KAYABA TECHNICAL REVIEW No. 66 APR. 2023

Technology
Explanation

Use of AI Technology in Development of SA Elements
OHUCHIDA Shun, MIYAUCHI Yuuki, SAGEHASHI Ryota

Abstract
Although there has been a third AI（artificial intelligence）boom in recent years, few organizations in theworld have been able to gain competitive advantage
through the use of AI.
In addition, KYB is focusing on the development ofsliding parts for shock absorbers（SA）to achieve bothride comfort and maneuverability at a high level. In orderto carry out this development efficiently, we have been
providing data-driven support for the development of SA
fluid.
This paper introduces the SA Element Development x
AI initiative led by Digital Transformation Improvement
Dept for these reasons.
When utilizing digital technologies such as AI, it isimportant to carry out data quality control and provide
user-friendly applications. Therefore, we have constructed a platform to safely manage and operate the driving

1

Introduction

We have been experiencing the third AI boom in recent years. Companies and research institutes throughout
the world have addressed social implementation Note 1) of
AI technology to resolve challenges in all fields.
Of the companies who have introduced AI, not lessthan 60% are still at the stage of Proof of Concept (PoC).
An investigation1) has revealed that only 12% of organizations in the world have been able to gain competitive
advantage through the use of AI.
Major reasons why they have not been able to actively
promote the use of AI are:[1] they have no proper knowledge and technology ofAI, and[2] they have not constructed a platform for AI operation.
Item [1] implies that they cannot step ahead out of thetest data collected during the evaluation of developed SA
products in auto vehicles, and developed an application
to easily search and analyze the data managed by theplatform.
With this platform at our disposal, we can now focus
on driving tests and data analysis, freeing us from the laborious data management tasks that used to be necessary.
We are already using this platform in some auto vehicle evaluations. Data useful for future SA development isbeing accumulated in the platform, and we feel that we
can realize a sustainable data-driven system to improve
development capability.
This paper describes AI-based frequency analysis technology that is the result of joint research with universities, along with the technology and philosophy behind
the AI services that utilize such frequency analysis technology for further development, and data analysis platform thus constructed.

PoC as they are not able to determine whether their challenges should be resolved by AI or not. In KYB, that is
why Digital Transformation Improvement Dept is addressing an AI human capital development activity Note 2)
for employees to learn not only AI technology but also
how to set challenges (for more information, refer to“Initiatives to Develop Digital Human Capital” includedin this issue of KAYABA Technical Review).
Item [2] above leads to cases where they cannot proceed to the operation phase because the development ofAI technology has advanced without establishing a system to collect and manage the necessary data for AI
learning. In addition, there may be another case of failingto utilize AI. AI technology can originally prove its worth
when combined with other digital technology, which finally resolves issues. However, optimal solutions or services may not be proposed for lack of knowhow on othernecessary technologies.

― 17 ―

Use of AI Technology in Development of SA Elements

This paper explains the SA Element Development x AI
Initiative, rather than describing the human capital development.
Recently, KYB has been focusing on the developmentof sliding parts for SAs to achieve both ride comfort andmaneuverability at a high level 2),3),4). Digital Transformation Improvement Dept has provided data-driven support
to the development of hydraulic fluids since its foundation in 2019. Specifically, the Department has analyzed
the driving test data collected for fluid evaluation using avariety of analysis approaches to compare the developed
fluid with conventional ones for verification. Based on
the results of the data analyses, the Department has supported the creation of ideas about evaluation techniques
and product development.
The analysis approaches that the Department has adopted for trial include frequency analysis using deeplearning, which is one of the latest AI technologies. Several analysis approaches including this AI-based analysis
method have turned out to be useful for the future development of SA elements. To establish a sustainable datadriven system to improve the development capability, we
have developed a “platform”, “application software”, and“AI services”, which will be described in the following
chapters.
Note 1) To apply and develop results of research to resolve social issues.
Note 2) K
 YB prefers to use the term "human capital " instead of"human resources".

