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Ansan University and Edcore Co., Ltd. signed an agreement to operate a socially customised curriculum
The Department of Artificial Intelligence Software, Ansan University, Gyeonggi Province, announced on the 12th that it had signed an agreement to operate an on-demand curriculum with Edcore Co., Ltd. on the 7th and held a SW license donation ceremony for the machine learning ‘Snotra ML’ product.
Through the agreement, the Department of Artificial Intelligence Software at Ansan University will operate an on-demand training course for talent nurturing of AI, big data, and security convergence from the first semester of 2022.
The donated product this time is an Easy artificial intelligence software tool. It is a program that can be easily used by people without advanced algorithm development skills only with data refining technology. The goal is to nurture capable human resources.
Daesoo Han, CEO of EDCORE, said, “Through this agreement, we hope that the talents of Ansan University will advance their practical competencies and create synergy between companies and universities. In the future, EDCORE will also spare no effort to provide support so that students can learn based on practical examples and problem-solving capabilities.”
Deok-ryeong Kim, head of the Department of Artificial Intelligence Software, said, “I received a license to use machine learning Snotra ML, which is worth 320 million won, for free from Edcore. Through this, students were able to build a field mirror-type practice space through artificial intelligence technology.”
Citations : Smart Times (http://www.smarttimes.co.kr)
Reporter: jinyong firstname.lastname@example.org
Industry 4.0 and the Smart Factory: How an Event Mesh Enables Digital Transformation in Manufacturing
Artificial intelligence (AI) is transforming the manufacturing industry and its processes, paving the way for the evolution of smart factories.
“The smart factory is a flexible system that can self-optimize performance across a broader network, self-adapt to and learn from new conditions in real or near-real time, and autonomously run entire production processes.”1
A smart factory requires AI-based systems that take advantage of real-time data and can use it to learn, optimize, and make predictions, with the end-goal of maximizing overall equipment effectiveness (OEE). There are a few ways to maximize OEE across manufacturing plants and production operations, but gathering real-time Industrial Internet of Things (IIoT) data is paramount for the new frontier of industry 4.0 and smart factories.
Developing or upgrading a system with event-driven architecture as the foundation is the best way to get real-time data to AI systems for analysis and modeling. It requires integrated and connected IT and OT ecosystems to automate manufacturing operations, including across the supply chain.
Digital Manufacturing Platforms for Connected Smart Factories: Signe-S and Event Mesh
Data Science & Engineering for Yield (DSEY) has developed an intelligent smart factory software and partnered with Solace to create a real-time data network called an event mesh. The Signe-S framework includes components for machine learning (Signe-S ML), virtual metrology (Signe-S VM), and advanced process control (Signe-S APC) which work together to maximize a plant’s OEE.
The benefits of this event-driven system include:
Application of machine learning with no coding
Real-time process prediction and control
Self-learning machine learning algorithms
Results from various perspectives
An event mesh provides the means to dynamically distribute data (in the form of events) from IoT devices or applications to other manufacturing systems and applications that require the information to perform their functions, no matter where they are deployed (on-premises, in the cloud, etc.).
Through event-driven architecture, a smart factory can be automated, analyze inefficiencies between multiple production lines and global locations, and predict downtime due to mechanical failures, supply chain issues, or logistics delays. With Signe-S and an event mesh, relevant events are communicated to processes and equipment in real-time so AI-based decisions and machine learning can be implemented.
This digital manufacturing platform leverages real-time connectivity to create a smart factory that is agile, adaptive, and intelligent.
Smart Factory Machine Learning with No Coding
Smart factories go beyond automation; they adapt and learn in real-time and optimize production processes — often on a global scale. Machine learning enables smart factories to ingest, interpret, and adapt to real-time data from connected equipment. Big data analysis and machine learning modeling can be time-consuming and costly. DSEY’s Signe-S ML product removes the trial and error aspect of data analysis and enables machine learning with a click of the mouse instead of complex coding. It has automated machine learning capabilities built-in, which have been optimized for the characteristics of manufacturing sites (pre-processing, data analysis, and engineering algorithms).
Real-Time Process Performance Predictions
By employing mathematical models, quality control can be enhanced to predict product quality drifts without conducting physical measurements. With the overarching goal in manufacturing of reducing OEE, quality control is a large component because product flaws can result in downtime. Virtual metrology is the method by which smart factories can predict variations and secure 100% control over quality activities. The challenge of virtual metrology systems is accurate prediction algorithms.
