Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献_第3页

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-25 DOI: 10.1002/aisy.202400927

Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan

{"title":"Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement","authors":"Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan","doi":"10.1002/aisy.202400927","DOIUrl":"https://doi.org/10.1002/aisy.202400927","url":null,"abstract":"Sweat rate measures key physiological states such as hydration levels and heat tolerance. Incorporating wearable technology with sweat rate sensors allows individuals to conveniently monitor their health, optimize workouts, and enhance occupational safety. However, challenges persist in such integration techniques, including intricate manufacturing, nonlinear responses to changes in sweat rates, and errors from the intermediate measurement of the distance sweat travels in the sensor. To address these issues, we present a comprehensive wearable platform that includes a fully printed, flexible sensor patch, readout electronics, and a mobile app for continuous, real-time monitoring of sweat rate. We fabricate a sensor patch with an area of 700 mm2 and a weight of 380 mg by utilizing direct 3D printing and scalable microfluidic fabrication. The microfluidic channels are 850 μm wide and 164 μm thick, with serpentine electrodes measuring sweat rate using capacitance. The custom readout electronics capture these changes in capacitance to accurately measure sweat rate, achieving a sensitivity of 0.01 μL min−1. The sensor's performance is validated against analytical models, simulations, and on-body trials with commercial sensors. This cost-effective, flexible, and fully integrated sweat-sensing solution has significant potential in precision health.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400927","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Performance of Hall Effect Ion Source Using Machine Learning 利用机器学习预测霍尔效应离子源的性能

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-25 DOI: 10.1002/aisy.202400555

Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su-Jin Shin, Young-Chul Ghim, Sanghoo Park, Wonho Choe

{"title":"Predicting Performance of Hall Effect Ion Source Using Machine Learning","authors":"Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su-Jin Shin, Young-Chul Ghim, Sanghoo Park, Wonho Choe","doi":"10.1002/aisy.202400555","DOIUrl":"https://doi.org/10.1002/aisy.202400555","url":null,"abstract":"Accurate performance prediction methods are essential for the development of high-efficiency Hall effect ion sources, which are employed in industries ranging from material surface treatment to spacecraft electric propulsion (known as Hall thrusters). Traditional methods rely on simplified scaling laws and computationally intensive numerical simulations. Herein, a robust machine learning model is introduced that uses a neural network ensemble to predict the performance of Hall effect ion sources based on design parameters such as discharge channel dimensions and magnetic field structure. The neural networks are trained using 18 000 data points generated from numerical simulations with input powers ranging from sub-kW- to kW-class. The accuracy of the developed machine learning model is demonstrated using untrained 700 W- and 1 kW-class Hall effect ion sources, producing results with deviations of less than 10% compared to the experimentally measured thrust and discharge current, thus surpassing the accuracy of conventional scaling laws. As a high-fidelity surrogate for numerical simulations, the proposed prediction tool provides high prediction accuracy and calculation speed, offering an excellent complement to conventional scaling laws and enhancing the understanding of Hall effect ion source performance characteristics.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-Distance Autonomous Navigation of Optical Microrobotic Swarms in Complex Environments

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470056

Zhihan Chen, Siyuan Huang, Yuebing Zheng

引用次数: 0

Classification of Vaginal Cleanliness Grades through Surface-Enhanced Raman Spectral Analysis via The Deep-Learning Variational Autoencoder–Long Short-Term Memory Model

