AI (Artificial Intelligence) and Management Issues
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.
The study which applies artificial intelligence and a genetic algorithm to various management issues is being performed at our laboratory. It's various management practice in the production management, the quality control and the enterprise to want to pay attention in particular. Declining birthrate aged society can be supported now with IT and IoT by automating these management practice by AI (Artificial Intelligence) and also automating a manufacturing premise by an industrial robot and automatic control.

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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.

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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.

・Research on cloud extraction by color identification from sky camera images and fluctuations in solar radiation,Otake,Toyotani et al., IPSJ SIG Technical Report Vol.2021-IS-158 No.3,2021
・AI-based cloud movement detection from sky camera images,sakoh,Toyotani et al.,College of Industrial Technology Nihon University 53rd Academic Lecture,5-34,2021
・AI and programming languages ​​for management,Japan Society of Directories 23rd National Convention, Research Report Proceedings,p.11-14,2018
・HR technology and human resource development utilizing AI,Toyotani et al.,Proceedings of IPSJapan 81st National Convention,6J-05,2018
・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.71-81,2016
・Toward Sustainable Operations of Supply Chain and Logistics Systems, Sala-ngam, Suzuki, Toyotani, Springer, p.207, 2015
・A Case of Intermediate Treatment Facilities in Chiba Japan, Sala-ngam, Suzuki, Toyotani, ICLS, Proc. The 10th Intrnational Congress on Logistics and SCM Systems(ICLS), TA21, 2015
・Optimum Position in Office of Delivering Using Guide API,Toyotani,JOURNAL OF THE JAPAN SOCIETY OF LOGISTICS SYSTEMS,11/ 1, p.91, 2011
・A Basic Research on SCM Strategy Formulation Model, Chen, Wakabayashi, Toyotani,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, ICLS, Proc. The 10th Intrnational Congress on Logistics and SCM Systems(ICLS), 2015