DSEY’s Signe-S VM is a self-learning based virtual measurement solution that makes predictions based on AI machine learning models while monitoring its own accuracy in real-time and processing maintenance tasks (which then “re-learn” with AI). Signe-S VM is optimized for manufacturing sites to solve frequent modeling issues from product/process changes and field noise problems, which are the main weaknesses of existing virtual measurement systems.
When used in combination with fault detection and classification (FDC), Signe-S VM can improve Run to Run Control (R2R) and further reduce manufacturing costs by reducing false determination.
Smart factories making use of DSEY’s virtual metrology solution receive the benefits of:
Real-time prediction & discrimination of future measurements
Automatic sensors to process changes
AI-based recommendations of measurement samplings to maximize productivity
Resolution of conventional FDC’s congenital problems with accuracy
Dramatically enhanced R2R applications
Rich measurement data for quality analysis
The Evolution of Advanced Process Control in Smart Factories
Advanced process control (APC) is not new, but the reliability and intelligence behind APC systems is drastically improving. For model-based APC software to be useful, it must accurately represent process dynamics and operations. The role of process management is another key factor that affects OEE, and Signe-S APC is equipped with advanced control algorithms that can perform parameter estimates and provide optimal process conditions in real-time.
The total cost of ownership of existing R2R systems can be reduced through Signe-S APC’s self-learning capabilities and auto-evolution without manually modifying programs or conducting software maintenance when a process changes. Since Signe-S APC evolves intelligently, it enables seamless and accurate process control even in changing processes, significantly reducing quality costs, process engineering, and R2R maintenance.
With evolving technical capabilities and advances in AI, smart factories are becoming viable options for manufacturers to accelerate their productions and lower their costs. Digital technologies such as event mesh and AI-based equipment like Signe-S provide powerful capabilities to manufacturers looking to modernize their processes and take advantage of intelligent systems that effectively eliminate human intervention.
Aligning IT and OT systems is making it possible for manufacturers to eliminate inefficiencies and make enterprise-wide decisions instead of plant-level decisions. Systems, processes, and applications having access to real-time data is crucial to a smart factory’s quality management, operating cost, sustainability – all things that drive OEE.
Implementing an intelligent digital manufacturing platform that detects, predicts, adapts, and controls process quality in real-time is how smart factory leaders can evolve their operations and differentiate themselves from the competition.
Rick Burke, Adam Mussomeli, Stephen Laaper, Martin Hartigan, Brenna Sniderman. “The smart factory.” Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/industry-4-0/smart-factory-connected-manufacturing.html# Accessed 1 December 2020.
Shiama Tilouche, Samuel Bassetto, Vahid Parvoti Nia. “Classification Algorithms for Virtual Metrology“ http://www.mgi.polymtl.ca/Samuel.Bassetto/papers/ICMIT2014.pdf Accessed 1 December 2020
Bespin Global and EDCORE sign an MOU for cooperation in AI and cloud and data business.
Bespin Global (www.bespinglobal.com) announced that Bespin has signed an MOU with Edcore (http://edcore.co.kr/), a company that develops intelligent predictive systems, for 'artificial intelligence, cloud and data business cooperation'.
EDCORE, a company specialising in intelligent smart factory solutions, launched the Industry 4.0 smart factory intelligent real-time prediction system (Snotra VM) in May of last year. Edcore's real-time prediction system can predict real-time measurement results for dynamic processes that have been difficult in the manufacturing field through its own AI reinforcement learning algorithm. Based on these technologies and solutions, we are getting closer to autonomous process control automation as a virtual manufacturing information provider with a digital twin concept that accurately predicts the manufacturing environment in real time.
The two companies plan to cooperate with each other based on their core competencies and technologies. EDCORE will provide AI solutions, big data collection/transmission solutions, and specialised technologies, while Bespin Global will discover cloud and data lake technologies and global informatization business opportunities. Through this, the two companies plan to expand cloud and data lake businesses and expand AI and big data collection/transmission businesses.
Park Sang-hyeon, Head of E1 Business Unit at Bespin Global, said, “We are realising various businesses such as smart factories and digital twins based on manufacturing environments by collaborating with EDCORE’s AI technology and big data collection capabilities and Bespin Global’s cloud-based data processing technology. We plan to discover business opportunities that can advance into the market.”
EDCORE launches AI-based Smart Factory 'Intelligent Platform' business full-scale.
