- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2019 Silicon Nanoelectronics Workshop (SNW) - Kyoto, Japan (2019.6.9-2019.6.10)] 2019 Silicon Nanoelectronics Workshop (SNW) - Error Crrection for Read-hot Data in 3D-TLC NAND Flash by Read-disturb Modeled Artificial Neural Network Coupled LDPC ECC
摘要： Read-disturb Modeled Artificial Neural Network Coupled LDPC ECC (RDNN-LDPC) is proposed to correct errors of read-hot data for 3D-TLC NAND flash. Conventional ANN-LDPC is optimized to correct errors of read-cold data. However, ANN-LDPC does not correct errors of read-hot data. To correct errors of read-hot data, this paper analyzes how input parameter and model change. As a result, measured results of proposed RDNN-LDPC extend acceptable read cycle of 3D-TLC NAND flash by 10-times.
关键词： LDPC ECC,Artificial Neural Network,Error correction,Read-disturb,3D-TLC NAND flash
更新于2025-09-11 14:15:04
-
[IEEE 2019 54th International Universities Power Engineering Conference (UPEC) - Bucharest, Romania (2019.9.3-2019.9.6)] 2019 54th International Universities Power Engineering Conference (UPEC) - Design of an intelligent MPPT based on ANN using a real photovoltaic system data
摘要： Maximum power point tracking (MPPT) methods are a fundamental part in photovoltaic (PV) system design for increasing the generated power of a PV array. Whilst several methods have been introduced, the artificial neural network (ANN) is an attractive method for MPPT due to its less oscillation and fast response. However, accurate training data is a big challenge to design an optimized ANN-MPPT technique. In this paper, an ANN-MPPT technique based on a large experimental training data is proposed to avoid the system from having a high training error. Those data are collected during one year from experimental tests of a PV system installed at Brunel University, London, United Kingdom. The irradiation and temperature of weather conditions are selected as the input, and the available power at MPP from the PV system as the output of the ANN model. To assess the performance, the Perturb and Observe (P&O) and the proposed ANN-MPPT methods are simulated using a MATLAB/Simulink model for the PV system. The results show that the proposed ANN method accurately tracks the optimal maximum power point and avoids the phenomenon of drift problem, whilst achieving a higher output power when compared with P&O-MPPT method.
关键词： photovoltaic (PV),Perturb and Observe (P&O),Artificial Neural Network (ANN),Maximum power point tracking (MPPT)
更新于2025-09-11 14:15:04
-
[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Artificial Neural Network Equalizers for PAM-4 using DML-on-Silicon
摘要： Nonlinear equalizers (NLEs) based on artificial neural networks (ANNs) are studied for short-reach 4-level pulse amplitude modulation (PAM-4) transmissions utilizing directly-modulated membrane lasers fabricated on silicon substrates (DMLs-on-Si). Using 28-GBaud signals over 2-km of single-mode fiber (SMF), we compare two ANN-NLEs with a previously developed reduced-complexity Volterra-NLE (VNLE).
关键词： Optical Transmission,DML-on-Silicon,Nonlinear Equalizers,PAM-4,Artificial Neural Network
更新于2025-09-11 14:15:04
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Vegetation Water Content Estimation for Corn by Means of Inverse Modeling from Simulations of the First-Order Scattering Model
摘要： Vegetation water content (VWC) is a key variable in land-atmosphere interactions and plays an important role in agriculture, climate and hydrology. Based on the first-order scattering model, simulation database of corn backscattering coefficients at L-band was established. The simulations were used to train an artificial neural network (ANN) to establish an inverse model for corn VWC estimation during corn growth periods. The inverse accuracy of the trained ANN was evaluated using ground corn samplings and radar data acquired by the Passive and Active L- and S-band (PALS) airborne microwave sensor during the Soil Moisture Experiments in 2002 (SMEX02). Moreover, the corn VWC inversion results were compared to those obtained from an empirical method using the radar vegetation index (RVI). Result showed that the ANN method is superior to the RVI method and capable of estimating corn VWC with a correlation coefficient (R) of 0.7987, a root mean square error (RMSE) of 0.6033 kg/m2 and a mean absolute relative error (MARE) of 12.00%.
关键词： artificial neural network,corn,active microwave remote sensing,first-order scattering model,vegetation water content
更新于2025-09-10 09:29:36
-
Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision
摘要： This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.
关键词： feature selection,machine vision,fish swarm intelligent,chips drying,artificial neural network
更新于2025-09-10 09:29:36
-
Identification of Diabetic Maculopathy Stages using Fundus Images
摘要： The impairment and blindness are caused due to Diabetes mellitus. Most of sick person are suffering with impairment diabetes and fifty to sixty percent of ill persons are suffering due to blindness diabetes. Present paper concentrated the identification process of diabetes mellitus using a computer-based intelligent system. The method consists of non-clinically and clinically significant maculopathy and usual eye images. With the help of morphological image processing techniques the fundus are extracted from the usual eye images. Later for comparison the extracted data is applied to classifiers named as (i) feed-forward ANN and (ii) (PNN) probabilistic neural network.
