Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux...Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.展开更多
Deterministic, probabilistic and composite-grading methods are used to get the possible locations of strong earth-quakes in the future in Norwest Beijing and its vicinity based on the quantitative data and their accur...Deterministic, probabilistic and composite-grading methods are used to get the possible locations of strong earth-quakes in the future in Norwest Beijing and its vicinity based on the quantitative data and their accuracy about active tectonics in the research area and by ordering, some questions in the results are also discussed. It shows that the most dangerous fault segments for strong earthquakes in the future include: segments B and A of the southern boundary fault of the Yangyuan basin, the southern boundary fault of the Xuanhua basin, the east segment of the southern Huaian fault and the east segment of the northern YanggaoTianzhen fault. The most dangerous area is YangyuanShenjing basin, the second one is TianzhenHuaianXuanhua basin and the third dangerous areas are WanquanZhangjiakou and northeast of Yuxian to southwest of Fanshan.展开更多
For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to p...For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.展开更多
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
In order to construct the data mining frame for the generic project risk research, the basic definitions of the generic project risk element were given, and then a new model of the generic project risk element was pre...In order to construct the data mining frame for the generic project risk research, the basic definitions of the generic project risk element were given, and then a new model of the generic project risk element was presented with the definitions. From the model, data mining method was used to acquire the risk transmission matrix from the historical databases analysis. The quantitative calculation problem among the generic project risk elements was solved. This method deals with well the risk element transmission problems with limited states. And in order to get the limited states, fuzzy theory was used to discrete the historical data in historical databases. In an example, the controlling risk degree is chosen as P(Rs≥2) ≤0.1, it means that the probability of risk state which is not less than 2 in project is not more than 0.1, the risk element R3 is chosen to control the project, respectively. The result shows that three risk element transmission matrix can be acquired in 4 risk elements, and the frequency histogram and cumulative frequency histogram of each risk element are also given.展开更多
Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.T...Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.展开更多
Sheet metal is widely used on auto-bodies, plane-bodies and metal furniture, etc. For instance, a typical auto-body commonly consists of hundreds of sheet metal stamping parts. Because of its complexity of structure a...Sheet metal is widely used on auto-bodies, plane-bodies and metal furniture, etc. For instance, a typical auto-body commonly consists of hundreds of sheet metal stamping parts. Because of its complexity of structure and manufacturing process, auto-bodies inevitably have geometrical variation results from a number of different sources, such as the geometrical variation of stamping parts, the transformation of assembly process parameters and even the improper design concept. As more than 30% quality defects of an auto-body are born from the dimensional deviation of Body-In-White originated during the manufacturing process, effective diagnosis and control of dimensional faults are essential to the continuous improvement of the quality of vehicles. Especially during the period of new car launching or model changing when the assembly process was changed and adjusted frequently. For continuously improving the quality of modern cars, rapid dimensional variation causes identification becomes a challenging but essential work. In this paper, main variation causes of auto-body was firstly been cataloged and analyzed, then, a dimensional variation diagnostic reasoning and decision approach was developed through the combination of data mining and knowledge discovery techniques. This approach is driven by variation pattern identification which can be discovered from the dispersive, isolated massive measured data: Correlation Analysis (CA) and Maximal Tree (MT) methods were applied to extract the large variation group from massive multidimensional measured data, while multivariate statistical analysis (MSA) approach was used to discovery the principle variation pattern. A Decision Tree (DT) approach based on the knowledge of product and assembly process was developed to fulfill the "Hypothesis and Validation" characterized variation causes reasoning procedure. An practical application case with sudden and severe dimension variation on rear end panel in up/down direction was analyzed and successfully solved aided by the devloped variation diagnostic method, which have proved that the approach is effective and efficient.展开更多
This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-...This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.展开更多
In order to rapidly and effectively meet the informative demand from commanding decision-making, it is important to build, maintain and mine the intelligence database. The type, structure and maintenance of military i...In order to rapidly and effectively meet the informative demand from commanding decision-making, it is important to build, maintain and mine the intelligence database. The type, structure and maintenance of military intelligence database are discussed. On this condition, a new data-mining arithmetic based on relation intelligence database is presented according to the preference information and the requirement of time limit given by the commander. Furthermore, a simple calculative example is presented to prove the arithmetic with better maneuverability. Lastly, the problem of how to process the intelligence data mined from the intelligence database is discussed.展开更多
This paper presents an integrated approach towards solving the problem of "Gene Prediction".The "Gene Prediction" problem solving undergoes well defined stages starting with a DNA
In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ...In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.展开更多
