Particle swarm optimization (PSO), inspired by social psychology principles and evolutionary computations, has been successfully applied to a wide range of continuous optimization problems. However, research on discrete problems has been done not much even though discrete binary version of PSO (BPSO) was introduced by Kennedy and Eberhart in 1997. In this paper, we propose a modified BPSO algorithm, which escapes from a local optimum by employing a bit change mutation. The proposed algorithm was tested on De jong's suite and its results show that BPSO with the proposed mutation outperforms the original BPSO.

An energy management circuit is proposed for self-powered ubiquitous sensor modules using vibration-based energy. With the proposed circuit, the sensor modules work with low duty cycle operation. Moreover, a two-tank circuit as a part of the energy management circuit is utilized to solve the problem that the average power density of ambient energy always varies with time while the power consumption of the sensor modules is constant and larger than it. In addition, the long start-up time problem is also avoided with the timing control of the proposed energy management circuit. The CMOS implementation and silicon verification results of the proposed circuit are also presented. Its validity is further confirmed with a vibration-based energy generation. The sensor module is used to supervise the vibration of machines and transfer the vibration signal discontinuously. A piezoelectric element acts as the vibration-to-electricity converter to realize battery-free operation.

It has been shown that the output information produced by the soft output Viterbi algorithm (SOVA) is too optimistic. To compensate for this, the output information should be normalized. This letter proposes a simple normalization technique that extends the existing sign difference ratio (SDR) criterion. The new normalization technique counts the sign differences between the a-priori information and the extrinsic information, and then adaptively determines the corresponding normalization factor for each data block. Simulations comparing the new technique with other well-known normalization techniques show that the proposed normalization technique can achieve about 0.2 dB coding gain improvement on average while reducing up to about 1/2 iteration for decoding.

To meet users' multi-service requests under dynamic and heterogenous environment with high-assurance, the Autonomous Network-Based Integration System based on Faded Information Field (FIF) has been proposed, which permits to actively integrate the correlated information services according to the current situation of the system. However, the increase in the total number of users' requests and changes in users' preferences cause the unbalancing load in the system and the overload in the locality. In this paper, based on the autonomous access distribution in the locality, a new approach of autonomous correlated services access is proposed to reduce the load of the system and achieve the adaptability and timeliness of correlated services utilization. We proved the effectiveness of the proposed technology through the simulation and the results show that the system can improve the average response time not only for joint requests of correlated services, but also for separate requests of each service under changing environments.

Wireless sensor networks are expected to play an essential role as a social infrastructure to realize our safe and secure living environment. In such a network, critical information must be transmitted faster and more reliably than other information. We propose a distributed transmission mechanism which enables emergency packets to be carried with high reliability and low latency along a preferential path, which is called an "assured corridor." In this self-organizing assured corridor mechanism (ACM), which works above the network layer and does not depend on any specific routing or MAC protocol, a corridor is gradually established as the first packet containing urgent information propagates to the base station. The nodes surrounding the corridor suppress the transmission of non-urgent information and nodes in the corridor are kept awake to forward emergency packets. ACM avoids packet loss and possible delay caused by collisions in the wireless transmission and normal sleep scheduling. An acknowledgment and retransmission scheme is incorporated into ACM in order to improve reliability of transmission of urgent information. Simulation experiments showed that, when only one node transmitted urgent information, the retransmission contributed to establish a corridor quickly and that ACM improved the delivery ratio and the delay of the urgent information transmission once a corridor is established. It was proved that ACM was effective to improve the reliability and the latency of urgent information as well in the cases where multiple nodes sent urgent information at once.

Expansion of imagination is crucial for lively creativity. However, such expansion is sometimes rather difficult and an environment which supports creativity is required. Because people can attain higher creativity by using words with a thematic relation rather than words with a taxonomical relation, we tried to extract word lists having thematic relations among words. We first extracted word lists from domain specific documents by utilizing inclusive relations between words based on a modifiee/modifier relationship in documents. Next, from the extracted word lists, we removed the word lists having taxonomical relations so as to obtain only word lists having thematic relations. Finally, based on the assumption what kind of knowledge a person can associate when he/she looks at a set of words correlates with how the word set is effective in creativity support, we examined whether the word lists direct us to informative pages on the Web for verifying the availability of our extracted word lists.