关键词： Non-CSME image,Non-clinically maculopathy,CSME image,Clinically maculopathy,Artificial Neural Network
更新于2025-09-10 09:29:36
-
[Advances in Intelligent Systems and Computing] Soft Computing for Problem Solving Volume 817 (SocProS 2017, Volume 2) || Image Compression Using Neural Network for Biomedical Applications
摘要： As images are of large size and require huge bandwidth and large storage space, an effective compression algorithm is essential. Hence in this paper, feedforward backpropagation neural network with the multilayer perception using resilient backpropagation (RP) algorithm is used with the objective to develop an image compression in the field of biomedical sciences. With the concept of neural network, data compression can be achieved by producing an internal data representation. This network is an application of backpropagation that takes huge content of data as input, compresses it while storing or transmitting, and decompresses the compressed data whenever required. The training algorithm and development architecture give less distortion and considerable compression ratio and also keep up the capability of hypothesizing and are becoming important. The efficiency of the RP is evaluated on x-ray image of rib cage and has given better results of the various performance metrics when compared to the other algorithms.
关键词： Artificial neural network,Backpropagation neural network,Gradient descent algorithm (GD),Resilient backpropagation algorithm (RP),Image compression
更新于2025-09-09 09:28:46
-
[IEEE 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - Odessa (2018.9.4-2018.9.7)] 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - Application of UWB Electromagnetic Waves for Subsurface Object Location Classification by Artificial Neural Networks
摘要： The problem of determination of object position in a plane is solved by the analysis of ultrawideband electromagnetic wave reflected from the subsurface object. The model of ground containing perfectly conducting object inside is irradiated by short impulse wave with Gaussian time dependence. The direct problem is solved by FDTD method to receive a time dependence of reflected wave amplitude. To recognize the presence of the object and depths of its position the multilayer artificial neural networks (ANN) is used. The amplitudes of electric component of the reflected field in different time and special points above the ground surface are the input data for multilayer ANN of different structures. The work of the trained ANN is verified for arbitrary depths of object position.
关键词： artificial neural network,object recognition,subsurface radar,impulse electromagnetic wave
更新于2025-09-09 09:28:46
-
Comparison of Artificial Intelligence and Physical Models for Forecasting Photosynthetically-Active Radiation
摘要： Different kinds of radiative transfer models, including a relative sunshine-based model (BBM), a physical-based model for tropical environment (PBM), an efficient physical-based model (EPP), a look-up-table-based model (LUT), and six artificial intelligence models (AI) were introduced for modeling the daily photosynthetically-active radiation (PAR, solar radiation at 400–700 nm), using ground observations at twenty-nine stations, in different climatic zones and terrain features, over mainland China. The climate and terrain effects on the PAR estimates from the different PAR models have been quantitatively analyzed. The results showed that the Genetic model had overwhelmingly higher accuracy than the other models, with the lowest root mean square error (RMSE = 0.5 MJ m?2day?1), lowest mean absolute bias error (MAE = 0.326 MJ m?2day?1), and highest correlation coefficient (R = 0.972), respectively. The spatial–temporal variations of the annual mean PAR (APAR), in the different climate zones and terrains over mainland China, were further investigated, using the Genetic model; the PAR values in China were generally higher in summer than those in the other seasons. The Qinghai Tibetan Plateau had always been the area with the highest APAR (8.668 MJ m?2day?1), and the Sichuan Basin had always been the area with lowest APAR (4.733 MJ m?2day?1). The PAR datasets generated by the Genetic model, in this study, could be used in numerous PAR applications, with high accuracy.
关键词： photosynthetically-active radiation,climate zones,physical models,artificial neural network,terrain features
更新于2025-09-09 09:28:46
-
A novel and fast technique for evaluation of plastic rod scintillators as position sensitive gamma-ray detectors using artificial neural networks
摘要： The problem of how to precisely estimate the radiation interaction position is an important parameter in medical and industrial imaging systems. This paper presents a new methodology for prediction of the incident position of the gamma rays based attenuation technique and multilayer perceptron (MLP) neural network system. The detection system is comprised of a gamma-ray source (60Co or 137Cs) and a plastic rod scintillator (BC400) coupled with just one PMT at one side. The experimental setup provides the required data for training and testing the network. Using this proposed method, the radiation interaction position was predicted in plastic rod scintillator with a mean absolute error less than 0.8 and 0.5 for 137Cs and 60Co sources, respectively. The mean relative error percentage was calculated less than 3.9% and 3.5%. Also, the correlation coefficient was measured 0.999 and 0.998, respectively. The results showed that the predicted interaction position using the ANN method is in good agreement with real data. The techniques and set up used in the previous position sensitive detectors were fairly complicated whereas the new set-up and proposed method are really very simple. Also, the radiation safety, cost and shielding, and electronics requirements are minimized and optimized.
关键词： Plastic rod scintillator,Radiation interaction position,Artificial Neural Network,Position sensitive detector
更新于2025-09-09 09:28:46