Important Dates Submission due November 15, 2005 Notification of acceptance December 30, 2005 Camera-ready copy due January 10, 2006 Workshop Scope Intelligence and Security Informatics (ISI) can be broadly defined as...Important Dates Submission due November 15, 2005 Notification of acceptance December 30, 2005 Camera-ready copy due January 10, 2006 Workshop Scope Intelligence and Security Informatics (ISI) can be broadly defined as the study of the development and use of advanced information technologies and systems for national and international security-related applications. The First and Second Symposiums on ISI were held in Tucson,Arizona,in 2003 and 2004,respectively. In 2005,the IEEE International Conference on ISI was held in Atlanta,Georgia. These ISI conferences have brought together academic researchers,law enforcement and intelligence experts,information technology consultant and practitioners to discuss their research and practice related to various ISI topics including ISI data management,data and text mining for ISI applications,terrorism informatics,deception detection,terrorist and criminal social network analysis,crime analysis,monitoring and surveillance,policy studies and evaluation,information assurance,among others. We continue this stream of ISI conferences by organizing the Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06). WISI’06 will provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. The workshop also welcomes contributions dealing with ISI challenges specific to the Pacific Asian region.展开更多
With the reform of rural network enterprise system,the speed of transfer property rights in rural power enterprises is accelerated.The evaluation of the operation and development status of rural power enterprises is d...With the reform of rural network enterprise system,the speed of transfer property rights in rural power enterprises is accelerated.The evaluation of the operation and development status of rural power enterprises is directly related to the future development and investment direction of rural power enterprises.At present,the evaluation of the production and operation of rural network enterprises and the development status of power network only relies on the experience of the evaluation personnel,sets the reference index,and forms the evaluation results through artificial scoring.Due to the strong subjective consciousness of the evaluation results,the practical guiding significance is weak.Therefore,distributed data mining method in rural power enterprises status evaluation was proposed which had been applied in many fields,such as food science,economy or chemical industry.The distributed mathematical model was established by using principal component analysis(PCA)and regression analysis.By screening various technical indicators and determining their relevance,the reference value of evaluation results was improved.Combined with statistical program for social sciences(SPSS)data analysis software,the operation status of rural network enterprises was evaluated,and the rationality,effectiveness and economy of the evaluation was verified through comparison with current evaluation results and calculation examples of actual grid operation data.展开更多
Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by ...Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.展开更多
Magnesium(Mg)is a promising alternative to lithium(Li)as an anode material in solid-state batteries due to its abundance and high theoretical volumetric capacity.However,the sluggish Mg-ion conduction in the lattice o...Magnesium(Mg)is a promising alternative to lithium(Li)as an anode material in solid-state batteries due to its abundance and high theoretical volumetric capacity.However,the sluggish Mg-ion conduction in the lattice of solidstate electrolytes(SSEs)is one of the key challenges that hamper the development of Mg-ion solid-state batteries.Though various Mg-ion SSEs have been reported in recent years,key insights are hard to be derived from a single literature report.Besides,the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs.In this viewpoint article,we analyze the structural characteristics of the Mg-based SSEs with high ionic conductivity reported in the last four decades based upon data mining-we provide big-data-derived insights into the challenges and opportunities in developing next-generation Mg-ion SSEs.展开更多
In this paper, we present a cluster-based algorithm for time series outlier mining.We use discrete Fourier transformation (DFT) to transform time series from time domain to frequency domain. Time series thus can be ma...In this paper, we present a cluster-based algorithm for time series outlier mining.We use discrete Fourier transformation (DFT) to transform time series from time domain to frequency domain. Time series thus can be mapped as the points in k -dimensional space.For these points, a cluster-based algorithm is developed to mine the outliers from these points.The algorithm first partitions the input points into disjoint clusters and then prunes the clusters,through judgment that can not contain outliers.Our algorithm has been run in the electrical load time series of one steel enterprise and proved to be effective.展开更多
A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial partic...A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.展开更多
The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) i...The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.展开更多
基金supported by the National Grand Fundamental Research "973" Program of China (2004CB318109)the National High-Technology Research and Development Plan of China (2006AA01Z452)the National Information Security "242"Program of China (2005C39).
文摘Anomaly detection has been an active research topic in the field of network intrusion detection for many years. A novel method is presented for anomaly detection based on system calls into the kernels of Unix or Linux systems. The method uses the data mining technique to model the normal behavior of a privileged program and uses a variable-length pattern matching algorithm to perform the comparison of the current behavior and historic normal behavior, which is more suitable for this problem than the fixed-length pattern matching algorithm proposed by Forrest et al. At the detection stage, the particularity of the audit data is taken into account, and two alternative schemes could be used to distinguish between normalities and intrusions. The method gives attention to both computational efficiency and detection accuracy and is especially applicable for on-line detection. The performance of the method is evaluated using the typical testing data set, and the results show that it is significantly better than the anomaly detection method based on hidden Markov models proposed by Yan et al. and the method based on fixed-length patterns proposed by Forrest and Hofmeyr. The novel method has been applied to practical hosted-based intrusion detection systems and achieved high detection performance.
