Results 131 to 140 of about 603,409 (311)

Multi-task reinforcement learning: shaping and feature selection

, 2012
Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge from previously experienced source tasks to speed up learning on a new target task.
Whiteson, S., Hutter, M., Snel, M., Sanner, S.   +3 more
core   +1 more source

Oxidized MoS2‐Based Multifunctional Memristive Hardware for Energy‐Efficient mmWave Signal Processing and In‐Memory Matrix Multiplication

Advanced Functional Materials, EarlyView.
Thermally oxidized MoS2‐based radio‐frequency switches enable a multifunctional platform that unifies broadband RF switching and in‐memory computation. The device achieves a cutoff frequency of 33.2 THz with high energy efficiency and supports hardware‐aware signal processing.
Juho Son, Changwoo Pyo, Sungmoon Park, Yoongi Cho, Seungchan Lee, Myungsoo Kim   +5 more
wiley   +1 more source

Efficient deep reinforcement learning based task scheduler in multi cloud environment

Scientific Reports
Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities.
Sudheer Mangalampalli, Ganesh Reddy Karri, M. V. Ratnamani, Sachi Nandan Mohanty, Bander A. Jabr, Yasser A. Ali, Shahid Ali, Barno Sayfutdinovna Abdullaeva   +7 more
doaj   +1 more source

Asymmetric Multi-task Learning Based on Task Relatedness and Loss

, 2016
We propose a novel multi-task learning method that can minimize the effect of negative transfer by allowing asymmetric transfer between the tasks based on task relatedness as well as the amount of individual task losses, which we refer to as Asymmetric ...
Giwoong Lee, Hwang, Sung Ju, Eunho Yang
core  

Application of Ibuprofen Sodium Dihydrate for Thermochemical Energy Storage

Advanced Functional Materials, EarlyView.
Ibuprofen sodium dihydrate is introduced as a durable organic salt hydrate for low‐temperature thermochemical energy storage, operating within 60°C–110°C with high energy density. At the material level, it delivers ∼99.9% cycling efficiency over 150 cycles without deliquescence, enabled by a dual energy‐storage mechanism coupling dehydration and phase ...
Kavin Chakravarthy Thangaraj, Hannah F. Chang, Shannon J. Lee, Mark E. Bowden, J. David Bazak, Vaithiyalingam Shutthanandan, Venkateshkumar Prabhakaran, Xiao Zhang, David M. Reed, Ajay S. Karakoti, Vijayakumar Murugesan   +10 more
wiley   +1 more source

A novel multi-task learning model based on Transformer-LSTM for wind power forecasting

International Journal of Electrical Power & Energy Systems
The integration of multi-task learning into multi-step deterministic and probabilistic prediction frameworks plays a pivotal role in augmenting the accuracy of wind power forecasts and mitigating associated operational uncertainties.
Rongquan Zhang, Siqi Bu, Yuxia Zheng, Gangqiang Li, Xiupeng Wan, Qiangqiang Zeng, Min Zhou   +6 more
doaj   +1 more source

Applying multi-criteria optimisation to develop cognitive models

, 2005
A scientific theory is developed by modelling empirical data in a range of domains. The goal of developing a theory is to optimise the fit of the theory to as many experimental settings as possible, whilst retaining some qualitative properties such as ...
Lane, PCR, Gobet, F
core  

Transparent and Robust LiCl–Organohydrogel Triboelectric Nanogenerator With Deep Learning Assisted Sensing

Advanced Functional Materials, EarlyView.
Develop a LiCl–PEI–PAM hydrogel with 3000% stretchability and excellent optical transparency. Through comparative studies of various salts, confirm that LiCl is the most suitable salt for high TENG output. Achieve excellent freeze‐resistant, dry‐resistant, and rapid self‐healing (10 s) properties even in extreme environments. Balance ionic conductivity,
Hai Anh Thi Le, Thien Trung Luu, Duc‐Manh Dinh, Kyehan Rhee, Kyungwho Choi, Dukhyun Choi, Yong Tae Park   +6 more
wiley   +1 more source

Multi-task learning for audio scene source counting and analysis

Machine Learning with Applications
Audio source counting is a fundamental task of audio scene analysis related to other audio tasks such as speaker diarization and sound event detection. It is also a relatively unexplored audio task that presents a complex challenge. In particular, source
Michael Nigro, Sridhar Krishnan
doaj   +1 more source

Advances in Sustainable and Wearable Textile Based Soft Robotics

Advanced Functional Materials, EarlyView.
This Review examines advances in wearable textile‐based soft robotics, focusing on sustainable materials, integrated sensing, and scalable actuation. It discusses manufacturing and system integration across healthcare, assistive robotics, prosthetics, and human–machine interfaces, and highlights key challenges in circular design, including life‐cycle ...
Zahir Abbas, Marzia Dulal, Sophia K. Novoselov, Victoria K. Novoselov, Kostya S. Novoselov, Shaila Afroj, Nazmul Karim   +6 more
wiley   +1 more source