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  3. コーピー 本社
  4. We will exhibit at the 3rd "Smart Factory EXPO Autumn": showcasing exterior inspection AI, worker analysis AI, and data generation management solutions.
SEMINAR_EVENT
  • Aug 29, 2024
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Aug 29, 2024

We will exhibit at the 3rd "Smart Factory EXPO Autumn": showcasing exterior inspection AI, worker analysis AI, and data generation management solutions.

コーピー コーピー 本社
We will be exhibiting at the "3rd Smart Factory EXPO Autumn - Manufacturing Innovation Exhibition through IoT/AI/FA," which will be held at Makuhari Messe from September 4 (Wednesday) to September 6 (Friday), 2024. Our exhibited products will include data management tools, appearance inspection AI, and worker analysis AI (certified system for IT subsidies 2024). We hope you will come visit us. Exhibited Products: - Appearance Inspection AI: Detects defects in manufactured products through AI learning. https://factory.confide.tech/visual-inspection/ - Worker Analysis AI: Visualizes and analyzes worker behavior. https://factory.confide.tech/workflow-analysis - Data Management: Generation of defective product data, centralized management of image data, verification and assurance of data quality. https://www.youtube.com/watch?v=Lvz2i7AHoDg About Smart Factory EXPO Autumn: This exhibition showcases the latest technologies and solutions such as IoT solutions, AI, and FA/robots that realize DX and efficiency in production and manufacturing.
Date and time Wednesday, Sep 04, 2024 ~ Friday, Sep 06, 2024
10:00 AM ~ 05:00 PM
Capital Makuhari Messe 〒261-8550 Chiba City, Mihama Ward, Nakase 2-1
Entry fee Free Pre-registration is required.
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【【品質管理者向け】AIによる外観検査とデータ管理ソリューション.jpg

AI-Based Visual Inspection and Data Management Solutions for Quality Managers

Attention: Improve work efficiency with the automation of image inspection! Easily and high-quality implement good product detection and defective product image generation with AI that provides understandable reasoning.

This is an AI solution designed for the manufacturing industry. It dramatically improves the automation of factory inspection tasks, enhances work efficiency, and increases the accuracy of quality control. It centralizes the generation and management of NG (non-good) data, streamlining the creation and management of AI training data. Additionally, it standardizes the skills required for inspection tasks, achieving skill-less operation. Key Features: - Intuitive operation: No need for AI expertise, easily operable by anyone. - Learning with a small amount of data: Efficient learning enables the quick construction of high-precision models. - Visualization of decision-making rationale: XAI technology clarifies the rationale behind AI decisions, enhancing reliability. - Generation and management of defective product data: Reduces the burden of data collection and optimizes quality control. Target Audience: - Production Management Department: Planning and managing production schedules, improving production efficiency, reducing defective products. - Producers: Optimizing production processes, introducing new technologies, maintaining equipment, utilizing automation technologies. - Quality Control Department: Conducting product quality inspections, detecting and addressing defective products, establishing quality assurance processes, confirming compliance with standards. - DX Promotion Department: Promoting digital transformation, implementing AI, improving operations through data analysis. - Companies that have already introduced image inspection machines and are facing issues with over-detection. *Please contact us for more information.

  • Visual Inspection Equipment
  • Image Processing Software
  • Image analysis software

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【製品資料】AI学習の生産性を劇的に向上!データ生成管理ツール.jpg

【Product Information】Dramatically improve the productivity of AI learning! Data generation/management tool

A must-see for those considering AI implementation! Streamline NG image generation and data management; preprocessing for machine learning has become surprisingly easy!

This is the ideal data management tool for manufacturers considering AI implementation. Preparing AI training data becomes easier, leading to reduced inspection time and lighter workloads. Challenges include: - Complex data management: Organizing image data is difficult, resulting in longer AI training times. - Insufficient NG data: There is a lack of defective product data, which hinders AI accuracy. - Difficult quality control: Data quality is inconsistent, causing variability in inspection results. Implementation is very simple! - Install the tool: Easy setup with cloud provision. - Upload data: Upload existing image data to the tool. - Generate NG samples: Automatically generate the necessary defective product data. - Train AI: Effectively train AI with high-quality data. Also perfect for those who have already implemented visual inspection! - Streamlined data management: Easily integrate and manage current visual inspection data. - Quality improvement: Support for evaluating and enhancing the quality of existing data. - Flexible response: Smooth integration with existing visual inspection AI. - AI validation support: Verify the performance of the AI model after implementation and assist with optimization. For more details, please refer to the 'Related Catalog' and contact us.

