Latent semantic analysis for vector space expansion and fuzzy logic-based genetic clustering

This paper proposes an improved latent semantic analysis (LSA) model to represent textual document and takes advantage of a fuzzy logic based genetic algorithm (FLGA) for clustering. The standard genetic algorithm (GA) in conventional vector space model is rather difficult to deal with because the high dimensional encoding of GA makes it explore the optimal solution in a complicated space which is prone to cause an overflow problem. The LSA-based corpus model not only reduces the dimensions drastically, but also creates an underlying semantic structure which enhances its ability of distinguishing documents in terms of concepts and indirectly improves the ability of GA for clustering (genetic clustering). A novel FLGA is proposed in conjunction with this semantic model in this study. According to the nature of biological evolution, several fuzzy controllers are given to adaptively adjust and optimize the behaviors of the GA which can effectively prevent the premature convergence to a suboptimum solution. The experiment results show that the fuzzy logic controllers enhance the ability of the GA to explore the global optimum solution, and the utilization of the LSA-based text representation method to FLGA further improves its clustering performance.     

Underdetermined Convolutive Blind Source Separation via Time–Frequency Masking

In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well known k -means or fuzzy c -means clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by using k-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.     

The moderating effects of psychological reactance and product involvement on online shopping recommendation mechanisms based on a causal map

The use of the Internet in the daily activities of individuals and firms has become entrenched, and online shopping has become commonplace. However, debates about how online shopping recommendation mechanisms (OREMs) should be designed have not been completely resolved. The challenge with traditional online shopping recommendation mechanisms (TR-OREMs) is that they focus too much on quantitative factors. Thus, they ignore causal interrelationships with qualitative factors that are believed to significantly impact quantitative factors. Considering only quantitative factors and ignoring qualitative ones likely distorts the final recommendation results. Another problem with TR-OREMs is that they ignore the perceived psychological reactance of consumers against the recommended products. Such consumer reactance may be reduced when the causal interrelationships among all the quantitative and qualitative factors are analyzed and incorporated properly into the OREM. To overcome these problems, we propose a causal map – online shopping recommendation mechanisms (CM-OREMs) based on a causal map. We analyzed possible causal relationships among quantitative and qualitative factors and incorporated them in the recommendation process to reduce consumer reactance against the recommendation results. Furthermore, an elaboration likelihood model (ELM) was used to build hypotheses about how the online shopping behavior of consumers is affected by OREMs based on the proposed causal map. Specifically, the performance of the proposed OREM was empirically analyzed by gathering experiment data from qualified respondents who were asked to refer to the proposed OREM before making purchasing decisions via mobile phones. Statistical results showed that the proposed OREMs could enhance consumer decision satisfaction, decision confidence, and attitude toward the recommended products. It could also positively affect consumer purchasing intentions. The OREM had a greater effect on the high-reactance group of participants than on the low-reactance group as well as on a high-involvement product versus a low-involvement product.     

Genetic algorithm for text clustering based on latent semantic indexing

In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with the vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Latent semantic indexing (LSI) is a successful technology in information retrieval which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. Meanwhile, LSI takes into account the effects of synonymy and polysemy, which constructs a semantic structure in textual data. GA belongs to search techniques that can efficiently evolve the optimal solution in the reduced space. We propose a variable string length genetic algorithm which has been exploited for automatically evolving the proper number of clusters as well as providing near optimal data set clustering. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. The superiority of GAL approach over conventional GA applied in VSM model is demonstrated by providing good Reuter document clustering results.     

Run-to-run control of inductively coupled C2F6 plasma etching of SiO2: Construction of a numerical process with a computational fluid dynamics code

A numerical process to simulate SiO₂ dry etching with inductively coupled C₂F₆ plasmas has been constructed using a commercial CFD code as a first step to design a run-to-run control system. The simulator was found to reasonably predict the reactive ion etching behavior of C₂F₆ plasmas and used to investigate the effects of plasma operating variables on the etch rate and uniformity. The relationship between the operating variables and the etching characteristics was mathematically modeled through linear regression for future run-to-run control system design.     