2

dardization of vehicle measurement and that automateddata processing and permanent data management on theplatform have been implemented by effectively using
Amazon Web Services (hereinafter "AWS"). We havealso developed a test condition recording application
with which anyone can record test conditions in the sameformat using the same wording. Details of the application
will be explained in the following chapters.
2. 2 Data Analysis Application
Data can deliver its value only after it is analyzed andutilized for decision making. The following describes therequirements for the “automatic analysis reporting application” that has been developed to be generally used forany kind of driving test.
The application shall:[1] allow for retrieval of data to be analyzed,[2] allow data analysts to automatically create graphsthat may be frequently referred to,[3] be able to promote the use of AI and Business Intelligence (BI) and to support idea creation,[4] be able to be intuitively operated, and,[5] be stably operated.
The easy-to-operate application we have developed
can provide several functions including “data retrieval”,“analysis reporting”, and “support to idea creation”. Withconsideration given to the micro serviceNote 3), the application was redesigned not to affect other functions during
modification of some functions. This application uses
AWS to ensure stable operation. The following chaptersprovide details of the application.
Note 3) An engineering method that divides a single application
into small services to enhance the development efficiency and minimize the affected range.

Requirements

In this development activity, we have constructed aplatform for implementing centralized management ofdriving test data including data retrieval and analysis.
The construction of the data analysis platform focusing on “data quality control” and the development of user-friendly data analysis applications were applied withthe following requirements.
2. 1 Data Analysis Platform
The requirements to ensure data quality control are:[1] Collected driving test data shall be standardized
and able to be saved in the platform.
[2] Records of test conditions and other descriptions
on the data in item [1] above shall be standardized
and able to be saved in the platform.
[3] Driving test data shall be linked to the test conditions and able to be managed on the platform.
[4] Data pretreatment including the linkage above shallbe able to be automated.
[5] No tampering or loss of data under management of
the platform shall occur.
The driving test data has been standardized in such amanner that sensor measurements are saved in the sameformat irrespective of who carried out the measurement
by using a dedicated logger that was developed for stan-

3 Overview of Data Analysis Platform
3. 1 System Configuration
Fig. 1 shows an overview of the system we developed.
The. system constructed on the AWS cloud is designed to
implement data collection, saving, visualization, andanalysis.
3. 2 Data Collection
For proper analysis of the driving test results, it is necessary to appropriately link and manage measurements ofthe driving test data to the spreadsheet meta data recording the test conditions. Thus, this system has a data collection line for the driving test data and another line forthe meta data.
The driving test data is measured using a dedicatedlogger developed with KYB's smart road monitoring system5). Unprocessed driving data measured with this dedicated logger is uploaded to AWS by an Edge PC. Withconsideration given to the expandability of the dedicatedlogger, Amazon S3 Note 4) has been selected as the data upload destination because S3 can save data in various formats including image and audio data sets that are not― 18 ―KAYABA TECHNICAL REVIEW No. 66 APR. 2023

currently collected.
The meta data can easily be recorded by test personnel
just by connecting to a dedicated website from their own
tablet or other similar data terminal and then inputting
the necessary information there. Information items to becollected as meta data have been standardized throughpreliminary discussion among those concerned. The dedicated website, particularly the information input screen,
has been designed to be user-friendly based on opinions
collected through interviews with those who are working
on the site. With the aim of reducing the burden of website operation and management as well as the operation
cost, the static website hosting function Note 5) of Amazon
S3 was introduced. The website is only allowed to be accessed by authorized users who have passed a user authentication through the in-house network, ensuring security as well.
Note 4) A data storage service that can save and protect datawith no limit on data type and capacity.
Note 5) A static website can be constructed by uploading web
contents to Amazon S3.

3. 3 Data Processing
Data measurement by the dedicated logger is automatically started at the time when the logger switch is turned
on. This means that even driving data of a vehicle running out of the testing section is measured too. Then, aprogram to only extract driving data of a vehicle runningfrom the testing section was built as a workflow of AWS
Step Functions. Note 6)
With input of the meta data and unprocessed driving
data, this workflow refers to the meta data and extracts
the driving data collected in the testing section. The extracted data is processed into an easy-to-handle format
and then saved in Amazon S3. The meta data can be
stored in a relational database (hereinafter the "RDB")
linked to the date and time of measurement of the driving
test data. This data management allows users to retrieve
the driving test data they want to analyze whenever necessary (Fig. 2).
The use of the cloud service has not only enabled thefast processing of the workflow but has also improved
fault tolerance and has established a backup environment
at low cost. The cloud service can also be used to relatively easily build and operate a workflow in which cost
only arises for using the necessary resources. For conventional data processing, the meta data has been prepared in a spreadsheet software application, from which
the driving test data has been manually extracted in several days. It has needed a number of man-hours to complete the data processing for organizing the driving test
data just for one day. From now on, the established workflow will automatically carry out a series of data process-Fig. 1 Overview of data analysis platform― 19 ―

Use of AI Technology in Development of SA Elements

ing tasks just with manual data entry of test conditions by
test personnel, substantially reducing the man-hours fordata processing.