With AI built in, more accurate and intelligent processing of equipment process progress information analysis/modeling, equipment process
measurement result prediction, real-time process quality automatic control can happen!
EDCORE (CEO Deasoo Han) is a company that develops intelligent foresight systems. EDCORE accounced on the 10th that the EDCORE will promote the industry 4.0 Smart Factory 'intelligent
production automation platfom' project.
EDCORE developed and launched a differentiate smart factory intelligent platfom equipped with the world's best artificial intelligence engine by applying its intelligent production automation
platform solution 'Snotra™'.
EDCORE's intelligent platform does more accurate and intelligently processes equipment process progress information analysis/modeling, equipment process measurement result prediction
(predictor), and real-time process quality automatic control (control) based on AI. Through this, the platform is expected to solve the issues of smart factory operation that manufacturers are
concerned about, such as operational efficiency and reduction of product production losses.
An EDCORE official said. "To enter the overseas smart factory solution market, we will participate in the overseas export in support of project of KOTRA's IT consortium this year."
EDCORE is conducting various promotions such as holding webinars and opening a YouTube channel to promote the intelligent automation platform business.
On the other hand, EDCORE has been selected for the 2020 Small and Medium Venture Business Startup Promotion Center- Startup leap package support project and is receiving professional
cutomised support such as commercialisation support and mentoring from the Gyeonggi Creative Economy Innovation Centre.
By staff reporter Mijoon Jeon (email@example.com)
'Real-time Prediction System (VM)'
‘EDCORE’, a company specialising in intelligent smart factory solutions, announced on the 12th that EDCORE has launched an innovative Industry 4.0 smart factory ‘Intelligent Real-Time Prediction System (Snotra™ VM, hereinafter ‘Real-Time Prediction System’).
EDCORE's real-time prediction system is a solution that can solve real-time measurement result prediction for dynamic processes, which was a difficult problem in the manufacturing site, through its own AI reinforcement learning algorithm.
An EDCORE official said, "The role of a 'Virtual Manufacturing Context Provider' with a digital twin concept that accurately predicts the changing manufacturing environment in real time."
In the case of semiconductor and display manufacturing, which are device industries that require large-scale investment, measurement is very important to maintain the competitiveness of ultra-gap products. However, due to site limitations (increased process time, sample measurement of less than 20%, etc.) and low accuracy of the existing measurement system (long-term analysis of the cause of low yield, insufficient rework rate improvement, SI redevelopment, etc.) I am facing the problem of not being able to connect.
According to the company, EDCORE developed a real-time prediction system to solve problems, autonomously detecting changes in dynamic objects at the manufacturing site and deriving causes in real time, autonomous self-learning, autonomous monitoring of predictive accuracy, and automating explainable AI.
In addition, it provides safe advanced process control and optimal smart factory OEE capabilities such as △reducing yield improvement lead time △reducing rework rate △smart sampling △real-time SPC/APC interworking of predictive data without manual DOE and coding.
EDCORE expects to diversify its customers in the intelligent smart solution business and increase sales by building a leading model for the Green New Deal smart factory. In the future, we decided to proceed with active marketing, such as promoting agency contracts and providing a Virtual Prototyping environment in connection with the website.
In addition, EDCORE plans to support the creation of an intelligent smart environment for small and medium-sized enterprises through AI-based intelligent smart solutions implemented in the cloud environment.
On the other hand, EDCORE was selected for the 2020 Small and Medium Venture Business Startup Promotion Center Startup Leap Package Support Project and is receiving professional customised support such as commercialization support and mentoring from the Gyeonggi Creative Economy Innovation Center.
Reporter: Jongmin Ko (firstname.lastname@example.org)
Gyeonggi-province, AI policy discovery begins in full scale... AI policy advisory group launched
The policy advisory group is composed of a total of 10 private experts in various fields, including MINDsLab CEO Taejoon Yoo. The general manager was Ki-duk Kim, the Gyeonggi-do AI Industry Strategy Officer.
Gyeonggi-do operates the Gyeonggi-do Artificial Intelligence Policy Advisory Group, which is composed of experts, in order to accelerate the discovery of major policies for the development of the artificial intelligence (AI) industry.
On the 19th, Gyeonggi Province 1st Vice Governor Yongcheol Lee held a meeting and appointment ceremony for the Gyeonggi AI Policy Advisory Group at the Gyeonggi Provincial Office in the presence of 10 experts in each field of artificial intelligence.
