Within the cell, several mechanisms exist to maintain homeostasis of the endoplasmic reticulum （ER）. One of the primary mechanisms is the unfolded protein response （UPR）. In this review, we primarily focus on the ...Within the cell, several mechanisms exist to maintain homeostasis of the endoplasmic reticulum （ER）. One of the primary mechanisms is the unfolded protein response （UPR）. In this review, we primarily focus on the latest signal webs and regulation mechanisms of the UPR. The relationships among ER stress, apoptosis, and cancer are also discussed. Under the normal state, binding immunoglobulin protein （BiP） interacts with the three sensors （protein kinase RNA-like ER kinase （PERK）, activating transcription factor 6 （ATF6）, and inositol-requiring enzyme la （IREla）） Under ER stress, misfolded proteins interact with BiP, resulting in the release of BiP from the sensors. Subsequently, the three sensors dimerize and autophosphorylate to promote the signal cascades of ER stress. ER stress includes a series of positive and negative feedback signals, such as those regulating the stabilization of the sensors/BiP complex, activating and inactivating the sensors by autophosphorylation and dephosphorylation, activating specific transcription factors to enable selective transcription, and augmenting the ability to refold and export. Apart from the three basic pathways, vascular endothelial growth factor （VEGF）-VEGF receptor （VEGFR）-phospholipase C-~ （PLCy）-mammalian target of rapamycin complex 1 （mTORC1） pathway, induced only in solid tumors, can also activate ATF6 and PERK signal cascades, and IREla also can be activated by activated RAC-alpha serine/threonine-protein kinase （AKT）. A moderate UPR functions as a pro-survival signal to return the cell to its state of homeostasis. However, persistent ER stress will induce cells to undergo apoptosis in response to increasing reactive oxygen species （ROS）, Ca2＋ in the cytoplasmic matrix, and other apoptosis signal cascades, such as c-Jun N-terminal kinase （JNK）, signal transducer and activator of transcription 3 （STAT3）, and P38, when cellular damage exceeds the capacity of this adaptive response.展开更多
We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to an...We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to analyze the trade-off between area and speed of the global signal networks for this staggered buffer insertion scheme, and the criterion for determining the design parameters is presented. The comparative analytic result shows that the methods in this paper are proven to be more efficient for FPGAs with a large array size.展开更多
Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in hi...Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.展开更多
The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and cl...The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.展开更多
The objectification of the pulse signal analysis is a practical problem. The classification of the pulse signal is studied based on the BP neural network. It is first analyzed how to select the characteristic factors ...The objectification of the pulse signal analysis is a practical problem. The classification of the pulse signal is studied based on the BP neural network. It is first analyzed how to select the characteristic factors of the pulse signal. Then the method of nondimensionalization/normalization on the pulse signal is presented to preprocess the characteristic factors. The classification of the pulse signal and the effects of the selection of characteristic factors are studied by using the normalized data and BP neural network. It is shown that nondimensionalization/normalization of the data is in favor of the training and forecasting of the network. The selection of characteristic factors affects the accuracy of forecasting obviously. The results of forecasting by selection of 8, 6 and 4 factors respectively show that the less the factors are, the worse the effects are.展开更多
To understand the organization of the biological networks that might potentially govern the pathogenesis of hormone refractory prostate cancer （HRPC）, we investigated the transcriptional circuitry and signaling in a...To understand the organization of the biological networks that might potentially govern the pathogenesis of hormone refractory prostate cancer （HRPC）, we investigated the transcriptional circuitry and signaling in androgen-dependent 22Rvl and MDA PCa 2b cells, androgen- and estrogen-dependent LNCaP cells, and androgen-independent DU 145 and PC-3 prostate cancer （PCa） cell lines. We used microarray analyses, quantitative real-time PCR, pathway prediction analyses, and determination of Transcription Factor Binding Site （TFBS） signatures to dissect HRPC regulatory networks. We generated graphical representations of global topology and local network motifs that might be important in prostate carcinogenesis. Many important putative biomarker ＇target hubs＇ were identified in the current study including AP-1, NF-KB, EGFR, ERK1/2, JNK, p38 MAPK, TGF beta, VEGF, PDGF, CD44, Akt, PI3K, NOTCH1, CASP1, MMP2 and AR. Our results suggest that complex cellular events including autoregulation, feedback loops and cross-talk might govern progression from early lesion to clinically diagnosed PCa, as well as metastatic potential of pre-existent high-grade prostate intraepithelial neoplasia （HG-PIN） and/or advancement to HRPC. The identification of TFBS signatures for TCF/LEF, SOX9 and ELK1 in the regulatory elements suggests additional biomarkers for the potential development of chemopreventive/therapeutic strategies against PCa. Taken together, in this study, we have identified putative biomarker ＇target hubs＇ in the architecture of PCa signaling networks, and investigated TFBS signatures that might enhance our understanding of key regulatory nodes in the progression and pathogenesis of HRPC.展开更多
