Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/land cover changes in the Rwizi catchment, south western Uganda from 19...Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/land cover changes in the Rwizi catchment, south western Uganda from 1989-2019 and projected the trend by 2040. Landsat images, field observations, key informant interviews and focus group discussions were used to collect data. Changes in cropland, forestland, built up area, grazing land, wetland and open water bodies were analyzed in ArcGIS version 10.2.2 and ERDAS IMAGINE 14 software and a Markov chain model. All the LULC classes increased in area except grazing land. Forest land and builtup area between 2009-2019 increased by 370.03% and 229.53% respectively. Projections revealed an increase in forest land and builtup area by 2030 and only built up area by 2040. LULCC in the catchment results from population pressure, reduced soil fertility and high value of agricultural products.展开更多
Background:Patients with type 2 diabetes are at high risk for developing multiple chronic complications.However,there is a lack of studies of the cumulative number of diabetic complications in China.Methods:A retrospe...Background:Patients with type 2 diabetes are at high risk for developing multiple chronic complications.However,there is a lack of studies of the cumulative number of diabetic complications in China.Methods:A retrospective cohort study was performed from 2009 to 2021.Type 2 diabetes patients who were first diagnosed after the age of 35 years between January 1,2009,and December 31,2017,were included.Five states were defined according to the number of chronic complications:no(S0),one(S1),two(S2),three(S3),and four or more complications(S4).A multi-state Markov model was constructed to estimate transition probability,transition intensity,mean sojourn time,and the possible factors for each state.Results:The study included 32653 type 2 diabetes patients(mean age,59.59 years;15929(48.8%)male),and mean follow-up time of 7.75 years.In all,4375 transitions were observed.The 12-year transition probability of from state S0 to S1 was the lowest at 16.4%,while that from S2 to S3 was the highest,at 45.6%.Higher fasting blood glucose,lower high-density lipoprotein cholesterol,higher total cholesterol,and an unhealthy diet were associated with higher risk of progression from S0 to S1.Being female,less than 60 years old,weekly physical activity,and vegetarian diet decreased this risk.Being female and less than 60 years old reduced the likelihood of transition from S1 to S2,whereas lower high-density lipoprotein cholesterol increased this likelihood.Conclusions:Following the occurrence of two complications in type 2 diabetes patients,the risk for accumulating a third complication within a short time is significantly increased.It is important to take advantage of the stable window period when patients have fewer than two complications,strengthen the monitoring of blood glucose and blood lipids,and encourage patients to maintain good living habits to prevent further deterioration.展开更多
Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers.It deals with the end user customers of a supply-chain network and therefore has to accommodate...Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers.It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities.The volume and volatility of the business makes it one of the prospectivefields for analytical study and data modeling.This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting,customer targeting,customized offers,value proposition etc.The segmentation could be on various aspects such as demographics,historic behavior or preferences based on the use cases.In this paper,historic retail transactional data is used to segment the custo-mers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm.This helps in calculating the futuristic value a segment or a customer brings to the business.Strategic marketing designs and budgeting can be implemented using these results.The study is specifically useful for large scale marketing in domains such as e-commerce,insurance or retailers to segment,profile and measure the customer lifecycle value over a short period of time.展开更多
The analysis of bird ringing data often comes with some potential sources of error and bias,as ring wear and/or loss could affect mark-recapture analyses and produce erroneous estimates of survival.Furthermore,ring we...The analysis of bird ringing data often comes with some potential sources of error and bias,as ring wear and/or loss could affect mark-recapture analyses and produce erroneous estimates of survival.Furthermore,ring wear and loss rates may differ between and within species based on the habitat they use or the species’ life-history traits and behaviour as well as the type of the ring.In this study we use resighting data from a long-term double marking experiment to directly estimate the rate of colour-ring loss among different Dalmatian Pelican colonies over time,evaluate any possible factors that could contribute to differential ring loss and assess how it may bias the results of mark-resighting analyses.Based on 14,849 resightings from 1275 individuals and using multi-state continuous-time hidden Markov models(HMMs) we showed that probability of ring loss was markedly different among colonies,ranging from 0.10 to 0.42 within the first year of marking,whereas the cumulative probability of losing a ring after ten years ranged 0.64 to 0.99.These rates are among the highest estimated when compared to previous studies in waterbirds.Our approach assessing the intra-specific variance in ring loss provided several factors potentially involved,such as the use of glue and the fledgling age accuracy and we could further hypothesise the effect of environmental factors.Finally,our results showed that ring loss can be a significant challenge for the assessment of the species’ population dynamics using mark-recapture methods as survival was consistently underestimated when not accounting for ring loss and varied significantly among different colonies.展开更多