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【品質管理者向け】判断根拠を理解。少量から学習可能な外観検査AI.jpg

[For Quality Managers] Understanding the Basis for Judgment. Appearance Inspection AI that can learn from small amounts of data.

Understanding the basis and risks of AI's judgments. Capable of learning from a small amount of data. Intuitive operation without the need for AI knowledge. Achieving continuous quality management that does not end with a PoC.

The practical "visual inspection AI" for manufacturing sites allows for high-precision learning and operation from a small amount of data with intuitive operation. It is a user-friendly and effective AI tool for site managers. Target Industries: Manufacturing (semiconductors, foundry, automotive, processed foods) / Chemicals / Pharmaceuticals Target Audience: - Production Management Department: Planning and managing production, improving production efficiency, reducing defective products - Production Technology Department: Optimizing production processes, introducing new technologies, maintaining equipment, utilizing automation technologies - Quality Control Department: Inspecting product quality, detecting and addressing defects, establishing quality assurance processes, confirming compliance with standards - DX Promotion Department: Promoting digital transformation, implementing AI, improving operations through data analysis - Companies that have already introduced image inspection machines and face challenges with over-detection Main Features: - Intuitive operation: No AI knowledge required. Easy from AI model creation to operation and quality management. - Learning from a small amount of data: Domain-specific data augmentation technology generates training data tailored to the on-site environment. - Quality management through visualization: XAI technology visualizes the reasoning behind AI decisions, making model improvement easier. - Quality verification for AI: QAAI enables quality verification of data and models, visualization, and management of change points.

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FAQ.jpg

[Free Offer] Comprehensive Coverage of Basic Knowledge with FAQ and Glossary on Appearance Inspection AI

【FAQ/Glossary】You can understand the overview of the FAQ and terminology related to the introduction of AI for difficult-to-understand visual inspections.

We will organize and provide FAQs and a glossary in an easy-to-understand manner for those considering the introduction of an appearance inspection AI system. This content is essential for companies that have already implemented appearance inspection and those that are considering doing so in the future. The appearance inspection AI system is a powerful tool designed to dramatically improve quality control in manufacturing. Our AI technology utilizes advanced image analysis and machine learning algorithms to detect defective products quickly and accurately. It achieves a highly transparent decision-making process through Explainable AI (XAI) and reliable quality assurance through Quality Assurance AI (QAAI). Furthermore, it can build high-precision models from a small amount of data using data augmentation techniques. It has the flexibility to learn from images taken with various cameras and can adapt to different imaging conditions. It maintains consistent detection accuracy even with images captured in different environments, such as those using rule-based image inspection, significantly improving factory production efficiency.

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【製造業向け】ワンストップソリューション(XAIQAAI).jpg

For the manufacturing industry: One-stop AI solution (XAI/QAAI)

Easy operation even for AI beginners! A next-generation AI platform that realizes factory automation and quality control.

This is an AI platform designed for the manufacturing industry. By using this tool, you can achieve factory automation and labor reduction, while also easily managing quality control. It supports tasks such as visual inspections and analysis of worker behavior with AI, assisting operations on the shop floor. Why it’s good: It offers intuitive operation that can be used without specialized AI knowledge. It can learn from a small amount of data, making implementation easy. Additionally, it incorporates the following advanced technologies: - XAI (Explainable AI): Clarifies the reasoning behind AI decisions, allowing users to understand AI behavior. - QAAI (Quality Assurance AI): Generates data and verifies quality while considering environmental changes and disturbances, providing a stable AI model. Why explainability and robust AI are necessary: In manufacturing environments, it is crucial to understand how AI makes decisions. XAI enhances reliability by making the reasoning behind AI decisions explicit, allowing for safe operation. Furthermore, QAAI provides a robust model capable of responding to disturbances and environmental changes, ensuring stable performance on the shop floor. *For more details, please refer to the "Related Catalog" and contact us.*

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【その他資料】無料:製造業技術者の暗黙知・属人化を解消する方法 (2).jpg

[Other Materials] Free: Methods to Eliminate Tacit Knowledge and Personalization in Manufacturing Engineers

DX transformation with operator analysis AI: Visualizing work processes and converting "tacit knowledge" into "explicit knowledge." We will streamline and optimize the manufacturing process!

