Zhanquan Sun

• Doctor of Philosophy
• Shandong Computer Science Center

Steganalysis is to detect whether or not the seemly innocent image hiding message. It is an important research topic in information security. With the development of steganography technology, steganalysis becomes more and more difficult. Some steganalysis methods have been proposed to improve the performance. Most research work concentrates on spec...

In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation of working memory. Howeve...

We propose an embedded/integrated feature selection method based on neural networks with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on the inputs to the network, i.e., for selection of useful features. Lasso based feature selection using a multi-layer perceptron usually requires an additional set of weights, w...

Conjugate gradient methods can be used with advantages such as fast convergence and low memory requirement in real applications. A conjugate gradient-based neuro-fuzzy learning algorithm for zero-order Takagi-Sugeno inference systems is proposed in this paper. Compared with the existing gradient-based algorithm, this method enhances the learning pe...

In order to effectively solve series of problems which affect grain production in implementation of the “Bohai granary” project, such as the wide varieties of factors (including soil fertility, soil salinity, pH, underground water level, water salinity, crop varieties and management etc.), the huge spatial and temporal variability, the difficulty o...

Chinese materia medica resource survey provides an important basis for the development of traditional Chinese Medicine (TCM) industry. During the Chinese materia medica resource survey process, millions of materia medica plant images are collected. The collected image dataset includes some images that are unqualified for image analysis, i.e. they c...

In this paper, by constructing a generalized Armijo search method, a novel conjugate gradient (CG) model has been proposed to training a common three-layer backpropagation (BP) neural network. Compared with the classical gradient descent method, this algorithm efficiently accelerates the convergence speed due to the existence of the additional conj...

Image classification is one of the most important problems for computer vision and machine learning. Many image classification methods have been proposed and applied to many application areas. But how to improve the performance of image classification is still an important research issue to be resolved. Feature extraction is the most important task...

In this paper, a novel multilayer backpropagation (BP) neural network model is proposed based on conjugate gradient (CG) method with generalized Armijo search. The presented algorithm requires low memory and performs fast convergent speed in practical applications. One reason is that the constructed conjugate direction guarantees the sufficient des...

The complex-valued neural networks are the class of networks that solve complex problems by using complex-valued variables. The gradient descent method is one of the popular algorithms to train complex-valued neural networks. Essentially, the established networks are integer-order models. Compared with classical integer-order models, the built mode...

Multi-label classification is one of the most challenging tasks in the computer vision community, owing to different composition and interaction (e.g. partial visibility or occlusion) between objects in multi-label images. Intuitively, some objects usually co-occur with some specific scenes, e.g. the sofa often appears in a living room. Therefore,...

Steganalysis is the opposite art to steganography, whose goal is to detect whether or not the seemly innocent objects like image hiding message. Image steganalysis is important research issue of information security field. With the development of steganography technology, steganalysis becomes more and more difficult. Regarding the problem of improv...

In big data era, digital information is growing rapidly. False and unlawful images influence our normal work and life, especially the exaggerated or fake propaganda of electronic commerce merchants. In this article, our purpose is to help people find out fake qualification certificate information automatically. Base on collecting and classifying we...

Text classification is a popular research topic in data mining. Many classification methods have been proposed. Feature selection is an important technique for text classification since it is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. In recent years, data ha...

Traffic congestion state identification is one of the most important tasks of ITS. Traffic flow is a nonlinear complicated system. Traffic congestion state is affected by many factors, such as road channelization, weather condition, drivers’ different driving behavior and so on. It is difficult to collect all necessary traffic information. Traffic...

Text clustering is an important research topic in data mining. Many text clustering methods have been proposed and obtained satisfactory results. Information Bottleneck algorithm, which is based on information loss, can measure complicated relationship between variables. It is taken as one of the most informative text clustering methods and has bee...

Feature selection is an important research topic in machine learning and pattern recognition. It is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. However, in recent years, data has become increasingly larger in both number of instances and number of features in...

Clustering is an important research topic of data mining. Information bottleneck theory-based clustering method is suitable for dealing with complicated clustering problems because that its information loss metric can measure arbitrary statistical relationships between samples. It has been widely applied to many kinds of areas. With the development...

Feature selection is an important research topic in machine learning and pattern recognition. It is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. With the development of computer science, data deluge occurs in many application fields. Classical feature selection...

Traffic flow forecasting is a popular research topic of Intelligent Transportation Systems (ITS). With the development of information technology, much historical electronic traffic flow data have been collected. How to take full use of the historical traffic flow data to improve the traffic flow forecasting precision is an important issue. As more...

Clustering is considered as one of the most important tasks in data mining. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. It has been widely applied to many kinds of areas. Many clustering methods have been studied, such as k-means, Fisher clustering method, Kohonen neural network and so on. In many kinds...

Finding optimal path in a given network is an important content of intelligent transportation information service. Static shortest path has been studied widely and many efficient searching methods have been developed, for example Dijkstra’s algorithm, Floyd-Warshall, Bellman-Ford, A* et al. However, practical travel time is not a constant value but...

Traffic congestion identification is a popular research topic of Intelligent Transportation System (ITS). Many identification methods, such as threshold value based methods, California, McMaster method and so on, have been studied. But the threshold values of these methods are difficult to be determined. A novel threshold value based traffic conges...

