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Big data analysis and prediction is classified into two research categories.

  • Intelligent Manufacturing Big Data Analysis and Prediction

  • In production 4.0, it is very important that in a production system, such as machine, warehouse, and machines in a production line can communicate with each other. IOT stores a lot of data. The idea of industrial applications is to let the machines do automatic operations, repair, self-diagnosis, bad pieces analysis, and fault tolerance process by reading and analyzing these big data. Factories face quality control problem and good production rate of their products. Every job-shop manufacturer wants to improve their profit, one way is to reduce their defective products. It can be done by processing the raw materials with machinery which is in very good condition. Then, keeping all working machines in very good condition means immediately repair the machines whose conditions are getting worse. To know which machine is getting worse, it could be seen from each machine yield rate data, in real-time. But sometimes the manufacturer did not have the real-time data, so alternatively could use their periodic data. Other previous research suggests using maintenance history, and/or machine deterioration rate. But sometimes those are not reliable and difficult to finds. On the other side, the machine yield rate data can’t be observed completely, because the manufacturer will not install inspection tools in all the machines. It’s costly and increases time-consuming. The manufacture will certainly install the inspection tool on only several machines/stations. The worst case is on the last step machines, for quality control purposes. Therefore, this research seeks to fill this gap by proposing a new approach to calculate unknown all machines yield rate based on inspection data. Therefore, bad pieces analysis program uses some of academic disciplines, such as non-linear mathematical model and data mining approach to solve the industry related problems. It also uses EM algorithm to complete machine learning task. The likelihood function that serves as the part of the EM algorithm was used to trace back the root cause by using the final data. Using Artificial Intelligence (AI) method, computer will infer under serious data inefficiency. In short, all of these efforts are done to help Taiwanese IT industries in maximizing their production outcomes.

    This lab consists of some professors and scholars who study the production 4.0 project. This lab also uses industrial key techniques and technologies, such as production due date model, special production routers, platform of Big Data Analysis, Regression Model(Download), Automated Optical Inspection(Download)(Download), and Online Analytical Processing (OLAP) to be a consultant of northern Taiwan IT industries, helping them to solve academic and practical problems. In the recent two years, this lab owns some of the Taiwanese Ministry of Science and Technology (MOST) grand projects and three industrial-academic collaboration grand projects. In the current time, the lab operates one MOST and one industrial-academic project.

  • Financial Prediction

  • Effective prediction of financially distressed firms is critical for financial institutions to make appropriate lending decisions. In general, the input variables (or features): financial ratios, the choice of feature selection methods, and the use of appropriate statistical and machine learning techniques, are the three important factors that affect the prediction performance. In the past decade, our research team has gained tremendous insights on the financial distress prediction problem (or FDP) in all three directions. In the recent studies, corporate governance indicators (CGIs) have been found to be another important type of input variables in addition to financial ratios (FRs). However, the performance obtained by combining CGIs and FRs has not been fully examined since only some selected CGIs and FRs have been used in related studies and the chosen features may differ from study to study. Therefore, the research in this direction is to assess the prediction performance obtained by combining seven and five different categories of FRs and CGIs respectively. The experimental results, based on a real-world dataset from Taiwan, show that the categories of solvency and profitability in FRs and the categories of board structures and ownership structures in CGIs are the most important features in bankruptcy prediction. In particular, the best prediction model performance is obtained with combination of prediction accuracy, Type I/II errors, ROC curve, and the misclassification cost. More details can be found in our recent publications [LLT16][LLL15].

    Since disputes remain regarding financial ratios as input features for model development, many studies consider feature selection as a pre-processing step in data mining before constructing the models. Apart from most studies, which have focused on applying one specific feature selection methods to FDP, we have conducted a comprehensive study to examine the effects of performing filter and wrapper based feature selection methods. In addition, the effect of feature selection on the prediction models obtained using various classification techniques is also investigated. In the experiments, two financial distress datasets are used. Moreover, three filter and two wrapper based feature selection methods combined with six different prediction models are studied. Our experimental results indicate that filter based feature selection methods perform better than models that are wrapper based. Moreover, depending on the chosen techniques, performing feature selection does not always improve the prediction performance. Interested readers can refer to [LTW15][LLY14].

