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When large amounts of data continuously arrive in streams, online updating is an effective way to reduce storage and computational burden. The key idea of online updating is that the previous estimators are sequentially updated only using the current data and some summary statistics of historical raw data. In this article, we develop a renewable learning method for a multiplicative regression model with streaming data, where the parameter estimator based on a least product relative error criterion is renewed without revisiting any historical raw data. Under some regularity conditions, we establish the consistency and asymptotic normality of the renewable estimator. Moreover, the theoretical results confirm that the proposed renewable estimator achieves the same asymptotic distribution as the least product relative error estimator with the entire dataset. Numerical studies and two real data examples are provided to evaluate the performance of our proposed method.

Additional file 5: Figure S1. TF analysis of RSGs in rump and longissimus lumborum cuts. Figure S2. The PPI network of RSGs. Figure S3. Amino acids and ion channel binding candidate gene expression profile. Figure S4. Protein processing candidate gene expression profile. Figure S5. Energy production and conversion candidate gene expression profile. Figure S6. Validation of the expression levels of candidate genes related to meat quality traits using RT-qPCR.

Additional file 8: Supplementary Table 1.

Additional file 5: Figure S1. TF analysis of RSGs in rump and longissimus lumborum cuts. Figure S2. The PPI network of RSGs. Figure S3. Amino acids and ion channel binding candidate gene expression profile. Figure S4. Protein processing candidate gene expression profile. Figure S5. Energy production and conversion candidate gene expression profile. Figure S6. Validation of the expression levels of candidate genes related to meat quality traits using RT-qPCR.

Additional file 9. Author checklist.

Additional file 9. Author checklist.

The RNA binding protein Lin28 is increased in most human malignancies, and elevated Lin28 is a biomarker for poor prognosis and contributes to cancer progression. Lin28 functions as a master oncogene and is involved in almost all hallmarks of cancer. In this review, we summarize the aberrant molecular expression mechanisms and pathological roles of Lin28 in cancer progression. Moreover, we elaborate on the established molecular mechanisms, from the transcriptional level to the post-transcriptional and translational levels, by which Lin28 regulates cancer progression.

This paper presents a hybrid approach for online fault detection in nonlinear processes. To solve the possible monitoring difficulties caused by nonlinear characteristics of industrial process data, two applications of the Kernel Method: Hypersphere Support Vector Machine (HSSVM) and Kernel Principal Component Analysis (KPCA) are used as fault detection methods. On top of that, to obtain the adaptive models for online monitoring and fault detection in unsteady-stage conditions, instead of the static ones established by traditional HSSVM and KPCA, multiple methods are adopted, including Recursive KPCA, Adaptive Control Limit (ACL) and Online Sequential Extreme Learning Machine (OS-ELM), all of which update the detection model in real time with dynamically adjusting. The T2 control limit of Recursive KPCA, the classification hyperspheres of HSSVM and the single hidden layer feedforward network (SLFN) trained with OS-ELM work collaboratively in monitoring the real time process data to detect the possible faults. The proposed approach was tested and validated via a set of experimental data collected from a bearing test rig. Experimental results show that this approach is adequate for fault detection while meets the needs of real time performance.

The failure rate of non-steady conditions is much higher than the failure rate of steady conditions. So, it is important to monitor non-steady conditions of system. The systems' monitoring results indicate that there are large false alarms or missing alarms based on traditional process control methods. The primary problems are higher data dimension, more complex correlation among variables, non-Gaussian distribution, the signal mutations and so on. Hence, this paper proposed a novel Longitudinal-standardization multi-period PCA fault detection strategy based on adaptive confidence limit (LMPCA-ACL) for periodic non-steady conditions. This LMPCA-ACL strategy comprises three helpful parts as follows. First one is to transform the non-Gaussian normal data into Gaussian data through a novel longitudinal-standardization (LS). The second part is to utilize the proposed multi-period PCA algorithm to reduce dimensions, remove correlation and improve the monitoring accuracy. The third part is to build the adaptive confidence limit to resolve the problems of signal mutations and real-time monitoring by the dynamic data window method. In this paper, the LMPCA-ACL strategy is applied to real-time monitor the motor cyclical process of loading and unloading. The examination results indicate that the LMPCA-ACL strategy is superior to other methods in fault detection if the system is under periodic non-steady conditions.

The multi-level inverter system is becoming a very promising candidate to replace the conventional two-level inverter, but system reliability remains an open issue. The most common reliability problem is that power switch transistors have open-circuit or short-circuit faults during operation. In order to improve the accuracy of the fault diagnosis and accelerate the operation speed in a cascaded H-bridge multilevel inverter system, a novel fault diagnosis strategy based on principle component analysis and multiclass relevance vector machine (PCA-mRVM) is presented in this paper. In this strategy, the output voltage of CHMLIS, which is preprocessed through the fast Fourier transform, is used to identify the type and location of occurring fault through the mRVM model. Then PCA is utilized to reduce input sample's dimension, which decrease the training time of diagnostic model. The PCA-mRVM strategy could not only achieve higher model sparsity and shorter diagnosis time, but also provide probabilistic outputs for every class membership. Hardware experimental results of simple-fault have shown that the PCA-mRVM could achieve the best diagnosis performance than traditional fault diagnosis methods. In addition, simulation results of complicated-fault have further validated that the PCA-mRVM strategy is useful in multi-fault diagnosis, which is better than other methods.

