Acoustical backscattering characteristics of Japanese anchovy can be estimated by Kirchhoffray mode model (KRM model) due to estimate exact body and swim-bladder shape of the fish, the samples were rapidly frozen by dry-ice and alcohol. X-ray photos for ventral and lateral direction for 6 samples were taken and the 3D coordinates of the body swim-bladder were estimated by digitizing from the photos. The angles between the axis of body and swim-bladder were about 9˚ at 38kHz and 7˚ at 120kHz, 200kHz. General formula of TS and BL estimated were 〈 TS38kHz 〉=20logBL-67.3, 〈 TS120kHz 〉=20logBL-66.6, 〈 TS200kHz 〉=20logBL-67.0. As a result, we confirmed KRM model is very useful to estimate TS (Target Strength) for design of experiment and it also can be applied to estimate the abundance of Japanese anchovy distributed by 2 frequency difference method in the survey area.

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other conc

High-performance concrete(HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance(MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction perfomance.

The study discusses the strength development of fresh concrete using fuzzy logic on Electromechanical impedance(EMI) data collected through Piezoelectric(PZT) sensors. For gaining data to estimate real time strength development of concrete through sensors, 3 specimens of concrete were casted for experiment. The PZT sensors were embedded in experimented concrete to perform dual job of actuating and sensing. In this technique with poring of concrete the PZT material starts actuating to produce vibrations, for the same time these materials work as sensors and measure the dynamic responses of the vibrations. Thus, with development of concrete strength the resonant frequencies of EMI start changing. The process of measuring data was continued for every hour for 28 days, after getting EMI data, it was treated by fuzzy logic technique to get a clear strength curve for defining strength development pattern. The change observed in EMI data curve leads to estimating concrete strength.