The consumption awareness of people in recent years has increased, with food safety becoming more and more important. While non-toxic products can be achieved by avoiding using too much antibiotics to control growth factors in a water environment, the measurement tools for dissolved oxygen on the market are very expensive and a great economic burden to fishermen. Thus, the purpose of this study is to design more economical measurement modules and algorithms for monitoring ponds. The research collected pond data through Oxidation-Reduction Potential (ORP), pH and temperature sensors, used regression analysis to infer Dissolved Oxygen (DO) by ORP and pH, and employed a real-time pond monitoring data map to figure out pond conditions. Compared with traditional equipment, findings show our approach reduces costs by about 20%, and increases production capacity and output value.

Decarburization layer depth has great influence on the quality of metal forming process. For determining the depth value, the inspector progresses the measurement by manual nearly, which is subjective and inconsistency. This paper presents a digital analysis approach for measuring the decarburization depth automatically. Meanwhile, this analysis can be carried out by a portable device. For the digital analysis approach, the morphology image process is developed by comparing the variation of gray gradient of image from edge to core region. While the histogram of grayscale gradient falling down sharply, the distance from edge to the point was defined as the ferrite decarburization depth. Meanwhile, if the histogram keeps decreasing and stopping the decrease at one point, then the distance from edge to that point was defined as the totally decarburization depth. For the portable device, the analysis software is settled on a cloud-based calculating system. The analyst can capture the metallographic image shown in the monitor by smart glasses. Then, the analysis result is shown in smart glasses directly. All analysis and calculation process are progressed on a cloud-computing system. Finally, a digital metallographic analysis approach for estimating decarburization depth of carbon steel specimen by a portable device can be carried out.

Inclusions in steel materials are generally composed of oxide, silicate from silica, sulfide, and other chemical compounds. These inclusions in steel cause stress concentration on the contact surface between inclusions and steel base, which result in a quenching crack and a decrease of fatigue strength. The inspection of inclusions in steel is high in demand, and is typically carried out manually. This study used a rule-based methodology to develop a division-making architecture for a digital evaluation system. The results from comparison experiments showed that the difference of evaluation grade between digital and manual evaluations was 0.5. Moreover, the time spent on digital evaluation was 30-50 times faster than manual evaluation. In summary, the digital evaluation developed by this study for inclusions is suitable for metallographic inspection.