預測

These stimuli-switchable nanoparticles coated with the cleavable outer polymer coatingmultifunctional peptides may afford a new platform for anticancer drugmiR. These nanoparticles possess characteristics of environmental response, active targeting,gene/chemo combinatorial therapeutics, thus providing spatiotemporal control of posttranｓｃｒｉｐｔional gene regulation, smart nuclear/mitochondrial localization,simultaneous inhibition of multiple progression pathways of tumor cells for enhancing antitumor efficacysafety of cancer therapy.

Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.

Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.

The IPMC system can detect possible abnormal signs in advancepredict the RUL of the machine before it fails so that it can effectively avoid unscheduled downtimeprevent enormous losses. The IPMC system also exploits the advantages of cloud computingContainer technology such as rapid deployment, failover,lightweight to enhance its performanceportability.

"The primary goal of this project is to establish a complete hospital-based liver cancer database, profiles for data feature extraction,develop different cancer, prediction models. A Medical AI program to predict the poster treatment (including operationradiofrequency ablation) recurrence of liver cancer will be established. The program system will assist doctors in the medical decision, identify high-risk patients,adjust clinical follow-up programs."

Through the technique, SamplingPrediction of Lithography Overlay Errors, the costtime of overlay error measurement can be reduced to improve the process efficiency. We identify key sampling through clusteringmachine learning models,design a new sampling algorithm in photolithography process. Due to the complexity of wafermany training factors of wafer data, we combine the clustering algorithm with incremental learning to meet customers＇ unique needsachieve the goal of optimally samplingreducing the costs.

This study proposes a 5G C-V2I (Cellular Vehicle-to-Infrastructure) enabled intelligent trajectory prediction and warning system, which can be implemented in a framework including RSUs (Road Side Units) with radar detection ability and 5G edge computing servers. This study exploits artificial intelligence to predict instant trajectories of vehicles at crossroads. The resulting augmented-awareness navigation information is then broadcasted to road users through 5G C-V2I with low latency. In practical applications, road users can obtain real-time dynamics of surrounding vehicles so that their level of safety can be effectively enhanced.

The core concept of the decision support system is to establish machine learning-based predictive modelsenable their interpretability to support physicians making clinical decisions in practice. The developed technique is part of the capstone project named "Smart Emergency Department," sponsored by the Ministry of ScienceTechnology. The system comprises NTUH (National Taiwan University Hospital) EMR (Electronic Medical Record) importance analysis, accurate clinical quality indicator prediction,similar medical record retrieval. It is expected to improve the patients＇ flowalleviate emergency department crowding. It has been verified in retrospective studieswill officially enter the clinical trial phase this year.

We propose an AI dynamics drug repurposing platform applicate on COVID-19. This platform analyzed a large number of structures of SARS-CoV, MERS,cross-species coronavirus 3CL protease-ligand complexes to construct uncovering six flexible active site conformationspharmacophore clusters for SARS-CoV-2 3CL protease screening all FDA drugsfound four inhibitors within three months. Among them, JM206 had even demonstrated ten times better efficacy than Remdesivir in-vitro assayalso show the effect on the in-vivo hamster model to alleviate the symptoms caused by COVID-19.

This technology＇s nanofluid/ultrasonic atomization micro-lubrication system is different from the general micro-lubrication technology that uses high-pressure air atomization. It is first to use cavitation characteristics to disperse a small amount of nanofluid into microscopic particleseffectively disperse the bondingagglomeration of nanoparticles due to the Van der Waals force. The second stage of atomization is carried out through high-pressure air. The oil mist particles of the nanofluid can easily enter the micro-machining cutting area for coolinglubrication.

The key technology of Patient Derived Tumor Spheroids (PDTS) is to create an in-vitro patient tumor microenvironmenttumor-like properties, furthermore, predict various cancer antidrug effectiveness to each patient via the high-throughputhigh-quality cell viability detectionbiomarker analysis. Therefore, this project established several technologies including extracellular matrix fabrication, bioink design, cell population identification,tumor physiological environment for the upon goal.

We propose a spatial-temporal inference model, which uses a large amount of spatialtemporal data in the city to help governmentsenterprises predict future long-short-term important urban indicator values, such as traffic flow, human mobility, pollution level, number of criminal caseseven commercial profitability. The SIM model exploits IoT to integrate multiple real-time geospatial big data, including population, the flow of people, geographical data, Traffic,real-time sensor values. SIM can make effective predictionsprovide explainability for making decisions.

Our brain waves consist of a lot of nonlinear characteristics. Holo-Hilbert cross-frequency phase clustering (HHCFPC) is a cross-frequency coupling connectivity analysis based on the nonlinear Holo-Hilbert Spectral Analysis (HHSA) method. It is achieved by clustering the vectors of "phase difference" between the phase of a first layer IMF (obtained by HHSA) from one EEG channelthe phase of a second layer IMF from the other EEG channel. The results of HHCFPC can be used as a guide to optimize the parameters (e.g. frequency,frequencydepth of AM) of non-invasive brain stimulations.