    Last but not least, the use of machine learning techniques to construct a prediction model is also a key factor to its performance. We introduce a classifier ensemble approach to reduce the misclassification cost. The outputs produced by multiple classifiers are combined by utilizing the unanimous voting (UV) method to find the final prediction result. Experimental results obtained based on four relevant datasets show that our UV ensemble approach outperforms many baseline single classifiers and classifier ensembles. More specifically, the UV ensemble not only provides relatively good prediction accuracy and Type I/II errors, but also produces the smallest misclassification cost.


    • On Going
    • Closed
      • IIoT Enabling Decision Support Platform for Smart Factory 4.0 (2017/11-2019/1) (Download)
      • Productivity 4.0 for ICT Industry using Machine Learning Technologies (2016/11-2017/10) (Download)

    • On Going
      • The development for aerial images of multiple crops interpretation modules based on deep learning technology(2023/1-2023/12) — collaboration: Ministry of Agriculture (Download)
      • 混合實境物件辨識技術與低延遲姿態動作辨識技術研究(2023/8-2023/11) — collaboration: Institute for Information Industry (Download)
    • Closed
      • Research on AI modeling with few sample(2023/3-2023/9) — collaboration: Institute for Information Industry (Download)
      • Pixel-level影像瑕疵檢測資料標記與模型學習輔助技術委託研究(2022/3-2022/11) — collaboration: Institute for Information Industry (Download)
      • The development for aerial images of multiple crops interpretation modules based on deep learning technology(2022/1-2022/12) — collaboration: council of agriculture (Download)
      • The Data Analysis & Practice (2020/9-2022/7) — collaboration: Ministry of Education
      • Knitted Fabric Defect Detection Research(2021/3-2021/11) — collaboration: Institute for Information Industry (Download)
      • The development for aerial images interpretation modules based on deep learning technology(2021/1-2021/12) — collaboration: council of agriculture (Download)
      • 瑕疵影像生成之GAN模型可解釋性委託研究(2021/6-2021/9) — collaboration: Institute for Information Industry (Download)
      • 瑕疵檢測資料採集軟硬體模組整合技術服務(2020/10-2020/11) — collaboration: Institute for Information Industry
      • The development of Smart Manufacturing and Equipment Optimization Technology for Fabric Industry(2020/4-2020/11) — collaboration: Institute for Information Industry
      • Deep learning technology-assisted remote-sensing images identification(2020/1-2020/12) — collaboration: council of agriculture (Download)
      • 自動光學檢測產業AI化推廣(2019/2-2019/12) — collaboration: Institute for Information Industry (Download)
      • Machine Learning and the Data Analysis & Practice (2018/12-2020/7) — collaboration: Ministry of Education
      • 運用機器學習法於電子廠生產優化及品質提升之產學專題型合作計畫(2019/1-2019/12) — collaboration: Industrial Development Bureau, MOEA
      • 海上無人載具傳輸頻寬自動調整軟體系統開發(2019/1-2019/3) — collaboration: National Chung-Shan Institute of Science & Technology
      • The Development Of The Testbed For Iiot Applications With High Availability (2016/4-2019/3) — collaboration: ADLINK Technology (Download)
      • The Research Of A Forecasting Prototype For Manufacturing Industry (2017/9-2017/12) — collaboration: Industrial Technology Research Institute (Download)
      • The Research Of Data Analysis Technology With Embedded Artificial Intelligence (2017/8-2017/11) — collaboration: Industrial Technology Research Institute (Download)

  • Authentication for Smartphone Users

  • With the advances in information technology and internet, the performance and features of handheld devices such as smartphone and tablet are rapidly increasing. This enables users to use such devices not only as communication tools but also with business applications such as m-banking and online shopping. These new applications raise concerns about security issues for smartphone users. Personal sensitive data stored in the phone such as credit card number and user credentials should be protected well in order to prevent financial loss when the device is stolen.

    The current protection mechanisms of these devices are usually based either on PIN codes, passwords or biometric-based methods, such as fingerprints or iris. Both fingerprints and password entry are intrusive in the sense that they require explicit action from the user. Fingerprints and password based authentication also vulnerable to be compromised when attacker have credentials needed for opening the devices.

