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軟體工程師
Avatar of 劉岳宬.
Avatar of 劉岳宬.
主任工程師 @創奕能源科技股份有限公司
2023 ~ Present
軟體工程師
Within one month
網IoT MQTT :整合 JavaScript 與 Python,實現 MQTT協議連線,建立即時通訊機制,應用於多種情境。 5.JavaScript 貪食蛇遊戲:開發 JavaScript 貪食蛇遊戲,藉此練習演算法和語法,展現對 JavaScript 的深入理解和實際應用。 前端技術: 1.使用 HTML、CSS 開發具有響應式設計(RWD)的前端網頁,確保在不同
Javascript(ES6)
Node.js
React.js
Employed
Ready to interview
Full-time / Interested in working remotely
10-15 years
大華科技大學
電子工程系
Avatar of 黃資恩.
Avatar of 黃資恩.
Engineer @鴻霖
2022 ~ Present
軟體工程師
Within one month
設計了一套框架,使其他同事減少開發新功能的學習成本,事後發展成其他產品也可套用這框架。在SOA Mapping的部分,我設計了演算法使原本需要數秒才能mapping到服務進而優化為數毫秒。此外,我也在團隊中積極導入CI/CD流程,這期間增強了許多有
HTML5
Ruby
CSS3
Employed
Ready to interview
Full-time / Interested in working remotely
6-10 years
國立中正大學
資訊工程
Avatar of the user.
Avatar of the user.
軟體工程師 @Wistron NeWeb Corporation 啟碁科技股份有限公司
2023 ~ 2023
軟體工程師
Within two months
Word
PowerPoint
Excel
Studying
Open to opportunities
Full-time / Interested in working remotely
4-6 years
國立中正大學(National Chung Cheng University)
Computer Science and Information Engineering
Avatar of the user.
Avatar of the user.
Android platform engineer @璟昇科技股份有限公司-美商ID TECH台灣分公司
2023 ~ Present
軟體工程師
Within one month
C/C++
JAVA
kotlin
Employed
Open to opportunities
Full-time / Interested in working remotely
6-10 years
朝陽科技大學
資訊工程
Avatar of Lewis Chang.
Avatar of Lewis Chang.
Senior Backend Engineer @Appier 沛星互動科技
2022 ~ Present
軟體工程師
Within one month
AngularJS to Angular. Software Engineer • AI4quant MaySepUsed NLP model and image processing help E-Commerce client to eliminate duplicate products. - Assessed NLP models for time series data. - Studied the state-of-the-art DL models like Efficient-Net, Transformer-XL. - Built a mobile app to receive data from various wearable devices via blue-tooth connection for monitoring user's health condition by DL models. AI Algorithm Development Engineer • Skyline Taiwan AugMayUsed MCTS and other ML techniques to build bots for card games. - Built an automated logs processing procedure to find c...
Python
Backend Development
Frontend Development
Employed
Not open to opportunities
Full-time
4-6 years
National Central University
Department of Optics and Photonics
Avatar of 徐子崴.
Avatar of 徐子崴.
軟體工程師 @台雲資訊
2016 ~ Present
軟體工程師
Within one month
架 React-native, Express, Django, Bootstrap, React, Vue, Flask, Koa 應用程式 Photoshop, Illustrator, Lightroom 論文 Yi-Wei Liu, Tz-Wei Hsu, Che-Yu Chang, Wen-Hung Liao and Jia-Ming Chang "PSLDoc3: A High-Throughput Protein Function Prediction by the Novel k - nearest neighbor and Voting algorithms",ISBRA2019, Barcelona, Spain) Che-Yu Chang*, Tz-Wei Hsu* and Jia-Ming Chang "PSLCNN: Protein Localization Prediction for Eukaryotes and Prokaryotes Using Deep Learning”(* Join First Author,accepted in the international track, EI,the 24th TAAI ) 競賽成績 科技部大專生研究計
PowerPoint
Microsoft Office
Node.js
Employed
Not open to opportunities
Full-time / Remote Only
4-6 years
國立台灣大學電信工程學研究所
資料科學與智慧網路
Avatar of Ivan Lee.
Offline
Avatar of Ivan Lee.
Offline
AI應用工程師 @碁仕科技
2018 ~ Present
軟體工程師
Within one month
Taiwan Research and developing visual recognition and control, using AI to solve multi-dimensional feature problems that cannot be handled by traditional methods, and making the algorithm to achieve generalization and desired speed. 1. Develop vision algorithms 2. Research control algorithms of robotic-arm 3. Deploy algorithms to embedded machines (Nvidia AGX, Nvidia NX) 4. Track the latest cutting-edge journals and technologies Project experiment Robotic-arm random bin picking(Depth-Image) Tensorflow、AlexNet、Cross entropy method Traditional algorithms can only specify a single or few items after modeling. Using an AI algorithm, it
Python
Reinforcement Learning
Computer Vision
Studying
Intern / Interested in working remotely
4-6 years
長榮大學
企管系
Avatar of 劉佳格.
Avatar of 劉佳格.
軟體工程師 @致伸科技
2019 ~ 2021
軟體工程師
Within one year
劉佳格 現服務於同致電子超聲波研發部門,專職於自動停車輔助系統的算法開發,畢業於台灣科技大學電子所,研究方向為機器學習、電腦視覺與深度學習。 台北市,,[email protected] 研究領域 Machine Learning Computer Vision Deep Learning 程式語言 C Python Java 開發環境/工具 Visual Studio Eclipse Docker
C
Employed
Full-time / Interested in working remotely
4-6 years
台灣科技大學
電子工程研究所
Avatar of the user.
Avatar of the user.
爬蟲工程師 @Biggo
軟體工程師
Within one month
python
vue.js
sql
Full-time / Interested in working remotely
6-10 years
輔英科技大學
軟體設計
Avatar of 陳文諭 Jesse Chen.
Avatar of 陳文諭 Jesse Chen.
Senior Engineer @TSMC 台積電
2023 ~ Present
軟體工程師
Within six months
Ltd • 2018 十一月四月 • 於 雲端研發處-軟體四部 任職 軟體工程師( 團隊介紹 ) 醫材讀數辨識服務 DHO MEOCR( 專案簡介 ) • 負責規劃專案架構、演算法運作流程、並開發排版分析演算法。 • 負責用 Django 撰寫網站前台、後台架構,分別架設於 Ubuntu、Linux 系統之機器上。 • 設計並撰寫 Restful
Python
Qt
OpenCV
Employed
Not open to opportunities
Full-time / Not interested in working remotely
4-6 years
國立清華大學 National Tsing Hua University
資訊工程

