Data driven prediction of fragment velocity distribution under explosive loading conditions by Donghwan Noh, Piemaan Fazily, Songwon Seo, Jaekun Lee, Seungjae Seo, Hoon Huh, Jeong Whan Yoon

Get full text

A fiber-optic traffic monitoring network trained with video inputs by Khen Cohen, Liav Hen, Ariel Lellouch

“…We use YOLO-derived vehicle location and classification from video inputs as labeled data to train a detection and classification neural network that uses DAS data only. …”
Get full text

Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples by Min Gu Kwak, Hyungu Kahng, Seoung Bum Kim

“…The existing methods assume that the class distributions of labeled and unlabeled data are equal; however, their performances are significantly degraded in class distribution mismatch scenarios where out-of-distribution (OOD) data exist in the unlabeled data. …”
Get full text

Gender Factor in Associative Links of Words: Dictionary and Distributive-Semantic Model Data by T. A. Litvinova, E. S. Kotlyarova, V. A. Zavarzina

“…The use of a set of methods for data mining (clustering, classification) and visualization of its results made it possible to establish the influence of gender on the composition of the semantic associates of the analyzed words (that is, words with close vectors in the distributive-semantic model) and the absence of such an effect in their associates recorded in the associative dictionary. …”
Get full text

Factors Affecting the Indifference of Players in Training for Competitions by Fatemeh Rahmati, Masoud Naderian Jahroni

“…This would eventually lead to reduced player indifference in professional training contexts. The findings also reflect the role of distributive justice in reducing perceived unfairness, which is a major cause of demotivation among players.…”
Get full text

Evaluating the role of training data origin for country-scale cropland mapping in data-scarce regions: A case study of Nigeria by Joaquin Gajardo, Michele Volpi, Daniel Onwude, Thijs Defraeye

“…Models trained on Nigeria-only or regional datasets outperformed those trained on global data, except for the multi-headed LSTM, which uniquely benefited from global samples when local data was unavailable. …”
Get full text

Equity in the cardiothoracic surgical workforce: addressing training gaps and workforce distribution in Africa– a narrative review by Samuel Mesfin Girma, Tsion Hiwot, Nanati Jemal Aliye, Elham Yimam, Ahmed K. Awad

Get full text

Secure Key Distribution Strategy in OFDM-PON by Utilizing the Redundancy of Training Symbol and Digital Chaos Technique by Shanshan Li, Mengfan Cheng, Lei Deng, Songnian Fu, Minming Zhang, Ming Tang, Ping Shum, Deming Liu

“…A secure key distribution scheme for orthogonal frequency division multiplexing (OFDM) passive optical network system is proposed and experimentally demonstrated. …”
Get full text

AI-Enabled Collaborative Distributed Computing in Networked UAVs by Bassem Mokhtar

“…Such architecture would help a suite of UAVs to train based on their local ML model and captured data and to collaborate with other UAVs in the same network to generate an aggregated ML model that improves the operation accuracy with acceptable performance speed. …”
Get full text

Defect detection in photolithographic patterns using deep learning models trained on synthetic data by Prashant P. Shinde, Priyadarshini P. Pai, Shashishekar P. Adiga, K. Subramanya Mayya, Yongbeom Seo, Myungsoo Hwang, Heeyoung Go, Changmin Park

Get full text

Adaptive Scattering Feature Awareness and Fusion for Limited Training Data SAR Target Recognition by Chenxi Zhao, Daochang Wang, Xianghui Zhang, Yuli Sun, Siqian Zhang, Gangyao Kuang

“…The physical laws inherent in ESF and azimuth angle guide the network training period, which improves feature discrimination and mitigates the pressure of data inadequacy. …”
Get full text

Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions by Liangjin Zhong, Huimin Lei, Jingjing Yang

“…While current deep learning (DL)‐related models trained on large data sets excel in spatial generalization, the direct applicability of these models in certain regions with unique hydrological processes can be challenging due to the limited representativeness within the training data set. …”
Get full text

Predicting indoor temperature distribution with low data dependency using recurrent neural networks by Jiahe Wang, Shohei Miyata, Keiichiro Taniguchi, Yasunori Akashi

“…This study proposes a prediction framework composed of two neural networks, enabling accurate indoor temperature distribution prediction with minimal training data in both temporal and spatial dimensions. …”
Get full text

Application of Remote Sensing Floodplain Vegetation Data in a Dynamic Roughness Distributed Runoff Model by Andre A. Fortes, Masakazu Hashimoto, Keiko Udo

“…The goal was to evaluate the applicability of remotely sensed vegetation data using the proposed method on a dynamic roughness distributed runoff model in the Abukuma River to assess the effect of vegetation on the typhoon Hagibis flood (12 October 2019). …”
Get full text

A Semantically Enhanced Label Prediction Method for Imbalanced POI Data Category Distribution by Hongwei Zhang, Qingyun Du, Shuai Zhang, Renfei Yang

“…Therefore, there is an urgent need to implement intelligent inference and enhancement processing for POI data labels. Conventional neural network models primarily target balanced data distribution, but they fail to address the issue of imbalanced distribution of POI data labels in terms of quantity. …”
Get full text

Dynamic display algorithm of sonar data based on grayscale distribution model and computational intelligence by Hongquan Lei, Diquan Li, Haidong Jiang

“…Multiple noise sources interfere with sonar signals, which affects both data precision and clarity. This article studies the dynamic display algorithm of sonar data based on grayscale distribution model and computational intelligence. …”
Get full text

A comparative framework to develop transferable species distribution models for animal telemetry data by Joshua A. Cullen, Camila Domit, Margaret M. Lamont, Christopher D. Marshall, Armando J. B. Santos, Christopher R. Sasso, Mehsin Al Ansi, Kristen M. Hart, Mariana M. P. B. Fuentes

“…SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. …”
Get full text

FLFT: A Large-Scale Pre-Training Model Distributed Fine-Tuning Method That Integrates Federated Learning Strategies by Yu Tao, Ruopeng Yang, Kaisheng Zeng, Changsheng Yin, Yiwei Lu, Wenxin Lu, Yongqi Shi, Bo Wang, Bo Huang

“…This paper proposes a distributed training strategy for large-scale pre-trained models that integrates federated learning strategies. …”
Get full text

Removing Instrumental Noise in Distributed Acoustic Sensing Data: A Comparison Between Two Deep Learning Approaches by Xihao Gu, Olivia Collet, Konstantin Tertyshnikov, Roman Pevzner

“…For the supervised learning (SL) approach, real DAS instrumental noise measured on an acoustically isolated coil is added to synthetic data to generate training pairs of clean/noisy data. …”
Get full text

Enhancing healthcare data privacy and interoperability with federated learning by Adil Akhmetov, Zohaib Latif, Benjamin Tyler, Adnan Yazici

Get full text