    Recently, researchers proposed the use of non-intrusive authentication methods using gait, touch and orientation patterns, and phone movement as a replacement for intrusive authentication. The non-intrusive authentication method require minimal explicit action form the user. Market research points out that there are about 60%-80% of smartphone users who choose to turn off intrusive authentication methods due to its inconvenience. That is the reason why non-intrusive authentication methods will be more acceptable to the users. The non-intrusive authentication methods also have promising advanced hijacking protection due to its difficulty to mimic other person’s behavior.

  • Driver Behavior Recognition

  • In 2014, traffic accidents claimed nearly 3500 lives each day. Studies reveal that most traffic accidents are caused by human-induced factors, such as abnormal driving behaviors. Therefore, detection of abnormal driving behavior is one of the significant studies of preventing traffic accidents.

    The smartwatch, which is equipped with many sensors, such as the accelerometer and orientation sensor, can be used for not only continuous motion analysis and health monitoring but also analysis of the driving behavior.

    The Gaussian mixture model (GMM), which is a mixture of Gaussian component densities, is used to model the distribution of the hand-movement feature of the driver obtained from the smartwatch. In the literature, such as text-independent speaker recognition and driver recognition, GMMs are often used in recognition issues.

    In recent, we have proposed a more robust and accurately system architecture based on traditional GMM approach. Then, the research results show our proposed system has significant improvements than the transitional GMM approach, and the equal error rate (EER) of our proposed system can reach 4~7%. In future work, the team members will keep on improving the EER, and try to apply this system to different applications.

  • Distracted Pedestrian Recognition (Download)


    • On Going
      • The Research of Resistance to Adversarial Attacks on the Biometric Authentication of Smartphones Based on Weak Features Manipulation(2023/8-2024/7) (Download)
    • Closed
      • The development of a light-weight mobile authentication mechanism with robust feature against impersonation attacks(2022/8-2023/7) (Download)
      • The Efficiency Improvement of Smart Devices User Authentication Mechanism based on Multiple Learning Strategies(2019/8-2022/7) (Download)
      • The Efficiency Improvement to Model Building of a Smartphone User Authentication Mechanism based on Multiple Learning Strategies(2018/8-2019/7) (Download)
      • The Development of Poses Adaptive and Multimodal User Authentication Mechanism for Smartphone Users: The Development of Poses Adaptive and Multimodal User Authentication Mechanism for Smartphone Users(2016/8-2018/7) (Download)
      • A poses adaptive authentication mechanism based on behavioral biometrics obtained from mobile devices (2015/8-2016/7) (Download)
      • A handheld device authentication mechanism for Cloud services (2014/8-2015/7) (Download)
      • A handheld device authentication mechanism to improve security level of Cloud services via securing the Cloud clients (2013/8-2014/7) (Download)
      • Developing a Mechanism and an Establishment of Testbed for Cloud Security Services (2010/8-2013/7)
      • A Monitoring and Protecting Mechanism for Abnormal Behavior of Mobile and Web services in Cloud Computing Environment (2010/8-2013/7) (Download)

    • On Going
    • Closed
      • Computational Cost Estimation Technology and Algorithm for Large-Scale Edge Server Deployment based on Fog Computing Paradigm(2019/7-2019/12) — collaboration: Institute for Information Industry (Download)
      • Optimization Technology For Streaming Hub Services (2017/6-2017/12) — collaboration: Institute for Information Industry (Download)
      • The Preliminary Research Of High Available Cloud Archives Management And Testing Platform (2016/6-2018/12) — collaboration: W&Jsoft Inc
      • The Development Of A Complete Cycle Real Time Active Safety Sensing Technology (2010/6-2010/11) — collaboration: Industrial Technology Research Institute

    Open Source: https://reurl.cc/V5ZzEy

    Fault tolerance is generally classified under two categories: the hardware fault tolerance and the software fault tolerance. The former receives significant attention from the traditional fault tolerance research community; on the contrary, the latter receives less attention and is regarded as a relatively novel field.

