manufacturing

In Progress

FPGA Edge AI – AOI Defect Classification

The Automated Optical Inspection, or AOI, is a fast and accurate optical inspection system based on machine vision technology. The advanced inspection method has multiple advantages over traditional inspections executed manually.AOI technique can be applied to business in multiple areas such as R&D in high tech, manufacturing, defense, healthcare, environmental protection, and electric utility with great potential to increase productivity with reduced costs and time.ITRI has dedicated in the R&D of flexible displays for decades and aims to improve accuracy of AOI systems to elevate product quality.We would like to invite data scientists from all different fields to join this competition. With the provided AOI image data to build a model to identify the correct defect type and strengthen the effectiveness of AOI inspection through data science.FPGAs (Field Programmable Gate Array) are often used to maximize application performance as workloads and algorithms evolve through reconfigurable fabric. In this topic, the participants are challenged to demostrate their ability to optimize machine learning model for FPGA data center accelerator cards (Xilinx® Alveo™ U50LV). Apart from accuracy of the model, time of model inference will also be taken into consideration. You have to upload the trained model to Aldea. The platform will load your model to FPGA accelerator card, where the model inference will be performed and scored. Aside from being familiar with AI algorithms, the ability to optimize models for AI accelerator devices such as FPGA cards is equally important in the flourishing future of Edge AI. Come accept the challenge and join the competition now! Participation FlowRegister and download training dataPrepare development environmentTrain float modelQuantizationCompilationPackage your model in a zip file. Upload it to Aldea for model inference and scoringFor detailed description, please refer to the Tutorial and Sample Code. Discussions and questions on the forum of this topic are welcomed as well. You may also send email to support@e-elements.com.tw for further questions regarding this competition.You may rest assured that your submitted model will not be used for any purpose other than the evaluation of this contest.

2021-08-26T16:00:00+00:00 ~ 2021-11-17T15:59:59+00:00
Closed

Edge AI Competition – AOI Defect Classification

The Automated Optical Inspection, or AOI, is a fast and accurate optical inspection system based on machine vision technology. The advanced inspection method has multiple advantages over traditional inspections executed manually.AOI technique can be applied to business in multiple areas such as R&D in high tech, manufacturing, defense, healthcare, environmental protection, and electric utility with great potential to increase productivity with reduced costs and time.ITRI has dedicated in the R&D of flexible displays for decades and aims to improve accuracy of AOI systems to elevate product quality.We would like to invite data scientists from all different fields to join this competition. With the provided AOI image data to build a model to identify the correct defect type and strengthen the effectiveness of AOI inspection through data science. Reference[1] https://en.wikipedia.org/wiki/Automated_optical_inspection CompetitionThis topic is mainly for testing your ability of applying NVIDIA Jetson Nano, an embedded platform. Apart from accuracy of the model, time of loading model as well as time of model inference, will also be taken into consideration. You may need to upload the trained model to Aldea. The platform will load the model to Jetson Nano, where the model inference will be done, and the score will be calculated. Instructions:Register the contest on Aldea.Download training set and train your modelPackage your model in a zip file. Upload it to Aldea for model inference and scoring. The maximum memory usage of the program on the platform is 3GB, and the maximum execution time is 20 minutes. If any limit is exceeded, it will be considered as a failure.Aside from being familiar with AI algorithms, the ability to optimize models for edge services is equally important in the flourishing future of Edge AI. Come accept the challenge now!For a more detailed description, please refer to the description file in the downloaded file. Discussions and questions on the forum of this topic are welcomed as well.description file You may rest assured that all the programs as well as models, will not be used for any purpose other than the evaluation of this contest. 

