Chung-Hong Lee

National Kaohsiung University of Science and Technology (NKUST) | NKUST · Department of Electrical Engineering

In this paper, we address a big-data analysis method for estimating the driving range of an electric vehicle (EV), allowing drivers to overcome range anxiety. First, we present an estimating approach to project the life of battery pack for 1600 cycles (i.e., 8 years/160 000 km) based on the data collected from a cycle-life test. This approach has t...

Although diverse groups argue about the potential and true value benefits from social-media big data, there is no doubt that the era of big data exploitation has begun, driving the development of novel data-centric applications. Big data is notable not only because of its size, but also because of the complexity caused by its relationality to other...

Due to the explosive growth of social-media applications, enhancing event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past events for tackling emerging real-world events, in this work we utilize t...

Due to the steady increase in the number of heterogeneous types of location information on the internet, it is hard to organize a complete overview of the geospatial information for the tasks of knowledge acquisition related to specific geographic locations. The text- and photo-types of geographical dataset contain numerous location data, such as l...

Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top...

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical...

The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical...

The total processing error of CNC machine tools essentially comprises geometric errors and thermal errors. Therefore, reducing the influence of thermal errors is necessary. In this study, 13 temperature sensors were utilized to measure temperature variations of heat sources on a machine. These sensors work in conjunction with a non-contact optical...

This paper aims to carry out the risk assessment of work safety in container dry ports (CDPs) by adopting a continuous risk matrix (CRM) based on a Fuzzy Analytic Hierarchy Process (AHP). The originality of this paper consists of (1) identifying risk factors (RFs) for work safety at CDPs, (2) adopting the fuzzy AHP approach to estimate the likeliho...

The driving behaviors of electric vehicle (EV) and hybrid electric vehicle (HEV) drivers have received considerable attention in the literature. The use of image recognition in combination with GPS and driving data has emerged as a popular approach to improving driver safety. However, such methods often generate sensitive personal information, incl...

Specialty coffee beans have a unique aroma and flavor. The aromas of coffee in the world are affected by several issues, including growing area, climate, postharvest processing (such as dry and wet methods), roasting treatment, etc. These issues significantly contribute to the development of coffee-bean aromas. Since humans have a limited ability t...

Grid faults are found to be one of the major issues in renewable energy systems, particularly in wind energy conversion systems (WECS) connected to the grid via back-to-back (BTB) converters. Under such faulty grid conditions, the system requires an effective regulation of the active (P) and reactive (Q) power to accomplish low voltage ride through...

“Smart medical” applications refer to the fusion of technology and medicine that connects all linked sensor equipment with the patients, including those that measure physiological signals, such as blood pressure, pulse, and ECG. In addition, these physiological signal data are highly private and should be safely protected. It takes much longer to c...

To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this st...

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e...

Recently, an emerging application field through Twitter messages and algorithmic computation to detect real-time world events has become a new paradigm in the field of data science applications. During a high-impact event, people may want to know the latest information about the development of the event because they want to better understand the si...

In recent years, many countries have provided promotion policies related to renewable energy in order to take advantage of the environmental factors of sufficient sunlight. However, the application of solar energy in the power grid also has disadvantages. The most obvious is the variability of power output, which will put pressure on the system. As...

The risk of supply chain disruption is usually related to daily disturbances in supply chain operations (e.g., demand fluctuations) and some emergency risks, such as earthquakes and epidemic outbreaks. During a crisis, companies need agility to quickly find new suppliers and open auxiliary sales channels to meet customer needs and remain competitiv...

Purpose Measuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a...

As electric vehicle (EV) emerges, it is important to understand how driver’s driving behavior is influencing power consumption in an electric vehicle. Driver’s personal driving behavior is usually quite distinctive and can be recognized by means of driving patterns after some driving cycles. This paper presents a method combining several machine le...

Introduction Predicting the sentiments and emotions of people from their texts is a critical issue in cognitive computing. The explosive growth of social network services has led to a tremendous increase of textual data, increasing the demand of the advanced analysis of these data. Sentiment analysis on textual social media data emerged in recent y...

With the consciousness of environmental protection is going to rising up globally, every government have seriously concerned the related issues of PM2.5. In essence, PM2.5 is the microscopic contaminated particles. The sizes of particles are just one of twenty-eighth of human's hair, in addition to penetrating the respiration system, carrying the h...

In an era of growing concern over climate change, several utility companies originally supplied wholesale and retail power mainly made by burning coal, have started to consider and build the clean-energy power systems for resolving global warming problems. Wind power is nowadays regarded as one of the predominant alternative sources of clean energy...