Our technologies are able to analyze customer behaviorsprovide personalized automatic services. We take two directions: (1) Establish a customer behavior predictionrecommendation system by analyzing consumption records, exploring behavior features,strengthening the link between marketing strategiesbehavior analysis (2) Collaborative EDM subjects generation by analyzing the relationship between customers’ click recordsconsumption for understanding the relationship between customer intentionsfinancial products,for generating personalized marketing strategies.

The DNN models of this technology include a LithoNet, an OPCNet,a layout novelty detection network. LithoNet is a learning-based pre-simulation model for layout-to-SEM contour prediction,OPCnet is a dual network of LithoNet for photomask optimization. Integrated with a well-trained LithoNet, our layout novelty detection network, consisting of a self-attention guided LithoNetan autoencoder, can check if there are layout patterns easily resulting in local distortions in contours of metal lines based on multi-modal (global-local) feature fusion.

By collecting the changes of kinds of vital signs(including heart beat / blood pressure / steps), we combined the results with the testers’ fatigue scores which belonged to the matched fatigue typestheir basic profiles. We extracted 780 types of structural features sets by cleaning raw data, then separating into training set, validation settesting set. With machine learning algorithm to auto-optimize regression analysis, finally we come out the correlative features between vital signsfatigue ,the overwork model as our deliverables.

1. Infrared wax physisorption kinetics (iR-WPK) provides a glyco-histopathological imaging analysis for examining tissue sections, which utilizes n-alkanes with carbon number (CN) from 20 to 34 and beeswax as glycan adsorbents for targeting similar longitudinal length of glycans of glycoconjugates anchoring in the cell surface. 2. It is an in-situ non-destructive method of examining tissue sections for cancer screening and prognosis prediction for chronic kidney disease by profiling aberrant glycans covalently-attached to both glycoconjugates anchored in tissue sections. 3. It can screen ten cancers including colon cancer, breast cancer, ovary cancer, cervical cancer, oral cavity cancer, gastric cancer, skin cancer, prostate cancer, intestinal neuroendocrine tumor and brain cancer.

In agriculture, the team has developed a “Chinese Medicinal Functional Compound Prediction A.I. System” that can predict compounds production during cultivation period. Moreover, in application, we have implemented a smart agricultural platform-AgriTalk to link the “Functional Compound Prediction System”“Smart Fertilization System” to provide a comprehensive management in farm production.

The HeaortaNet is developed by the TW-CVAI team. The HeaortaNet is a deep learning model trained by 70,000 axial images from 200 patients with verified annotations of the pericardiumaorta. It shortens the time for data processing from 60 minutes, by manual segmentation of both pericardiumaorta, to 0.4 seconds. The segmentation accuracy, as assessed by dice similarity coefficient, is 94.8 for the pericardium,91.6 for the aorta. The imaging-based Cardiovascular Risk Prediction module was constructed by analyzing data from the National Health Insurance Databank.

In the context of FinTech, we present Financial Graph Attention Networks (FinGAT) to recommend high-profitable stocks in terms of return ratio using time series of stock pricessector info. Our FinGAT can learn the long-short-term price tendency,model the latent interactionsinfluence between stockssectors without any hand-crafted effort. Experiments conducted on Taiwan Stock, S&P 500,NASDAQ stock markets exhibit remarkable accuracy of FinGAT, comparing to state-of-the-arts (by 11, 14,12 performance improvement). The tech is published in IEEE TKDE 2021.

Integrating deep learning, 3D information analysis, hyperspectral analysis, computer vision analysiscombined the multi-source images to develop fruit quality monitoring techniques, including: planting area monitoring, position monitoring, yield monitoring, harvesting time prediction, fruit maturity measurement, fruit quality testing, to achieve the goal of enhancing industrial value.

This technology aims to automatically extractdefine features from a data-driven perspective through a series of data engineering, machine learningensemble learning technologies via the big data collected from equipment sensorsquality characteristic measurement results. For products that have no quality measurement, a virtual metrology resultpredicted confidence score are provided as a decision-making basis for process control.

PCA-BM includes two models: “Automatic BMs Segmentation AI Model”" Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size,number of brain metastases. The latter uses radiomics to extract numerous radiographic featuresemploys machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patientsimproves personalizedaccurate overall stereotactic radiosurgery planning.

The technology is video-based blood pressure monitoring which will help people know the continuous BP status in real time. Also, the contactless measurement can make a daily health record. We have conducted clinical trials in hospitalsestablished a large database to optimize AI solution. The system joins the physicians＇ specialty to analyze the users＇ health status with AI,takes care of human life with technology.

1.Low-Power Processing-in-sensor CMOS Image Sensor 2.Implementation of Low-PowerLow-Latency Deep Learning Chip based on Neuromorphic Intelligence (collaborated with MOST 108-2622-8-007-009) 3.Development of Neuromorphic Chip based on The Fruit Fly VisualSpatial Sensory Systems 4.Hardware-Software Co-Design for Neuromorphic AI Chips