AI-enabled Service Assurance Platform for 5G Vertical Application

This study proposes a 5G C-V2I (Cellular Vehicle-to-Infrastructure) enabled intelligent trajectory predictionwarning system, which can be implemented in a framework including RSUs (Road Side Units) with radar detection ability5G 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.

This study proposes a 5G C-V2I (Cellular Vehicle-to-Infrastructure) enabled intelligent trajectory predictionwarning system, which can be implemented in a framework including RSUs (Road Side Units) with radar detection ability5G 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.

AI-enabled Service Assurance Platform for 5G Vertical Application: The platform can help customers quickly import 5G vertical applications by providing overall customized solutions, including equipment evaluation, network deployment, application importing, network maintenance, and operation optimization. The platform can bridge the gap between the telecom industries and the vertical application industries and provide a total solution for the industries to import 5G vertical applications.

AI農情調查之UAV群眾協作平台後台支援自動鑲嵌建模，更具備四項突破技術：(1)巨量影像格化技術；(2)平行運算技術；(3)任務規格標準化；(4)UAV任務媒合。致力於打造空中UBER協作服務，未來能應用於農作物分佈調查、大範圍災情調查、農業保險、農地違法使用調查與休耕補助調查等面向。

Taiwan was formerly the Shrimp Capital of the World. With the changing of the timesincreasingly updated technology, AI smart farming is the new milestone for Taiwan’s agriculturefisheries.

This integrated research project follows the Taiwan's 2030 Science & Technology Vision and takes LOHAS community and inclusive technology as the major research direction. We aim to develop trustable AI technologies, and introduce them to future smart services. That will realize the development of human-centric smart technology, and strengthen the governance and application of emerging technologies. The integrated project consists of 7 sub-projects led by PIs from National Taiwan University, National Tsing-Hua Universiy and Academia Sinica and composed of top AI technological teams. These sub-projects are divided into 3 clusters, including machine learning (sub-projects 1 and 2), computer vision (sub-projects 3 and 4), and human-centric computing (sub-projects 5, 6 and 7). We will deal with the issues of bias, fairness, transparency, explainability, traceability, and so on, from the aspects of data collection, technology, and application landing. Each sub-project will implement specific smart services to reflect the benefits and practical applications of the developed technologies. The NTU Joint Research Center for AI Technology and All Vista Healthcare, an AI Innovation Research Center supported by MOST, is responsible for management, planning, and execution of the integrated research project. We will propose a plan that can be generalized and applied to the intelligent service industry.

The Pericardium/Aorta Segmentation and Cardiovascular Risk Prediction AI Total Solution Model, HeaortaNet, is a deep learning model based on UNet and attention gate, and had been trained by >70,000 axial images with verified annotations of the pericardium and aorta. It shortens the time for data processing from 60 minutes, by manual segmentation of both pericardium and aorta, to 0.4 seconds. The segmentation accuracy is 94.8% for the pericardium, and 91.6% for the aorta. The applicability of HeaortaNet had been demonstrated by analyzing the non-contrast chest CT scans (>5,000 cases) deposited in the mega-image bank of National Health Insurance Databank.

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

As we age, our memory also gradually begin deteriorating. At what point should one seek out a consultation with a doctor? Statistics estimate that worldwide, every 3 seconds someone experiences dementia.

Quote:br / “It is worth giving up some things because of dream pursuing” Professor SHOU-DE, LIN  at the department of computer scienceInformation Engineering in National Taiwan University, Chief Machine Learning Scientist in Appier, said “An escape from comfort zone to seek new challenges makes my life become more colorful.”br /  br / Content:br /  br / Given qualified for being as the freshman of National Taiwan University College of Medicine, Professor Lin chose the department of electrical engineering in NTU as the first priority in Joint College Entrance Examination (JCEE). Though the undergraduate education  did not cultivate him the passion on the field of electrical engineering, Professor Lin said, however, he was still recommended for further study at the graduate institute of electronics engineering in NTU due to his talentsoutstanding academic performance.

This project goes on developing embedded AI deep learning technology focus on the ADAS/Self-driving applications. We develop the technology from five aspects, including automatic object labeling toollabeled datasets, deep learning softwarehardware technology development, various ADAS/Self-driving object detectionbehavior prediction technology, self-driving control technology as well as virtual simulation environment establishment for ADAS/Self-driving applications.