This paper presents a machine learning approach to analyze driving behaviors that allows a better understanding of electric vehicle. We implement a pattern recognition process to model the driving pattern according to the energy consumption for both a single driver and a fleet. The growing hierarchical self-organizing maps (GHSOM) is applied to lea...

Self-organizing maps (SOM) have been applied on numerous data clustering and visualization tasks and received much attention on their success. One major shortage of classical SOM learning algorithm is the necessity of predefined map topology. Furthermore, hierarchical relationships among data are also difficult to be found. Several approaches have...

In this work, an "analytical data model of mosquito vector" was developed to perform analytical computation to the character of the dengue vectors. Our goal is to investigate a way to understand how the temporal trend of collected dataset correlates with the incidence dengue as identified by national health authorities. Based upon the mosquito-vect...

In this paper, we describe our work on developing a model and method for extracting key entities from the online social messages regarding emergent events for enhancing ontology engineering, enabling a sensible solution for prevention of similar disasters. Our work started with the development of an event modelling system using a data-cluster slici...

Growth of Electric vehicles (EV) starts to change the way that people transit. Several factors that affact the performance of EVs and environment including energy efficiency, safety, product durability, climate, geographical factor, infrastructure, and grid capacity need to be further investigated to cope with upcoming challenges. These issues main...

In recent years, the concept of human-in-the-loop has been utilized to support environment sensing. Along with IoT (Internet of Things) and wearable computing technologies, connecting people and devices to the internet provides a significant advantage for real-time emergency management. For development of context-aware applications, it is important...

With explosive growth of the Internet, the amount of information in text form is growing rapidly and the demand for data analysis is also increases. We can perform sentiment analysis on a large set of text messages to discover valuable knowledge and obtain enormous benefits in national security, business, politics, economics, etc. However, text mes...

This book constitutes the refereed proceedings of the 2014 Multidisciplinary International Social Networks Research, MISNC 2014, held in Kaohsiung, Taiwan, in September 2014. The 37 full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on electronic commerce, e-business ma...

In this paper, we describe our work on extracting entities from the online social messages regarding emergent events for ontology learning, which can contribute to a solution for quick response of emerging disastrous events. Our work started with the development of a real-time event detection system using a data-cluster slicing approach which combi...

In this work we utilize the social messages to construct an extensible event ontology model for learning the experiences and knowledge to cope with emerging real-world events. We develop a platform combining several text mining and social analysis algorithms to cooperate with our stream mining approach to detecting large-scale disastrous events fro...

Self-organizing map (SOM) algorithm has been applied widely in tasks such as data clustering and visualization. Two major deficiencies of classical SOM are the need of predefined map structure and the lack of hierarchy generation. Several approaches have been devised to tackle these deficiencies. One of our previous works, namely the topic-oriented...

Social-media text streams have great potentials for the development of ontology based systems owing to the contents of messages posted by mobile device users exhibit relevant event-feature information, such as the distribution of temporal features, spatial entities, emotions, and relations amount different friends, etc. In this work, we propose a f...

The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be...

Intelligence collection and analysis always play a major role in a company's growth. Traditional intelligence management process was most concealed and required massive human effort. It also has the disadvantages of rarity and danger. Therefore open source intelligence (OSINT) emerged as a major intelligence collection and analysis approach. Differ...

Due to the explosive growth of social-media applications, enabling event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past microblogs for tackling emerging real-world events, in this work we utiliz...

The microblogs archives keep a valuable collection of records for past disastrous events and related stories. To learn the experiences from past microblogging messages for coping with emerging real-world events, in this work we utilize the social-media messages to characterize events for relatedness analysis. First, we established an online cluster...

Nowadays lots of information are stored as Web pages for ease of sharing and searching. It is rather impractical for users to browse such gigantic amount of Web pages to obtain target information. Therefore, users often count on search engines to retrieve information they needed. Most of the search engines rely on the contents of Web pages, which a...

Microblog is a social network service which is able to aggregate messages to explore new knowledge. Nowadays, more and more users contribute what they found and what they thought by posting short messages. This phenomenon makes people spend lots of time monitoring text streams of microblogging messages, in order for acquisition of information regar...

One major approach for information finding in the WWW is to navigate through some Web directories and browse them until the goal pages were found. However, such directories are generally constructed manually and may have disadvantages of narrow coverage and inconsistency. Besides, most of existing directories provide only monolingual hierarchies th...

Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top...

Social book marking Web sites have emerged recently for collecting and sharing of interesting Web sites among users. People can add Web pages to such sites as bookmarks and allow themselves as well as others to manipulate them. One of the key features of the social book marking sites is the ability of annotating a Web page when it is being bookmark...