Efficient scheme of verifying integrity of application binaries in embedded operating systems

Currently, embedded systems have been widely used for ubiquitous computing environments including digital setup boxes, mobile phones, and USN (Ubiquitous Sensor Networks). The significance of security has been growing as it must be necessarily embedded in all these systems. Up until now, many researchers have made efforts to verify the integrity of applied binaries downloaded in embedded systems. The research of problem solving is organized into hardware methods and software-like methods. In this research, the basic approach to solving problems from the software perspective was employed. From the software perspective, unlike in the existing papers (Seshadri et al., Proc. the IEEE symposium on security and privacy, 2004; Seshadri et al., Proc. the symposium on operating systems principals, 2005) based on the standardized model (TTAS.KO-11.0054. 2006) publicized in Korea, there is no extra verifier and conduct for the verification function in the target system. Contrary to the previous schemes (Jung et al. , 2008; Lee et al., LNCS, vol. 4808, pp. 346–355, 2007), verification results are stored in 1 validation check bit, instead of storing signature value for application binary files in the i-node structure for the purpose of reducing run-time execution overhead. Consequently, the proposed scheme is more efficient because it dramatically reduces overhead in storage space, and when it comes to computing, it performs one hash algorithm for initial execution and thereafter compares 1 validation check bit only, instead of signature and hash algorithms for every application binary. Furthermore, in cases where there are frequent changes in the i-node structure or file data depending on the scheme application, the scheme can provide far more effective verification performance compared to the previous schemes.     

Microstructures and tensile behavior of carbon nanotube reinforced Cu matrix nanocomposites

Carbon nanotubes (CNTs) have been considered as an ideal reinforcement to improve the mechanical performance of monolithic materials. However, the CNT/metal nanocomposites have shown lower strength than expected. In this study, the CNT reinforced Cu matrix nanocomposites were fabricated by spark plasma sintering (SPS) of high energy ball-milled nano-sized Cu powders with multi-wall CNTs, and followed by cold rolling process. The microstructure of CNT/Cu nanocomposites consists of two regions including CNT/Cu composite region, where most CNTs are distributed, and CNT free Cu matrix region. The stress–strain curves of CNT/Cu nanocomposites show a two-step yielding behavior, which is caused from the microstructural characteristics consisting of two regions and the load transfer between these regions. The CNT/Cu nanocomposites show a tensile strength of 281 MPa, which is approximately 1.6 times higher than that of monolithic Cu. It is confirmed that the key issue to enhance the strength of CNT/metal nanocomposite is homogeneous distribution of CNTs.     

Modified atmosphere packaging of fresh produce using microporous earthenware material

Microporous earthenware sheets of 5.5–5.6mm thickness were fabricated with or without a glazing treatment by passing through a sequential firing procedure in a furnace. Their microstructure and gas permeability against oxygen and carbon dioxide were measured and examined for their usability in modified atmosphere packaging of fresh produce. Compared with plastic packaging materials, earthenware sheets with a high proportion of micropores had very high gas permeability and gave CO₂:O₂ permeability ratios close to 1. Glazing treatment smoothed the surface by clogging the pores on the surface and significantly decreased gas permeability, without affecting the internal microstructure of the earthenware. When the earthenware sheet was combined with a plastic box and used in packaging for strawberries and enoki mushrooms at 5°C, its unique permeability properties developed a modified atmosphere that was beneficial for preserving the quality of the produce. Changes in the earthenware's permeability characteristics due to moisture adsorption and condensation need to be resolved so that the dynamic changes occurring in package atmosphere over time can be better understood. Copyright © 2006 John Wiley & Sons, Ltd.     

Hybrid accident simulation methodology using artificial neural networks for nuclear power plants

A hybrid accident simulation methodology for nuclear power plants is proposed to enhance the capabilities of compact simulator by introducing artificial neural networks. Two neural networks are trained with the target values obtained from the analyses of detailed computer codes and trained results are combined with the compact simulator to perform the following roles: (i) compensation for inaccuracies of a compact simulator occurring from simplified governing equation and reduced number of physical control volumes, and (ii) prediction of the critical parameter usually calculated from the sophisticated computer code: the autoassociative neural network improves the computational results of the compact simulator up to the accuracy level of detailed best estimate computer code, while the backpropagation neural network predicts the minimum departure from nucleate boiling ratio (DNBR). Simulations are carried out to verify the applicability of the proposed methodology for the loss of flow accidents and the results show that the neural networks can be used as a complementary tool to improve the results of a compact simulator.