We will clarify the tasks in manufacturing and logistics that are prone to errors and time-consuming, and achieve appropriate personnel placement, training, workload verification, and improvement of work mistakes. To this end, we will promote the digital transformation (DX) of operations, which can enhance productivity and quality. To address issues such as labor shortages and the transmission of skills, we will utilize the "Worker Analysis AI" to smoothly implement the SECI model (Socialization, Externalization, Combination, Internalization), promoting personnel training, efficient work, and knowledge sharing. The "Worker Analysis AI" strengthens corporate competitiveness and supports sustainable growth. **The Necessity of Converting Tacit Knowledge into Explicit Knowledge** In various operations, the experience of skilled workers tends to accumulate as tacit knowledge within individuals. Converting tacit knowledge into explicit knowledge offers the following benefits: - Standardization: Ensures consistency in work procedures and quality. - Efficient Training: Enables rapid training of new employees. - Knowledge Sharing: Shares knowledge across the organization, improving performance. - Risk Mitigation: Reduces risks associated with worker transfers and resignations. - Continuous Improvement: Promotes process improvements, optimizing efficiency and costs. *For more details, please refer to the "Related Catalog" and "Other Materials" and contact us.*

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【【現場管理者向け】映像から作業工程を分析・可視化:作業者解析AI.jpg

[For Site Managers] Analyze and Visualize Work Processes from Video: Worker Analysis AI

Significant efficiency improvements in manual processes with the help of AI! Analyzing work processes from videos of the manufacturing site.

What is Operator Analysis? Operator Analysis AI is an innovative tool that visualizes work on the manufacturing line in real-time and recorded footage, enabling efficient production processes. It can dramatically improve productivity and quality in manufacturing settings through accurate understanding of cycle times, optimization of process flows, improvement of work actions, optimization of personnel allocation, detection of work omissions, and estimation of work loads. Benefits of Work Analysis 1. Remarkable Efficiency - Eliminates waste through detailed analysis of work processes, significantly improving work efficiency. 2. Assurance of Top Quality - Reduces work omissions and errors through consistent procedures, dramatically enhancing product quality. 3. Improved Safety - Monitors work actions in real-time, allowing for early detection and correction of risky behaviors, thereby providing a safe working environment. 4. Cost Reduction - Reduces operational costs by eliminating unnecessary work and duplication, optimizing personnel and resources. Reasons to Choose Our Products We offer AI solutions specialized for the manufacturing industry, with extensive know-how and flexible customization options. We ensure transparency with XAI and QAAI technologies, achieving intuitive operability and rapid implementation. Our pre-validation minimizes risks and guarantees high implementation effectiveness.

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【製造業向け】動画をAI解析で現場作業を比較・分析 – ボトルネックを特定し、IE手法で作業効率を劇的に改善.jpg

Elimination of bottlenecks in the manufacturing line: Improving work efficiency with worker analysis AI.

[For the manufacturing industry] Analyze and compare on-site work using AI video analysis – Identify bottlenecks and dramatically improve work efficiency with IE methods.

The "Operator Analysis AI" visualizes routine work on the manufacturing line using AI, enabling the understanding of cycle times, optimization of process flows, improvement of work actions, optimization of personnel allocation, detection of work omissions, and estimation of work loads. 【Optimal Operations】 - Comparison among operators: Improve consistency and efficiency of work using the ECRS (Eliminate, Combine, Rearrange, Simplify) method. - Creation of manuals: Promote the 3S (Sort, Set in order, Shine) principles. - Continuous operation: Review and optimize the 3M (Muri, Muda, Mura). 【Target Audience】 - Production Technology Department: For those aiming for process improvement and efficiency, promoting TQC (Total Quality Control) methods. - Production Management: For those wanting to accurately grasp cycle times and optimize production flows, supporting KY (Kiken Yochi) activities. - Quality Control: For those aiming to improve quality through the detection of work omissions and estimation of work loads. - DX Promotion Department: For those promoting digital transformation and aiming for work efficiency through IE (Industrial Engineering) methods. - Shipping Management: For those looking to enhance the efficiency and accuracy of the shipping process. You can view actual implementation examples and demos. *For details, please refer to the "Separate Catalog" and contact us.