The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are us...The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.展开更多
Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homo...Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.展开更多
The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical c...The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.展开更多
An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformat...An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.展开更多
There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR）...There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.展开更多
This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and d...This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and divides the output of a sensor into'Signal dominant component'and'Noise dominant component'because the pattern of sensor failure often appears in the'Noise dominant component'.With an ARMA model built for'Noise dominant component'using artificial neural network,such sensor failures as bias failure,hard failure,drift failure,spike failure and cyclic failure may be detected through residual analysis,and the type of sensor failure can be indicated by an appropriate indicator.The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.展开更多
A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational ...A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.展开更多
Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted conside...Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics field.Recently,Bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell data.However,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named PDABC.Specifically,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric.The experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.展开更多
This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Netwo...This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Networks (ANN) in Structural Health Monitoring (SHM). Namely, the robustness of an ANN SHM system is assessed through an airfoil case study based on the sensitivity of delamination location and size predictions, when the ANN is imposed to noisy input. In light of the observed poor performance of the original network, even when its architecture was carefully optimized, it had been proposed to weigh the input layer of the ANN by a set of signal-to-noise (SN) ratios and then trained the network. Both damage location and size predictions of the latter SHM approach were increased to above 90%. Practical aspects of the proposed robust SN-ANN SHM have also been discussed.展开更多
Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward contr...Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.展开更多
In this paper, a new approach is proposed to estimate pseudo noise(PN) sequence in the lower SNR DS/SS signals blindly. This method utilizes the characteristics of self-organization, principal components analysis and ...In this paper, a new approach is proposed to estimate pseudo noise(PN) sequence in the lower SNR DS/SS signals blindly. This method utilizes the characteristics of self-organization, principal components analysis and extraction of unsupervised neural networks adequately, in addition to its higher-speed operation ability, successfully solve the difficult problem about PN sequence blind estimation. The theoretic analysis and experimental results show that this approach can work very well on lower SNR input signals.展开更多
An approach based on discrete Karhunen-Loeve transformation of the DS/SS signals is proposed to estimate PN sequence in lower S/N ratio DS/SS signals. Characteristics of self-organization and principle components extr...An approach based on discrete Karhunen-Loeve transformation of the DS/SS signals is proposed to estimate PN sequence in lower S/N ratio DS/SS signals. Characteristics of self-organization and principle components extraction of unsupervised neural networks are exploited adequately. Theoretical analysis and experimental results are provided to show that this approach can work well on the lower S/N ratio input signals.展开更多
An approach by using neural network signal processing in associate with embedded fiberoptic sensing array for the newly developed “smart material systems and structures” is discussed in this paper.The principle,stru...An approach by using neural network signal processing in associate with embedded fiberoptic sensing array for the newly developed “smart material systems and structures” is discussed in this paper.The principle,structure of this approach and suitable neural network algorithms are described.The results of simulation experiments are also given.展开更多
基金Project supported by the National Basic Research Program(973)of China(No.2012CB518900)the National Natural Science Foundation of China(Nos.31160240 and 31260621)+2 种基金the National Major Scientific and Technological Special Project during the Twelfth Five-year Plan Period of China(No.2012ZX10002006)the Hangzhou Normal University Supporting Project(No.PE13002004042)the Natural Science Foundation of Jiangxi Province(No.20114BAB204016),China
