Results 51 to 60 of about 60,295 (249)

Application of Quantum Genetic Optimization of LVQ Neural Network in Smart City Traffic Network Prediction

IEEE Access, 2020
Accurate prediction of traffic flow in urban networks is of great significance for smart city management. A short-term traffic flow prediction algorithm of Quantum Genetic Algorithm - Learning Vector Quantization (QGA-LVQ) neural network is proposed to ...
Fuquan Zhang, Tsu-Yang Wu, Yiou Wang, Rui Xiong, Gangyi Ding, Peng Mei, Laiyang Liu   +6 more
doaj   +1 more source

In Situ Quantization with Memory‐Transistor Transfer Unit Based on Electrochemical Random‐Access Memory for Edge Applications

Advanced Science, EarlyView.
By combining ionic nonvolatile memories and transistors, this work proposes a compact synaptic unit to enable low‐precision neural network training. The design supports in situ weight quantization without extra programming and achieves accuracy comparable to ideal methods. This work obtains energy consumption advantage of 25.51× (ECRAM) and 4.84× (RRAM)
Zhen Yang, Yuxiang Yang, Baiqian Wang, Yaoyu Tao, Zelun Pan, Lei Cai, Teng Zhang, Longhao Yan, Xianbin Li, Yuchao Yang   +9 more
wiley   +1 more source

MEASUREMENT OF CLASSIFICATION PERFORMANCE WITH THE LEARNING VECTOR QUANTIZATION METHOD ON COVID-19 VACCINATION DATA AT THE PARUMPANAI HEALTH CENTER

Jurnal Matematika UNAND
In the midst of the COVID-19 pandemic, various countries are always trying their best to restore global stability. One effective way is the discovery of several vaccines to prevent transmission of the virus.
ADHIYAKSA PRANANDA, Siswanto Siswanto, Sri Astuti Thamrin, A. Muh. Amil Siddik   +3 more
doaj   +1 more source

Divergence-based classification in learning vector quantization [PDF]

Neurocomputing, 2011
We discuss the use of divergences in dissimilarity-based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular, we introduce and study divergence based learning vector quantization (DLVQ). We derive cost
Mwebaze, E., Schneider, P., Schleif, Frank-Michael, Aduwo, J.R., Quinn, J.A., Haase, S., Villmann, T., Biehl, M.   +7 more
openaire   +2 more sources

Hierarchical Reconfigurable Metasurface Based on Scenario‐Guided Functional Modules and Programmable Core

Advanced Science, EarlyView.
This paper proposes a hierarchical reconfigurable metasurface architecture (HRMA) to achieve comprehensive electromagnetic parameter modulation and on‐demand polymorphic function switching. The reconfigurable metasurface is divided into a programmable core (PC) and scenario‐guided functional modules (FMs), exhibiting significant flexibility and ...
Lihao Zhu, Jiaqi Han, Zhe Zheng, Qiang Feng, Dexiao Xia, Xiangjin Ma, Yajie Mu, Guoliang Luo, Rui Li, Haixia Liu, Hao Xue, Long Li   +11 more
wiley   +1 more source

Intelligent design of network multimedia using big data and virtual Artificial Intelligence technology

IET Networks, EarlyView., 2023
The topic discussed was the intelligent design of network multimedia using BD and virtual AI technology. First of all, the authors gave a brief overview of its relevant research background, and then comprehensively analysed the advantages and disadvantages of previous scholars' research on network multimedia.
Xin Zhang
wiley   +1 more source

Learning a Deep Vector Quantization Network for Image Compression

IEEE Access, 2019
Deep convolutional neural network (DCNN) based image codecs, consisting of encoder, quantizer and decoder, have achieved promising image compression results. The major challenge in learning these DCNN models lies in the joint optimization of the encoder,
Xiaotong Lu, Heng Wang, Weisheng Dong, Fangfang Wu, Zhonglong Zheng, Guangming Shi   +5 more
doaj   +1 more source

Fast Quantization of Stochastic Volatility Models

, 2017
Recursive Marginal Quantization (RMQ) allows fast approximation of solutions to stochastic differential equations in one-dimension. When applied to two factor models, RMQ is inefficient due to the fact that the optimization problem is usually performed ...
Kienitz, Joerg, McWalter, Thomas A., Platen, Eckhard, Rudd, Ralph   +3 more
core   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Klasifikasi Stroke Berdasarkan Kelainan Patologis dengan Learning Vector Quantization

Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), 2014
Dampak yang ditimbulkan stroke diantaranya kelumpuhan sebagian atau keseluruhan organ tubuh sampai kematian. Tingginya angka kematian akibat stroke disebabkan karena penanganan yang lambat.
Aji Seto Arifianto, Moechammad Sarosa, Onny Setyawati   +2 more
doaj   +1 more source