The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these pro...The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.展开更多
Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.Th...Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.展开更多
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
Dictionary has many functions, in which the function of definition is of very importance because the main purpose of dictionary is providing the entry's meaning information for the readers so that the readers can ...Dictionary has many functions, in which the function of definition is of very importance because the main purpose of dictionary is providing the entry's meaning information for the readers so that the readers can understand and use the entry-word and the realization of the purpose completely depends on lexicographical definition. However, the function of definition is limited, which need the exemplification to assist it. Therefore, the exemplification becomes very important, too. Good exemplification can assist definition, provide grammatical information, and supplement the information usage and so on. Many researches studied the exemplification of dictionary, its principles and so on. Dictionary changed much with the development of technology and many kinds of electronic dictionaries appeared. Few studies are involved with the new-type dictionary. Based on the general principles of the exemplification in a learner's printed dictionary, it is necessary to construct the general principles about the exemplification in the electronic learner's dictionary.展开更多
The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the tr...The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.展开更多
The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-dom...The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-domain methods have been partly successful in identifying small cracks, but not so successful in estimating crack size, especially in strong backscattering noise. Sparse signal representation can provide sparse information that represents the signal time-frequency signature, which can also be used in processing ultrasonic nondestructive signals. A novel ultrasonic nondestructive signal processing algorithm based on signal sparse representation is proposed. In order to suppress noise, matching pursuit algorithm with Gabor dictionary is selected as the signal decomposition method. Precise echoes information, such as crack location and size, can be estimated by quantitative analysis with Gabor atom. To verify the performance, the proposed algorithm is applied to computer simulation signal and experimental ultrasonic signals which represent multiple backscattered echoes from a thin metal plate with artificial holes. The results show that this algorithm not only has an excellent performance even when dealing with signals in the presence of strong noise, but also is successful in estimating crack location and size. Moreover, the algorithm can be applied to data compression of ultrasonic nondestructive signal.展开更多
Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisf...Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.展开更多
Comments were made on the ＂word-for-word＂ literal translation method used by Mr. Nigel Wiseman in A Practical Dictionary of Chinese Medicine. He believes that only literal translation can reflect Chinese medical con...Comments were made on the ＂word-for-word＂ literal translation method used by Mr. Nigel Wiseman in A Practical Dictionary of Chinese Medicine. He believes that only literal translation can reflect Chinese medical concepts accurately. The so-called ＂word-for-word＂ translation is actually ＂English-word-for- Chinese-character＂ translation. First, the authors of the dictionary made a list of Single Characters with English Equivalents, and then they gave each character of the medical term an English equivalent according to the list. Finally, they made some minor modifications to make the rendering grammatically smoother. Many English terms thus produced are confusing. The defect of the word-for-word literal translation stems from the erroneous idea that a single character constitutes the basic element of meaning corresponding to the notion of ＂word＂ in English, and the meaning of a disyllabic or polysyllabic Chinese word is the simple addition of the constituent characters. Another big mistake is the negligence of the polysemy of Chinese characters. One or two English equivalents can by no means cover all the various meanings of a single character which is a polysemous monosyllabic word. Various examples were cited from this dictionary to illustrate the mistakes.展开更多
Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In t...Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.展开更多
Mr. Wiseman believes that Western medical terms chosen as equivalents of Chinese medical terms should be the words known to all speakers and not requiring any specialist knowledge or instrumentation to understand or i...Mr. Wiseman believes that Western medical terms chosen as equivalents of Chinese medical terms should be the words known to all speakers and not requiring any specialist knowledge or instrumentation to understand or identify, and strictly technical Western medical terms should be avoided regardless of their conceptual conformity to the Chinese terms. Accordingly, many inappropriate Western medical terms are selected as English equivalents by the authors of the Dictionary, and on the other hand, many ready-made appropriate Western medical terms are replaced by loan English terms with the Chinese style of word formation. The experience gained in solving the problems of translating Western medical terms into Chinese when West- ern medicine was first introduced to China is helpful for translating Chinese medical terms into English. However, the authors of the Dictionary adhere to their own opinions, ignoring others＂ experience. The English terms thus created do not reflect the genuine meaning of the Chinese terms, but make the English glossary in chaos. The so-called true face of traditional Chinese revealed by such terms is merely the Chinese custom of word formation and metaphoric rhetoric. In other words, traditional Chinese medicine is not regarded as a system of medicine but merely some Oriental folklore.展开更多
In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adapt...In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adaption. We expand the time-frequency dictionary library with Ricker, Morlet, and mixed phase seismic wavelets, to make the method more suitable for seismic signal time-frequency decomposition. In this paper, we demonstrated the algorithm theory using synthetic seismic data, and tested the method using synthetic data with 25% noise. We compared the matching pursuit results of the time-frequency dictionaries. The results indicated that the dictionary which matched the signal characteristics better would obtain better results, and can reflect the information of seismic data effectively.展开更多
Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising met...Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.展开更多
文摘The niche discipline of Indo-European Studies has proven itself to be prevailingly au courant by launching several projects in the field of online etymological dictionaries.My paper will offer an overview of these projects(including the Lexicon Etymologicum Digitale Indoeuropaeum(LEDI)directed by me)and analyse their approaches,features,and peculiarities(e.g.,commercial vs.open access).Special attention will paid to the projects’inclusions of phonetic rules and affixes,which makes derivation transparent and is helpful for didactic purposes.
