Results 111 to 120 of about 278 (159)

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A wireless wearable sweat rate sensor system is presented, featuring digital 3D direct‐write printing on a flexible substrate with microfluidic layers for continuous, real‐time monitoring. Printed encapsulated metal electrodes are used for capacitance measurements, achieving high sensitivity (0.01 μL min−1) while maintaining a compact and lightweight ...
Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan   +6 more
wiley   +1 more source

ÐÑганизаÑÐ¸Ñ ÑелÑеÑа и комплекÑное благоÑÑÑÑойÑÑва поÑелка «ХемÑлÑ» в РеÑпÑблике ÐаÑелиÑ

, 2017
Алена Александровна Паршина
openalex   +1 more source

Nonplanar Atomic Layer Deposition (ALD)‐Niobium Oxide (NbOx) Neurons for Oscillatory Neural Network Applications

Advanced Intelligent Systems, EarlyView.
This article presents a nonplanar niobium oxide (NbOx) neuron device fabricated using an atomic layer deposition (ALD) method for use in oscillatory neural networks (ONNs). Potentially, such nonplanar geometry allows for high‐density arrays. The desired threshold switching (TS) characteristics are achieved through an interfacial method using a thin ...
Jaehyun Moon, Kitae Kim, Jihyun Kim, Soohyung Park, Yeonjin Yi, Jiyong Woo, Seung‐Youl Kang   +6 more
wiley   +1 more source

ÐиÑеÑенÑÑÑÐ²Ð°Ð½Ð½Ñ Ð²Ð¸Ð¼Ð¾Ð³, Ñо пÑед'ÑвлÑÑÑÑÑÑ Ð´Ð¾ генеÑаÑоÑÑв ÐÐÐ, викоÑиÑÑовÑÐ²Ð°Ð½Ð¸Ñ Ð² ÑÑÑлÑÐ½Ð¸ÐºÐ¾Ð²Ð¸Ñ ÑиÑÑÐµÐ¼Ð°Ñ Ð·Ð²'ÑзкÑ

, 2009
К Б; ДП «Київський коледж зв 'язку» Нікіфоренко, Н М; ДАТ КБ «Дніпровське» Согіна   +1 more
openalex   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Ðплив модÑлÑÑÑÑ Ñа нелÑнÑйноÑÑÑ Ð¿ÐµÑедаваÑа на ÑÑанÑмÑÑÑÑ ADS-B повÑÐ´Ð¾Ð¼Ð»ÐµÐ½Ñ ÑеÑез ÑÑпÑÑниковий OFDM канал звâÑзкÑ

, 2014
Volodymyr Kharchenko, Bo, Wang; Ningbo University of Technology, China, Grekhov, Andrii; National Aviation University, Kyiv, Ukraine, Bezsmertna, Daria; National Aviation University, Kyiv, Ukraine   +3 more
openalex   +1 more source

ÐÑÑоÑное оÑиÑное здание Ñ Ð¸ÑполÑзованием ÑÑалÑной ÑибÑÑ Ð´Ð»Ñ Ð°ÑмиÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÐ¾Ð»Ð¾Ð½Ð½

, 2019
Варвара Клещевникова
openalex   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

ÐоÑлÑÐ´Ð¶ÐµÐ½Ð½Ñ Ð¼Ð¾Ð´Ð°Ð»ÑÐ½Ð¸Ñ ÑаÑакÑеÑиÑÑик вÑлÑно обпеÑÑÐ¾Ñ Ñа конÑолÑÐ½Ð¾Ñ Ð±Ð°Ð»Ð¾Ðº пÑд дÑÑÑ ÑоÑÐºÐ¾Ð²Ð¸Ñ Ñа ÑозподÑÐ»ÐµÐ½Ð¸Ñ Ð³Ð°ÑмонÑÑÐ½Ð¸Ñ Ð·Ð±ÑдженÑ

, 2006
Vitaly Makarenko
openalex   +1 more source

РеализаÑÐ¸Ñ Ð¾ÑновнÑÑ Ð½Ð°Ð¿Ñавлений ÑÑÑаÑегии вÑÑ Ð¾Ð´Ð° на внеÑний ÑÑнок пÑедпÑиÑÑиÑ

, 2017
Ольга Дашкова
openalex   +1 more source