The policy advisory group includes MINDsLab CEO YTaejoon Yoo, law, system, and ethics (Soyoung Lee, lawyer at Jipyeong Law Firm, R&D (Okki Min, head of the Korea Electronics and Telecommunications Research Institute), talent training (Song Jung, dean of KAIST AI Graduate School), cultural contents (Pilki Hong, Seoul). Digital University professor), financial distribution (Chaemi Kim, head of Korea Trade and Information and Communication Center), life service (Hyejoo Kim, managing director of Shinhan Bank), biomedical (Seungkyu Lee, vice president of Korea Bio Association), semiconductor display (Deasoo Han, CEO of EDCORE Co., Ltd.), mobility (Dongsoo Ahn, Kia) It is composed of a total of 10 private experts in various fields such as automobile department), etc. The leader was Kiduk Kim, director of AI industry strategy in Gyeonggi-do.
During their two-year term, they plan and discover new AI-related policies and materialize AI-related businesses, while coordinating and advising on matters that require multi-departmental participation and coordination due to the nature of AI technology.
The policy advisory group meeting is held frequently when there are major issues related to artificial intelligence, and the policy advisory committee plans to hold industry-university-research seminars for each issue to establish a network and materialize the business.
Lieutenant Governor Lee Yong-cheol said, “The importance of big data and artificial intelligence is growing even more during the COVID-19 outbreak, and it is expected to spread rapidly in the administrative and corporate sectors. As it is a homework assignment, we will actively reflect the opinions of the advisory group.”
In the first seminar held following the appointment ceremony, the direction of nurturing talents in Gyeonggi-do, which will lead the AI era, and the plan for Gyeonggi-do biomedical service using artificial intelligence technology were discussed.
On the other hand, Gyeonggi Province signed an agreement with Gwangju and Busan on the 29th of last month to promote the 'Hyper-connected artificial intelligence healthcare platform establishment' project, and the Gyeonggi-do AI convergence service, where residents discover tangible artificial intelligence technology-based convergence services It is taking the lead in creating an artificial intelligence convergence ecosystem in the province, such as promoting a demonstration project.
Reporter Kwangmin Choi email@example.com
[Solace Webinar] “Efficient data utilisation depends on ‘event-driven architecture’
Solace and EDCORE introduce the importance of event-driven architecture at ‘Datanet TV’
Data after AI execution should be reflected in re-learning…
Event platform with simple structure and high stability is essential.
Provides sticky load balancing for personalised hyperconnected services… Ease of continuous scalability.
As we enter the 4th industrial era, efficient use of data is leading to competitiveness of companies. Companies that are already leading the industry are creating new business models while increasing productivity through activities such as linking various data and applying artificial intelligence (AI) based architecture is attracting attention.
Solace, a company specialising in message brokers, and EDCORE, a company specialising in smart factory solutions, presented an overview of event-driven architecture in the 'Event-Driven Architecture for AI-based Smart Factory and Connected Car' webinar with <Datanet TV>. Introduced the necessity and composition method.
Leveraging event meshes for smart AI learning
Daesoo Han, CEO of EDCORE, who was the first speaker, introduced the essence of 'Industry 4.0', a recent topic in the manufacturing field, and ways to achieve it under the theme of 'Artificial Intelligence-based Smart Factory Construction Plans and Cases'.
What the manufacturing industry is paying attention to is ‘Industry 4.0’, which aims to maximise overall equipment efficiency (OEE) such as non-stop equipments, maximum production, and zero defects. In the meantime, the manufacturing industry has achieved computerisation and automation of the production process through continuous quality innovation, but losses are still occurring.
Continuous engineering is continuing to reduce this, but it is true that it is difficult with human power alone. Every time a process changes, a new process must be designed and controlled, because excessive manual engineering not only increases costs but also has the potential to cause quality accidents.
Therefore, intelligent AI is recommended as a new quality innovation method. AI can analyse processes and create control models on behalf of humans, and the models can also be updated frequently according to changes in the environment. In order to improve the quality of AI, humans only need to discover various data and learn from it and cooperate with AI.
CEO Deasoo Han says, “AI learning is important to make smart AI. In addition to learning by data, re-learning reflecting real-time events after execution is required, and for this, multi-faceted event exchange is required.”
Solace's event mesh platform is characterised by a simple structure but strong performance and stability, and EDCORE uses an event platform with Solace technology in its smart factory solution.
Reporter: HyunKi Yoon