2021-05-18T16:00:00+00:00 ~ 2021-08-18T15:59:59+00:00
Closed

生產智慧排程規劃決賽

本議題供「生產智慧排程規劃」決賽使用,第一階段成績優秀之參賽者,受邀參加。 傳統的生產排程以人工使用EXCEL表作業,因工序中可能需額外處理或臨時的工時異動,因此常造成原定排程不準,無論規劃或調整都缺乏效率和彈性。導入AI技術建立智慧排程可以有效面對即時的影響因素變動,讓生產可以快速調整,提高準確度。 本議題提供訂單資訊、物件清表(BOM)、出勤表、生產日曆等資訊,希望在特定期間與有限資源下,找出最佳的排程規劃,讓工作站/資源銜接等待時間愈短愈好,以符合目標交期且產能利用率最高為最優排程結果。 獎項獲獎條件:決賽成績之前三名,並於 11/2 前繳交書面報告者。 第一名 hicloud 15 萬點優惠點數 + 50,000 元獎金(含稅)第二名 hicloud 10 萬點優惠點數 + 30,000 元獎金(含稅)第三名 hicloud 5 萬點優惠點數 + 20,000 元獎金(含稅)佳作 hicloud 5 萬點優惠點數 (數名,視最終結果決定) hicloud 點數由中華電信提供 備註1:hicloud 優惠點數可折抵服務費用,折抵完後自動開始依牌價7折計費 備註2:中華電信保有資格審核、優惠內容修改與中止權利 點數試算範例請參考:https://aidea-web.tw/computing 報告內容包含硬體規格、軟體規格、資料前處理、模型選擇、參數調校等。

2020-10-22T00:00:00+00:00 ~ 2020-10-25T15:59:59+00:00
Closed

多樣態光電製程品質預測

本議題資料來源為某光電公司,目標為同時預測多種產品的製程品質,針對每一組製程參數,需要預測 4 個不同類型目標的結果值。影響製程的變數包含溫度、壓力、材料、機台參數等,不同產品的製程,各種變數影響的強弱程度有所不同。藉由機器學習為製程中使用的參數建立模型,以此預測結果值。不同的產品有不同的結果標準值以及上、下限,一個預測精準的模型,可以幫助了解製程中各變數的重要性,對結果值的影響程度,有助於製程的分析及最佳化,可提升產品品質並節省成本。獎項獲獎條件:在 Private Leaderboard 低於 baseline (Weighted MAE 第一名 hicloud 15 萬點優惠點數 + 50,000 元獎金(含稅)第二名 hicloud 10 萬點優惠點數 + 30,000 元獎金(含稅)第三名 hicloud 5 萬點優惠點數 + 20,000 元獎金(含稅)佳作 hicloud 5 萬點優惠點數 (數名,視最終結果決定)hicloud 點數由中華電信提供備註1:hicloud 優惠點數可折抵服務費用,折抵完後自動開始依牌價7折計費備註2:中華電信保有資格審核、優惠內容修改與中止權利點數試算範例請參考:https://aidea-web.tw/computing 報告內容包含硬體規格、軟體規格、資料前處理、模型選擇、參數調校等,格式請參考 「多樣態光電製程品質預測 - 報告格式.docx」。

2020-08-11T16:00:00+00:00 ~ 2020-09-23T15:59:59+00:00
Closed

生產智慧排程規劃

傳統的生產排程以人工使用EXCEL表作業,因工序中可能需額外處理或臨時的工時異動,因此常造成原定排程不準,無論規劃或調整都缺乏效率和彈性。導入AI技術建立智慧排程可以有效面對即時的影響因素變動,讓生產可以快速調整,提高準確度。本議題提供訂單資訊、物件清表(BOM)、出勤表、生產日曆等資訊,希望在特定期間與有限資源下,找出最佳的排程規劃,讓工作站/資源銜接等待時間愈短愈好,以符合目標交期且產能利用率最高為最優排程結果。獎項獲獎條件:在第一階段得分超過 baseline (> 0.75) 之前十名,得受邀參加第二階段決賽,以決賽成績為排名依據。決賽成績之前三名,並於 10/9 前繳交書面報告者。第一名 hicloud 15 萬點優惠點數 + 50,000 元獎金(含稅)第二名 hicloud 10 萬點優惠點數 + 30,000 元獎金(含稅)第三名 hicloud 5 萬點優惠點數 + 20,000 元獎金(含稅)佳作 hicloud 5 萬點優惠點數 (數名,視最終結果決定)hicloud 點數由中華電信提供備註1:hicloud 優惠點數可折抵服務費用,折抵完後自動開始依牌價7折計費備註2:中華電信保有資格審核、優惠內容修改與中止權利點數試算範例請參考:https://aidea-web.tw/computing 報告內容包含硬體規格、軟體規格、資料前處理、模型選擇、參數調校等。

