Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protection

The multilevel per cell technology and continued scaling down process technology significantly improves the storage density of NAND flash memory but also brings about a challenge in that data reliability degrades due to the serious noise. To ensure the data reliability, many noise mitigation technologies have been proposed. However, they only mitigate one of the noises of the NAND flash memory channel. In this paper, we consider all the main noises and present a novel neural network-assisted error correction (ANNAEC) scheme to increase the reliability of multi-level cell (MLC) NAND flash memory. To avoid using retention time as an input parameter of the neural network, we propose a relative log-likelihood ratio (LLR) to estimate the actual LLR. Then, we transform the bit detection into a clustering problem and propose to employ a neural network to learn the error characteristics of the NAND flash memory channel. Therefore, the trained neural network has optimized performances of bit error detection. Simulation results show that our proposed scheme can significantly improve the performance of the bit error detection and increase the endurance of NAND flash memory.

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An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210509 ◽

2021 ◽

pp. 1-11

Author(s):

Yuan Zou ◽

Daoli Yang ◽

Yuchen Pan

Keyword(s):

Neural Network ◽

Markov Chain ◽

Gross Domestic Product ◽

Error Correction ◽

Historical Data ◽

Markov Chain Model ◽

Back Propagation ◽

The United States ◽

Domestic Product ◽

Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

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Determination of quantum toric error correction code threshold using convolutional neural network decoders

Chinese Physics B ◽

10.1088/1674-1056/ac11e3 ◽

2021 ◽

Author(s):

Hao-Wen Wang ◽

Yun-Jia Xue ◽

Yu-Lin Ma ◽

Nan Hua ◽

Hong-Yang Ma

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Error Correction ◽

Error Correction Code

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Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

Computational Intelligence and Neuroscience ◽

10.1155/2021/1026978 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Shaobo Lu

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Error Correction ◽

Relative Error ◽

Bp Neural Network ◽

Arima Model ◽

Forecast Model ◽

Price Prediction ◽

The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

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Multi-path Error Correction Method for Slope Monitoring Based on BP Neural Network

10.1007/978-981-16-3138-2_11 ◽

2021 ◽

pp. 101-110

Author(s):

Mengfei Lei ◽

Xiaodong Liang ◽

Jinyi Tang ◽

Junhua Zhou

Keyword(s):

Neural Network ◽

Error Correction ◽

Bp Neural Network ◽

Correction Method ◽

Error Correction Method ◽

Slope Monitoring

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Decoding surface code with a distributed neural network–based decoder

Quantum Machine Intelligence ◽

10.1007/s42484-020-00015-9 ◽

2020 ◽

Vol 2 (1) ◽

Cited By ~ 2

Author(s):

Savvas Varsamopoulos ◽

Koen Bertels ◽

Carmen G. Almudever

Keyword(s):

Neural Network ◽

Renormalization Group ◽

Error Correction ◽

Quantum Error Correction ◽

Noise Model ◽

Error Correction Codes ◽

Exponential Increase ◽

Main Challenge ◽

Surface Code ◽

Quantum Error

Abstract There has been a rise in decoding quantum error correction codes with neural network–based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network–based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the renormalization group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network–based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the blossom algorithm and a neural network–based decoder. We show that a similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network–based decoders.

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Integration of Convolutional Neural Network and Error Correction for Indoor Positioning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020074 ◽

2020 ◽

Vol 9 (2) ◽

pp. 74

Author(s):

Eric Hsueh-Chan Lu ◽

Jing-Mei Ciou

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Error Correction ◽

Spatial Information ◽

Rapid Development ◽

Indoor Positioning ◽

Experimental Results ◽

Global Navigation Satellite Systems ◽

Signal Interference ◽

Position Vector

With the rapid development of surveying and spatial information technologies, more and more attention has been given to positioning. In outdoor environments, people can easily obtain positioning services through global navigation satellite systems (GNSS). In indoor environments, the GNSS signal is often lost, while other positioning problems, such as dead reckoning and wireless signals, will face accumulated errors and signal interference. Therefore, this research uses images to realize a positioning service. The main concept of this work is to establish a model for an indoor field image and its coordinate information and to judge its position by image eigenvalue matching. Based on the architecture of PoseNet, the image is input into a 23-layer convolutional neural network according to various sizes to train end-to-end location identification tasks, and the three-dimensional position vector of the camera is regressed. The experimental data are taken from the underground parking lot and the Palace Museum. The preliminary experimental results show that this new method designed by us can effectively improve the accuracy of indoor positioning by about 20% to 30%. In addition, this paper also discusses other architectures, field sizes, camera parameters, and error corrections for this neural network system. The preliminary experimental results show that the angle error correction method designed by us can effectively improve positioning by about 20%.

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