文摘Within the cell, several mechanisms exist to maintain homeostasis of the endoplasmic reticulum （ER）. One of the primary mechanisms is the unfolded protein response （UPR）. In this review, we primarily focus on the latest signal webs and regulation mechanisms of the UPR. The relationships among ER stress, apoptosis, and cancer are also discussed. Under the normal state, binding immunoglobulin protein （BiP） interacts with the three sensors （protein kinase RNA-like ER kinase （PERK）, activating transcription factor 6 （ATF6）, and inositol-requiring enzyme la （IREla）） Under ER stress, misfolded proteins interact with BiP, resulting in the release of BiP from the sensors. Subsequently, the three sensors dimerize and autophosphorylate to promote the signal cascades of ER stress. ER stress includes a series of positive and negative feedback signals, such as those regulating the stabilization of the sensors/BiP complex, activating and inactivating the sensors by autophosphorylation and dephosphorylation, activating specific transcription factors to enable selective transcription, and augmenting the ability to refold and export. Apart from the three basic pathways, vascular endothelial growth factor （VEGF）-VEGF receptor （VEGFR）-phospholipase C-~ （PLCy）-mammalian target of rapamycin complex 1 （mTORC1） pathway, induced only in solid tumors, can also activate ATF6 and PERK signal cascades, and IREla also can be activated by activated RAC-alpha serine/threonine-protein kinase （AKT）. A moderate UPR functions as a pro-survival signal to return the cell to its state of homeostasis. However, persistent ER stress will induce cells to undergo apoptosis in response to increasing reactive oxygen species （ROS）, Ca2＋ in the cytoplasmic matrix, and other apoptosis signal cascades, such as c-Jun N-terminal kinase （JNK）, signal transducer and activator of transcription 3 （STAT3）, and P38, when cellular damage exceeds the capacity of this adaptive response.
文摘We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to analyze the trade-off between area and speed of the global signal networks for this staggered buffer insertion scheme, and the criterion for determining the design parameters is presented. The comparative analytic result shows that the methods in this paper are proven to be more efficient for FPGAs with a large array size.
基金supported by the ‘‘Detection of very low-flux background neutrons in China Jinping Underground Laboratory’’ project of the National Natural Science Foundation of China(No.11275134)
文摘Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.
文摘The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.
文摘The objectification of the pulse signal analysis is a practical problem. The classification of the pulse signal is studied based on the BP neural network. It is first analyzed how to select the characteristic factors of the pulse signal. Then the method of nondimensionalization/normalization on the pulse signal is presented to preprocess the characteristic factors. The classification of the pulse signal and the effects of the selection of characteristic factors are studied by using the normalized data and BP neural network. It is shown that nondimensionalization/normalization of the data is in favor of the training and forecasting of the network. The selection of characteristic factors affects the accuracy of forecasting obviously. The results of forecasting by selection of 8, 6 and 4 factors respectively show that the less the factors are, the worse the effects are.
基金National Institutes of Health(Grant No.RO1 CA118947 and RO1 CA152826 to Ah-Ng Tony Kong and R21 CA133675 to Li Cai)
文摘To understand the organization of the biological networks that might potentially govern the pathogenesis of hormone refractory prostate cancer （HRPC）, we investigated the transcriptional circuitry and signaling in androgen-dependent 22Rvl and MDA PCa 2b cells, androgen- and estrogen-dependent LNCaP cells, and androgen-independent DU 145 and PC-3 prostate cancer （PCa） cell lines. We used microarray analyses, quantitative real-time PCR, pathway prediction analyses, and determination of Transcription Factor Binding Site （TFBS） signatures to dissect HRPC regulatory networks. We generated graphical representations of global topology and local network motifs that might be important in prostate carcinogenesis. Many important putative biomarker ＇target hubs＇ were identified in the current study including AP-1, NF-KB, EGFR, ERK1/2, JNK, p38 MAPK, TGF beta, VEGF, PDGF, CD44, Akt, PI3K, NOTCH1, CASP1, MMP2 and AR. Our results suggest that complex cellular events including autoregulation, feedback loops and cross-talk might govern progression from early lesion to clinically diagnosed PCa, as well as metastatic potential of pre-existent high-grade prostate intraepithelial neoplasia （HG-PIN） and/or advancement to HRPC. The identification of TFBS signatures for TCF/LEF, SOX9 and ELK1 in the regulatory elements suggests additional biomarkers for the potential development of chemopreventive/therapeutic strategies against PCa. Taken together, in this study, we have identified putative biomarker ＇target hubs＇ in the architecture of PCa signaling networks, and investigated TFBS signatures that might enhance our understanding of key regulatory nodes in the progression and pathogenesis of HRPC.