The traditional mathematical modeling is nonrepresentational and it is hard for understanding. In oder to model the real system in a intuitive method, a novel Local BP Neural Network (LBPNN) model has been proposed to imitate arbitrary feed-forward topologies of networks and the weights' training algorithm-constrained stochastic gradient descent (CSGD) is also introduced in this paper. The network model could be used to approach functions which could improve the training speed and reduce the training complexity. With the LBPNN model and the CSGD training algorithm, network's parameter of weight could be identified within constrains. The training algorithm's robustness and effectiveness are verified in the LBPNN. Finally, the LBPNN model is used to map the fuzzy petri net(FPN) of a power system and the parameters of weights in the FPN are identified by training the LBPNN model with the CSGD algorithm.

In order to improve the accuracy of the fault diagnosis and accelerate the operation speed in a cascaded H-bridge multilevel inverter system (CHMLIS), a fault diagnosis strategy based on Relative Principle Component Analysis-Support Vector Machine (RPCA-SVM) is presented in this paper. In this strategy, the output voltage of CHMLIS, which is preprocessed through the fast Fourier transform (FFT), is used to identify the type and location of occurring fault through a SVM model. Then RPCA is utilized to reduce input sample's dimension. A lower dimensional input sample will reduce the time necessary to train the SVM model, and the reduced noise may improve the mapping performance. Compared with other traditional fault diagnosis methods, the proposed strategy has much higher computing efficiency and diagnosis accuracy in fault diagnosis. Simulation results and experimental results have validated the RPCA-SVM fault diagnosis strategy in CHMLIS.

The principal component analysis method is usually used for fault detection under the steady conditions, however, when system works under the non-steady conditions, the false alarm rate and the missing alarm rate, tested by the T2 control limit, are so high. The main reason for this situation is that the sampled data is accord with normal distribution under the steady conditions, whereas the data does not satisfy normal distribution under the non-steady conditions, but T2 control limit can only detect fault effectively for the data conforming to normal distribution. So this paper firstly analyzes the characteristics of the periodic non-steady conditions and then puts forward a normalization PCA (NPCA) model according to the precondition of effective detection under the T2 control limit. This model deals with the measured data for normalization data based on the longitudinal standardization, and then uses T2 control limit to detect fault. At the end, it is applied to wind power generation system, and the results verify the effectiveness of the model.

The principal component analysis method is usually used for fault detection under the steady conditions, however, when system works under the non-steady conditions, the false alarm rate and the missing alarm rate, tested by the T2 control limit, are so high. The main reason for this situation is that the sampled data is accord with normal distribution under the steady conditions, whereas the data mostly does not satisfy normal distribution under the non-steady conditions, but T2 control limit can only detection fault effectively for the data conforming to normal distribution. So this paper firstly analyzes two characteristics of the periodic non-steady conditions and mathematical theory and Q-Q figures have verified the characteristics, then a normalization PCA (NPCA) is proposed. Finally it is applied to the system of the motor cyclical process and alleviating the load, the test result is good and can verify the effectiveness of the model.

This paper presents a multi-dimension predictive model PDRNN based on the diagonal recurrent neural networks with a parallel learning algorithm. This model can be used to predict not only values, but also some points in the multi-dimension space. And also its applications in multi-dimension prediction will be discussed in the paper. Some analysis results show the significant improvement to multi parameters simultaneous prediction using the PDRNN algorithm.

Clustering analysis is an important approach of data mining. This paper presents an ANN-based clustering analysis algorithm with dynamic data window (DDW). Comparing with k-means algorithm merged in density-based and integrated clustering analysis algorithm, the new clustering analysis algorithm based on artificial neural networks and combining with DDW has more valuable in data mining. This algorithm can immensely avoid the effect on accumulation points from boundary points, and can automatically find representative accumulation points in all kings of shapes. Furthermore its applications in CIS will be discussed in the paper. Some analysis results show the significant improvement to ship-routing design with the clustering analysis algorithm based on ANN and DDW in database of CIS.

This paper presents a new multi-dimension predictive model based on the diagonal recurrent neural networks (PDRNN) with a parallel learning algorithm. This method can be used to predict not only values, but also some points in the multi-dimension space. And also its applications in data mining are discussed in the paper. Some analysis results show the significant improvement to ship route prediction using the PDRNN algorithm in database of geographic information system (CIS).

By studying of DFT, some interesting phenomena relating to special frequency of sinusoidal signal that are similar to the beat phenomenon in physical vibration theory are found. In this paper, the characteristics of its spectrogram are described in detail, and we have analyzed the reasons that cause this kind of phenomenon by the angle of phase difference.

We present the standardized parallel sorting algorithm. Instead of the most basic CCI (compare and conditional interchange) operation in traditional sorting algorithms, this algorithm compares sorting data with a standard value sequence simultaneously and synchronously on an SIMD array processor. The algorithm possesses three significant advantages. First, the running time T is independent of the problem size, T=0(1). The second attractive feature of the algorithm is that it is not necessary to build a communication network between PEs. Finally, no bottleneck occurs in output. We have implemented the algorithm in an SIMD array processor that is designed by our research group. The experiment verifies all the theoretical predictions. For these merits the algorithm is of significance in both theory and practice. >

In this paper, a parallel coordinative visual model-the revised plate parallel retrieval model (Wang Tianzhen et al, 1994) is presented based on the analysis of the global effect of Chinese characters and the recent neurobiological research on the function of neuroglia in learning and memory. The theory assumes that visual neurons possess the function of memory and retrieval in addition to the commonly recognised function of signal transformation. It supposes all the pixels in the array of a Chinese character are encoded, stored and retrieved simultaneously and synchronously by each neuron separately. In the model, neuroglia cells play an important role in the integration of the visual signal. The model cannot only satisfactorily explain both the global effect and strokes effect, but can also explain the implicit memory of Chinese characters as well. We have experimented on an SIMD array processor to prove the viability of the PPR algorithm. The results are satisfactory. The model can be extended to general shape vision with some conditions.