Continuous Monitoring System for the Wastewaters Having Multiply, Randomly, and Small Effluent Characteristics -Approarch to Analysis of Chemical Oxygen Demand by Complete Flow Process-

A simple system was developed for the fully automatic and continuous measurement of chemical oxygen demand (COD) in wastewater samples based on colorimetry of dichromate. A sample and a solution of sulfuric acid (1+1) containing 2mM potassium dichromate are continuously pumped with a double-reciprocating micro-pump at each flow rate of 0.3 ml/min. The wastewater sample is filtered at first with a 100-mesh stainless filter and then mixed with the dichromate solution in the mixing joint. The mixture is introduced into a reaction coil made of poly(tetrafluoroethylene) tubing (1 mm i.d., 3 mm o.d., and 20 m length), being placed in an oil bath (120℃). After reaction, the mixture passes into a quartz tubular flow-through cell (10 mm path length, 18 μl volume) in a spectrophotometer, and the absorbance is measured at 445 nm. The COD value of the sample is automatically estimated from the amount of decreased absorbance. The system was successfully applied to COD measurement of some waters, and to continuous monitoring of COD in wastewater of university laboratories. The system was also evaluated by comparing with the flow injection analyzer system previously developed by the authors

Investigation of evolution strategy and optimization of induction heating model

An optimal design method using the finite element method and the evolution strategy (ES) is investigated. The evolution strategy is applied to the optimization of induction heating model. The position of auxiliary coil, frequency and ampere-turns are optimized so that the distribution of eddy current density on the surface of steel becomes uniform. It is shown that the selection of the appropriate parameter is important in the practical application of ES</p

A Novel Weight-Shared Multi-Stage CNN for Scale Robustness

Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures have achieved even higher performance recently thanks to their robustness to the parallel shift of objects in images as well as their numerous parameters and the resulting high expression ability. However, CNNs have a limited robustness to other geometric transformations such as scaling and rotation. This limits the performance improvement of the deep CNNs, but there is no established solution. This study focuses on scale transformation and proposes a network architecture called the weight-shared multi-stage network (WSMS-Net), which consists of multiple stages of CNNs. The proposed WSMS-Net is easily combined with existing deep CNNs such as ResNet and DenseNet and enables them to acquire robustness to object scaling. Experimental results on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for image classification tasks with only a minor increase in the number of parameters and computation time.Comment: accepted version, 13 page