文摘Denoising is an important preprocessing step in seismic exploration that improves the signal-to-noise ratio(SNR)and helps identify oil and minerals.Dictionary learning(DL)is a promising method for noise attenuation.The DL extracts sparse features from noisy seismic data using over-complete dictionaries and performs denoising based on a threshold.However,the choice of threshold in DL greatly impacts the denoising results and the improvement in output SNR.Ramanujan’s sum(s)(RS)is a signal processing tool that exhibits derivative behavior and finds applications in edge detection and noise estimation of signals.Hence,we propose a novel DL method with threshold estimation based on RS to improve the output SNR.In this work,we estimate the noise variance of seismic data based on RS and use it as a threshold value for the DL method to perform denoising.We analyze the results of the proposed work on synthetically generated and field data sets.We perform simulations on noisy seismic data across a wide range of SNR values and tabulate the denoised results using the performance metrics SNR and mean squared error.The results indicate that the proposed method provides superior SNR and reduced mean squared error compared to MAD,SURE-based,and adaptive soft-thresholding techniques.
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
文摘Dictionary has many functions, in which the function of definition is of very importance because the main purpose of dictionary is providing the entry's meaning information for the readers so that the readers can understand and use the entry-word and the realization of the purpose completely depends on lexicographical definition. However, the function of definition is limited, which need the exemplification to assist it. Therefore, the exemplification becomes very important, too. Good exemplification can assist definition, provide grammatical information, and supplement the information usage and so on. Many researches studied the exemplification of dictionary, its principles and so on. Dictionary changed much with the development of technology and many kinds of electronic dictionaries appeared. Few studies are involved with the new-type dictionary. Based on the general principles of the exemplification in a learner's printed dictionary, it is necessary to construct the general principles about the exemplification in the electronic learner's dictionary.
基金supported by The National 973 program (No. 2007 CB209505)Basic Research Project of PetroChina's 12th Five Year Plan (No. 2011A-3601)RIPED Youth Innovation Foundation (No. 2010-A-26-01)
文摘The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.
基金supported by National Natural Science Foundation of China (Grant No. 60672108, Grant No. 60372020)
文摘The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-domain methods have been partly successful in identifying small cracks, but not so successful in estimating crack size, especially in strong backscattering noise. Sparse signal representation can provide sparse information that represents the signal time-frequency signature, which can also be used in processing ultrasonic nondestructive signals. A novel ultrasonic nondestructive signal processing algorithm based on signal sparse representation is proposed. In order to suppress noise, matching pursuit algorithm with Gabor dictionary is selected as the signal decomposition method. Precise echoes information, such as crack location and size, can be estimated by quantitative analysis with Gabor atom. To verify the performance, the proposed algorithm is applied to computer simulation signal and experimental ultrasonic signals which represent multiple backscattered echoes from a thin metal plate with artificial holes. The results show that this algorithm not only has an excellent performance even when dealing with signals in the presence of strong noise, but also is successful in estimating crack location and size. Moreover, the algorithm can be applied to data compression of ultrasonic nondestructive signal.
基金supported by the National Natural Science Foundation of China (No. 51201182)
文摘Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.
文摘Comments were made on the ＂word-for-word＂ literal translation method used by Mr. Nigel Wiseman in A Practical Dictionary of Chinese Medicine. He believes that only literal translation can reflect Chinese medical concepts accurately. The so-called ＂word-for-word＂ translation is actually ＂English-word-for- Chinese-character＂ translation. First, the authors of the dictionary made a list of Single Characters with English Equivalents, and then they gave each character of the medical term an English equivalent according to the list. Finally, they made some minor modifications to make the rendering grammatically smoother. Many English terms thus produced are confusing. The defect of the word-for-word literal translation stems from the erroneous idea that a single character constitutes the basic element of meaning corresponding to the notion of ＂word＂ in English, and the meaning of a disyllabic or polysyllabic Chinese word is the simple addition of the constituent characters. Another big mistake is the negligence of the polysemy of Chinese characters. One or two English equivalents can by no means cover all the various meanings of a single character which is a polysemous monosyllabic word. Various examples were cited from this dictionary to illustrate the mistakes.
基金supported in part by the National Natural Science Foundation of China(61302041,61363044,61562053,61540042)the Applied Basic Research Foundation of Yunnan Provincial Science and Technology Department(2013FD011,2016FD039)
文摘Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.
文摘Mr. Wiseman believes that Western medical terms chosen as equivalents of Chinese medical terms should be the words known to all speakers and not requiring any specialist knowledge or instrumentation to understand or identify, and strictly technical Western medical terms should be avoided regardless of their conceptual conformity to the Chinese terms. Accordingly, many inappropriate Western medical terms are selected as English equivalents by the authors of the Dictionary, and on the other hand, many ready-made appropriate Western medical terms are replaced by loan English terms with the Chinese style of word formation. The experience gained in solving the problems of translating Western medical terms into Chinese when West- ern medicine was first introduced to China is helpful for translating Chinese medical terms into English. However, the authors of the Dictionary adhere to their own opinions, ignoring others＂ experience. The English terms thus created do not reflect the genuine meaning of the Chinese terms, but make the English glossary in chaos. The so-called true face of traditional Chinese revealed by such terms is merely the Chinese custom of word formation and metaphoric rhetoric. In other words, traditional Chinese medicine is not regarded as a system of medicine but merely some Oriental folklore.
文摘In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adaption. We expand the time-frequency dictionary library with Ricker, Morlet, and mixed phase seismic wavelets, to make the method more suitable for seismic signal time-frequency decomposition. In this paper, we demonstrated the algorithm theory using synthetic seismic data, and tested the method using synthetic data with 25% noise. We compared the matching pursuit results of the time-frequency dictionaries. The results indicated that the dictionary which matched the signal characteristics better would obtain better results, and can reflect the information of seismic data effectively.
基金financially supported the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA14020102)the National Natural Science Foundation of China (Nos. 41774125,41530320 and 41804098)the Key National Research Project of China (Nos. 2016YFC0303100,2017YFC0601900)。
文摘Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.