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イプロス画像(製品・サービス).jpg

[Case Study] Work Analysis of Yokogawa Manufacturing's Production Site Using AI

[Free Case Study Available] Discover efficiency improvement points in the assembly processes of the world's six major manufacturers using AI! Results of the comparison between veterans and newcomers included!

Facing significant challenges in the manufacturing industry, Yokogawa Manufacturing Corporation has implemented Copy's work analysis AI, achieving remarkable results. This case study introduces the detailed processes and specific outcomes. 【Challenges Addressed】 1. Personalization of tasks 2. Issues in human resource development and technology transfer 3. Workload of operators 4. Variability in quality 【Some of the Effects of Implementation】 1. Early detection of process anomalies: Using Copy's AI technology, outliers in process cycle times were identified, uncovering process anomalies such as missed preparations for necessary parts. 2. Realization of efficiency: Waiting times for assembly equipment were identified, and efficient cart operations by veteran workers were discovered. The work efficiency of veterans and newcomers was compared, clarifying each group's strengths and areas for improvement. 3. Comparative analysis of workers: The movements of newcomers and veterans were analyzed over approximately 200 cycles, clarifying differences in work efficiency and areas for improvement. Through these initiatives, reductions in work time, improvements in quality consistency, and contributions to human resource development have been achieved, establishing new standards for productivity improvement and quality management across the manufacturing industry. For detailed case studies, specific data, and analysis results, please download and view the "Related Catalog."

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朝日電装導入事例.png

[Case Study] Digitizing Asahi Electric's Manufacturing Site with AI

[Free Case Study Available] Achieving Efficiency and Quality Improvement in Production Sites with AI! Publicizing Asahi Densou's Success Case.

Asahi Denso Co., Ltd. has introduced Confide for Factory to address important challenges faced by the manufacturing industry, achieving remarkable results. This case study presents the detailed process and specific outcomes. 【Challenges Addressed】 - Complexity of paper-based document management - Decrease in data accuracy - Difficulty in grasping real-time progress and quality data - Improvement of operational efficiency 【Some Effects of the Implementation】 - Improved operational efficiency: Reduced data entry effort, resulting in smoother and more accurate work. - Visualization and sharing of information: Real-time information sharing enables effective progress management. - Reduction in working hours: A reduction of work hours equivalent to six people per day. By moving away from paper-based document management and embracing digitalization, transparency in business processes has improved. Employee productivity has significantly increased, and errors and mistakes have decreased. Additionally, communication between departments has become smoother, strengthening the overall cooperation of the team. Confide for Factory is evaluated as an essential tool that supports digital transformation in the manufacturing industry and enhances the overall competitiveness of companies.

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[White Paper] Appearance Inspection AI: FAQ and Glossary "Fundamental Knowledge and Practical Information on Appearance Inspection Using AI Technology!"

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[Notice of Exhibition Participation by Fukuda Co., Ltd.] 27th Interphex Week Tokyo

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We would like to extend our heartfelt congratulations on the continued prosperity of your esteemed company. Our company will be exhibiting at the following trade show, where we will showcase a variety of products, including new ones. We sincerely apologize for the inconvenience during your busy schedule, but we would like to invite you to attend this event. 【 Exhibition Name 】 27th INTERPHEX Week Tokyo https://www.interphex.jp/tokyo/ja-jp.html 【 Dates 】 July 9 (Wednesday) - 11 (Friday), 2025 10:00 AM - 5:00 PM 【 Venue 】 Tokyo Big Sight (3-11-1 Ariake, Koto-ku, Tokyo 135-0063)

Feb 13, 2026

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[Notice of Exhibition Participation by Fukuda Co., Ltd.] 2025 YOKOHAMA Automotive Technology Exhibition

