Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets (POCS) based image restoration method into the seismic data...Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets (POCS) based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.展开更多
In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive conve...In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.展开更多
The issue of "headedness" is a product of Chomsky's (1988) notion of UG (Universal Grammar) that led to the development of a framework known as P&P (Principles and Parameters) theory. It is this theory we ha...The issue of "headedness" is a product of Chomsky's (1988) notion of UG (Universal Grammar) that led to the development of a framework known as P&P (Principles and Parameters) theory. It is this theory we have adopted for our analysis in this paper. The purpose of this paper is to examine the inconsistency in the value of Head Parameter with reference to the value of DP (determiner phrase) in Yorfib^i. As a native speaker of Yorfib~, the author has adopted an introspective method of data collection and used the intuitive knowledge of other native speakers of the language for necessary clarifications. Despite the fact that English and Yorfib~ are both head-initial, the structure of the NPs (noun phrases) in English shows that the head noun is always pre-modified, making the NP "head-final"--a violation of the value of Head Parameter in the language. This necessitated the need for Abney's (1987) DP hypothesis; in which the determiner heads its own phrase, thereby making a NP in English head-initial. This solves the problem of Head Parameter in English. However, since nouns in Yor/lb^i are post-modified, adopting "DP-analysis" will automatically produce head-final--a violation of the value of Head Parameter in the language. Given the inconsistency in the specification of head-complement order among the noun phrases in English and Yorfib~, this paper proposes to set a parameter for SVO (Subject-Verb-Object) languages with pre-modification (like English) to adopt "DP-analysis", and those with post-modification (like Yorfib^t) to adopt "NP-analysis". This will ensure "head-initial" value for the two categories of SVO languages展开更多
One of the most important problems of clustering is to define the number of classes. In fact, it is not easy to find an appropriate method to measure whether the cluster configuration is acceptable or not. In this pap...One of the most important problems of clustering is to define the number of classes. In fact, it is not easy to find an appropriate method to measure whether the cluster configuration is acceptable or not. In this paper we propose a possible and non-automatic solution considering different criteria of clustering and comparing their results. In this way robust structures of an analyzed dataset can be often caught (or established) and an optimal cluster configuration, which presents a meaningful association, may be defined. In particular, we also focus on the variables which may be used in cluster analysis. In fact, variables which contain little clustering information can cause misleading and not-robustness results. Therefore, three algorithms are employed in this study: K-means partitioning methods, Partitioning Around Medoids (PAM) and the Heuristic Identification of Noisy Variables (HINoV). The results are compared with robust methods ones.展开更多
The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collect...The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.展开更多
Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities...Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved: prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher's Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.展开更多
基金financially supported by National 863 Program (Grants No.2006AA 09A 102-09)National Science and Technology of Major Projects ( Grants No.2008ZX0 5025-001-001)
文摘Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets (POCS) based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.
基金The National Key R&D Program of China(No.2018YFB1500800)the Specialized Development Foundation for the Achievement Transformation of Jiangsu Province(No.BA2019025)+1 种基金Pre-Research Fund of Science and Technology on Near-Surface Detection Laboratory(No.6142414190405)the Open Project of the Key Laboratory of Wireless Sensor Network&Communication of Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences(No.20190907).
文摘In order to maximize the value of information(VoI)of collected data in unmanned aerial vehicle(UAV)-aided wireless sensor networks(WSNs),a UAV trajectory planning algorithm named maximum VoI first and successive convex approximation(MVF-SCA)is proposed.First,the Rician channel model is adopted in the system and sensor nodes(SNs)are divided into key nodes and common nodes.Secondly,the data collection problem is formulated as a mixed integer non-linear program(MINLP)problem.The problem is divided into two sub-problems according to the different types of SNs to seek a sub-optimal solution with a low complexity.Finally,the MVF-SCA algorithm for UAV trajectory planning is proposed,which can not only be used for daily data collection in the target area,but also collect time-sensitive abnormal data in time when the exception occurs.Simulation results show that,compared with the existing classic traveling salesman problem(TSP)algorithm and greedy path planning algorithm,the VoI collected by the proposed algorithm can be improved by about 15%to 30%.
文摘The issue of "headedness" is a product of Chomsky's (1988) notion of UG (Universal Grammar) that led to the development of a framework known as P&P (Principles and Parameters) theory. It is this theory we have adopted for our analysis in this paper. The purpose of this paper is to examine the inconsistency in the value of Head Parameter with reference to the value of DP (determiner phrase) in Yorfib^i. As a native speaker of Yorfib~, the author has adopted an introspective method of data collection and used the intuitive knowledge of other native speakers of the language for necessary clarifications. Despite the fact that English and Yorfib~ are both head-initial, the structure of the NPs (noun phrases) in English shows that the head noun is always pre-modified, making the NP "head-final"--a violation of the value of Head Parameter in the language. This necessitated the need for Abney's (1987) DP hypothesis; in which the determiner heads its own phrase, thereby making a NP in English head-initial. This solves the problem of Head Parameter in English. However, since nouns in Yor/lb^i are post-modified, adopting "DP-analysis" will automatically produce head-final--a violation of the value of Head Parameter in the language. Given the inconsistency in the specification of head-complement order among the noun phrases in English and Yorfib~, this paper proposes to set a parameter for SVO (Subject-Verb-Object) languages with pre-modification (like English) to adopt "DP-analysis", and those with post-modification (like Yorfib^t) to adopt "NP-analysis". This will ensure "head-initial" value for the two categories of SVO languages
文摘One of the most important problems of clustering is to define the number of classes. In fact, it is not easy to find an appropriate method to measure whether the cluster configuration is acceptable or not. In this paper we propose a possible and non-automatic solution considering different criteria of clustering and comparing their results. In this way robust structures of an analyzed dataset can be often caught (or established) and an optimal cluster configuration, which presents a meaningful association, may be defined. In particular, we also focus on the variables which may be used in cluster analysis. In fact, variables which contain little clustering information can cause misleading and not-robustness results. Therefore, three algorithms are employed in this study: K-means partitioning methods, Partitioning Around Medoids (PAM) and the Heuristic Identification of Noisy Variables (HINoV). The results are compared with robust methods ones.
基金International Joint Research Program from the Ministry of Science and Technology of Chinagrant number:20070667+1 种基金Education Commission of Chongqing of Chinagrant number:KJ081209
文摘The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 and 0.08. When the confidence degree is 0.05, the confidence interval of the normal population mean of complexity measures for the control group is (0.2871,0.3652), and (0.4944,0.5552) for the normal group. The statistic results show that the normal samples and patient samples can be clearly distinguished by the value of measures. In clinical medicine, the results can be used to be a reference to evaluate the function or state, to diagnose disease, to monitor the rehabilitation progress of the brain.
文摘Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved: prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher's Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.