Based on four phases of TM images acquired in 1990, 1995, 2000 and 2005, this paper took Kitakyushu in Japan as a case study to analyze spatial change of land use landscape and corresponding effects on environmental i...Based on four phases of TM images acquired in 1990, 1995, 2000 and 2005, this paper took Kitakyushu in Japan as a case study to analyze spatial change of land use landscape and corresponding effects on environmental issues guided by landscape ecology theory in virtue of combining technology of Remote Sensing with GIS. Firstly, land use types were divided into 6 classes (farmland, mountain, forestland, water body, urban land and unused land) according to national classification standard of land use, comprehensible ability of TM image and purpose of this study. Secondly, following the theory of landscape ecology analysis, 11 typical landscape indices were abstracted to evaluate the environmental effects and spatial feature changes of land use. Research results indicated that land use has grown more and more diversified and unbalanced, human activities have disturbed the landscape more seriously. Finally, transfer matrix of Markov was applied to forecast change process of land use in the future different periods, and then potential land use changes were also simulated from 2010 to 2050. Results showed that conversion tendency for all types of land use in Kitakyushu into urban construction land were enhanced. The study was anticipated to help local authorities better understand and address a complex land use system, and develop improved land use management strategies that could better balance urban expansion and ecological conservation.展开更多
Text information is principally dependent on the natural languages.Therefore,improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researche...Text information is principally dependent on the natural languages.Therefore,improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researchers encounter.Content authentication and tampering detection of digital contents have become a major concern in the area of communication and information exchange via the Internet.In this paper,an intelligent text Zero-Watermarking approach SETZWMWMM(Smart English Text Zero-Watermarking Approach Based on Mid-Level Order and Word Mechanism of Markov Model)has been proposed for the content authentication and tampering detection of English text contents.The SETZWMWMM approach embeds and detects the watermark logically without altering the original English text document.Based on Hidden Markov Model(HMM),Third level order of word mechanism is used to analyze the interrelationship between contexts of given English texts.The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,SETZWMWMM has been implemented and validated with attacked English text.Experiments were performed on four datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results show that our method is more sensitive and efficient for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.展开更多
Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide.The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroenceph...Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide.The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram(EEG) as a noninvasive procedure to record neuronal activities in the brain.EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals.Shannon entropy,collision entropy,transfer entropy,conditional probability,and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform.Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification.Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector.The accuracy of the proposed approach is higher for Q=2 and J=10.Transfer entropy is observed to be significant for different class combinations.Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time.The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals.The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.展开更多
This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model (HHMM) is used to represent a temporal profile of normal behavior in...This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model (HHMM) is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system's normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.展开更多
As the increasing popularity and complexity of Web applications and the emergence of their new characteristics, the testing and maintenance of large, complex Web applications are becoming more complex and difficult. W...As the increasing popularity and complexity of Web applications and the emergence of their new characteristics, the testing and maintenance of large, complex Web applications are becoming more complex and difficult. Web applications generally contain lots of pages and are used by enormous users. Statistical testing is an effective way of ensuring their quality. Web usage can be accurately described by Markov chain which has been proved to be an ideal model for software statistical testing. The results of unit testing can be utilized in the latter stages, which is an important strategy for bottom-to-top integration testing, and the other improvement of extended Markov chain model (EMM) is to present the error type vector which is treated as a part of page node. this paper also proposes the algorithm for generating test cases of usage paths. Finally, optional usage reliability evaluation methods and an incremental usability regression testing model for testing and evaluation are presented. Key words statistical testing - evaluation for Web usability - extended Markov chain model (EMM) - Web log mining - reliability evaluation CLC number TP311. 5 Foundation item: Supported by the National Defence Research Project (No. 41315. 9. 2) and National Science and Technology Plan (2001BA102A04-02-03)Biography: MAO Cheng-ying (1978-), male, Ph.D. candidate, research direction: software testing. Research direction: advanced database system, software testing, component technology and data mining.展开更多