Support Vector Machines (SVM) are powerful classification and regression tools. They have been widely studied by many scholars and applied in many kinds of practical fields. But their compute and storage requirements increase rapidly with the number of training vectors, putting many problems of practical interest out of their reach. For applying SV...

Selection of treatment according to the differentiation of syndromes is the kernel theory of traditional Chinese medicine. Different symptoms play different roles in the differentiation of syndromes. It is important to know the contribution rate of each symptom quantitatively. How to find the most informative symptoms combination served as the diff...

Travel time forecasting is an important content of dynamic traffic navigation. Dynamic traffic data collection is the precondition of forecasting. Many traffic data collection methods have been adopted, such as loop inductive vehicle detector, radar detector, video detector, GPS floating car and so on. Due to the widely distribution, GPS floating c...

Reliability assessment is an important issue in reliability engineering. Classical reliability-estimating methods are based on precise (also called “crisp”) lifetime data. It is usually assumed that the observed lifetime data take precise real numbers. Due to the lack, inaccuracy, and fluctuation of data, some collected lifetime data may be in the...

The realization of the Intelligent Transportation Systems will effectively solve the problem of traffic congestion and urban traffic pollution, improve the road capacity and traffic safety. A crucial key of the realization of the ITS is the estimate and prediction of travel time: how to make and continuously update prediction of travel time for sev...

The paper proposes a space–time autoregressive integrated moving average (STARIMA) model to predict the traffic volume in urban areas. The methodological framework incorporates the historical traffic data and the spatial features of a road network. Moreover, the spatial characteristics in a way that reflects not only the distance but also the avera...

With the increasing of vehicle quantity, traffic violations occur frequently. It is an efficient means to avoid traffic accident through controlling traffic violation. Based on the study of traffic accidents, black spot of traffic violation can be determined. But little research work has been done on this subject at present. This paper proposed a t...

For improving traffic flow forecasting precision, a forecasting method that combines nonlinear regression Support Vector Machines (SVM) with Principal Component Analysis (PCA) was proposed. PCA was used to extract features from forecasting variables and produce fewer principal components. These principal components were input to nonlinear regress S...

It is a crucial part for ATMS to accurately identify and forecast traffic state from real-time traffic data. To improve the identification rate of traffic state, multisource information should be used. The multisource information fusion method is important. Information fusion is divided into three levels, i.e. data level, feature level, and decisio...

Traffic congestion identification is a popular research topic of intelligent transport systems. Identification rate of existing methods usually cannot meet the practical requirements. To improve the identification rate and reduce the computation cost, a novel intelligent identification method is proposed. The proposed method combines principal comp...

According to the feature of software in bioinformatics field, this paper proposes a suite of software integration solution based on grid service - SoSIS. In the method, other solutionspsila ideas are used for referenced, such as PISE, myGrid and VINCA. Firstly, bio-software is encapsulated into grid service. Secondly, the grid service is abstracted...

Traffic flow forecasting is a popular research topic of intelligent transportation systems (ITS). Some forecasting models have been developed, but these methods' precision usually can't meet with practical requirement. The Traffic models of different time sections in a day have diversities. The forecasting precision could be improved if the models...

Phase space reconstruction is the base of chaotic traffic flow prediction. To determine objectively the embedding delay time tau and embedding dimension m of real urban traffic flow time series chaotic phase space, we put forward a new kind of average calculation method. For delay time value, we first calculated its value of single day with auto-co...

Mutual information can measure arbitrary statistical dependencies. It has been applied to many kinds of fields widely. But when mutual information is used as the correlation measure, the features with more values are apt to be chosen. To solve this problem, a novel definition of correlation degree is proposed in this paper. It can avoid the shortco...

Traffic flow forecasting is an important issue for the application of intelligent transportation systems (ITS). How to improve the traffic flow forecasting precision is a crucial problem. Traffic models in different time sections have great differences. The forecasting precision could be improved if the traffic flow forecasting models were built on...

Traffic flow forecasting is a popular research topic of intelligent transport systems (ITS). Some efficient forecasting models have been developed. In practice, many feature variables are available for traffic flow forecasting. It is important that how to choose the most informative forecasting variable combination, which can save lots of computati...

Based on current work about high order Boltzmann machine (BM) and unsupervised BM, an unsupervised learning algorithm based on high order BM is proposed. It is different from supervised BM in that it has no training samples for output units. In the unsupervised BM, the maximization of the mutual information based on Shannon entropy is used as an un...

Differentiation of syndromes of traditional Chinese medicine (TCM) mainly depends on the information obtained from four diagnosis methods. Now many physicochemical parameters are available in clinic. There exists great correlation between TCM syndromes and physicochemical parameters. The objective of the paper is to analyze the correlation between...

Differentiation of syndromes is the kernel theory of Traditional Chinese Medicine (TCM). How to diagnose syndromes correctly with scientific means according to symptoms is the first problem in TCM. Several modern approaches have been applied, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support Vecto...

In Traditional Chinese Medicine (TCM), it is difficult to interpret how to judge syndrome according to clinic information in scientific means because TCM depends mostly on experience and subjective judgment. Now there are more and more people begin to study TCM with modern techniques in many countries. Neural network techniques have developed quick...