    Software Implemented fault tolerance deals with faults that cannot be dealt with by hardware fault-tolerant methods via the use of the software-based detection and recovery methods, which include software hangs and crashes. With software implemented fault tolerance, the possibility of operational business loss due to the software malfunction can be minimized.

    The foundation of cloud computing platform is the virtual machine management system (VMM). The fault tolerance of the VMM that is installed on Industrial personal computers (IPCs) becomes increasingly important as the cloud computing applications burgeon across the IT industry today (IPCs have been widely employed as the core components of many applications, such as networking devices, automation control devices, medical electronic equipment... etc). These applications are known for their strict demands on reliability and robustness while operating in extreme working conditions. However, our industry partners have found that the cost and quality of the hardware are no longer the key factors to achieve high profit margin, instead, the ability to develop domain-specific and value-added applications over the Intelligent Platform Management Interface(IPMI) platform, a high-end IPC, have become increasingly important in market competitiveness in the future. Consequently, the IPMI-enabled servers with value-added virtualization technology will be the key products for the next generation of IPCs, since they provide high flexibility and yet maintain high availability. However, the technology to integrate high-availability IPCs and virtualization technology is still at its infantile stage. To improve its competitiveness in the next three to five years, the IPC industry in Taiwan will need to develop the following four key technologies: fault-tolerance for virtualization on the IPMI servers, software and hardware integrated testing technology, as well as value-added applications based on IPMI servers.

    Therefore, we plan to provide fault tolerance technology for virtualization, as well as automatic testing technologies and tools. Our team also plan to develop new industrial-technology-oriented courses, to provide related teaching material, and to train our students accordingly.The NCU team has obtained the following achievements in the past year: (1) the fault tolerant VM (the Zero-Down-time Fault Tolerance) is about three times as fast as the existing tool, Micro-Checkpointing, when measured in operations per minute, while the response time is reduced by 67% and the bandwidth consumption is reduced to 41% (2) the automatic regression test technology and tools that can reduce the testing time up to 80% in product line and reduce manual labor cost by 70%, with an increase in developmental speed of new products by 50% (3) the offering of two practical courses featuring over 30 lecturers from the industry, and (4) the engagement of joint research projects with our industrial partners: over 4.5 million NTD in R&D fund, 300,000 NTD in equipment donation, and over 2.7 million NTD in labor.


    • On Going
    • Closed
      • On the Development of Core Technologies for High Quality Virtualized Software Services (2016/10-2020/9) (Download)
      • On the Development of the Virtual Technology for Industrial Computers (2015/1-2015/12) (Download)
      • On the Development of the Virtual Technology for Industrial Computers (2014/1-2014/12) (Download)
      • On the Development of the Virtual Technology for Industrial Computers (2013/1-2013/12) (Download)

    • On Going
    • Closed
      • Event-Triggered Data Stream Processing and Management(2022/6-2022/12) — collaboration: Institute for Information Industry (Download)
      • Integration of Edge Computing and Service Mesh over Cloud-Edge Federated Clusters(2021/5-2021/12) — collaboration: Institute for Information Industry (Download)
      • Microservice Circuit Breaker and Automatic Service Discovery on the Cloud(2020/5-2020/11) — collaboration: Institute for Information Industry
      • Optimization Technology For Streaming Hub Services (2017/6-2017/12) — collaboration: Institute for Information Industry (Download)
      • The Preliminary Research Of High Available Cloud Archives Management And Testing Platform (2016/6-2017/12) — collaboration: W&Jsoft Inc
      • Iiot Automated Testing Technology For Web Service Application (2016/4-2016/5) — collaboration: Industrial Technology Research Institute
      • The Development Of Automated Testing Service Portal (2015/8-2015/12) — collaboration: Industrial Technology Research Institute
      • The Research And Development Of High Reliability Architecture Virtual Machines (2015/3-2015/11) — collaboration: Institute for Information Industry
      • On The Development Of The Virtual Technology For Industrial Computers (2013/1-2015/12) — collaboration: ADLINK Technology
      • The Research Of Service Orientation Iot Data Acquisition Prototype Platform (2011/4-2011/12) — collaboration: Institute for Information Industry