The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict...The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.展开更多
Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measur...Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.展开更多
Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultur...Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.展开更多
With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at lo...With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.展开更多
To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov rand...To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.展开更多
In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance...In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.展开更多
A new feature extraction method based on 2D-hidden Markov model(HMM) is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tes...A new feature extraction method based on 2D-hidden Markov model(HMM) is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.展开更多
The paper aims to analyze land use/land cover (LULC) changes in western part and the populated area of Amman governorate and to identify the process of urbanization and urban expansion within the study area for the pe...The paper aims to analyze land use/land cover (LULC) changes in western part and the populated area of Amman governorate and to identify the process of urbanization and urban expansion within the study area for the period of 1984-2014. It also aims to predict future LULC map for the year 2030 using Markov Model to provide city planners and decision makers with information about the past and current spatial dynamics of LULC change and strictly urban expansion towards successful management and better planning in the future. Images from Landsat 5-TM for the years 1984, 1999 and from Landsat 8-OLI for the year 2014 were used to investigate LULC within the study area during 1984-2014 and the resulted LULC maps in 1999 and 2014 were used to predict future LULC map based on Markov Model. The results indicated that the urban/built up area expanded by 147% during the period from 1984 to 2014 and predicted to expand by 43.9% from 2014 to 2030 based on Markov model predictions. The areas in the western, northwest and southwest parts of Amman as well as the areas of Marka and Uhud, the northeast of the study area, were predicted to witness the major urban expansion in 2030. And these are the areas where city planners and decision makers should take into consideration in future plans of Amman. The urban expansion was mainly attributed to the high population growth rate and large number of immigrants from neighboring countries and other socio-economic changes.展开更多
The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necess...The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis (ICA) and the Discrete Hidden Markov Model (DHMM), a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.展开更多
With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods re...With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model(HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people’s eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced(38.4%) and work-oriented(61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people’s behavior in urban mobility research.展开更多
Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link accomplishment.Given the issu...Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link accomplishment.Given the issues above,route failure is prevalent in ad hoc mobile cloud computing networks,which increases energy consumption and delay and reduces stability.These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network.To address these weaknesses,which raise many concerns about privacy and security,this study formulated clustering-based storage and search optimization approaches using cross-layer analysis.The proposed approaches were formed by cross-layer analysis based on intrusion detection methods.First,the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks.Moreover,delay,energy consumption,network lifetime,and link accomplishment are highly addressed by the proposed algorithm.The hidden Markov model is used to maintain the data transition and distributions in the network.Every data communication network,like ad hoc mobile cloud computing,faces security and confidentiality issues.However,the main security issues in this article are addressed using the storage and search optimization approach.Hence,the new algorithm developed helps detect intruders through intelligent cross layer analysis with theMarkov model.The proposed model was simulated in Network Simulator 3,and the outcomes were compared with those of prevailing methods for evaluating parameters,like accuracy,end-to-end delay,energy consumption,network lifetime,packet delivery ratio,and throughput.展开更多
文摘Analysis of catchment Land use/Land cover (LULC) change is a vital tool in ensuring sustainable catchment management. The study analyzed land use/land cover changes in the Rwizi catchment, south western Uganda from 1989-2019 and projected the trend by 2040. Landsat images, field observations, key informant interviews and focus group discussions were used to collect data. Changes in cropland, forestland, built up area, grazing land, wetland and open water bodies were analyzed in ArcGIS version 10.2.2 and ERDAS IMAGINE 14 software and a Markov chain model. All the LULC classes increased in area except grazing land. Forest land and builtup area between 2009-2019 increased by 370.03% and 229.53% respectively. Projections revealed an increase in forest land and builtup area by 2030 and only built up area by 2040. LULCC in the catchment results from population pressure, reduced soil fertility and high value of agricultural products.
