‐EHD matrix can open the route for a multipurpose platform driven by physical intelligence and can be used for driving locomotion and operate manifolds functionalities in many areas of science and technology at microscale as well as nanoscale with advantages to be activated by the sole thermal stimulus, controlled remotely, and in noncontact mode.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"200 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80128380","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}

引用次数: 10

Modeling Previous Trial Effect in Human Manipulation through Iterative Learning Control 基于迭代学习控制的人类操作前试效应建模

Advanced Intelligent Systems Pub Date : 2020-07-01 DOI: 10.1002/aisy.201900074

Lorenzo Cenceschi, C. D. Santina, Giuseppe Averta, M. Garabini, Qiushi Fu, M. Santello, M. Bianchi, A. Bicchi

{"title":"Modeling Previous Trial Effect in Human Manipulation through Iterative Learning Control","authors":"Lorenzo Cenceschi, C. D. Santina, Giuseppe Averta, M. Garabini, Qiushi Fu, M. Santello, M. Bianchi, A. Bicchi","doi":"10.1002/aisy.201900074","DOIUrl":"https://doi.org/10.1002/aisy.201900074","url":null,"abstract":"In the execution of repetitive tasks, humans can capitalize on experience to improve their motor performance. Prominent examples of this ability can be recognized in our capacity of grasping and manipulating in uncertain conditions. With the aim of providing a mathematical description for such behavior, experiments are considered where participants are required to lift an object with an unexpected mass distribution. By repeating multiple times the same lifting action, participants can learn the correct motor command for task accomplishment. Three models are proposed that combine reactive terms and a learned anticipatory action to explain experimental data. The models feature intratrial and intertrial memory, and the effect of slowly and fast adaptive sensory receptors. The architectures’ effectiveness in explaining experimental data is compared with a general‐purpose state of the art model. The proposed algorithms conspicuously outperform the state of the art in all the considered validation routines. Global and within‐trial human behavior is predicted with 88% of accuracy in nominal conditions. When the object's center of mass is moved, the accuracy is maintained up to 83%. Finally, convergence properties of proposed algorithms are analytically discussed, and their stability and robustness against measurement noise are evaluated in simulation.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90208478","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}

引用次数: 0