[Notice of Exhibition Participation by Fukuda Co., Ltd.] 2025 YOKOHAMA Automotive Technology Exhibition

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We would like to extend our heartfelt congratulations on the continued prosperity of your esteemed company. Our company will be exhibiting at the following trade show, where we will showcase a variety of products, including new ones. We sincerely invite you to visit us during this busy time. 【 Exhibition Name 】 Automotive Technology Exhibition 2025 YOKOHAMA https://aee.expo-info.jsae.or.jp/ja/yokohama/ (An "online exhibition" will also be held during and around the event period) 【 Dates 】 May 21 (Wednesday) - 23 (Friday), 2024 10:00 AM - 6:00 PM *On the final day, 9:00 AM - 4:00 PM (Online exhibition: May 14 (Wednesday) - June 4 (Wednesday)) 【 Venue 】 Pacifico Yokohama (1-1-1 Minatomirai, Nishi-ku, Yokohama, Kanagawa Prefecture) Our booth number: "Concourse 505"

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[Seminar] How Photonic-Electronic Integration is Changing the Semiconductor Hegemony Structure

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[Speaker] Ichiyoshi Economic Research Institute, Inc. Senior Analyst, Corporate Research Department, Mitsuru Ozawa [Key Lecture Content] Optoelectronic fusion is a key area of enhancement included in several items of the government's Japan Growth Strategy Council's 17 priority strategies, and there is an urgent need to address issues such as the power demand of data centers, which is driving the need for accelerated implementation in the future. This lecture will organize the key technological themes and challenges related to optoelectronic fusion, explain the trends of global players centered around CPOs, and discuss the positioning of Japanese companies, exploring the direction we should aim for in the future. We will also examine national projects in the semiconductor industry and introduce a wide range of Japanese companies beyond just the major players, providing a comprehensive explanation of the current status and prospects of optoelectronic fusion. [Lecture Items] 1. Overview and technical points of optoelectronic fusion 2. Trends of global companies regarding optoelectronic fusion 3. Japanese players and their positions 4. Optoelectronic fusion as a national strategy 5. Summary 6. Q&A / Business card exchange

Feb 12, 2026

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"Bio-Life Science Technology Special Feature" 31st University-Originated Seed Matching Seminar

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We will hold the 31st Matching Seminar "Special Feature on Bio and Life Science Technologies." We look forward to your participation. Date and Time: March 4, 2026 (Wednesday) from 13:30 to around 15:30 Application Deadline: February 26, 2026 (Thursday) Format: Online via Zoom Participation Fee: Free Program 13:30 to around 14:00: "Fundamental Technologies for Delivering Proteins and Antibodies to Target Cells and Their Applications in Medicine" Naruhiro Yoshimura, Professor, Faculty of Science, Kyushu University / Special Professor, Graduate School of Biostudies, Kyoto University 14:00 to around 14:30: "Construction of Super Bio-tissues Using Interface-Induced Cell Sheet Formation Method" Yoshihiro Sasaki, Professor, Department of Polymer Chemistry, Graduate School of Engineering, Kyoto University 14:30 to around 15:00: "Selective Reduction of Treatment Toxicity for B-cell Lymphoma by Modifying TNFRSF Signaling" Momoko Nishikiori, Professor, Department of Clinical Medical Science, Graduate School of Medicine, Kyoto University 15:00 to around 15:30: "Spatial Reconstruction Based on Molecular Proximity Networks" Hirofumi Shintaku, Professor, Department of Life System Research, Institute for Advanced Medical Research, Kyoto University

Feb 12, 2026

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■Notice of the "2025 Attack Fair in Meiko"!■ 【July 23, 2025 (Wednesday) - July 24, 2025 (Thursday)】

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Nitto Kohki will hold the "2025 Attack Fair in Meiko" for two days from July 23 (Wednesday) to July 24 (Thursday), 2025, at the "Kowa Seminar Plaza 2F Training Room NO.12 (inside Kowa Sports Land)." The Attack Fair is an exhibition where you can actually "see, touch, and operate" Nitto Kohki's products. Additionally, there will be wonderful souvenirs for attendees and many fun activities planned. We look forward to welcoming everyone!

Feb 12, 2026

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