With the continuously increasing needs of location information for users around the world, applications of geospatial information have gained a lot of attention in both research and commercial organizations. Extraction of semantics from the image content for geospatial information seeking and knowledge discovery has been thus becoming a critical pr...

Online microblogging services such as Twitter allow users to post very short messages related to everything ranging from mundane daily life routines to breaking news events. This phenomenon has changed the way for information acquisition. In this paper, we present an instinctive method with a time-decay function which corresponds to the natural pro...

Social bookmarking or tagging Web sites, or folk-sonomies, emerge recently since the result of traditional context-driven search of Web pages could be improved by user-annotated tags. People could retrieve, organize, and comprehend Web pages thorough such tags. However, spam tags were significantly increased and deteriorated the effectiveness of so...

Self-organizing map (SOM) learning algorithm has been widely applied in solving various tasks in pattern recognition, machine learning, and data mining, etc. Recently, it has been used to cluster documents and produced reasonable results. Traditional SOM algorithm learns from data using a fixed map. Approaches have been proposed to allow adaptable...

Social tags are annotations for Web pages collaboratively added by users. It will be much easier to understand the meaning of Web pages and classify them according to their tags. The precision in retrieving Web pages may also increase using such tags. Nowadays social tags are mostly annotated manually by users via social bookmarking Web sites. Such...

With the increasing needs of location information, applications of geospatial information have gained a lot of attention in both research and commercial organizations. Extraction of geospatial knowledge from the information content has been thus becoming a important process. Among theses applications, a typical example is to discover relationships...

The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be...

In this paper, we describe a location based text mining approach to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones. We first mapped documents into corresponding zones by adaptive affinity propagation (adaptive AP) clustering technique, and then framed...

Due to the improvement of digital image technology and increasing amount of digital image data, the issue of automatic image annotation technology and applications becomes more and more important. In order to retrieval image efficiently, it is important to extract and represent the semantics of images. Traditional image semantics extraction and rep...

Due to the increasing needs of location information, applications of geospatial information have gained a lot of attention in both research and commercial organizations. Extraction of geospatial semantics from the information content has been thus becoming a critical process. Unfortunately, the available geographic images may be blurred, either too...

In this paper we describe our work on developing a novel technique for discovery of implicit knowledge about patents from multilingual patent information sources. In this work we developed a system platform to support locating similar and relevant multilingual patent documents. The platform was implemented using a multilingual vector space based on...

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge about patents from multilingual patent information sources. Traditional techniques of multi- and cross-language patent retrieval are mostly based on the process of translation. One major problem of those is that it is difficult to find r...

Due to the availability of a huge amount of textual data from a variety of sources, users of internationally distributed information regions need effective methods and tools that enable them to discover, retrieve and categorize relevant information, in whatever language and form it may have been stored. This drives a convergence of numerous interes...

Image retrieval has attracted lots of attention from both researchers and practitioners. Different methodologies as well as commercial systems have been proposed and developed to tackle this task. Most of these systems are based on one or both of two major image retrieval schemes, namely annotation-based image retrieval and content-based image retr...

With the increasing number of multilingual texts in the internet, multilingual text retrieval techniques have become an important research issue. However, the discovery of relationships between different languages remains an open problem. In this paper we propose a method, which applies the growing hierarchical self-organizing map (GHSOM) model, to...

In this paper, we describe a two-stage hybrid approach to select gene features and produce dominant patterns for evaluating the pathological probability. To discover suitable genes as experiment samples for distinguishing the status of gene regulation, we utilized receiver operating characteristic (ROC) method to eliminate non-significant genes of...

Multilingual information retrieval has attracted lots of attention in recent years due to the explosive increase of multilingual Web pages. It will not be easy to retrieve documents written in languages other than the query if the relationships among entities of different languages were not found. In this work, we will develop a method based on sel...

Web directories cluster Web pages into categories and usually organize them into hierarchies. Many users used them to browse for interesting Web pages in a coarse-to-fine manner. Nowadays most of the Web directories access monolingual Web pages and provide only monolingual interface which may limit the coverage and accessibility of Web pages for us...

The Web pages nowadays were written in various languages including English, Chinese, Spanish, etc. There are increasing needs in searching Web pages of different languages using single query. This task is called multilingual information retrieval (MLIR). However, MLIR is difficult to achieve since we need some kind of method to find the association...

Research work related to plagiarism detection methods in dealing with monolingual texts (e.g. English texts) have been well established in recent years. However, little attention has been paid to facilitate plagiarism detection in cross-lingual text collections (e.g. English and Chinese texts). In this paper we present a system platform to evaluati...