基金National major basic-theory planning project Mechanism and Prediction of Strong Earthquake (95130105) and the Key Project from China Seismological Bureau (95040803).
文摘Deterministic, probabilistic and composite-grading methods are used to get the possible locations of strong earth-quakes in the future in Norwest Beijing and its vicinity based on the quantitative data and their accuracy about active tectonics in the research area and by ordering, some questions in the results are also discussed. It shows that the most dangerous fault segments for strong earthquakes in the future include: segments B and A of the southern boundary fault of the Yangyuan basin, the southern boundary fault of the Xuanhua basin, the east segment of the southern Huaian fault and the east segment of the northern YanggaoTianzhen fault. The most dangerous area is YangyuanShenjing basin, the second one is TianzhenHuaianXuanhua basin and the third dangerous areas are WanquanZhangjiakou and northeast of Yuxian to southwest of Fanshan.
基金supported by the National Natural Science Foundation of China(61371172)the International S&T Cooperation Program of China(2015DFR10220)+1 种基金the Ocean Engineering Project of National Key Laboratory Foundation(1213)the Fundamental Research Funds for the Central Universities(HEUCF1608)
文摘For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘In order to construct the data mining frame for the generic project risk research, the basic definitions of the generic project risk element were given, and then a new model of the generic project risk element was presented with the definitions. From the model, data mining method was used to acquire the risk transmission matrix from the historical databases analysis. The quantitative calculation problem among the generic project risk elements was solved. This method deals with well the risk element transmission problems with limited states. And in order to get the limited states, fuzzy theory was used to discrete the historical data in historical databases. In an example, the controlling risk degree is chosen as P(Rs≥2) ≤0.1, it means that the probability of risk state which is not less than 2 in project is not more than 0.1, the risk element R3 is chosen to control the project, respectively. The result shows that three risk element transmission matrix can be acquired in 4 risk elements, and the frequency histogram and cumulative frequency histogram of each risk element are also given.
基金Projects(61001188,1161140319)supported by the National Natural Science Foundation of ChinaProject(2012ZX03001034)supported by the National Science and Technology Major ProjectProject(YETP1202)supported by Beijing Higher Education Young Elite Teacher Project,China
文摘Objective speech quality is difficult to be measured without the input reference speech.Mapping methods using data mining are investigated and designed to improve the output-based speech quality assessment algorithm.The degraded speech is firstly separated into three classes(unvoiced,voiced and silence),and then the consistency measurement between the degraded speech signal and the pre-trained reference model for each class is calculated and mapped to an objective speech quality score using data mining.Fuzzy Gaussian mixture model(GMM)is used to generate the artificial reference model trained on perceptual linear predictive(PLP)features.The mean opinion score(MOS)mapping methods including multivariate non-linear regression(MNLR),fuzzy neural network(FNN)and support vector regression(SVR)are designed and compared with the standard ITU-T P.563 method.Experimental results show that the assessment methods with data mining perform better than ITU-T P.563.Moreover,FNN and SVR are more efficient than MNLR,and FNN performs best with 14.50% increase in the correlation coefficient and 32.76% decrease in the root-mean-square MOS error.
文摘Sheet metal is widely used on auto-bodies, plane-bodies and metal furniture, etc. For instance, a typical auto-body commonly consists of hundreds of sheet metal stamping parts. Because of its complexity of structure and manufacturing process, auto-bodies inevitably have geometrical variation results from a number of different sources, such as the geometrical variation of stamping parts, the transformation of assembly process parameters and even the improper design concept. As more than 30% quality defects of an auto-body are born from the dimensional deviation of Body-In-White originated during the manufacturing process, effective diagnosis and control of dimensional faults are essential to the continuous improvement of the quality of vehicles. Especially during the period of new car launching or model changing when the assembly process was changed and adjusted frequently. For continuously improving the quality of modern cars, rapid dimensional variation causes identification becomes a challenging but essential work. In this paper, main variation causes of auto-body was firstly been cataloged and analyzed, then, a dimensional variation diagnostic reasoning and decision approach was developed through the combination of data mining and knowledge discovery techniques. This approach is driven by variation pattern identification which can be discovered from the dispersive, isolated massive measured data: Correlation Analysis (CA) and Maximal Tree (MT) methods were applied to extract the large variation group from massive multidimensional measured data, while multivariate statistical analysis (MSA) approach was used to discovery the principle variation pattern. A Decision Tree (DT) approach based on the knowledge of product and assembly process was developed to fulfill the "Hypothesis and Validation" characterized variation causes reasoning procedure. An practical application case with sudden and severe dimension variation on rear end panel in up/down direction was analyzed and successfully solved aided by the devloped variation diagnostic method, which have proved that the approach is effective and efficient.
