Abstract Railway emergency management has gradually shifted from reaction planning to scenario planning when an emergency occurs. As such, an effective approach to integrated information management based on the "scenario-response" mode has become of considerable value in establishing a scenario model for railway emergency. First, the scenario elements of railway emergencies were classified by scenario analysis to set up a hierarchical network model for railway emergencies. After the analysis of... Railway emergency management has gradually shifted from reaction planning to scenario planning when an emergency occurs. As such, an effective approach to integrated information management based on the "scenario-response" mode has become of considerable value in establishing a scenario model for railway emergency. First, the scenario elements of railway emergencies were classified by scenario analysis to set up a hierarchical network model for railway emergencies. After the analysis of the requirements, sources, and characteristics of information for scenario building, an approach to integrated information service for railway emergency scenarios based on the Service- Oriented Adaptive Agent Collaborative Model was proposed. Such approach could meet the requirements for autonomy and flexibility of an integrated information service under railway emergency conditions. Agent technology was adopted, and the Web service proxy was provided by the software agents in the proposed approach. The global scenario model was divided into many local procedure models in different departments, which distributed the control of integrated information service to numerous service nodes. Experimental results showed that the integrated information service system based on the proposed approach is significantly more autonomous and flexible than the integrated model based on the Business Process Execution Language for Web Services. Read More Read Less

Abstract Digital image clustering algorithms can classify pixels according to their data characteristics without the pre-input of training samples. The number of categories and center point value are difficult to determine because of the large size of pixels and several features of digital image data. This paper proposed a digital image clustering algorithm based on multi-agent center optimization (DICA-MCO). The proposed algorithm established a problem optimization and solving system composed... Digital image clustering algorithms can classify pixels according to their data characteristics without the pre-input of training samples. The number of categories and center point value are difficult to determine because of the large size of pixels and several features of digital image data. This paper proposed a digital image clustering algorithm based on multi-agent center optimization (DICA-MCO). The proposed algorithm established a problem optimization and solving system composed of agents. To achieve fuzzy evaluation, DICA-MCO mapped the digital image clustering problem as a problem of intelligent agent movement in a multidimensional solution space. Results demonstrat that compared with traditional algorithms, DICA-MCO can select the optimal number of categories and value of center points and has high classification accuracy, Kappa coefficient, and classification effect. Read More Read Less

Abstract Sentiment analysis is a new task related to text mining that extracts opinions from textual data and classifies them into positive, negative or neutral. The goal of this paper is to determine the effect of applying stemming and n-gram techniques for Arabic texts (tweets) on sentiment classification. This study also aims at investigating the impact of feature selection on the performance of the classifier. For this reason,... Sentiment analysis is a new task related to text mining that extracts opinions from textual data and classifies them into positive, negative or neutral. The goal of this paper is to determine the effect of applying stemming and n-gram techniques for Arabic texts (tweets) on sentiment classification. This study also aims at investigating the impact of feature selection on the performance of the classifier. For this reason, three classifiers Support Vector Machines (SVM), Naïve Bayes, (NB), and K-nearest neighbor (KNN) are used. The obtained results showed that the best results of performance are obtained when applying a hybrid representation which includes tokens with character 3- grams. The experiment results also revealed that the use of feature selection technique improves significantly the accuracy of the three classifiers for the task of opinion classification. Regarding The classifiers, SVM outperforms the other classifiers when using all the features, while when selecting the most relevant features by the SVM feature selection technique, SVM and NB provided the best results. Read More Read Less

Abstract This paper describes a quantitative study on interdisciplinary technological relatedness represented by patent co-classification. Taking patents in the electricity sector as samples, classification analysis was conducted on the basis of 4-digit codes of International Patent Classification. Then, social network analysis and visualization technique were conducted. The relatedness between different subclasses was measured and visualized. The focus and trend were detected. The results indicate that "H04L: Transmission of digital information" is... This paper describes a quantitative study on interdisciplinary technological relatedness represented by patent co-classification. Taking patents in the electricity sector as samples, classification analysis was conducted on the basis of 4-digit codes of International Patent Classification. Then, social network analysis and visualization technique were conducted. The relatedness between different subclasses was measured and visualized. The focus and trend were detected. The results indicate that "H04L: Transmission of digital information" is the most active subclass that can be considered as the focus in the field of electricity. It also suggests that hot domains characterized by high interdisciplinarity may involve some emerging technologies, driven by knowledge relatedness and characterized by technological diversification. The results obtained in this study are expected to provide an improved understanding of technological development and evolving characteristics, as well as provide insights that can contribute to further research and development. Read More Read Less