2020-08-05T16:00:00+00:00 ~ 2020-10-21T15:59:00+00:00
Closed

Quality Prediction in Optoelectronic Manufacturing Process

This topic is about the quality prediction of manufacturing processes. By adopting Machine Learning methods, parameters used during the manufacturing processes will be used to build models for quality prediction. The data is provided by an opto-electronic technology company. By analyzing the data, results of the manufacturing process can be accurately predicted. This can not only improve product quality, but also lower the costs of manufacturing.The complexity of the manufacturing processes puts challenges on analyzation and optimization. Many factors will influence product quality, such as machine parameters, the amount and ratio of materials (primary materials and accessory materials), temperature, pressure, time, etc., and these factors can impact the result either directly or indirectly. Different products have different requirements, or acceptable quality level. An accurate model can help understand the relative importance of different factors and identify how the parameters can influence the quality, and thus can directly improve manufacturing process analyzation and optimization. In this topic, participants need to predict two target numbers regarding each set of parameters. PrizeThe following awards will be given to the participants who have achieved all of the following: the top three participants whose score on the Private Leaderboard is lower than the baseline (MAE < 0.007) and the report is submitted before 7/22.First place: 150,000 discount points of hicloud + NTD 50,000 (Tax included) Second place: 100,000 discount points of hicloud + NTD 30,000 (Tax included) Third place: 50,000 discount points of hicloud + NTD 20,000 (Tax included) Honorable mention: 50,000 discount points of hicloud (multiple winners, depends on the final result) * Hicloud discount points were provided by Chunghwa TelecomNotes:1. Hicloud points can redeem service charge. After the discount, the price will be charged at 30% off based on the list price automatically.2. Chunghwa Telecom deserves the right to make changes to the terms and conditions herein.3. The example of discount points calculation can be referred to https://aidea-web.tw/computingReport content should include hardware and software specifications, data preprocessing, choice of models, and parameters adjustments, and etc. 

2020-06-11T16:00:00+00:00 ~ 2020-07-15T15:59:59+00:00
academic only
Closed

Trend forecast of Second-line chemical reactivity

This topic is provided by China Petrochemical Development Corporation (CPDC), whose core business is in the production of petrochemical intermediates and related engineering plastics, synthetic resins, raw materials for chemical fiber and other derivative products. The data of this topic is the production record of products, and this product is produced by adopting chemical catalytic reactions. The production record includes operating parameter (A1~A7)and forecast of product conversion(B1~B12), B4 is the main product and B5,B7 is the by-product. Personnels can determine the changes of future product conversion by referring the known operating parameter, and thus can adjust the operating parameter in order to keep B4- the main product, in the best condition. By doing so, we can reduce the usage of raw materials and improve the productivity of B4. Everyday we will go through a complete analysis assay of chemical catalytic reactions during Monday to Friday, and we will adjust the parameters refer to the product conversion of the day. By observing the product conversion daily, we can make sure that B4 can remain its best condition. On this issue, we would like to forecast the product conversion by referring the time trend, mainly the product conversion of B4, B5 and B7, so that we are more likely to adjust the parameter beforehand, to make sure the product conversion can go above the standard. reward The first place 100,000 discount points of hicloud The second place 50,000 discount points of hicloud The third place 50,000 discount points of hicloud honorable mention 50,000 discount points of hicloud( multiple winners, depends on the final result) Rewards were provided by Chunghwa Telecom Note: 1.Hicloud points can redeem service charge. After the discount, the price will be charged at 30% off based on the list price automatically. 2.Chunghwa Telecom deserves the right to make changes to the terms and conditions herein. The example of calculating points can be referred to https://aidea-web.tw/computing

2019-06-11T16:00:00+00:00 ~ 2019-09-30T15:59:59+00:00