文摘This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.
文摘In order to rapidly and effectively meet the informative demand from commanding decision-making, it is important to build, maintain and mine the intelligence database. The type, structure and maintenance of military intelligence database are discussed. On this condition, a new data-mining arithmetic based on relation intelligence database is presented according to the preference information and the requirement of time limit given by the commander. Furthermore, a simple calculative example is presented to prove the arithmetic with better maneuverability. Lastly, the problem of how to process the intelligence data mined from the intelligence database is discussed.
文摘This paper presents an integrated approach towards solving the problem of "Gene Prediction".The "Gene Prediction" problem solving undergoes well defined stages starting with a DNA
文摘In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.
文摘Important Dates Submission due November 15, 2005 Notification of acceptance December 30, 2005 Camera-ready copy due January 10, 2006 Workshop Scope Intelligence and Security Informatics (ISI) can be broadly defined as the study of the development and use of advanced information technologies and systems for national and international security-related applications. The First and Second Symposiums on ISI were held in Tucson,Arizona,in 2003 and 2004,respectively. In 2005,the IEEE International Conference on ISI was held in Atlanta,Georgia. These ISI conferences have brought together academic researchers,law enforcement and intelligence experts,information technology consultant and practitioners to discuss their research and practice related to various ISI topics including ISI data management,data and text mining for ISI applications,terrorism informatics,deception detection,terrorist and criminal social network analysis,crime analysis,monitoring and surveillance,policy studies and evaluation,information assurance,among others. We continue this stream of ISI conferences by organizing the Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06). WISI’06 will provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. The workshop also welcomes contributions dealing with ISI challenges specific to the Pacific Asian region.
基金Supported by Funding(2017RAXXJ075)from Harbin Applied Technology Research and Development Project
文摘With the reform of rural network enterprise system,the speed of transfer property rights in rural power enterprises is accelerated.The evaluation of the operation and development status of rural power enterprises is directly related to the future development and investment direction of rural power enterprises.At present,the evaluation of the production and operation of rural network enterprises and the development status of power network only relies on the experience of the evaluation personnel,sets the reference index,and forms the evaluation results through artificial scoring.Due to the strong subjective consciousness of the evaluation results,the practical guiding significance is weak.Therefore,distributed data mining method in rural power enterprises status evaluation was proposed which had been applied in many fields,such as food science,economy or chemical industry.The distributed mathematical model was established by using principal component analysis(PCA)and regression analysis.By screening various technical indicators and determining their relevance,the reference value of evaluation results was improved.Combined with statistical program for social sciences(SPSS)data analysis software,the operation status of rural network enterprises was evaluated,and the rationality,effectiveness and economy of the evaluation was verified through comparison with current evaluation results and calculation examples of actual grid operation data.
文摘Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.
基金supported by the Ensemble Grant for Early Career Researchers 2022-2023 and the 2023 Ensemble Continuation Grant of Tohoku University,the Hirose Foundation,and the AIMR Fusion Research Grantsupported by JSPS KAKENHI Nos.JP23K13599,JP23K13703,JP22H01803,JP18H05513,and JP23K13542.F.Y.and Q.W.acknowledge the China Scholarship Council(CSC)to support their studies in Japan.
文摘Magnesium(Mg)is a promising alternative to lithium(Li)as an anode material in solid-state batteries due to its abundance and high theoretical volumetric capacity.However,the sluggish Mg-ion conduction in the lattice of solidstate electrolytes(SSEs)is one of the key challenges that hamper the development of Mg-ion solid-state batteries.Though various Mg-ion SSEs have been reported in recent years,key insights are hard to be derived from a single literature report.Besides,the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs.In this viewpoint article,we analyze the structural characteristics of the Mg-based SSEs with high ionic conductivity reported in the last four decades based upon data mining-we provide big-data-derived insights into the challenges and opportunities in developing next-generation Mg-ion SSEs.
文摘In this paper, we present a cluster-based algorithm for time series outlier mining.We use discrete Fourier transformation (DFT) to transform time series from time domain to frequency domain. Time series thus can be mapped as the points in k -dimensional space.For these points, a cluster-based algorithm is developed to mine the outliers from these points.The algorithm first partitions the input points into disjoint clusters and then prunes the clusters,through judgment that can not contain outliers.Our algorithm has been run in the electrical load time series of one steel enterprise and proved to be effective.
文摘A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.
文摘The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.