A practical optical character reader is required to deal with not only common fonts but also complex designed fonts. However, recognizing various kinds of decorative character images is still a challenging problem in the field of document image analysis. Since appearances of such decorative characters are complicated, most general character recognition systems cannot give good performances on decorative characters. In this paper, an algorithm that recognizes decorative characters by structural analysis using a graph-matching technique is proposed. Character structure is extracted by using topographical features of multi-scale images, and the extracted structure is represented by a graph. A character image is recognized by matching graphs of the input and standard patterns. Experimental results show the effectiveness of the proposed algorithm.

In this paper, we present a method to automatically acquire a large-scale vocabulary of evaluative expressions from a large corpus of blogs. For the purpose, this paper presents a semi-supervised method for classifying evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, our semi-supervised method can classify evaluative expressions in a corpus by their polarities, starting from a very small set of seed training examples and using contextual information in the sentences the expressions belong to. Our experimental results with real Weblog data as our corpus show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions. We also show that a reasonable amount of evaluative expressions can be really acquired.

Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.

From lessons learned in medical data mining projects we show that integration of advanced computation techniques and human inspection is indispensable in medical data mining. We proposed an integrated approach that merges data mining and text mining methods plus visualization support for expert evaluation. We also appropriately developed temporal abstraction and text mining methods to exploit the collected data. Furthermore, our visual discovery system D2MS allowed to actively and effectively working with physicians. Significant findings in hepatitis study were obtained by the integrated approach.

Machine learning techniques have long been applied in many fields and have gained a lot of success. The purpose of learning processes is generally to obtain a set of parameters based on a given data set by minimizing a certain objective function which can explain the data set in a maximum likelihood or minimum estimation error sense. However, most of the learned parameters are highly data dependent and rarely reflect the true physical mechanism that is involved in the observation data. In order to obtain the inherent knowledge involved in the observed data, it is necessary to combine physical models with learning process rather than only fitting the observations with a black box model. To reveal underlying properties of human speech production, we proposed a learning process based on a physiological articulatory model and a coarticulation model, where both of the models are derived from human mechanisms. A two-layer learning framework was designed to learn the parameters concerned with physiological level using the physiological articulatory model and the parameters in the motor planning level using the coarticulation model. The learning process was carried out on an articulatory database of human speech production. The learned parameters were evaluated by numerical experiments and listening tests. The phonetic targets obtained in the planning stage provided an evidence for understanding the virtual targets of human speech production. As a result, the model based learning process reveals the inherent mechanism of the human speech via the learned parameters with certain physical meaning.

Dividing a lecture speech into segments and providing those segments as learning objects are quite general and convenient way to construct e-learning resources. However it is difficult to assign an appropriate title to each object that reflects its content. Since there are various aspects of analyzing discourse segments, it is inevitable that researchers will face the diversity when describing the "meanings" of discourse segments. In this paper, we propose the assignment of discourse segment titles from the representation of their "meanings." In this assigning procedure, we focus on the speaker's evaluation for the event or the speech object. To verify the effectiveness of our idea, we examined identification of the segment boundaries from the titles that were described in our procedure. We confirmed that the result of the identification was more accurate than that of intuitive identification.

An application profile specifies a set of terms, drawn from one or more standard namespaces, for annotation of data, and constrains their usage and interpretations in a particular local application. An approach to representation of and reasoning with application profiles based on the OWL and OWL/XDD languages is proposed. The former is a standard Web ontology language, while the latter is a definite-clause-style rule language that employs XML expressions as its underlying data structure. Semantic constraints are defined in terms of rules, which are represented as XDD clauses. Application of the approach to defining application profiles with fine-grained semantic constraints, involving implicit properties of metadata elements, is illustrated. A prototype application profile development environment equipped with metadata validation features has been implemented based on the proposed framework.

In mobile computing environments, cache invalidation techiniques are widely used. However, theses techniques require a large-sized invalidation report and show low cache utilization under high server update rate. In this paper, we propose a new cache-level cache invalidation technique called TTCI (Timestamp Tree-based Cache Invalidation technique) to overcome the above two problems. TTCI also supports selective tuning for a cache-level cache invalidation. We show in our experiment that our technique requires much smaller size of cache invalidation report and improves cache utilization.

Educational websites contain rich knowledge components on a web page. Detecting student attention on web pages fulfills the recommendation of adequate knowledge components to students based on students' current interests. Previous studies have shown the application of learner attention in intelligent learning systems. This study proposes a methodology to analyze student on-line mouse movement patterns that indicate student attentions. The methodology can be combined with learning systems that implement pedagogical models such as inquiry-based learning and problem-solving learning activities. The feasibility and effectiveness of the proposed methodology have been evaluated by student mouse movements in problem-solving scenarios.