With the increasing amount of multilingual texts in the Internet, multilingual text retrieval techniques have become an important research issue. However, the discovery of relationships between different languages remains an open problem. In this paper we propose a method, which applies the growing hierarchical self- organizing map (GHSOM) model, t...

Traditional content-based image retrieval (CBIR) systems often fail to meet a user’s need due to the ‘semantic gap’ between the extracted features of the systems and the user’s query. The cause of the semantic gap is the failure of extracting real semantics from an image and the query. To extract semantics of images, however, is a difficult task. M...

The functions of automatically identify relationships between cancer diseases and external factors from medical records for supporting cancer diagnosis would be a valuable contribution in public health fields. Unfortunately, so far little attention has been paid on providing effective solutions to such a problem domain. In this work, we propose a f...

In this paper, we develop a platform framework for categorization of cancer related abstracts using support vector machines (SVMs) based text categorization techniques with a one-against-all (OAA) learning algorithm for classification decisions. The corpora for the work were selected from the Website of PubMed database. By using information derived...

The WWW provides an ultimate source of information for all kinds of knowledge in various kinds of languages. There are emerging needs for searching documents in different languages, causing multilingual information retrieval an active research topic recently. The performance of such task depends on the degree of understanding for the relationships...

The ability to automatically identify relationships between cancer diseases and external factors from medical records for supporting cancer diagnosis would be a valuable contribution in public health fields. Unfortunately, so far little attention has been paid on such a problem domain to developing effective solutions. In this work, we propose a pr...

We propose a method that could automatically annotating images with some keywords that could feasibly describe the semantics of the images. A set of training images as well as their annotations are trained to find the relationships between images as well as between keywords. New image could then be annotated and retrieved according to such relation...

In this paper we discuss the implementation of the leading supervised and unsupervised approaches for multilingual text categorization. We selected support vector machines (SVM) and latent semantic indexing (LSI) techniques as representatives of supervised and unsupervised methods for system implementation, respectively. The preliminary results sho...

Latent semantic indexing is a well known technique in information retrieval, especially in dealing with polysemy and synonymy. LSI use SVD process to decompose the original term-document matrix into a lower dimension triplet. The triplet (the resulted matrices) is the approximation to original matrix and can capture the latent semantic relation bet...

Recently users of internationally distributed information networks need tools and methods that enable them to discover, retrieve and categorize relevant information, in whatever language and form it may have been stored. This drives a convergence of numerous interests from diverse research communities focusing on the issues related to multilingual...

One major approach for information finding in the WWW is to navigate through some Web directories and browse them for the goal pages. However, such directories are generally constructed manually and have disadvantages of narrow coverage and inconsistency. In this work, we propose a machine learning approach that automatically constructs a navigatio...

One major approach for information finding in the WWW is to navigate through some web directories and browse them for the goal pages. However, such directories are generally constructed manually and have disadvantages of narrow coverage and inconsistency. In this work, we propose NaviSOM, a machine learning approach to automatically construct a nav...

Topic maps provide a general, powerful, and user-oriented way to navigate the information resources under consideration in any specific domain. A topic map provides a uniform framework that not only identifies important subjects from an entity of information resources and specifies the resources that are semantically related to a subject, but also...

The research on automatic hypertext construction emerges rapidly in the last decade because there exists a urgent need to translate the gigantic amount of legacy documents into web pages. Unlike traditional ‘flat’ texts, a hypertext contains a number of navigational hyperlinks that point to some related hypertexts or locations of the same hypertext...

Recently research on text mining has attracted lots of attention from both industrial and academic fields. Text mining concerns of discovering unknown patterns or knowledge from a large text repository. The problem is not easy to tackle due to the semi-structured or even unstructured nature of those texts under consideration. Many approaches have b...

The semantic Web has emerged to replace the World Wide Web (WWW or the Web) as the unique platform for information sharing. Applications such as e-commerce will be and could be plausible only if we can annotate the Web pages with their semantics. For newly developed semantic Web resources, such annotation can be done manually or by help of some aut...

The quantification of evaluating semantic relatedness among texts has been a challenging issue that pervades much of machine learning and natural language processing. This paper presents a hybrid approach of a text-mining technique for measuring semantic relatedness among texts. In this work we develop several text classifiers using support vector...

The World Wide Web (WWW) has been recognized as the ultimate and unique source of information for information retrieval and knowledge discovery communities. Tremendous amount of knowledge are recorded using various types of media, producing enormous amount of web pages in the WWW. Retrieval of required information from the WWW is thus an arduous ta...