3. 4. 2 Ad Hoc Analysis Using BI Tools
While the analysis reports certainly characterize thedriving test data, an ad hoc analysis environment using
BI tools is available to enable even deeper analyses.
KYB provides a data analysis environment using Tableau
not only for this development activity but also across thecompany.
We have also developed and provided a function
whereby anyone can easily retrieve any given driving test
data by connecting the RDB managing the meta data toTableau (Fig. 3). With this search function, analyzers can
now efficiently conduct statistical analysis of driving test
data sets collected under similar test conditions or comparison and verification of similar driving test data setsthat are only different in specific conditions. Anotherfunction available is the downloading of search results.
This may be useful for sharing the search results or data
pretreatment for “advanced analysis using AI”, which
will be described later.

Fig. 2 Overview of workflow
Note 6) A service with which users can create a series of workflows by allocating two or more AWS services.

3. 4 Data Analysis
The data analysis platform provides the following
analysis approaches applicable to the managed driving
test data, so that analyzers can proceed with the analysis
step by step:[1] Simplified analysis using standard reports[2] Ad hoc analysis Note 7) using BI tools[3] Advanced analysis using AI
Note 7) One-off data analysis that is conducted as necessary
with no analysis items or details predetermined.

3. 4. 1 S
 implified Analysis Using Standard Reports
Once the driving test data is managed on the platform,
data analyzers can first view the analysis report automatically output by an internally built web-based application.
This web-based application was developed by consulting
KYB's analysis techniques. Just by selecting any given
driving test data, anyone can easily create a standard report on the test results with the analysis knowhow accumulated in the past. Details of the functions provided bythis web-based application will be explained in Chapter
5.
With the web-based application, the creation of standard reports, which was difficult without dedicated tools
or well-informed people, has now been standardized. In
addition, the safe data management by the platform releases analyzers from management work on data anddedicated tools and allows them to concentrate on analysis, research, and development tasks.

Fig. 3 Driving test data search screen

3. 4. 3 Advanced Analysis Using AI
An environment to implement machine learning Note 8)
is available to support more advanced analysis approaches for the driving test data. Digital Transformation Improvement Dept is promoting an activity to develop AI
human capital capable of effectively utilizing Amazon
SageMaker. Note 9) In liaison with the promotion work, theDepartment has also advanced the development of thissystem.
Currently, the Department provides SageMaker Notebook Note 10) as a data analysis environment in the test
phase. In the development phase with the aim of proceeding to the product phase including machine learning
implementation, the Department recommends carryingout development from local PCs using a dedicated Software Development Kit (SDK) for AWS. Not only bysimply providing an environment for machine learning
implementation but also by distributing sample codes already organized and actively disclosing the know-how
for Amazon SageMaker use accumulated in the company,
the Department supports the promotion of AI utilization.
The environment provided by the Department allows human capital who have already utilized Amazon SageMaker to quickly obtain any given driving test data man-― 20 ―KAYABA TECHNICAL REVIEW No. 66 APR. 2023

aged by the platform and conduct advanced analysis byleveraging various analysis technologies including AI.
Finally, the AI that was developed in this activity has
been incorporated into an AI service, which is now available. The AI service includes a function with which anyone with no knowledge about AI can freely obtain analysis results given by AI so long as data is available.
Details of the AI that was developed as well as the AI
service will be described in Chapter 6 onward.
Note 8) A technology with which a computer can learn rules
and patterns from a variety of data including numericvalues and text to determine the current condition andprospect the future.
Note 9) A service that provides an environment to quickly develop, learn, and distribute machine learning models.
Note 10) A service that can interactively implement a series oftasks including data visualization and machine learning model building.

