Results 261 to 270 of about 559,125 (302)

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Laser‐Induced Graphene from Waste Almond Shells

Advanced Functional Materials, EarlyView.
Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova, Anna Chiara Bressi, Marina Galliani, Attilio Marino, Gianni Ciofani, Eduardo Machado‐Charry, Hilda Gomez Bernal, Alessandra Francini, Luca Sebastiani, Francesco Greco   +9 more
wiley   +1 more source

Slight Truncation Changes in Iron Oxide Nanocubes Strongly Affect Their Magnetic Properties

Advanced Functional Materials, EarlyView.
Subtle variations in nanoparticle morphology can lead to significant changes in functional properties. An automated shape‐fitting method captures minor differences in corner truncation between iron oxide nanocubes of similar sizes synthesized under identical conditions, revealing pronounced disparities in their magnetic and hyperthermia behavior ...
Kingsley Poon, Raúl López‐Martín, Verner Håkonsen, Nader Yaacoub, Francisco J. Terán, Alberto López Ortega, Jose A. De Toro, Gurvinder Singh   +7 more
wiley   +1 more source

K Means Algorithm

, 2020
This chapter is concerned with the k means algorithm as the most popular clustering algorithm. This chapter begins with the unsupervised version of the KNN algorithm. With respect to the clustering process, we study the two main versions of the k means algorithm: the crisp k means algorithm and the fuzzy k means algorithm.
Achmad Thoriq, Luh Putu Gayatri Wirastuti, Deanda Bevani Aletha (8517924), nuraini rakhmawati (8517915), Achmad Thoriq (8517918), Deanda Bevani Aletha, Alexander Sidum Laka Kaki (8517927), nuraini rakhmawati, Luh Putu Gayatri Wirastuti (8517921), Alexander Sidum Laka Kaki   +9 more
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A Modified K-Means Algorithm - Two-Layer K-Means Algorithm

2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014
In this paper, a modified K-means algorithm is proposed to categorize a set of data. K-means algorithm is a simple and easy clustering method which can efficiently classify a large number of continuous numerical data of high-dimensions. Moreover, the data in each cluster are similar to one another.
Chen-Chung Liu, Shao Wei Chu, Yung-Kuan Chan, Shyr-Shen Yu   +3 more
openaire   +1 more source

Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm

2010 Third International Symposium on Intelligent Information Technology and Security Informatics, 2010
Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the ...
Na Shi, Xumin Liu, Yong Guan
openaire   +1 more source

K*-Means: An Effective and Efficient K-Means Clustering Algorithm

2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), 2016
K-means is a widely used clustering algorithm in field of data mining across different disciplines in the past fifty years. However, k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering.
Jianpeng Qi, Yanwei Yu, Lihong Wang, Jinglei Liu   +3 more
openaire   +1 more source

Implementation of K-means algorithm on FGGA

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
K-means algorithm is one of the clustering algorithms that increase in popularity day by day. The intensive mathematical operations and the continuous increase of the data size while clustering on large data using the K-means algorithm prevent the algorithm from operating at high performance.
ŞAHİN, SUHAP, Turkoglu, Bahadir, ALTUNCU, MEHMET ALİ, Cavuslu, Mehmet Ali   +3 more
openaire   +2 more sources

Noise identification with the k-means algorithm

16th IEEE International Conference on Tools with Artificial Intelligence, 2005
The presence of noise in a measurement dataset can have a negative effect on the classification model built. More specifically, the noisy instances in the dataset can adversely affect the learnt hypothesis. Removal of noisy instances will improve the learnt hypothesis; thus, improving the classification accuracy of the model.
Wei Tang, Taghi M. Khoshgoftaar
openaire   +1 more source

Capped Robust K-means Algorithm

2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017
K-means algorithm is a classical algorithm and has been widely used in many applications. However, the traditional K-means algorithm is easily influenced by outliers and it usually obtains an unstable clustering result and poor clustering accuracy. In this paper, aiming at K-means algorithm resistant to outliers, we proposed a Capped Robust K-means ...
Ting Zhang, Fang Yuan, Liu Yang
openaire   +1 more source