文摘The ship hydraulic pressure signal is one of the important characters for the target detection and recognition. At present, most of the researches on the detection focus on the ways in the time domain. The ways are usually invalid in the large wind wave background. In order to solve the problem efficiently, we present an effectual way to detect the ship using the ship hydraulic pressure signal. Firstly, the signature in the proposed method is decomposed by wavelet-transform technique and reconstructed at the low-frequency region. Then,a predictive model is set up by using the radial basis function(RBF) neural network. Finally, the signature predictive error is regarded as the testing signal which can be used to judge whether the target exists or does not.The practical result shows that the method can improve the signal to noise ratio(SNR) obviously.
基金supported by the National Natural Science Foundation of China(Grant No.61231010)the Fundamental Research Funds for the Central Universities,China(Grant No.HUST No.2012QN076)
文摘Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.
文摘The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.
基金Supported by the National Natural Science Foundation of China(64601500)
文摘An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2014 ZX03001027)
文摘There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.
文摘This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and divides the output of a sensor into'Signal dominant component'and'Noise dominant component'because the pattern of sensor failure often appears in the'Noise dominant component'.With an ARMA model built for'Noise dominant component'using artificial neural network,such sensor failures as bias failure,hard failure,drift failure,spike failure and cyclic failure may be detected through residual analysis,and the type of sensor failure can be indicated by an appropriate indicator.The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.
基金Supported partly by Natural Science Foundation of ChinaAviation Science Grant of China
文摘A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.
基金National Natural Science Foundation of China,Grant/Award Numbers:62106009,62276010R&D Program of Beijing Municipal Education Commission,Grant/Award Numbers:KM202210005030,KZ202210005009。
文摘Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells,which has attracted considerable attention within the bioinformatics field.Recently,Bayesian network(BN)techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single-cell data.However,current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells.A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony(PDABC),named PDABC.Specifically,PDABC is a score-based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric.The experimental results on several simulated datasets,as well as a previously published multi-parameter fluorescence-activated cell sorter dataset,indicate that PDABC surpasses the existing state-of-the-art methods in terms of performance and computational efficiency.
文摘This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Networks (ANN) in Structural Health Monitoring (SHM). Namely, the robustness of an ANN SHM system is assessed through an airfoil case study based on the sensitivity of delamination location and size predictions, when the ANN is imposed to noisy input. In light of the observed poor performance of the original network, even when its architecture was carefully optimized, it had been proposed to weigh the input layer of the ANN by a set of signal-to-noise (SN) ratios and then trained the network. Both damage location and size predictions of the latter SHM approach were increased to above 90%. Practical aspects of the proposed robust SN-ANN SHM have also been discussed.
基金supported by Grant-in-Aid for Scientific Research(C) (No. 20560248) of Japan
文摘Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.
文摘In this paper, a new approach is proposed to estimate pseudo noise(PN) sequence in the lower SNR DS/SS signals blindly. This method utilizes the characteristics of self-organization, principal components analysis and extraction of unsupervised neural networks adequately, in addition to its higher-speed operation ability, successfully solve the difficult problem about PN sequence blind estimation. The theoretic analysis and experimental results show that this approach can work very well on lower SNR input signals.
文摘An approach based on discrete Karhunen-Loeve transformation of the DS/SS signals is proposed to estimate PN sequence in lower S/N ratio DS/SS signals. Characteristics of self-organization and principle components extraction of unsupervised neural networks are exploited adequately. Theoretical analysis and experimental results are provided to show that this approach can work well on the lower S/N ratio input signals.
文摘An approach by using neural network signal processing in associate with embedded fiberoptic sensing array for the newly developed “smart material systems and structures” is discussed in this paper.The principle,structure of this approach and suitable neural network algorithms are described.The results of simulation experiments are also given.