基金supported by the National Natural Science Foundation of China(grant No.72074011)the Real World Study Project of Hainan Boao Lecheng Pilot Zone(Real World Study Base of NMPA)(HNLC2022RWS012)+1 种基金the fundamental research funds for central public welfare research institutes(2023CZ-11)National Natural Science Foundation of China(No.82003536).
文摘Background:Patients with type 2 diabetes are at high risk for developing multiple chronic complications.However,there is a lack of studies of the cumulative number of diabetic complications in China.Methods:A retrospective cohort study was performed from 2009 to 2021.Type 2 diabetes patients who were first diagnosed after the age of 35 years between January 1,2009,and December 31,2017,were included.Five states were defined according to the number of chronic complications:no(S0),one(S1),two(S2),three(S3),and four or more complications(S4).A multi-state Markov model was constructed to estimate transition probability,transition intensity,mean sojourn time,and the possible factors for each state.Results:The study included 32653 type 2 diabetes patients(mean age,59.59 years;15929(48.8%)male),and mean follow-up time of 7.75 years.In all,4375 transitions were observed.The 12-year transition probability of from state S0 to S1 was the lowest at 16.4%,while that from S2 to S3 was the highest,at 45.6%.Higher fasting blood glucose,lower high-density lipoprotein cholesterol,higher total cholesterol,and an unhealthy diet were associated with higher risk of progression from S0 to S1.Being female,less than 60 years old,weekly physical activity,and vegetarian diet decreased this risk.Being female and less than 60 years old reduced the likelihood of transition from S1 to S2,whereas lower high-density lipoprotein cholesterol increased this likelihood.Conclusions:Following the occurrence of two complications in type 2 diabetes patients,the risk for accumulating a third complication within a short time is significantly increased.It is important to take advantage of the stable window period when patients have fewer than two complications,strengthen the monitoring of blood glucose and blood lipids,and encourage patients to maintain good living habits to prevent further deterioration.
文摘Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers.It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities.The volume and volatility of the business makes it one of the prospectivefields for analytical study and data modeling.This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting,customer targeting,customized offers,value proposition etc.The segmentation could be on various aspects such as demographics,historic behavior or preferences based on the use cases.In this paper,historic retail transactional data is used to segment the custo-mers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm.This helps in calculating the futuristic value a segment or a customer brings to the business.Strategic marketing designs and budgeting can be implemented using these results.The study is specifically useful for large scale marketing in domains such as e-commerce,insurance or retailers to segment,profile and measure the customer lifecycle value over a short period of time.
基金supported by MAVA Foundation and Tour du Valatsupported financially by the MAVA Foundationby the Prespa Ohrid Nature Trust (PONT)。
文摘The analysis of bird ringing data often comes with some potential sources of error and bias,as ring wear and/or loss could affect mark-recapture analyses and produce erroneous estimates of survival.Furthermore,ring wear and loss rates may differ between and within species based on the habitat they use or the species’ life-history traits and behaviour as well as the type of the ring.In this study we use resighting data from a long-term double marking experiment to directly estimate the rate of colour-ring loss among different Dalmatian Pelican colonies over time,evaluate any possible factors that could contribute to differential ring loss and assess how it may bias the results of mark-resighting analyses.Based on 14,849 resightings from 1275 individuals and using multi-state continuous-time hidden Markov models(HMMs) we showed that probability of ring loss was markedly different among colonies,ranging from 0.10 to 0.42 within the first year of marking,whereas the cumulative probability of losing a ring after ten years ranged 0.64 to 0.99.These rates are among the highest estimated when compared to previous studies in waterbirds.Our approach assessing the intra-specific variance in ring loss provided several factors potentially involved,such as the use of glue and the fledgling age accuracy and we could further hypothesise the effect of environmental factors.Finally,our results showed that ring loss can be a significant challenge for the assessment of the species’ population dynamics using mark-recapture methods as survival was consistently underestimated when not accounting for ring loss and varied significantly among different colonies.
