The spread of services using mobile lines and data centers (DCs) and the future spread of IOWN services requires the rapid isolation and restoration of faulty sections in the event of a large-scale disaster or failure. Our development promotes remote-operated optical-fiber node technology that constantly monitors lines from a remote location and instantly isolates the faulty section in the event of a line failure.
This section introduces an AI that generates images of equipment to predict the progression of deterioration several years later from images taken of road bridges. This AI can predict the change over time of corrosion on steel parts of road bridges based on image data learned in advance and environmental information (temperature, precipitation, etc.) of the installation site and generate images showing the progression of corrosion. By utilizing this AI, the frequency of inspections, which are currently conducted at regular intervals, can be reviewed, repair plans can be optimized, and maintenance costs can be reduced.
This section introduces an inspection system for road bridges that utilizes drones and AI, which was jointly tested by Kumagaya City and NTT Group. In this system, AI detects corrosion on steel materials from images taken by drones and estimates the depth of corrosion. This enables the system to calculate the bearing capacity of road bridges and optimize the timing of repairs, thereby reducing maintenance costs.
Road collapses, which have been gaining social attention, occur when the ground becomes unstable and voids form due to factors such as damage to aging underground pipelines or soil erosion caused by subway excavation work. Currently, ground cavity investigations are conducted from the surface to detect voids in advance. However, conventional underground radar surveys using electromagnetic waves are fundamentally incapable of probing deeper than approximately 2 meters. By establishing a measurement technology that utilizes cosmic rays (muons) that penetrate the ground, it becomes possible to investigate depths beyond 2 meters. We will introduce our current efforts and conduct a demonstration using the measurement equipment employed in our verification process.
We introduce laser removal technology for asbestos containing materials. We succeeded in reducing working time by optimizing laser parameters.
When using high-power laser for rust removal, it is essential to avoid accidentally irradiating other objects or people. We will demonstrate that if the distance from the laser head to the target, measured with LiDAR, deviates from the preset range, the laser irradiation will be immediately halted to ensure safety.
Regarding cable traction technology using drones, technology to calculate the optimum cable traction route in the traction environment by the load simulation technology applied to the cable will be exhibited.
In addition, hardware with the function necessary for cable traction is exhibited.
With the aim of establishing smart pole construction, which is an automatic drilling method to replace hand digging, we will exhibit the PoC system of an underground exploration method that enables the detection of buried pipes in narrow sites for poles.
A lightweight utility pole made of new resin materials for more efficient construction
Exhibition of a Technology for Reinforcing Steel Poles with Advanced Ground-Level Corrosion by Inserting Reinforcement Materials, Eliminating the Need for Pole Replacement
We will exhibit a method of automatically generating work processes by integrating information such as operation logs and procedure manuals. This technology enables automation of work processes, including non-routine tasks, suggests improvements, and generates operation manuals automatically. In this exhibition, we will demonstrate how work processes can be generated for non-routine tasks, such as data processing, while interacting with systems and files, and how improvements to operations can be suggested.
Integrating the technologies of infrastructure, equipment, and work support to visualize the future of field work
We will exhibit a holographic hand-model for supporting hand-work. This hand-model is easy to understand visually
and aims to contribute to shortening work time and improving work safety. Work simulation, remote work support, and remote training are being considered as possible targets for application of this technology.
Using technologies cultivated in communications, we are researching and developing technologies to realize proactive responses to large-scale earthquakes and intensifying torrential rain disasters by predicting damage to infrastructure facilities such as water supply and roads. In this article, we will introduce a visual image of road damage prediction.
Onshore and offshore wind power generation is drawing attention as one of the power sources that can increase domestic energy self-sufficiency.
The developed method aims to detect the presence of blade damage and motor abnormalities using drones without stopping the operation of wind turbines. With the this method, it will be possible to increase the amount of power generated by wind power and maintain it safely and efficiently.
There will be a static display of images and drones showing the demonstration experiment.
To eliminate workplace accidents caused by human error, it is necessary to prevent mistakes by determining the cognitive state of workers. We aim to tackle this challenge by estimating the cognitive state using eye movement (gaze and pupil). In this exhibition, we introduce an effective training system that can evaluate whether or not a dangerous area has been recognized, using eye movement information from commercially available devices, targeting a dangerous training scene in a VR environment.