4E ffort for Establishing an Appropriate
Development and Operation System

4. 1 D evelopment Taking Expandability Into AccountWe carry out system development using Infrastructure
as Code (hereinafter "IaC") taking subsequent expandability and maintainability into account. IaC provides away of managing the configuration of infrastructure using
source codes. It uses source codes to define server environments and application settings. When building a system, IaC can be used to reduce the man-hours, increase
efficiency in reutilization, and reduce human errors inmanual operation. This system uses Serverless Framework for AWS Step Functions and other applications (including the workflow described above) and HashiCorp
Terraform for network- or security-related infrastructure.
4. 2 Scheme for Team Development
Besides IaC, we use another scheme for team development in various applications. That is the source code version management tool GitLab with which we can promote daily development jobs safely and quickly. Source
codes created by development personnel are reviewed bywell-informed people before being merged into the production environment. Once a service is formally released
under a CI/CD Note 11) environment, the latest versionof source codes is tagged, which is one of the GitLabfunctions. The use of this rule ensures that, should a service release fail in future, the service can be swiftly
rolled back to the last version that has proven to provide
normal operation (Fig. 4).
Note 11) Refers to a system that automates the build, test, anddevelopment processes. CI and CD are acronyms forContinuous Integration and Continuous Delivery respectively.

Fig. 4 System development using tagging

4. 3 Multi-account Cloud Operation
As the number of systems running in the company increases in future, it will be necessary to establish an appropriate operation rule for AWS accounts. KYB is promoting a multi-account operation that uses the single
sign-on method to switch over multiple AWS accounts
depending on the type and environment of the system(Fig. 5). Developers and maintainer are only allowed toaccess specific operations according to their own set of
authorizations invested to individual service accounts.

Fig. 5 Overview of multi-account operation

Distribution of authorizations for services and functions to different accounts can help control cloud security
and eliminate the need of setting maximum usage of eachaccount. This can be expected to reduce the burden of
AWS administrators and users. This operation rule is also
applied to the construction of this system.

 evelopment of Automatic Analysis Re5 D
porting Application

As amounts of data increase, data visualization requires
more time and more man-hours. Then, we have developed a web-based data analysis application that canquickly visualize the results of the analysis of driving test
data and then automatically output a report (hereinafterthe "web-based analysis application") without the laborious tasks that used to be necessary.

― 21 ―

Use of AI Technology in Development of SA Elements

the characteristics of the data obtained from more than
one sensor, substantially reducing the time needed tocomplete the process from data collection to visualization.

Fig. 6 Screens of the web-based analysis application

Using the web-based analysis application, you can easily access the driving test data uploaded in the cloud (Fig.
6). Just by selecting a set of driving test data you want toanalyze, the web-based analysis application automatically visualizes the data set, eliminating the need for complicated setting or operation. You may also extract data
you want to analyze in detail (for example, a data set collected for a certain period immediately after the start of atest) as necessary, and then view the data in different
scales. The driving test data may also be filtered by test
conditions, which also serve as a description of the data
to be analyzed, on the Tableau screen shown in Fig. 3,
Chapter 3. Data retrieved in this way will also be automatically visualized on the application. We assume thatthe web-based analysis application will be used in theseways.
There are three modes in the web-based analysis application as described later. You can select the desired mode
depending on the application.
5. 1 General Analysis Mode
General analysis mode is available for the typical visualization formats, which data analyzers frequently use to
create drawings. Through hearing from those concerned
who were involved in data analysis, data visualization
formats that were popular among them were selected.
Specifically, these formats include line graphs, scatter diagrams, and histograms. In addition to these views, theapplication is designed to be able to output sensor measurement statistics, correlation maps, and other relational
data just with a simple mouse operation on the webbased application.
Fig. 7 shows an example of the visualization of thedriving test data collected for the evaluation of SA fluids
using actual auto vehicles. The use of the web-based
analysis application made it possible to quickly identify

Fig. 7 Case example of using the web-based analysis
application (general analysis mode): (a) Histogram of Sensor
A, (b) Scatter diagram with X-axis for Sensor A and Y-axis
for Sensor B, (c) Scatter diagram with X-axis for Sensor A
and Y-axis for Sensor C