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470059

Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang

{"title":"Classification of Vaginal Cleanliness Grades through Surface-Enhanced Raman Spectral Analysis via The Deep-Learning Variational Autoencoder–Long Short-Term Memory Model","authors":"Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang","doi":"10.1002/aisy.202470059","DOIUrl":"https://doi.org/10.1002/aisy.202470059","url":null,"abstract":"Deep-Learning-Guided Surface-Enhanced Raman Spectroscopy\u0000 In article number 2400587, Muhammad Usman, Liang Wang, and co-workers present a novel approach combining deep-learning-guided surface-enhanced Raman spectroscopy (SERS) and a variational autoencoder (VAE) with a long short-term memory (LSTM) neural network to classify vaginal cleanliness levels rapidly and accurately. Enhanced spectral quality and an optimized VAE–LSTM model yielded an 85% accuracy on blind test data. This method, which improves signal-to-noise ratios and diagnostic efficiency, shows strong potential for clinical applications in assessing vaginal cleanliness through SERS analysis of vaginal secretions.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture>\u0000 </div>\u0000 </figure>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"6 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoupling Implantation Prediction and Embryo Ranking in Machine Learning: The Impact of Clinical Data and Discarded Embryos

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470058

Itay Erlich, Sotirios H. Saravelos, Cristina Hickman, Assaf Ben-Meir, Iris Har-Vardi, James A. Grifo, Semra Kahraman, Assaf Zaritsky

引用次数: 0

AS-XAI: Self-Supervised Automatic Semantic Interpretation for CNN

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470055

Changqi Sun, Hao Xu, Yuntian Chen, Dongxiao Zhang

引用次数: 0

A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo-Rigid Body Modeling

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-15 DOI: 10.1002/aisy.202400637

Haiyun Zhang, Kelvin HoLam Heung, Gabrielle J. Naquila, Ashwin Hingwe, Ashish D. Deshpande

{"title":"A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo-Rigid Body Modeling","authors":"Haiyun Zhang, Kelvin HoLam Heung, Gabrielle J. Naquila, Ashwin Hingwe, Ashish D. Deshpande","doi":"10.1002/aisy.202400637","DOIUrl":"https://doi.org/10.1002/aisy.202400637","url":null,"abstract":"Controlling soft robots, especially soft hand grasping, is complex due to their ubiquitous deformation, prompting the use of reduced model-based controllers to provide sufficient state information for high dynamic response control performance. However, most modeling techniques face computational efficiency and complexity of parameter identification issues. To alleviate this, a paradigm coupling an analytical modeling approach based on pseudo-rigid body modeling and the logarithmic decrement method (PRBM + LDM) for parameter estimation is proposed. Using a soft robot hand test bed, the PRBM + LDM model for a closed-loop position controller is applied and is compared with a simple proportional–integral–derivative controller (PID controller) static shape control of soft continuum robots using deep visual inverse kinematic models. Furthermore, the PRBM + LDM model-based force controller is compared with simple constant pressure grasping control by pinching tasks on low-weight, small objects—a screwdriver, a potato chip, and a brass coin. The PRBM + LDM-based position controller outperforms the simple PID position controller, and the PRBM + LDM-based force controller achieves a higher success rate than the constant pressure grasping control in the pinching tasks. In conclusion, the PRBM + LDM modeling technique proves to be a convenient and efficient way to model the dynamic behavior of soft actuators closely and can be applied to build high-precision position and force controllers.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Risks and Rewards of Embodying Artificial Intelligence with Cloud-Based Laboratories

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-15 DOI: 10.1002/aisy.202400193

Nicolas Rouleau, Nirosha J. Murugan

{"title":"The Risks and Rewards of Embodying Artificial Intelligence with Cloud-Based Laboratories","authors":"Nicolas Rouleau, Nirosha J. Murugan","doi":"10.1002/aisy.202400193","DOIUrl":"https://doi.org/10.1002/aisy.202400193","url":null,"abstract":"Autonomous, cloud-based laboratories (CBLs) are transforming scientific research by democratizing access to advanced instruments that accelerate high-throughput discovery. As artificial intelligences (AIs) become integrated or “embodied” with CBLs and gain independence from human oversight, efforts to identify novel pharmaceuticals, renewable energies, and agricultural biotechnologies will accelerate. AI-driven CBLs can perform tasks more efficiently and accurately than human scientists at lower costs, achieving results in weeks rather than years. However, as AI systems approach or exceed human intelligence, their decision-making abilities could outpace the need for human input, raising ethical, economic, and safety concerns. Aligning AI goals with human values is critical, as unregulated systems could pose existential risks, including global health hazards or the distortion of knowledge-generating systems. AI-driven misinformation in research highlights the need for transparency and data integrity, which may be achieved by aligning incentivizes and engineered fail-safes to promote long-term human flourishing. To mitigate risks, strict compartmentalization of AI systems and CBLs with third-party supervision at fine temporal resolutions will be necessary. While current CBLs are piloted by humans, future AI systems may relegate humans to the role of co-pilot. Anticipating increased AI-CBL integration, policies must balance innovation with caution to maximize benefits and avoid unintended harm.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Skin-Conformal Myography for Real-Time Hand Tracking Using a Laser-Induced Graphene Strain Sensor Array

