Results 261 to 270 of about 336,714 (315)

In-sample prediction accuracy measures MAE, RMSE, MAPE, and MDA for MKSARMAX, SARMA, SARMAX, additive Holt-Winters, and KARMA models fit of observations from to 138 of the Guarapiranga UV dataset. The best value for each measure is shaded light gray.

Aline Armanini Stefanan (21395087), Murilo Sagrillo (21395090), Bruna G. Palm (21395093), Fábio M. Bayer (9187943)   +3 more
openalex   +1 more source

Voxel‐SLAM: A Complete, Accurate, and Versatile Light Detection and Ranging‐Inertial Simultaneous Localization and Mapping System

Advanced Intelligent Systems, EarlyView.
: In this work, Voxel‐SLAM (simultaneous localization and mapping) is introduced: a complete, accurate, and versatile LiDAR (light detection and ranging) ‐inertial SLAM system consisting of five modules: initialization, odometry, local mapping (LM), loop closure (LC), and global mapping (GM), all employing the same map representation, an adaptive voxel
Zheng Liu, Haotian Li, Chongjian Yuan, Xiyuan Liu, Jiarong Lin, Rundong Li, Chunran Zheng, Bingyang Zhou, Wenyi Liu, Fu Zhang   +9 more
wiley   +1 more source

PENENTUAN NILAI TERKECIL ROOT MEAN SQUARRED ERROR (RMSE) METODE HOLT-WINTERS EXPONENTIAL SMOOTHING PADA EKSPOR KOPI TUJUAN JERMAN

Wahyu Nur Achmadin, Dwi Agustin Retnowardani, Dewi Mashitasari, Fita Fatimah, Indah Noor Dwi Kusuma Dewi   +4 more
openalex   +2 more sources

Modeling and Characterization of a Self‐Sensing Soft Hydraulic Muscle

Advanced Intelligent Systems, EarlyView.
This article presents the self‐sensing soft hydraulic muscle (SSHM), a novel soft actuator capable of simultaneously sensing force and length without external sensors. A comprehensive model accurately predicts SSHM behavior, validated experimentally with minimal errors. Using propylene glycol enhances durability and reduces hysteresis.
Nhu An Phan, Phuong Linh Dao, Duc Tu Vu, The Doan Le, Sy Trung Ngo, Minh Tri Phan, T. Thang Vo‐Doan, Ke Wu, Mai Thanh Thai   +8 more
wiley   +1 more source

Validation metric for all four learning models a) R2, b) RMSE, c) MSE, d) MAE.

Krishnamoorthy Ramalingam (22056959), Mohd. Zulkifly Abdullah (22056962), Perumal Venkatesan Elumalai (22056965), Allam Sangeetha (22056968), Xu Yong (12675832), Nasim Hasan (22056971), Wei Shangzhi (22056974)   +6 more
openalex   +1 more source

Ultralow‐Power Real‐Time On‐Chip Thermal Prediction via Finite Element Method–Machine Learning Codesign and Field‐Programmable Gate Array Deployment

Advanced Intelligent Systems, EarlyView.
A lightweight machine learning (ML)‐based thermal prediction framework is demonstrated and implemented on a field‐programmable gate array (FPGA). Using measured temperature data from a real chiplet, the approach enables real‐time, die‐level heat‐map inference with low power consumption, validating practical on‐chip thermal monitoring for advanced ...
Jun Ho Lee, Jae Gyu Kim, Sung Jin Kim, Jae Yong Song, Byoung Don Kong   +4 more
wiley   +1 more source

HBP-net for robust remote heart rate estimation using heartbeat probability. [PDF]

iScience
Ye X, Zhou C, Wei Y, Dahlweid FM, Sun H, Wang C, Zhang X.   +6 more
europepmc   +1 more source

RMSE and Winning Model calculation

Lee, Jeungchan
openalex   +1 more source