5. 2 Frequency Analysis Mode
Frequency analysis mode allows users to view spectrograms of power spectra and time-frequency analysis. Detailed parameters for creating frequency analysis graphs
can be individually set. Basically, the web-based analysis
application automatically sets parameters in response to
the data input, allowing users to easily visualize the data
without paying attention to detailed settings.
5. 3 AI Analysis Mode
AI analysis mode is where all users can easily carryout frequency analysis with AI. Detailed information
about the AI-based approach we developed will be described in the next chapter.
5. 4 Downloading of Driving Test Data
A function that allows users to separately download
the driving test data selected on the web-based analysis
application as CSV files is also available. A signed URL
with an expiration date is issued to target users so thatthey can download the data just through a simple mouse
operation.
This function can meet the needs of users who want tovisualize data in their own way, using familiar analysis
tools.

― 22 ―KAYABA TECHNICAL REVIEW No. 66 APR. 2023

6

Note 12) The spectrograms were generated with Fourier trans-

Use of AI and Setting-up of Challenges

form to visualize vibration. Part of the information is

Deep learning is one of the latest AI technologies anda general-purpose method to be utilized to resolve any
challenge. On the other hand, in comparison with conventional proven technologies, deep learning delivers justequivalent or possibly inferior performance depending on
the challenge. Therefore, it is important to discuss thefollowing questions before starting to develop an AIbased technology:[1] Is the use of AI is the best way of resolving thechallenge?
[2] Can the challenge be adjusted so that the use of AI
is the best solution?
You can easily answer question [1] if you have proper
knowledge about AI. You may hardly be able to answer
question [2] unless you have general knowledge and experience in interpreting challenges in addition to knowledge about AI. The following describes specific examplesof setting up challenges:
6. 1 Background and Challenges
KYB has promoted the development of SA sliding
parts to achieve both ride comfort and maneuverability ata high level. It was known that sensory factors including
ride comfort would be substantially affected by a changeof SA fluids only. However, the relationship between thefluid and the vehicle behavior had not been fully clarified.
At first, we had the existing product and a new productthat was identical to the existing one except for an improved fluid. These products were tested on actual auto
vehicles for evaluation and frequency analysis (Fig. 8). A
comparison of the results of fluid analysis between theexisting and new products only identified a minor difference between the two. Was this minor difference attributable to the fluids or to any environmental factors such as
road noise? Or might it be caused by differences in driving manner? Note 13) There arose the problem that the causeof the minor difference was difficult to determine.

Fig. 8 Results of analysis of SA sprung frequency for
different fluids Note 12)

confidential and has been intentionally deleted or
blurred.
Note 13) Even under the same test conditions, the operation of
the accelerator and/or steering wheel of auto vehicles
may slightly vary.

6. 2 Setting up Challenges for AI
KYB has addressed the development of advanced
anomaly detection technologies6),7) utilizing deep learning, which is one of the latest AI technologies, including“appearance inspection through image processing” and“poor weld detection”. By effectively using insights fromthis experience, we decided to treat the acceleration information, among the driving test data items related to
the SA fluid, as follows:[1] Acceleration of existing product: Reference data[2] Acceleration of new product: Comparative data
The AI-based anomaly detection technologies wereapplied to develop a technology to only learn the acceleration information (reference data) of the existing productand detect what is new in the new product. Note 14)
In short, the challenge of "detecting what is new in the
new product" was interpreted as "comparing the resultsof frequency analysis between the existing and new products and only detecting the differences attributable to thefluids". The latter was further replaced by the challengeof "anomaly detection for which the use of AI is effective". These considerations were made in the initial stageof the development phase before the full-scale development was launched.
Note 14) An AI-based technology that compares acceleration
between the existing and new products and identifies
frequency bands with substantial differences.

G eneral AI-based and Proposed Ap7 
proaches for Anomaly Detection

The newly developed approach is an application of AIbased encoder and decoder. The approach is generally
used to input image or other data that is dense with information to an encoder where the information is compressed and then further input the compressed information to a decoder where given data can be reconfigured.
This approach has the following two major advantages:[1] Difficult-to-handle high dimensional data can beconverted into low dimensional data, which can beused as a feature value for machine learning.
[2] The data compression/reconfiguration process is
expected to deliver a noise removal effect. (In otherwords, only distinctive data can be reconfigured).
If the encoder and decoder learn to reconfigure input
data Note 15), determining the difference between the reconfigured data (output data) and the input data can locate
noise. This noise corresponds to flaws or stains that arefound in appearance inspections. Thus, this approach isoften used at sites requiring anomaly detection (Fig. 9).