基金Sasakawa Scientific Foundation of Japan, No.20-238 National Basic Research Program of China (973 Program), No.2006CB403200+1 种基金 National Natural Science Foundation of China, No.40261002 No.40561006
文摘Based on four phases of TM images acquired in 1990, 1995, 2000 and 2005, this paper took Kitakyushu in Japan as a case study to analyze spatial change of land use landscape and corresponding effects on environmental issues guided by landscape ecology theory in virtue of combining technology of Remote Sensing with GIS. Firstly, land use types were divided into 6 classes (farmland, mountain, forestland, water body, urban land and unused land) according to national classification standard of land use, comprehensible ability of TM image and purpose of this study. Secondly, following the theory of landscape ecology analysis, 11 typical landscape indices were abstracted to evaluate the environmental effects and spatial feature changes of land use. Research results indicated that land use has grown more and more diversified and unbalanced, human activities have disturbed the landscape more seriously. Finally, transfer matrix of Markov was applied to forecast change process of land use in the future different periods, and then potential land use changes were also simulated from 2010 to 2050. Results showed that conversion tendency for all types of land use in Kitakyushu into urban construction land were enhanced. The study was anticipated to help local authorities better understand and address a complex land use system, and develop improved land use management strategies that could better balance urban expansion and ecological conservation.
基金The author extends his appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(R.G.P.2/55/40/2019),Received by Fahd N.Al-Wesabi.www.kku.edu.sa。
文摘Text information is principally dependent on the natural languages.Therefore,improving security and reliability of text information exchanged via internet network has become the most difficult challenge that researchers encounter.Content authentication and tampering detection of digital contents have become a major concern in the area of communication and information exchange via the Internet.In this paper,an intelligent text Zero-Watermarking approach SETZWMWMM(Smart English Text Zero-Watermarking Approach Based on Mid-Level Order and Word Mechanism of Markov Model)has been proposed for the content authentication and tampering detection of English text contents.The SETZWMWMM approach embeds and detects the watermark logically without altering the original English text document.Based on Hidden Markov Model(HMM),Third level order of word mechanism is used to analyze the interrelationship between contexts of given English texts.The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,SETZWMWMM has been implemented and validated with attacked English text.Experiments were performed on four datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results show that our method is more sensitive and efficient for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.
文摘Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide.The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram(EEG) as a noninvasive procedure to record neuronal activities in the brain.EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals.Shannon entropy,collision entropy,transfer entropy,conditional probability,and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform.Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification.Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector.The accuracy of the proposed approach is higher for Q=2 and J=10.Transfer entropy is observed to be significant for different class combinations.Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time.The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals.The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.
基金Supported by the Science and Technology Development Project Foundation of Tianjin (033800611, 05YFGZGX24200)
文摘This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model (HHMM) is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system's normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.
文摘As the increasing popularity and complexity of Web applications and the emergence of their new characteristics, the testing and maintenance of large, complex Web applications are becoming more complex and difficult. Web applications generally contain lots of pages and are used by enormous users. Statistical testing is an effective way of ensuring their quality. Web usage can be accurately described by Markov chain which has been proved to be an ideal model for software statistical testing. The results of unit testing can be utilized in the latter stages, which is an important strategy for bottom-to-top integration testing, and the other improvement of extended Markov chain model (EMM) is to present the error type vector which is treated as a part of page node. this paper also proposes the algorithm for generating test cases of usage paths. Finally, optional usage reliability evaluation methods and an incremental usability regression testing model for testing and evaluation are presented. Key words statistical testing - evaluation for Web usability - extended Markov chain model (EMM) - Web log mining - reliability evaluation CLC number TP311. 5 Foundation item: Supported by the National Defence Research Project (No. 41315. 9. 2) and National Science and Technology Plan (2001BA102A04-02-03)Biography: MAO Cheng-ying (1978-), male, Ph.D. candidate, research direction: software testing. Research direction: advanced database system, software testing, component technology and data mining.