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Within one month
AI應用工程師 @ 碁仕科技
碁仕科技
2018 ~ Present
Taipei, 台灣
Professional Background
Current status
Studying
Job Search Progress
Professions
Machine Learning Engineer, AR/VR Engineer, Full Stack Development
Fields of Employment
Software
Work experience
4-6 years
Management
I've had experience in managing 1-5 people
Skills
Python
Reinforcement Learning
Computer Vision
ROS
Languages
Job search preferences
Positions
軟體工程師
Job types
Intern
Locations
Remote
Interested in working remotely
Freelance
Yes, I freelance in my spare time
Educations
School
長榮大學
Major
企管系
Print

Ivan Lee 李逸帆

Master of Computer Science

Hsinchu, Taiwan

       

Research and develop algorithms in my company. Cooperate with domestic and foreign manufacturers to use AI technology to solve problems that traditional algorithms cannot overcome. Completed or ongoing projects include defect detection, text recognition (OCR), airplane detection, random bin picking, bottle inspection, point cloud image cutting, and robotic arm grasping by reinforcement learning.

Cellphone: +886 0952152828
Gmail: [email protected]

Personal Web: https://www.ivan-lee.me/ 
Blog: https://medium.com/change-the-world-with-technology


Work experience



AI senior engineer

G4 technology company

FEB 2021 - JUL 2022
Taipei, Taiwan

Recruit AI researchers and RD. Planning and project management.

1. Plan project development and project process
2. Organize the paper-sharing club
3. Plan coding style python with team members
4. Lead newcomers to familiarize the project and development 

AI junior engineer

G4 technology company

Jan 2018 - Jan 2021
Taipei, Taiwan

Research and developing visual recognition and control, using AI to solve multi-dimensional feature problems that cannot be handled by traditional methods, and making the algorithm to achieve generalization and desired speed.

1. Develop vision algorithms
2. Research control algorithms of robotic-arm
3. Deploy algorithms to embedded machines (Nvidia AGX, Nvidia NX)
4. Track the latest cutting-edge journals and technologies

Project experiment

Robotic-arm random bin picking(Depth-Image)

Tensorflow、AlexNet、Cross entropy method

Traditional algorithms can only specify a single or few items after modeling. Using an AI algorithm, it can grasp various daily necessities or stationery (universal). As long as the width of the object gripper and a specific distance are met, 95% of daily life and household items can be grasped. In cooperation with the robotic arm team, the company's project has been able to grab a variety of daily necessities, including fried chicken legs and bundled wires (flexible objects).