― 23 ―

Use of AI Technology in Development of SA Elements

Fig. 9 AI-based approach for fault detection ofhand-written text
Note 15) An approach for an encoder and a decoder to learn toensure that the input data is identical to the output datais called an autoencoder.

7. 1 Proposed Approach
In the proposed approach, the encoder learns to receive
input of spectrograms of the unsprung acceleration of theexisting product and the decoder learns to output (or reconfigure) spectrograms of the sprung acceleration (Fig.
10).

Fig. 10 Overview of proposed approach

Learning huge amounts of data from the existing product alone can generate a dedicated model for the existingproduct. The model here means a computing unit that
outputs the sprung spectrogram of the existing productafter being input with its unsprung spectrogram.
If part of the existing product data that was not used

for learning is input to this model, the sprung acceleration with noise removed can be output. Identifying thedifference between this output data and the actual measurement data allows visualization of the noise (Fig.
11(a)). This visualized noise is expected to be able to beinterpreted as "test errors including road noise".
If data from the new product is input to this model inturn, only the frequency bands that correspond to thoseof the existing product data can be output. Identifying thedifferences between this output data and the actual measurement data allows observation of the frequency bands
that failed to be reconfigured well as noise (Fig. 11(b)).
This visualized noise is expected to be able to be interpreted as "differences in frequency bands attributable tothe fluids".
When visually checking the spectrograms for reconfiguration errors, however, users may discover minor variations in color by unavoidably relying on their subjective
determination. Then, we have proposed another plotting
method that averages spectrograms out in the direction of
the axis for frequency and visualizes shifts in frequency
bands using a line graph (Fig. 12). We believe that thisplotting method allows users to determine the differences
between fluids regardless of users' expertise.
Another advantage of this proposed approach is that
only a relatively small amount of data from the new
product is needed while large amounts of learned data (theexisting product data in this case) need to be available.
This means that, as long as large amounts of driving test
data to be benchmarked are collected in the initial stageof the development phase, AI-based frequency analysis
can be made even with small amounts of data from the(a) Learned model with existing product data as input(b) Learned model with new product data as input
Fig. 11 Overview of proposed approach― 24 ―KAYABA TECHNICAL REVIEW No. 66 APR. 2023

Fig. 12 AI-based visualization of differences in sprung frequency bands between existing and new products

new product. Note that the model generated in this proposed approach is a black box. The grounds on which thedata was reconfigured is unknown. It must be remembered that analysis results should be used for reference
purpose only.

8 AI Support for Development of SA Elements
Digital Transformation Improvement Dept uses Python
for development of AI-based technologies including the proposed approach described in Chapter 7. Therefore, expertise on AI and Python as well as a dedicatedoperating environment are needed for effective use of the
proposed approach. We thought that, in fact, many people
faced a hurdle to obtain expertise and prepare the operating environment. We then developed an application embedded with AI (the proposed approach) to establish anenvironment where anyone can try the AI-based frequency analysis.
Note 16)

Note 16) One of the general-purpose programming languages.
Python is the de facto standard language for AI development.

8. 1 C ase Example of Utilizing Internally Built
AI Services
The AI service we developed is available as one of thefunctions of the “automatic analysis reporting application” described in Chapter 5. One thing that is different
from the other functions is that the AI service also supports data that is not managed by the platform Note 17) Any
users can temporarily upload their own data via the webbased application to analyze the data using the AI service.
This AI service has the following objectives:[1] To allow users to use the AI service only with thetest data available even if they are unable to easily
carry out a driving test, lowering the hurdle for AI
technology.
[2] To implement AI-based analysis of virtual driving
test data generated by simulation, based on the as-

sumption that a high-precision physical SA model
including fluid will be created in future.
[3] To allow expanded usage of the AI service to takeplace by not limiting users or data types.
Finally, Fig. 13 shows an example of using the AI service for any given driving test data. The accuracy of detecting what is new in the new product is expected to risethrough comparison of analysis results between the conventional and proposed approaches.

Fig. 13 Comparison of results of frequency analysis between
conventional and proposed approaches
Note 17) Must be in a specified data format.

