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The upper left figure below is the input image. On the right side, an AI technology called Instance Segmentation is used to indicate the recognized person in a square frame, which is filled in according to the shape. The bottom left is the result of removing the display such as the frame and only filling it with the same method. And the lower right is the result of classifying all pixels (pixels) using Panoptic segmentation, and the ceiling, walls, roads, etc. are also identified. They can be applied to a 24-hour surveillance system in places where people should not enter. In addition, this technology can be applied to automatic driving, medical image analysis, quality inspection, etc. And in the past, it is possible to automate the work that people used to do visually.
For example, the following image shows the simulation when dealing with the optimal placement problem for an automated robotics warehouse. You can use data science and AI to find the optimal placement based on the big data of the large stock in the past.
I'm working on the most suitable allocation problem of physical distribution center and a poisonous snake by a genetic algorithm (GA) in the field of the logistics and the supply chain management (SCM).
The next figure is the example which solved a optimum arrangement problem of distribution centre in an actual area in GA. The left side is in the state grouped together at random first. That, ton x Kiro(baggage amount x distances) in each business office and physical distribution center repeats alternation of generations in GA, and searches the combination which becomes smallest. And the optimum arrangement result obtained finally starts to be a right figure.
Following results are examples of AI analyzing concrete images and automatically judging the presence or absence of cracks. Work in high and dangerous places can be replaced with automatic work by robots. In addition, AI will be able to automatically judge symptoms that only experts could see. The figure below is examples of concrete images. Normal is a normal image without cracks, and Abnormal is an image with cracks. Also, in parentheses is the result of AI making a decision using a machine learning model called CNN. And the numbers show the percentage of that possibility.
The results below show which part of the image the AI refers to when making decisions. Visualization with GRAD-CAM as technology. The cracked area will be lighter in color. This is the evidence that AI refers to to determine the presence or absence of cracks.
We are researching the use of AI to predict the amount of power generated by solar power generation. In addition to statistical methods, we are conducting research to predict illuminance with AI based on cloud images seen from the ground and information from illuminometers.
The following is the result of automatically taking pictures from the ground using an astronomical camera at regular intervals, excluding the sun, and automatically extracting only the cloud part (colored green in the image) by the program.
References
・A Study on Consolidated Optimal Stock Locations for lmport and Export Freight Flows in Thailand, Sarinya Sala-ngam, Yataka Karasawa, Jun Toyotani et al., International Journal of Logistics and SCM systems, Vol.9, p.71,2016
・Toward Sustainable Operations of Supply Chain and Logistics Systems, Sala-ngam, Suzuki, Toyotani et.al., Springer, p.207, 2015
・A Case of Intermediate Treatment Facilities in Chiba Japan, Sala-ngam, Suzuki, Toyotani et.al., ICLS, Proc. The 10th Intrnational Congress on Logistics and SCM Systems(ICLS), TA21, 2015
・A Basic Research on SCM Strategy Formulation Model, Chen, Wakabayashi, Toyotani et.al.,ICLS, Proc. The 10th Intrnational Congress on Logistics and SCM Systems(ICLS), TA23, 2015
・A Case Study of the Optimization of the Location Problem and the Delivery Vehicle Routing Problem for Post Office Center in Bangkok, Sala-ngam, Toyotani et.al., ICLS, Proc. The 10th Intrnational Congress on Logistics and SCM Systems(ICLS), 2015
・Logistics Operations, Supply Chain Management and Sustainability, Sala-ngam, Suzuki, Toyotani et.al., Springer, p.525, 2014
・Optimum Position in Office of Delivering Using Guide API,Toyotani et.al.,JOURNAL OF THE JAPAN SOCIETY OF LOGISTICS SYSTEMS,11/ 1, p.91, 2011
Papers
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