基金National Key Research and Development Program of China(Grant No.2020YFB1710300)National Natural Science Foundation of China(Grant No.52005042)+2 种基金National Defense Fundamental Research Foundation of China(Grant No.JCKY2020203B039)Equipment Pre-research Foundation of China(Grant No.80923010101)Beijing Institute of Technology Research Fund Program for Young Scholars.
文摘The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.
基金This project is supported by National Natural Science Foundation of China(No.50375153).
文摘Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.
基金support provided from the Cooperative Education Fund of China Ministry of Education(201702113002 and 201801193119)Hunan Natural Science Foundation(2018JJ2138)Degree and Graduate Education Reform Project of Hunan Province(JG2018B096)are greatly appreciated by the authors.
文摘Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.
基金This study was funded by the National Natural Science Foundation of China(Grant No.41975027)the Natural Science Foundation of Jiangsu Province(Grant No.BK20171457)the National Key R&D Program on Monitoring,Early Warning and Prevention of Major Natural Disasters(Grant No.2017YFC1501401).
文摘With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.
基金the National Natural Science Foundation of China(Grant No.11471004)the Key Research and Development Program of Shaanxi Province,China(Grant No.2018SF-251)。
文摘To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.
文摘In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.
基金This project is supported by National Natural Science Foundation of China(No.50075079).
文摘A new feature extraction method based on 2D-hidden Markov model(HMM) is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.
文摘The paper aims to analyze land use/land cover (LULC) changes in western part and the populated area of Amman governorate and to identify the process of urbanization and urban expansion within the study area for the period of 1984-2014. It also aims to predict future LULC map for the year 2030 using Markov Model to provide city planners and decision makers with information about the past and current spatial dynamics of LULC change and strictly urban expansion towards successful management and better planning in the future. Images from Landsat 5-TM for the years 1984, 1999 and from Landsat 8-OLI for the year 2014 were used to investigate LULC within the study area during 1984-2014 and the resulted LULC maps in 1999 and 2014 were used to predict future LULC map based on Markov Model. The results indicated that the urban/built up area expanded by 147% during the period from 1984 to 2014 and predicted to expand by 43.9% from 2014 to 2030 based on Markov model predictions. The areas in the western, northwest and southwest parts of Amman as well as the areas of Marka and Uhud, the northeast of the study area, were predicted to witness the major urban expansion in 2030. And these are the areas where city planners and decision makers should take into consideration in future plans of Amman. The urban expansion was mainly attributed to the high population growth rate and large number of immigrants from neighboring countries and other socio-economic changes.
基金supported by the National Natural Science Foundation of China under Grant No.60672184
文摘The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis (ICA) and the Discrete Hidden Markov Model (DHMM), a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.
文摘With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model(HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people’s eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced(38.4%) and work-oriented(61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people’s behavior in urban mobility research.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Ad hoc mobile cloud computing networks are affected by various issues,like delay,energy consumption,flexibility,infrastructure,network lifetime,security,stability,data transition,and link accomplishment.Given the issues above,route failure is prevalent in ad hoc mobile cloud computing networks,which increases energy consumption and delay and reduces stability.These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network.To address these weaknesses,which raise many concerns about privacy and security,this study formulated clustering-based storage and search optimization approaches using cross-layer analysis.The proposed approaches were formed by cross-layer analysis based on intrusion detection methods.First,the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks.Moreover,delay,energy consumption,network lifetime,and link accomplishment are highly addressed by the proposed algorithm.The hidden Markov model is used to maintain the data transition and distributions in the network.Every data communication network,like ad hoc mobile cloud computing,faces security and confidentiality issues.However,the main security issues in this article are addressed using the storage and search optimization approach.Hence,the new algorithm developed helps detect intruders through intelligent cross layer analysis with theMarkov model.The proposed model was simulated in Network Simulator 3,and the outcomes were compared with those of prevailing methods for evaluating parameters,like accuracy,end-to-end delay,energy consumption,network lifetime,packet delivery ratio,and throughput.