8. 2 Technologies Supporting the AI Service
The development of this AI service has used Container, which is a virtualization technology. Container technology specifically refers to the method of collectively
managing and operating AI applications and its operating
environment in a single package. A Container containing
applications and operating environment can be started upeither locally or in the cloud, minimizing environmentdependent problems. Note 18)
AWS provides many different Container-related services. Users can freely select these services according totheir needs.
We do not actually use the AI service (the proposed
approach) so often in our daily development activity. So,
we selected the AWS Lambda service for which we only
pay for what we use. Although it takes several minutes to― 25 ―

Use of AI Technology in Development of SA Elementscomplete the start-up process with AWS Lambda, containers can be started and used at minimum cost, implementing the operation and management of an environment where anyone can use AI services safely wheneverneeded (around No.9 in Fig. 1).
Note 18) In some cases, modification is needed depending on
the container start-up environment.

9

Outlook

Under this development project, the construction of adata analysis platform for improved capability for SA development has been completed. In fact, it is difficult todetermine whether the platform has contributed to improved development capability or not at this moment because the platform has little track record of operation yet.
Still, the platform did provide users with the following
value:[1] Users have been released from data managementwork.
[2] An environment where users can focus on driving
tests and data analysis has become available.
The platform has been constructed to be able to manage and operate a variety of driving test data. If a driving
test that cannot be assumed at this moment is carried out
in future, however, the platform may fail to cope with theanalysis. So, we would like to develop a maintenancesystem ensuring that the platform can be operated over along time and can handle even unexpected problems.
The “AI service” described in this paper was developed by applying the results of the joint research of Gifu
University and Digital Transformation Improvement
Dept of KYB. The service is available in a user-friendly
manner even to users who are not AI experts. From now
on, we would like to introduce AI-based technologies
and algorithms developed not only by Digital Transformation Improvement Dept but also by other functions
into the service. To achieve this as well, we plan to establish a scheme to respond to inquiries on how to use theplatform or the development environment from AI human capital or advanced IT human capital in various
functions.

10 Concluding Remarks
This development project has constructed a system toeffectively use driving test data. We understand that some
employees may have inhibitions on the Work Style Reform using digital technology. Still, we will promote digital transformation (DX) by adequately explaining thefull value of accepting DX.
The know-how accumulated in this development project can be also applied to building platforms for types ofdata other than driving test data. Any functions facing
difficulties with data utilization can collaborate with us
for resolving their challenges.
Finally, I would like to take this opportunity to sincerely thank all those concerned in the related functions
who extended great support and cooperation to this development project.

References
1) Accenture: Survey Report, https://www.accenture.com/us-en/
insights/artificial-intelligence/aimaturity-and-transformation(Survey period: August through September 2021).
2) MURATA: "Development of Sliding Parts for Shock Absorber(Introduction of ProsmoothTM)", KYB Technical Review
No.58 (April 2019) .
3) SANO: "Development for Analytical Technology of Shock Absorber Valve Characteristics", KYB Technical Review No.58(April 2019) .
4) KATO, SASAKI: "Study of Evaluation Index Characterizing
Dynamic Friction Characteristics in Shock Absorber", KYB
Technical Review No.59 (October 2019) .
5) SHUTO, TAKAMATSU: "Development of a Smart Road Monitoring System", KYB Technical Review No.61 (October 2020) .
6) NAITO: "AI x IoT Initiatives in the Production Technology Domain at KYB", KYB Technical Review No.60 (April 2020) .
7) FURUKAWA, ISASHI: "Development of an Equipment Predictive Maintenance System", KYB Technical Review No.63 (Oc-

― 26 ―

tober 2021) .

KAYABA TECHNICAL REVIEW No. 66 APR. 2023

Authors 

OHUCHIDA ShunMIYAUCHI Yuuki

Joined the company in 2013.
Digital Transformation Improvement
Dept, Engineering Div.
Engaged in development of AI systems

Joined the company in 2017.
Digital Transformation Improvement
Dept, Engineering Div.
E ngaged in construction of data
analysis platforms.

SAGEHASHI Ryota
Joined the company in 2019.
Digital Transformation Improvement
Dept, Engineering Div.
E ngaged in development of backends related to data analysis.

― 27 ―