Ontology based user query interpretation for semantic multimedia contents retrieval

Users who are familiar with the existing keyword-based search have problems of not being able to configure the formal query because they don’t have generic knowledge on knowledge base when using the semantic-based retrieval system. User wants the search results which are more accurate and match the user’s search intents with the existing keyword-based search and the same search keyword without the need to recognize what technology the currently used retrieval system is based on to provide the search results. In order to do the semantic analysis of the ambiguous search keyword entered by users who are familiar with the existing keyword-based search, ontological knowledge base constructed based on refined meta-data is necessary, and the keyword semantic analysis technique which reflects user’s search intents from the well-established knowledge base and can generate accurate search results is necessary. In this paper, therefore, by limiting the knowledge base construction to multimedia contents meta-data, the applicable prototype has been implemented and its performance in the same environment as Smart TV has been evaluated. Semantic analysis of user’s search keyword is done, evaluated and recommended through the proposed ontological knowledge base framework so that accurate search results that match user’s search intents can be provided.     

Item recommendation based on context-aware model for personalized u-healthcare service

A personalized service in the ubiquitous environment is to provide services or items, which reflect personal tastes, attitudes, and contexts. It is impossible to reflect the context information generated in u-healthcare environments due to the existing recommendation system performing the recommendation using the information directly input by users and application usage record only. This study develops a context-aware model using the context information provided by the context information model. The study applies it to the extraction of the missing value in a collaborative filtering process. The context-aware model reflects the information that selects items by users according to the appropriate context using the C-HMM and provides it to users. The solution of the missing value in the preference significantly affects the recommendation accuracy in a preference based item supply method. Thus, this study developed a new collaborative filtering for ubiquitous environments by reflecting the missing preference value and reflecting it to the collaborative filtering using the context-aware model. Also, the validity of this method will be evaluated by applying it to menu services in u-healthcare services.     

An intensive case study on kernel-based relation extraction

Relation extraction refers to a method of efficiently detecting and identifying predefined semantic relationships within a set of entities in text documents. Numerous relation extractionfc techniques have been developed thus far, owing to their innate importance in the domain of information extraction and text mining. The majority of the relation extraction methods proposed to date is based on a supervised learning method requiring the use of learning collections; such learning methods can be classified into feature-based, semi-supervised, and kernel-based techniques. Among these methods, a case analysis on a kernel-based relation extraction method, considered the most successful of the three approaches, is carried out in this paper. Although some previous survey papers on this topic have been published, they failed to select the most essential of the currently available kernel-based relation extraction approaches or provide an in-depth comparative analysis of them. Unlike existing case studies, the study described in this paper is based on a close analysis of the operation principles and individual characteristics of five vital representative kernel-based relation extraction methods. In addition, we present deep comparative analysis results of these methods. In addition, for further research on kernel-based relation extraction with an even higher performance and for general high-level kernel studies for linguistic processing and text mining, some additional approaches including feature-based methods based on various criteria are introduced.     

Adsorption and desorption kinetics of hydrocarbons in FCC catalysts studied using a tapered element oscillating microbalance (TEOM). Part 2: numerical simulations

The factors affecting the adsorption and desorption kinetics in a TEOM are reviewed in detail with particular attention given to the assumptions required to obtain physical transport parameters from the data. Two models are presented to simulate TEOM adsorption data in the case that concentration differences down the catalyst bed can be neglected, as is appropriate when the amount of catalyst used is small, the carrier gas flowrate is large, and/or the adsorbate partial pressure is low. In the first model, the effective diffusion coefficient, D_e, is taken to be constant. In the second model, the effective diffusion coefficient is assumed to obey the Darken equation, D_e=D₀/(1−θ). The TEOM results obtained on n-hexane, n heptane, n-octane, toluene and p-xylene on a commercial FCC catalyst and on pure rare-earth exchanged zeolite Y under non-reacting conditions (373-) are analysed in detail. It is found that intracrystalline diffusion is not the limiting factor affecting the overall rates of adsorption and desorption for the systems studied. Instead, it is the transport of molecules between the adsorbed and vapour phases at the edge of zeolite crystallites that is the limiting transport step affecting the overall kinetics. For the FCC catalyst, the limiting step is the transport of molecules at the zeolite-matrix interface rather than, say, the matrix-vapour interface. Local rate constants for the desorption of the hydrocarbons at the rate-controlling interface have been obtained.     

Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment

To provide context-based personalized services utilizing smart appliances in a smart home environment, we propose a framework for PersonAlized Service disCovery Using FuZZY-based CBR and Context Ontology (PASCUZZY). Basically, the PASCUZZY framework is implemented on case-based context ontology. To generate and manage the case instances on the case-based context ontology, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment. The context is transposed to linguistic-type context instances on the context ontology. In addition, to formalize and manage the context and services as multi-attributed data, the context ontology was developed reflecting the structure of cases borrowed from case-based reasoning. Furthermore, we propose adaptation methods to adjust the generic fuzzy membership functions depending on the inhabitants’ context. It is performed by modifying the values of the membership number and/or modifying the numbers of the linguistic terms that are based on the inhabitants’ context to affect the membership numbers. The adapted membership functions return the personalized degree of memberships depending on the specialized context of a specific fuzzy variable. Inevitably, the number of cases on the case-based context ontology will be increased from time to time. We apply Ward’s method not only to reduce the search effort via a hierarchical clustering on the case-based context ontology but also to find the most similar service as a solution to the new context. To verify the superiority of the PASCUZZY framework, we perform two kinds of evaluations. First, we evaluate the effectiveness of the adaptation of the fuzzy membership functions. Second, we verify the effectiveness of the application of a clustering method to the case instances of the case-based context ontology to identify the most similar service. Results of the experiment verified the effectiveness and superiority of the PASCUZZY framework.     

A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

Building cooling load prediction is critical to the success of energy-saving measures. While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model’s parameters. This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model’s mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model’s performance according to other ANN models. It is also successfully applied to predicting the cooling load of a commercial building in Hong Kong. The occupancy areas and concentration of carbon dioxide inside the building are successfully adopted to mimic the building’s internal cooling load. Training data was adopted from actual measurements taken inside the building. Its results show reasonable agreement with actual cooling loads.     

Real-time stochastic optimal control for traffic signals of multiple intersections

In this paper, a real-time stochastic optimal control method of traffic signal is modified. In addition, H-GA-PSO algorithm is proposed to search optimal traffic signals based on the stochastic model. The H-GA-PSO algorithm is a modified Hierarchical Particle Swarm Optimization (H-PSO) algorithm based on Genetic Algorithm (GA) processing. Finally, the effectiveness of the stochastic optimal control method with H-GA-PSO algorithm is shown through simulations at multiple intersections using a micro-traffic simulator.     

SVD‐Based Accurate Identification and Compensation of the Coupling Hysteresis and Creep Dynamics in Piezoelectric Actuators

In this paper, the coupling hysteresis and creep in piezoelectric actuators are identified and compensated for accurate tracking. First, we present the coupling hysteresis and creep model in smart actuators. Next, a complete identification strategy is designed according to the properties of the Preisach model. Then, an approach for parameter updating of the coupling model is provided. With the identified hysteresis and creep, the model‐based inversion compensation is designed. Finally, we apply the model identification and compensation to a piezoelectric stage to demonstrate the effectiveness of the proposed approaches. Significant reduction of the tracking error is achieved with the model‐based inversion feedforward compensator in which the relative errors at 10 Hz and 50 Hz are reduced to 1.85% and 4.53%, respectively. In addition, the model‐based feedforward is augmented with an integral feedback controller. With the composite controller, the relative errors at 10 Hz and 50 Hz are reduced to 0.42% and 3.04%, respectively.     

用动态剪切模量估算NR与SBR硫化胶的化学交联密度

用美国Ｍｏｎｓａｎｔｏ公司的橡胶加工过程分析仪测定ＮＲ和ＳＢＲ硫化胶或混炼胶料的动态剪切模量（Ｇ＇），进而根据Ｇ＿Ｎ￣０＝ｇ×２Ｘ＿０ＲＴ和Ｇｅ＝ｇ＇×２Ｘ＿（ｐｈ）ＲＴ估算其硫化胶的化学交联密度（Ｘ＿ｃｈ），结果发现：ＮＲ／过氧化二异丙苯体系硫化胶和混炼胶料的Ｇ＇是不同的，但用Ｇ＇估算的Ｘ＿ｃｈ与用化学分析法测定的结果基本相近；对ＳＢＲ／促进剂ＴＭＴＤ体系，用这种方法估算的Ｘ＿（ｃｈ）接近用应力－应变法和ＮＭＲ法的估算值；对ＮＲ／促进剂ＴＭＴＭ体系，用Ｇ＇估算的Ｘ＿（ｃｈ）与用溶胀法获得的数据基本接近。用Ｇ＇估算Ｘ＿（ｃｈ）法，十分简单、方便。     

核交换法对禾草类木质素结构的研究

本文采用核交换法对麦草粉、Nkny－HCHO－AQ（SFP－AQ）浆、NaOH—Na2SO3一AQ（ASP—AQ）浆和NaOH—AQ（AP—AQ）浆中残余木质素的化学结构特性进行了研究。通过核交换法结合硝基苯氧化反应，测定麦草原本木质素和浆中残余本质素的各类型酚核的数量。结果表明麦草原本木质素主要由25．35mol％非缩聚G单元，17．60mol％非缩聚S单元和57．05mol％缩聚G单元所组成。与AP—AQ浆相比，SFP浆含有较少的DPM结构单元，这可能是SFP浆漂白性能好的原因之一。