Abstract With the development of marine traffic information digitalization, proactive information service has become increasingly important in maritime intelligent traffic systems (ITS). Trajectory prediction is one of the kernel problems that must be addressed to realize proactive information service. This study proposes an intelligent model to solve the issue of the trajectory prediction of vessels based on data mining and machine learning methods. The spatial clustering algorithm of data mining... With the development of marine traffic information digitalization, proactive information service has become increasingly important in maritime intelligent traffic systems (ITS). Trajectory prediction is one of the kernel problems that must be addressed to realize proactive information service. This study proposes an intelligent model to solve the issue of the trajectory prediction of vessels based on data mining and machine learning methods. The spatial clustering algorithm of data mining is used to cluster the historical trajectories of vessels, and the cluster results represent the distribution patterns of these historical trajectories. The support vector machine algorithm of machine learning is used to train the classifiers. The classifiers define the pattern of the new trajectory of the vessel, which must be predicted. In the experiment, the information on 2862 trajectories is used as input to the model in chronological order to simulate the data flow in realtime situation. The predicted trajectories are compared with the actual trajectories of vessels. Experimental results show that future trajectories can be predicted efficiently and accurately. The intelligent model can also solve prediction problems with little human intervention and can automatically adapt to dynamic applications. The prediction results can provide accurate and reliable data for proactive information service. The model promotes the development of maritime ITS. Read More Read Less

Abstract Text information is increasing at an explosive speed with the advent of the Internet. However, this situation has given rise to the problem of abundant information with relative deficiency of knowledge. Therefore, finding a way to seek target information rapidly and accurately has become a research hotspot. This study presented a method to improve web text clustering accuracy and integrity. First, the dk-means algorithm was modified, and the... Text information is increasing at an explosive speed with the advent of the Internet. However, this situation has given rise to the problem of abundant information with relative deficiency of knowledge. Therefore, finding a way to seek target information rapidly and accurately has become a research hotspot. This study presented a method to improve web text clustering accuracy and integrity. First, the dk-means algorithm was modified, and the k-means algorithm based on average density optimization was proposed. Second, a web text clustering model was designed, and indepth research on the key technology of web text clustering was conducted. Finally, the k-means algorithm based on average density optimization (Adk-means algorithm) was applied to the web text clustering model, and clustering and classification of web text were completed. Experiment showed that the purity and mutual trust values of the Adk-means algorithm are higher than those of the dk-means algorithm, and the modified algorithm is greatly improved in terms of accuracy, integrity, and performance of partitioned clusters. When clustering text, the Adk-means algorithm has high polymerization and similarity within classification. Research results were applicable to text clustering. When used in Internet text searching, the Adk-means algorithm is a highly efficient information retrieval technology that can improve searching speed and accuracy. Read More Read Less

Abstract Power dispatching automation system is an automatic application for monitoring and controlling electronic power network dispatching in electric power industry operation and a complex, huge and important engineering concerning national economy and the people's livelihood. With the improved requirement of national economy on power quality and development of computer and information technology, power dispatching automation tends to be stronger in monitoring and controlling electronic power network. Focusing on power dispatching... Power dispatching automation system is an automatic application for monitoring and controlling electronic power network dispatching in electric power industry operation and a complex, huge and important engineering concerning national economy and the people's livelihood. With the improved requirement of national economy on power quality and development of computer and information technology, power dispatching automation tends to be stronger in monitoring and controlling electronic power network. Focusing on power dispatching automation system in data management of power plant substation, this study discussed over how to process data information of power plant substation and improve preciseness of data during acquisition, transmission and controlling under the condition of increased access volume in power plant substation, which is beneficial to promote safe, reliable and economic operation of electronic power network and economic benefit of power plant. Read More Read Less

Abstract Social tags have been considered to indirectly reflect authorized opinions of taggers. This paper proposes an unsupervised method which derives implicit sentiment information from social tags to decide, in one document, which sentences are opinionated, as well as to annotate them with proper polarity labels. First, for a social tag, its opinion degree is measured by aggregating the opinion degree of related sentiment words, in proportion to the... Social tags have been considered to indirectly reflect authorized opinions of taggers. This paper proposes an unsupervised method which derives implicit sentiment information from social tags to decide, in one document, which sentences are opinionated, as well as to annotate them with proper polarity labels. First, for a social tag, its opinion degree is measured by aggregating the opinion degree of related sentiment words, in proportion to the co-occurrence relations between sentiment words and the tag. Second, the opinion degree of a sentence is determined by a combination function of the opinion degree of the tags, in proportion to the similarity between the sentence and each tag. Finally, sentences are sorted in order of their opinion degree, followed by a partition of the ranked list to distinguish sentences into positively opinionated, negatively opinionated, neutral, and non-opinionated ones. The proposed method is examined using the Chinese dataset of the NTCIR Opinion Analysis Task Test Collection and found to perform well. Experimental results testify that social tags are positively conducive to opinion analysis. Read More Read Less