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-15 DOI: 10.1002/aisy.202400812

Vinay Kammarchedu, Monther AlSiyabi, Aida Ebrahimi

{"title":"Skin-Conformal Myography for Real-Time Hand Tracking Using a Laser-Induced Graphene Strain Sensor Array","authors":"Vinay Kammarchedu, Monther AlSiyabi, Aida Ebrahimi","doi":"10.1002/aisy.202400812","DOIUrl":"https://doi.org/10.1002/aisy.202400812","url":null,"abstract":"Despite significant progress in developing wearable systems for hand tracking, most devices are still bulky, restrictive to the user or to the placement of the exoskeleton systems, and sensitive to skin preparation and impedance. In this work, we develop a wristband that integrates an array of 10 skin-conformal strain sensors based on laser-induced graphene, which is optimized for continuous measurement of skin strain. The device is characterized to identify several hand gestures and tasks while simultaneously using an optical camera-based hand-tracking system to estimate the joint locations for ground truth generation. Machine learning models are developed to predict gestures as well as specific hand joint angles with high accuracy of >90% and >95%, respectively. The findings show that the sensors placed closer to actuation-specific anatomical features contribute more toward the high accuracy. The sensor array is also integrated with a wearable readout system that wirelessly transmits the data in real time in order to control a robotic arm as a proof of concept for human–robot interaction applications. The developed skin-conformal device is expected to find wide applications in rehabilitation, sports sciences, and human–computer interaction, paving the way for low-profile prosthetic and orthotic control systems.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hand Gesture Recognition Using Frequency-Modulated Continuous Wave Radar on Tactile Displays for the Visually Impaired

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-15 DOI: 10.1002/aisy.202400663

Ahmed Hamza, Santosh Kumar Prabhulingaiah, Pegah Pezeshkpour, Bastian E. Rapp

{"title":"Hand Gesture Recognition Using Frequency-Modulated Continuous Wave Radar on Tactile Displays for the Visually Impaired","authors":"Ahmed Hamza, Santosh Kumar Prabhulingaiah, Pegah Pezeshkpour, Bastian E. Rapp","doi":"10.1002/aisy.202400663","DOIUrl":"https://doi.org/10.1002/aisy.202400663","url":null,"abstract":"Touchscreens are essential parts of many electronics in daily lives of sighted people in the digital information era. On the other hand, visually impaired users rely on tactile displays as one of the key communication devices to interact with the digital world. However, due to their working mechanism and the uneven surface of tactile displays, one of the key features of screens for sighted users is surprisingly challenging to implement: precision touch input. To overcome this, a hand gesture recognition system is developed using a frequency-modulated continuous wave millimeter-wave radar. A multifeature encoder method is used to obtain the range and velocity information from the radar to translate the data into spectrogram images. Gesture recognition is implemented for common input gestures: single/double-click, swipe-right/left, scroll-up/down, zoom-in/out, and rotate-anticlockwise/clockwise. The gesture recognition and classification are based on machine learning, support vector machines, deep learning, and convolutional neural network approaches. The chosen model You-Only-Look-Once (YOLOv8) shows a high accuracy of 97.1% by iterating only 30 epochs with only 500 collected data samples per gesture. This research paves the way toward using radar sensors not only for tactile displays but also for other digital devices in human–computer interaction.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 2","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400663","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0