Detecting the defects of industrial products

Tensorflow、Segmentation、Unet

Cooperate with well-known factories and manufacturers to detect the defects of various items. Traditional algorithms need to design multiple layers of logic and processing for complex images, but general-purpose neural networks can overcome this problem. Design a general-purpose model, so that the model (Segmentation) can be generalized and learned effectively. The objects include 17 kinds of items, such as tires, keyboards, PCB boards, metal welding objects, passive components, etc. Compared with traditional algorithms, it can effectively reduce development by 80%.

Robotic-arm automatic grasping (RGB-Image)

Tensorflow、Cross entropy method、Pybullet、Q-learning

The traditional algorithm relies on line scanning and surface scanning. After obtaining the point cloud, it performs a clamping calculation, path planning, and collision detection. The neural-like control method can directly use the color camera, save the expensive point cloud camera, and save all the aforementioned calculation processes, and dynamically execute the grasping strategy. At present, research in the virtual environment has achieved results. After transferring the model to the real world, it is expected to save 25% of the hardware cost of the gripping project and speed up the gripping time by 3 times. demo video:https://youtube.com/shorts/17ROS385zy4

Detect airplane

Pytorch、Yolov3、Jetson AGX、Jetson NX

Cooperate with a large domestic institution to detect aircraft on satellite images and actually deploy them on embedded machines after training.

Recognize nutrition label

Pytorch、OCR、crnn、scikit-image

Cooperate with well-known domestic retailers to test the nutrition labels and ingredients on bento boxes. The text recognition software on the market cannot detect special Chinese characters (words for nutrition), and the arrangement is too narrow. Therefore, according to the font used on the label, and the image characteristics of the actual scene, such as deformation and skewness, we provide customized products for customers. And passed the customer stress test: in addition to normal words, it can also issue warnings when there are defects or typos in the text, with an accuracy of 99%.

Detect bottles(RGB-Image、Depth-Image)

Tensorflow、Segmentation、Edge detection、Surface rebuild、Open3d

Transparent objects have always been a difficult problem in traditional algorithm detection. No matter the line scan, area scan, or depth camera, there will be refraction and transmission, and complete imaging cannot be achieved. Taking advantage of the neural-like feature of processing multi-dimensional information, a variety of models are used to restore the smooth point cloud on the transparent surface of the bottle, overcoming the problem that traditional algorithms cannot solve.

Point Cloud segmentation

Tensorflow、Mask RCNN

In the traditional method of point cloud calculation, the calculation time is too time-consuming, and the calculation is slow for the final prediction and grasping of traditional CAD. Combined with Instance segmentation to calculate point cloud, the efficiency is accelerated by 2~4 times.

Skills

Tools


  • Python
  • Tensorflow
  • Keras
  • Pytorch

OS


  • Ubuntu
  • ROS

Others


  • Git
  • Slack
  • Jira
  • MySQL

Education




National Yang Ming Chiao Tung University

Computer Science

2022/7 - present
Taipei, Taiwan

Research for reinforcement learning, robotic arm, computer vision, GAN, VR.

Institute for Information Industry

BigData Data Scientist Class

2017/2 - 2017/8

Learning Hadoop distributed systems, database planning, Linux operation, agile management.

Chang Jung Christian University

Bachelor of Business Administration

2012 - 2016

Organization and project management.

Lecture at Central University (2020/11 AI introduction)


Resume
Profile

Ivan Lee 李逸帆

Master of Computer Science

Hsinchu, Taiwan

       

Research and develop algorithms in my company. Cooperate with domestic and foreign manufacturers to use AI technology to solve problems that traditional algorithms cannot overcome. Completed or ongoing projects include defect detection, text recognition (OCR), airplane detection, random bin picking, bottle inspection, point cloud image cutting, and robotic arm grasping by reinforcement learning.

Cellphone: +886 0952152828
Gmail: [email protected]

Personal Web: https://www.ivan-lee.me/ 
Blog: https://medium.com/change-the-world-with-technology


Work experience



AI senior engineer

G4 technology company

FEB 2021 - JUL 2022
Taipei, Taiwan

Recruit AI researchers and RD. Planning and project management.

