AI and management problems

Object Recognition and Pedestrian Counting by AI

For privacy protection, individuals are obscured, and AI is seen counting the number of pedestrians walking down the street. In addition to people, various objects such as cars and bicycles can be recognized through machine learning. Currently, such traffic surveys are conducted outdoors by humans, which can be challenging, especially during harsh weather conditions like winter or summer. By using AI, these surveys can be conducted not only during those times but continuously, throughout the year. However, there are issues; for example, when two people overlap, they may be recognized as a single individual.
Furthermore, if this technology is implemented within stores, it can automatically count the number of pedestrians year-round. This enables high-precision sales forecasting by analyzing factors such as weather, pedestrian traffic, and past sales data, contributing to the optimization of staff, inventory, and addressing food loss issues in line with SDGs.


Automate visual evaluation by veteran professional engineers using AI after retirement


 For example, this is an example of cracks in concrete, and on-site professionals with extensive experience visually evaluate deterioration. However, there is an issue of age, and I will reach retirement age in a few years. Although it is necessary to pass on the technology, AI machine learning can be said to be optimal. By using machine learning on a huge amount of past evaluation data, AI can instantly give the same evaluation as a veteran.
The figure below shows the locations evaluated by the AI ​​in color, allowing you to see the basis for where the AI ​​was looking. Normal AI is a black box, but this is a new technology that is attracting a lot of attention.


Optimal problem for automated robotic warehouse

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.


Distribution Warehouse Optimal Placement Problem

We are 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.

eye_tracker eye_tracker

Automatic crack detection of concrete by AI

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.

eye_tracker eye_tracker

Solar power generation prediction by AI

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.


Object Recognition/Segmentation

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.


・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, 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
・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
・Logistics Operations, Supply Chain Management and Sustainability, Sala-ngam, Suzuki, Toyotani, Springer, p.525, 2014
・Optimum Position in Office of Delivering Using Guide API,Toyotani,JOURNAL OF THE JAPAN SOCIETY OF LOGISTICS SYSTEMS,11/ 1, p.91, 2011 etc.