Study on the common criteria methodology for secure ubiquitous environment construction

The ubiquitous environment has been developed since the extension of IT technology. The term ‘Ubiquitous’ is referred firstly by Mark Weiser, which means the user based environment that provides network service ‘any time, anywhere’. In the ubiquitous environment, the computing environment should have the device that recognizes the user and the environment. But there is not a verification process when dealing in sensitive information, and it may cause serious errors or malfunction. In order to complement the problem, members of the CCRA utilized CC(Common Criteria) to build up an assessment system and create a secured ubiquitous environment.     

Face recognition using fuzzy Integral and wavelet decomposition method

In this paper, we develop a method for recognizing face images by combining wavelet decomposition, Fisherface method, and fuzzy integral. The proposed approach is comprised of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. As a result of this decomposition, we obtain four subimages (namely approximation, horizontal, vertical, and diagonal detailed images). The second stage of the approach concerns the application of the Fisherface method to these four decompositions. The choice of the Fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The two last phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. Both Sugeno and Choquet type of fuzzy integral are considered as the aggregation method. In the experiments we use n-fold cross-validation to assure high consistency of the produced classification outcomes. The experimental results obtained for the Chungbuk National University (CNU) and Yale University face databases reveal that the approach presented in this paper yields better classification performance in comparison to the results obtained by other classifiers.     

Effects of Annealing Temperature on Microstructure and Tensile Properties in Ferritic Lightweight Steels

An investigation was conducted into the effects of annealing temperature on microstructure and tensile properties of ferritic lightweight steels. Two steels were fabricated by varying the C content, and were annealed at 573 K to 1173 K (300 °C to 900 °C) for 1 hour. According to the microstructural analysis results, κ-carbides were formed at about 973 K (700 °C), which was confirmed by equilibrium phase diagrams calculated from a THERMO-CALC program. In the steel containing low carbon content, needle-shaped κ-carbides were homogeneously dispersed in the ferrite matrix, whereas bulky band-shaped martensites were distributed in the steel containing high carbon content. In the 973 K (700 °C)-annealed specimen of the steel containing high carbon content, deformation bands were formed throughout the specimen, while fine carbides were sufficiently deformed inside the deformation bands, thereby resulting in the greatest level of strength and ductility. These results indicated that the appropriate annealing treatment of steel containing high carbon content was useful for the improvement of both strength and ductility over steel containing low carbon content.     

Generalized mean for feature extraction in one-class classification problems

Biased discriminant analysis (BDA), which extracts discriminative features for one-class classification problems, is sensitive to outliers in negative samples. This study focuses on the drawback of BDA attributed to the objective function based on the arithmetic mean in one-class classification problems, and proposes an objective function based on a generalized mean. A novel method is also presented to effectively maximize the objective function. The experimental results show that the proposed method provides better discriminative features than the BDA and its variants.     

Vibration suppression of cart-pendulum system by combining the input-shaping control and the position-input position-output feedback control

Journal of Mechanical Science and Technology - This study is concerned with the active vibration control of a cart-pendulum system. The input-shaping control alone is not sufficient to suppress...     

Physicochemical properties of nanopowdered eggshell

This study was carried out to investigate the physicochemical properties of nanopowdered eggshell (NPES). These characteristics were determined based on the particle size, particle morphology, zeta potential, moisture sorption behaviour, purity and crystallinity. Homogeneous, aggregated and spherical crystals of NPES particles were observed through scanning electron microscope and transmission electron microscope. The average particle sizes of NPES and powdered eggshell (PES) were 202.3 ± 28.9 nm and 113.89 ± 79.37 μm, respectively. Zeta potentials of NPES (?15.41 mV) suggested an incipient instability of the colloidal system. Moisture sorption analysis indicated a higher water adsorption capacity of NPES than that of PES. X‐ray diffractometer (XRD) analysis confirmed the presence of calcite in both NPES and PES. The chemical compositions of the NPES and PES particles were strongly associated with the presence of calcium carbonate, as determined by the Fourier transform infrared (FTIR) spectra. Therefore, eggshell nanopowder has a great potential to be utilised as a component for biomedical applications.     

Preparation and evaluation of poly(2-hydroxyethyl aspartamide)-hexadecylamine-iron oxide for MR imaging of lymph nodes

The purpose of this study was to synthesize biocompatible poly(2-hydroxyethyl aspartamide)–C₁₆-iron oxide (PHEA-C₁₆-iron oxide) nanoparticles and to evaluate their efficacy as a contrast agent for magnetic resonance imaging of lymph nodes. The PHEA-C₁₆-iron oxide nanoparticles were synthesized by coprecipitation method. The core size of the PHEA-C₁₆-iron oxide nanoparticles was about 5 to 7 nm, and the overall size of the nanoparticles was around 20, 60, and 150 nm in aqueous solution. The size of the nanoparticles was controlled by the amount of C₁₆. The 3.0-T MRI signal intensity of a rabbit lymph node was effectively reduced after intravenous administration of PHEA-C₁₆-iron oxide with the size of 20 nm. The in vitro and in vivo toxicity tests revealed the high biocompatibility of PHEA-C₁₆-iron oxide nanoparticles. Therefore, PHEA-C₁₆-iron oxide nanoparticles with 20-nm size can be potentially useful as T2-weighted MR imaging contrast agents for the detection of lymph nodes.     

Multistage PRIM: patient rule induction method for optimisation of a multistage manufacturing process

Industries such as automotive, LCD, PDP, semiconductor and steel produce products through multistage manufacturing processes. In a multistage manufacturing process, performances of stages are not independent. Therefore, the relationship between stages should be considered when optimising the multistage manufacturing process. This study proposes a new procedure of optimising a multistage manufacturing process, called multistage PRIM (patient rule induction method). Multistage PRIM extends the scope of process optimisation from a single stage to the multistage process, and it can use the information encapsulated in the relationship between stages when maximising each stage's performance. A case study in a multistage steel manufacturing process is conducted to illustrate the proposed procedure.     

Situation dependent decision selector for production control in testing and rework cell

This paper presents a production control approach for the testing and rework cell in a dynamic and stochastic computer integrated manufacturing (CIM) system. The proposed situation dependent decision selector (SDDS) observes the status of the system and jobs at every decision point, and makes its three different decisions on dispatching, pre-emption, and dispatching within pre-emption in real time. This paper also develops a new feature selection algorithm for the SDDS. Experimental results show that the SDDS is better than other static control rules and another dynamic control approach with respect to the number of tardy jobs.     

TESLA: an extended study of an energy-saving agent that leverages schedule flexibility

This paper presents transformative energy-saving schedule-leveraging agent (TESLA), an agent for optimizing energy usage in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead to significant energy savings. This paper provides four key contributions: (i) online scheduling algorithms, which are at the heart of TESLA, to solve a stochastic mixed integer linear program for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; (iii) an extensive analysis on energy savings achieved by TESLA; and (iv) surveys of real users which indicate that TESLA’s assumptions of user flexibility hold in practice. TESLA was evaluated on data gathered from over 110,000 meetings held at nine campus buildings during an 8-month period in 2011–2012 at the University of Southern California and Singapore Management University. These results and analysis show that, compared to the current systems, TESLA can substantially reduce overall energy consumption.