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114/5/15（四）志聖工業股份有限公司 梁又文總經理

• 演講時間：114/5/15（四）15:30-17:00

 

• 演講地點：國立清華大學工程一館107演講廳

 

• 演講者： 梁又文 總經理 (志聖工業股份有限公司)

 

• 講題： 西部黃金廊道鑽石鏈 (AI West Golden Corridor: The Shining Diamond Chain of Advanced Packaging)
• 摘要：

紮根台灣，一起省思台灣在高速變化的時代，擁有的在地優勢。

Rooted in Taiwan — Let us reflect on the unique local advantages Taiwan holds in this era of rapid transformation.

國際化浪潮勢在必行，國際觀將成人才必要條件。

Globalization is inevitable, and a global perspective will become an essential quality for future talents.

邊緣AI的發展軌跡日趨明朗，臺灣產業與學界是否做好充分準備？

The development path of edge AI is becoming increasingly clear — are Taiwan’s industries and academia ready for it?

在ESG驅動與即時溝通需求雙重推動下，關鍵晶圓代工大廠在地採購的承諾，將是半導體國家隊的重要指標。

Driven by ESG imperatives and the demand for instant communication, the local sourcing commitment of major foundries has become a key benchmark for Taiwan’s semiconductor national team.

114/5/8(四) 達詳自動化股份有限公司 黃晧倫 總經理

• 演講時間：114/5/8（四）15:30-17:00

 

• 演講地點：國立清華大學工程一館107演講廳

 

• 演講者： 黃晧倫 總經理 (達詳自動化股份有限公司)

 

• 講題： 從需求出發，開創藍海新事業 (Expert in Smart Manufacturing for the Metal Industry)

114/5/1(四) 國立臺灣大學機械工程學系 蔡孟勳 特聘教授

• 演講時間：114/5/1（四）15:30-17:00

 

• 演講地點：國立清華大學工程一館107演講廳

 

• 演講者： 蔡孟勳 特聘教授 (國立臺灣大學 機械工程學系)

 

• 講題： 「應用數位孿生技術於五軸工具機之智能化技術研發」(Development of Intelligent Technologies for Five-Axis Machine Tools by Application of Digital Twins Technology)
• 摘要：

本次演講首先將簡介工具機所面臨的技術挑戰，並以五軸工具機為例，說明如何透過數位雙生技術來預測加工精度、加工時間以及解析加工紋路，以提升機台的性能，最後將介紹如何應用AI人工智慧於加工程式生成、控制器參數優化、加工紋路鑑別、刀具磨耗偵測以及溫昇補償等技術。

 

This presentation will begin by introducing the technology challenges of the machine tool. Then using five-axis machine tools as an example to illustrate how digital twin technology can be employed to predict machining accuracy, machining time, and analyze cutting marks such that the performance of the machine tool can be enhanced significantly. Finally, it will explore the application of AI in areas such as NC program generation, controller parameter optimization, cutting mark identification, tool wear detection, and thermal compensation.

114/4/24 (四) 國立臺灣科技大學機械工程學系 林彥樺 助理教授

• 演講時間：114/4/24（四）15:30-17:00

 

• 演講地點：國立清華大學工程一館107演講廳

 

• 演講者： 林彥樺 助理教授 (國立臺灣科技大學 機械工程學系)

 

• 講題： 「從體現人工智慧到結構進化機器人：探索智慧與機器融合的前沿」(Embodied Artificial Intelligence Towards Structural Evolutionary Robotics: Exploring the Frontiers of Intelligence and Machine Integration.)
•  
• 摘要：

Embodied intelligence bridges artificial intelligence and the physical world through real-world interaction. Intelligence emerges not only from computation but from the interaction between a system's physical body and its environment. As intelligent agents interact with the world, their physical structures can evolve, shaping and enhancing their capabilities.

 

This perspective has renewed focus in robotics on the role of a robot’s mechanical form in shaping its intelligence. Current robotic research falls into two domains. Rigid robots are known for their high precision and force capacity, and soft robots are known for their compliance, adaptability, and safety in unstructured environments. However, both types face limitations in real-world tasks that demand a balance of strength and flexibility.

 

This talk introduces reconfigurable robotics, a class of robotic systems capable of dynamically altering physical configurations. Furthermore, the metamorphic mechanisms can switch topologies and degrees of freedom without disassembly, enabling robots to adapt efficiently to diverse tasks and environments with minimal energy cost. By integrating mechanisms intelligence with control intelligence, we edge closer to a new generation of embodied intelligent robots—machines that are intelligent not just in code, but also in structure.

114/4/17(四) 國立陽明交通大學資訊工程學系 黃敬群 教授

• 演講時間：114/4/17（四）15:30-17:00

 

• 演講地點：國立清華大學工程一館107演講廳

 

• 演講者： 黃敬群 教授 (優貝克科技股份有限公司)

 

• 講題： 「結合動態、保真與品質的盲人臉部修復方法」(Dynamic Blind Face Restoration: Bridging Fidelity and Quality for Real-World Applications)
• 大綱：

Face restoration in the wild is a challenging task, especially when the input comes from low-quality surveillance systems. This talk introduces a dynamic blind face restoration framework designed to handle open-set cross-resolution face recognition (CRFR) problems.

The method targets key challenges including:

• Resolution discrepancy between high-res galleries and low-res probes
• Large pose and texture variations in low-res faces
• Arbitrary image sizes and unknown degradations

We propose a unified solution that includes:

• Towards Understanding Cross Resolution Feature Matching for Surveillance Face Recognition
• Lipschitz-Conditioned for Resolution Robust Face Recognition
• Arbitrary-Resolution and Arbitrary-Scale Face Super-Resolution with Implicit Representation Networks

Theoretical analysis and extensive experiments on benchmark datasets such as SCface, CRLFW, and QMUL-Tinyface demonstrate the effectiveness of the proposed method.

Join us to explore how this framework bridges fidelity, visual quality, and real-world applicability in surveillance scenarios.