Recently, application areas based on M2M (Machine-to-Machine communications) and IoT (Internet of Things) technologies are expanding rapidly. Accordingly, water flow and water quality management improvements are being pursued by applying this technology to water and sewage facilities. Especially, water management will collect and store accurate data based on various ICT technologies, and then will expand its service range to remote meter-reading service using smart metering system. For this, the error in flow rate data transmitting should be minimized to obtain credibility on related additional service system such as real time water flow rate analysis and billing. In this study, we have identified the structural problems in transmitting process and protocol to minimize errors in flow rate data transmission and its handling process which is essential to water supply pipeline management. The result confirmed that data acquisition via communication system is better than via analogue current values and pulse, and for communication method case, applying the industrial standard protocol is better for minimizing errors during data acquisition versus applying user assigned method.

Concrete has recently been modified to have various performance and properties. However, the conventional method for predicting the compressive strength of concrete has been suggested by considering only a few influential factors. so, In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were used for learning the Deep Learning Algorithm to derive a prediction model. The purpose of this study is to suggest a method of constructing a prediction model that predicts the compression strength with high accuracy based on Deep Learning Algorithms.

In this study, according to the reference setting based on the runoff video of 9:00 where the highest water level of 3.94 m has been recorded during the runoff of Cheon-mi Stream in Jeju Island by the attack of Typhoon no. 16 Sanba on September 17th, 2012, the error rate of long-distance and short-distance velocimetry and real-distance change rate by input error have been calculated and the input range value of reference point by stream has been suggested. In the reference setting process, if a long-distance reference point input error occurs, the real-distance change rate of 0.35 m in the x-axis direction and 1.35 m in y-axis direction is incurred by the subtle input error of 2~11 pixels, and if a short-distance reference point input error occurs, the real-distance change rate of 0.02 m in the x-axis direction and 0.81 m in y-axis direction is incurred by the subtle input error of 1~11 pixels. According to the long-distance reference point setting variable, the velocity error rate showed the range of fluctuation of at least 14.36% to at most 76.06%, and when calculating flux, it showed a great range of fluctuation of at least 20.48% to at most 78.81%.