Reliable Multicast has been applied to large-scale contents delivery systems for distributing digital contents to a large number of users without data loss. Reliable contents distribution is indispensable for software updates and management data sharing in actual delivery services. This paper evaluates the implementation and performance of RMTP; a reliable multicast protocol for bulk-data transfer, through the developments of contents delivery systems. Software configuration is also examined including operation functions such as delivery scheduling. Furthermore, applicability of reliable multicast to emerging broadband networks is also discussed based on the experimentation results. Through the deployment of the protocol and the software, performance estimation has played a key role for constructing the delivery systems as well as for designing the communication protocol.

This study initiates a systematic evaluation of web search engine performance using queries written in Thai. Statistical testing indicates that there are some significant differences in the performance of search engines. In addition to compare the search performance, an analysis of the returned results is carried out. The analysis of the returned results shows that the majority of returned results are unique to a particular search engine and each system provides quite different results. This encourages the use of metasearch techniques to combine the search results in order to improve the performance and reliability in finding relevant documents. We examine several metasearch models based on the Borda count and Condorcet voting schemes. We also propose the use of Evolutionary Programming (EP) to optimize weight vectors used by the voting algorithms. The results show that the use of metasearch approaches produces superior performance compared to any single search engine on Thai queries.

This paper addresses a discrete-time consensus problem with non-linear performance functions over dynamically changing communication topologies. Each agent has a performance value based on its internal information state and exchanges the performance value with other agents to achieve consensus. We derive sufficient conditions for a global consensus using algebraic graph theory.

This research aims to develop automatic knowledge mining of causality from texts for supporting an automatic question answering system (QA) in answering 'why' question, which is among the most crucial forms of questions. The out come of this research will assist people in diagnosing problems, such as in plant diseases, health, industrial and etc. While the previous works have extracted causality knowledge within only one or two adjacent EDUs (Elementary Discourse Units), this research focuses to mine causality knowledge existing within multiple EDUs which takes multiple causes and multiple effects in to consideration, where the adjacency between cause and effect is unnecessary. There are two main problems: how to identify the interesting causality events from documents, and how to identify the boundaries of the causative unit and the effective unit in term of the multiple EDUs. In addition, there are at least three main problems involved in boundaries identification: the implicit boundary delimiter, the nonadjacent cause-consequence, and the effect surrounded by causes. This research proposes using verb-pair rules learnt by comparing the Naïve Bayes classifier (NB) and Support Vector Machine (SVM) to identify causality EDUs in Thai agricultural and health news domains. The boundary identification problems are solved by utilizing verb-pair rules, Centering Theory and cue phrase set. The reason for emphasizing on using verbs to extract causality is that they explicitly make, in a certain way, the consequent events of cause-effect, e.g. 'Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.'. The outcome of the proposed methodology shown that the verb-pair rules extracted from NB outperform those extracted from SVM when the corpus contains high occurence of each verb, while the results from SVM is better than NB when the corpus contains less occurence of each verb. The verb-pair rules extracted from NB for causality extraction has the highest precision (0.88) with the recall of 0.75 from the plant disease corpus whereas from SVM has the highest precision (0.89) with the recall of 0.76 from bird flu news. For boundary determination, our methodology can handle very well with approximate 96% accuracy. In addition, the extracted causality results from this research can be generalized as laws in the Inductive-Statistical theory of Hempel's explanation theory, which will be useful for QA and reasoning.

Target location estimation is one of many promising applications of wireless sensor networks. However, until now only few studies have examined location estimation performances in real environments. In this paper, we analyze the effect of walking people on target location estimation performance in three experimental locations. The location estimation is based on received signal strength indicator (RSSI) and maximum likelihood (ML) estimation, and the experimental locations are a corridor of a shopping center, a foyer of a conference center and a laboratory room. The results show that walking people have a positive effect on the location estimation performance if the number of RSSI measurements used in the ML estimation is equal or greater than 3, 2 and 2 in the case of the experiments conducted in the corridor, foyer and laboratory room, respectively. The target location estimation accuracy ranged between 2.8 and 2.3 meters, 2.5 and 2.1 meters, and 1.5 and 1.4 meters in the case of the corridor, foyer and laboratory room, respectively.