Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.     

Characterization of a novel antibody immobilization combining protein G with parylene-H for surface plasmon resonance immunosensors

Here we present a method for selectively and efficiently immobilizing antibodies to enhance the detection performance of surface plasmon resonance immune-sensors (SPRIs) for diagnostic applications. To improve the performance of antibody arrays, protein G was used as antibody-selective linkage layer with aldehyde functionalized poly-(para-xylylene) film. To estimate the efficiency of antibody immobilization, immunoglobulin G (IgG) was measured using the anti-IgG immobilized SPRIs. To demonstrate the proof-of-concept validation, the signal detected from the IgG using parylene-H film was compared with that of a combination of parylene-H and protein G in SPRIs. The results showed that the detection of IgG on the immobilized anti-IgG layer using the combination of parylene-H and protein G has a larger change of signal than that of using parylene-H layer. These results also imply that the anti-IgG was densely and efficiently immobilized on the modified surface with the linkage layer in a combination with parylene-H and protein G. Therefore, we believe that this combinatorial approach could selectively immobilize the antibodies, and also be applied for detection and diagnosis of immune diseases in the field of many SPRIs applications.     

A frame‐based EM‐simulation for design of LC oscillator with MoM capacitor banks

This article presents a simulation method for the design of a digitally controlled oscillator (DCO). Electromagnetic (EM) simulations are essential and inevitable for modern LC oscillator design. Although EM‐simulators provide high accuracy, the EM‐simulation time is very long when metal‐oxide‐metal (MoM) capacitors are present. The proposed frame‐based EM‐simulation can significantly reduce the EM‐simulation time even in the presence of MoM capacitors without influencing the accuracy. To verify the proposed method, a DCO was fabricated using a 55‐nm CMOS process. Measurements of the DCO are in good agreement with the frame‐based post‐layout simulation results. In addition, the DCO has good performances with a low power consumption of approximately 0.68 mW.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Photopyroelectric measurement of the thermal diffusivity of recrystallized high purity aluminum

Thermal diffusivity measurements on high purity (99.99% pure) aluminum samples are reported, using chirped and sinusoidally-modulated laser excitation and photopyroelectric detection of the thermal wave response. The results are found to be dependent on the degree of recrystallization of the specimen: for cold-rolled aluminum, the thermal diffusivity is measured to be =3.10×10^–5 m²/s, which rises to 3.88×10^–5 m²/s for fully recrystallized samples, and then falls to 3.53×10^–5 m²/s for samples exhibiting the initial stages of grain growth.     

Monthly Joint Operations for the Nakdong Multi-reservoir System in Korea

Abstract

This study applies a state-of-art optimization technique, SSDP/ESP (Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction), to derive a monthly joint operating policy for the Nakdong multi-reservoir system in Korea. A rainfall-runoff model, SSARR (Streamflow Synthesis And Reservoir Regulation), is linked to the SSDP/ESP model to provide ESP scenarios for runoff during the next month in the Nakdong River basin. The primary advantage of the SSDP/ESP is that it updates the derived operating policy as new ESP forecasts become available. Another SSDP model that employs historical runoff scenarios (SSDP/Hist) is also developed. The main difference between the two SSDP models is that SSDP/Hist is an off-line model whereas the SSDP/ESP is on-line. The developed operating policies are tested with a simulation model using an object-oriented simulation software, STELLA. The simulation results show that SSDP/ESP is superior to SSDP/Hist with respect to the water supply criterion, although both models perform similarly with respect to the hydroelectric energy production criterion.     

Getting closer and experiencing together: Antecedents and consequences of psychological distance in social media-enhanced real-time streaming video

Social media services such as YouTube and Flickr have become online necessities for millions of users worldwide. Social media are online services that enable users to share contents, opinions, and perspectives that support communication with other users. Social media places an emphasis on the shared experience between users, which we call co-experience. However, the online characteristics of social media increase psychological distance between users, which, in turn, results in a decrease in the quality of co-experience. Hence, as the goal of this study, we theoretically modeled and empirically verified the antecedents and user experience-based consequences of psychological distance in a social media-enhanced real-time streaming video service. In order to reduce psychological distance, we introduced two system elements: inhabited space (the degree of being situated in context and in a meaningful place) and isomorph effects (the degree of preserving the structure of a user’s actions). We constructed a social media-enhanced real-time streaming video service prototype and conducted a field experiment with actual social media users. The prototype, which streamed a live baseball game, enabled users to simultaneously view the game from remote locations and to interact with each other through cheering tools. The results indicate that inhabited space and isomorph effects reduce psychological distance between users, and this, in turn, enhances co-experience. This paper ends with theoretical as well as practical implications of the study.     

Localizing slab identification numbers in factory scene images

This paper deals with algorithms for text localization and character segmentation in images for process automation in the steel-making industry. Each character which comprises slab identification numbers may be corrupted severely before it is captured by network cameras. Therefore, proper processing is required to localize the target texts successfully. In this paper, we propose (1) a method to evaluate the closeness of an edge patch to the form of a closed contour, (2) an edge inspection method to determine character colors and estimate font thickness, and (3) three reasonable binarization methods to increase the performance of the algorithm for the detection of the left and right boundaries of the text rectangle. The experimental results show that the proposed algorithms are reliable.