1. Plan project development and project process
2. Organize the paper-sharing club
3. Plan coding style python with team members
4. Lead newcomers to familiarize the project and development 

AI junior engineer

G4 technology company

Jan 2018 - Jan 2021
Taipei, Taiwan

Research and developing visual recognition and control, using AI to solve multi-dimensional feature problems that cannot be handled by traditional methods, and making the algorithm to achieve generalization and desired speed.

1. Develop vision algorithms
2. Research control algorithms of robotic-arm
3. Deploy algorithms to embedded machines (Nvidia AGX, Nvidia NX)
4. Track the latest cutting-edge journals and technologies

Project experiment

Robotic-arm random bin picking(Depth-Image)

Tensorflow、AlexNet、Cross entropy method

Traditional algorithms can only specify a single or few items after modeling. Using an AI algorithm, it can grasp various daily necessities or stationery (universal). As long as the width of the object gripper and a specific distance are met, 95% of daily life and household items can be grasped. In cooperation with the robotic arm team, the company's project has been able to grab a variety of daily necessities, including fried chicken legs and bundled wires (flexible objects).

Detecting the defects of industrial products

Tensorflow、Segmentation、Unet

Cooperate with well-known factories and manufacturers to detect the defects of various items. Traditional algorithms need to design multiple layers of logic and processing for complex images, but general-purpose neural networks can overcome this problem. Design a general-purpose model, so that the model (Segmentation) can be generalized and learned effectively. The objects include 17 kinds of items, such as tires, keyboards, PCB boards, metal welding objects, passive components, etc. Compared with traditional algorithms, it can effectively reduce development by 80%.

Robotic-arm automatic grasping (RGB-Image)

Tensorflow、Cross entropy method、Pybullet、Q-learning

The traditional algorithm relies on line scanning and surface scanning. After obtaining the point cloud, it performs a clamping calculation, path planning, and collision detection. The neural-like control method can directly use the color camera, save the expensive point cloud camera, and save all the aforementioned calculation processes, and dynamically execute the grasping strategy. At present, research in the virtual environment has achieved results. After transferring the model to the real world, it is expected to save 25% of the hardware cost of the gripping project and speed up the gripping time by 3 times. demo video:https://youtube.com/shorts/17ROS385zy4

Detect airplane

Pytorch、Yolov3、Jetson AGX、Jetson NX

Cooperate with a large domestic institution to detect aircraft on satellite images and actually deploy them on embedded machines after training.

Recognize nutrition label

Pytorch、OCR、crnn、scikit-image

Cooperate with well-known domestic retailers to test the nutrition labels and ingredients on bento boxes. The text recognition software on the market cannot detect special Chinese characters (words for nutrition), and the arrangement is too narrow. Therefore, according to the font used on the label, and the image characteristics of the actual scene, such as deformation and skewness, we provide customized products for customers. And passed the customer stress test: in addition to normal words, it can also issue warnings when there are defects or typos in the text, with an accuracy of 99%.

Detect bottles(RGB-Image、Depth-Image)

Tensorflow、Segmentation、Edge detection、Surface rebuild、Open3d

Transparent objects have always been a difficult problem in traditional algorithm detection. No matter the line scan, area scan, or depth camera, there will be refraction and transmission, and complete imaging cannot be achieved. Taking advantage of the neural-like feature of processing multi-dimensional information, a variety of models are used to restore the smooth point cloud on the transparent surface of the bottle, overcoming the problem that traditional algorithms cannot solve.

Point Cloud segmentation

Tensorflow、Mask RCNN

In the traditional method of point cloud calculation, the calculation time is too time-consuming, and the calculation is slow for the final prediction and grasping of traditional CAD. Combined with Instance segmentation to calculate point cloud, the efficiency is accelerated by 2~4 times.

Skills

Tools


  • Python
  • Tensorflow
  • Keras
  • Pytorch

OS


  • Ubuntu
  • ROS

Others


  • Git
  • Slack
  • Jira
  • MySQL

Education




National Yang Ming Chiao Tung University

Computer Science

2022/7 - present
Taipei, Taiwan

Research for reinforcement learning, robotic arm, computer vision, GAN, VR.

Institute for Information Industry

BigData Data Scientist Class

2017/2 - 2017/8

Learning Hadoop distributed systems, database planning, Linux operation, agile management.

Chang Jung Christian University

Bachelor of Business Administration

2012 - 2016

Organization and project management.

Lecture at Central University (2020/11 AI introduction)