Zinc ore supplier evaluation and recommendation method based on nonlinear adaptive online transfer learning

<p style='text-indent:20px;'>Purchasing decisions determine the purchasing cost, which is the largest section of the production cost of zinc smelting enterprise(ZSE). An excellent supplier recommendation is significant for ZSE to reduce the cost. However, during the supplier recommendation process, the nonlinear demand feature of purchasing department varies with the production environment, and there are wrong samples that can affect the supplier recommendation effect. To handle these problems, the recommendation strategy based on a multiple-layer perceptron adaptive online transfer learning algorithm(AOTLMLP) are proposed. In this method, the original prediction function is modified based on MLP nonlinear projective function and adaptive loss function, which enables the AOTLMLP algorithm to tackle the nonlinear classification problems and efficiently follow the demand change of purchasing department, thereby improving the result of the recommendation. The performance of the AOTLMO algorithm is evaluated through a common dataset and a purchasing dataset from a zinc smelter that generated by a supplier evaluation model. It can be assumed that AOTLMLP can ignore the influence of wrong samples and provide an effective recommendation confronting the characteristic of zinc ore purchasing.</p>

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Retraction Note to: Mobile network intrusion detection for IoT system based on transfer learning algorithm

Cluster Computing ◽

10.1007/s10586-021-03251-1 ◽

2021 ◽

Vol 24 (1) ◽

pp. 589-589

Author(s):

Lianbing Deng ◽

Daming Li ◽

Xiang Yao ◽

Haoxiang Wang

Keyword(s):

Intrusion Detection ◽

Transfer Learning ◽

Learning Algorithm ◽

Mobile Network ◽

Network Intrusion Detection ◽

Network Intrusion

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Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

Neural Computing and Applications ◽

10.1007/s00521-021-05741-0 ◽

2021 ◽

Author(s):

Yongming Li ◽

Xinyue Zhang ◽

Pin Wang ◽

Xiaoheng Zhang ◽

Yuchuan Liu

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Transfer Learning ◽

Learning Algorithm ◽

Insight Into

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Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽

10.3390/s21030885 ◽

2021 ◽

Vol 21 (3) ◽

pp. 885 ◽

Cited By ~ 1

Author(s):

Zhongzheng Fu ◽

Xinrun He ◽

Enkai Wang ◽

Jun Huo ◽

Jian Huang ◽

...

Keyword(s):

Transfer Learning ◽

Activity Recognition ◽

Human Activity ◽

Sensor Node ◽

Learning Algorithm ◽

Joint Probability ◽

Human Activity Recognition ◽

Sensor Technology ◽

Measurement Unit ◽

Wearable Sensor

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

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Event detection of different English data sources based on transfer learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189798 ◽

2021 ◽

pp. 1-11

Author(s):

Yanan Huang ◽

Yuji Miao ◽

Zhenjing Da

Keyword(s):

Transfer Learning ◽

Event Detection ◽

Visual Analysis ◽

Learning Algorithm ◽

Data Sources ◽

Data Set ◽

Data Source ◽

Single Data Source ◽

The Difference ◽

Single Data

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.

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Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors

Journal of Marine Science and Engineering ◽

10.3390/jmse9010047 ◽

2021 ◽

Vol 9 (1) ◽

pp. 47

Author(s):

Magnus Gribbestad ◽

Muhammad Umair Hassan ◽

Ibrahim A. Hameed

Keyword(s):

Transfer Learning ◽

Data Transfer ◽

Health Management ◽

Correct Diagnosis ◽

Vital Role ◽

Research Field ◽

Remaining Useful Life ◽

Prognostic Models ◽

Classification Problems ◽

Air Compressors

Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. Due to the requirements of system safety and reliability, the correct diagnosis or prognosis of abnormal condition plays a vital role in the maintenance of industrial systems. It is expected that new requirements in regard to autonomous ships will push suppliers of maritime equipment to provide more insight into the conditions of their systems. One of the stated challenges with these systems is having enough run-to-failure examples to build accurate-enough prognostic models. Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. In this paper, various deep learning models are used to predict the remaining useful life (RUL) of air compressors. Here, transfer learning is applied by building a separate prognostics model trained on turbofan engines. It has been found that several of the explored transfer learning architectures were able to improve the predictions on air compressors. The research results suggest transfer learning as a promising research field towards more accurate and reliable prognostics.

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Transfer Learning algorithm in image analysis with Augmented Reality headset for Industry 4.0 technology

2020 International Conference Mechatronic Systems and Materials (MSM) ◽

10.1109/msm49833.2020.9201739 ◽

2020 ◽

Author(s):

Mateusz Kozek

Keyword(s):

Image Analysis ◽

Augmented Reality ◽

Transfer Learning ◽

Industry 4.0 ◽

Learning Algorithm

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Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA

Algorithms ◽

10.3390/a14010018 ◽

2021 ◽

Vol 14 (1) ◽

pp. 18

Author(s):

Michael Li ◽

Santoso Wibowo ◽

Wei Li ◽

Lily D. Li

Keyword(s):

Data Analysis ◽

Conceptual Framework ◽

Spectral Data ◽

Learning Algorithm ◽

Structural Parameters ◽

Inverse Function ◽

Principal Component ◽

Learning Task ◽

Classification Problems ◽

Spectral Data Analysis

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.

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Exploring the Uncertainty Space of Ensemble Classifiers in Face Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415560029 ◽

2015 ◽

Vol 29 (03) ◽

pp. 1556002 ◽

Cited By ~ 7

Author(s):

Juan Luis Fernández-Martínez ◽

Ana Cernea

Keyword(s):

Face Recognition ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Majority Voting ◽

Borda Count ◽

Discrete Wavelet ◽

Final Decision ◽

Ensemble Classifiers ◽

Classification Problems ◽

Uncertainty Space

In this paper, we present a supervised ensemble learning algorithm, called SCAV1, and its application to face recognition. This algorithm exploits the uncertainty space of the ensemble classifiers. Its design includes six different nearest-neighbor (NN) classifiers that are based on different and diverse image attributes: histogram, variogram, texture analysis, edges, bidimensional discrete wavelet transform and Zernike moments. In this approach each attribute, together with its corresponding type of the analysis (local or global), and the distance criterion (p-norm) induces a different individual NN classifier. The ensemble classifier SCAV1 depends on a set of parameters: the number of candidate images used by each individual method to perform the final classification and the individual weights given to each individual classifier. SCAV1 parameters are optimized/sampled using a supervised approach via the regressive particle swarm optimization algorithm (RR-PSO). The final classifier exploits the uncertainty space of SCAV1 and uses majority voting (Borda Count) as a final decision rule. We show the application of this algorithm to the ORL and PUT image databases, obtaining very high and stable accuracies (100% median accuracy and almost null interquartile range). In conclusion, exploring the uncertainty space of ensemble classifiers provides optimum results and seems to be the appropriate strategy to adopt for face recognition and other classification problems.

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