This project goes on developing embedded AI deep learning technology focus on the ADAS/Self-driving applications. We develop the technology from five aspects, including automatic object labeling toollabeled datasets, deep learning softwarehardware technology development, various ADAS/Self-driving object detectionbehavior prediction technology, self-driving control technology as well as virtual simulation environment establishment for ADAS/Self-driving applications.

As artificial intelligence rapidly develops and enhances human’s quality of life, another cause for concern is the dangers and possible threats to humanity. “Moral judgement” will be a big issue that needs to be addressed as these intelligent machines become more and more prevalent in society. As with any new technologies, the topic of applied ethics often arises. So, the extension of that is AI ethics issues.

The invention belongs to the field of low-temperature electrochemical graphitization and is improved by the FFC-Cambridge Process. In comparison to the traditional process, the invention can be at a low temperature of 850oC and shorter process time. This strategy can convert carbon blacks into high-value graphite (graphene). The method is simple, low temperature, without adding catalyzer, and low-cost. The products have a wide range of applications such as energy storage, catalysis, absorption, separation, processing metal cutter, precision die, aeronautics, and astronautics.

Assisted by in-house AI deep compression toolchain (ezLabel, ezModel, ezQUANT, ezHybrid-M), the proposed technology supports automatic AI model design and optimization with the integrated performance of 120x model size reduction and 70x power reduction in 2D CNN model, and develops a world-first 1/2/4/8-bit CNN model realized by the developed high efficiency Hybrid fixed point CNN NPU (Hybrid-NPU), which has been verified in Xilinx ZCU102 FPGA and achieves the performance up to 2.5 TOPS(8-b)/ 20TOPS(1-b)＠28nm technology running at 550MHz and 4TOPS/W energy efficiency.

As artificial intelligence rapidly developsenhances human’s quality of life, another cause for concern is the dangerspossible threats to humanity. “Moral judgement” will be a big issue that needs to be addressed as these intelligent machines become moremore prevalent in society. As with any new technologies, the topic of applied ethics often arises. So, the extension of that is AI ethics issues.

With the rapid advancement of artificial intelligenceIoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming.

This project collaborates with the international team to collect a very large-scale Chinese emotional corpus. In terms of technology, the fairness of speech emotion recognition is also discussed to solve social issues that may be encountered regarding the usability of emotion recognition. Among them, it is found that the database annotations are all labeled with the unfair perspective of men and women, which leads to biases in the trained model. In order to solve this problem, there have been preliminary achievements in the technological development of fairness, and will be submitted in the near future.

The dialogue system is the main subsystem of the visually impaired navigation system, which provides destinations for the navigation system through multiple dialogues. We use the knowledge graph as the basis for reasoning. In terms of close-range navigation, deep learning technology is used to develop RGB camera detection depth algorithm, indoor semantic cutting algorithm, integrated detection depth estimation and indoor semantic cutting in indoor obstacle avoidance, etc. The whole system uses the CellS software design framework to integrate distributed AIoT systems.

AI is applied to assist data cleaning and cross-database authority control of names and institutions in the bibliographic materials such as patents, papers and research projects.

The proposed indoor radar sensing system includes two enabling technology, activity recognition and respiration rate estimation. In the former, the proposed framework consists of four major components: denosing, enhanced voxelization, data augmentation, and dual-view machine-learning to lead to accurate and efficient human-activity recognition. In the latter, the proposed system leverages the variation of the phase information of a specific frequency bin of the range profiles, and proposes a dynamic adaptive respiration waveform filtering algorithm to improve accuracy.

1. Identification of host factor, which is required for virus replication, by “EditCell Virology Platform”.2. The host factor will be as a target to develop antivirals.3. The antiviral effects of the candidates can be validated in vitroin vivo.4. Two FDA-approved drugs are repurposed for COVID-19 treatment.

The liver is a silent organ, yet it affects the health of many. In Taiwan, there are over 10,000 deaths annually due to chronic liver
disease, cirrhosis and liver cancer. Liver cancer is currently the second leading cancer in Taiwan.

Did you know that Tuberculosis is still a highly infectious disease affecting Taiwan and is one of the top 10 causes of death worldwide? TB is an airborne illness that can be contracted by breathing in germs. Early TB detection greatly reduces the risk of spreading the illness.

A famous commercial ad stated: “If the liver is unwell, life becomes black and white.” Those who have seen this are reminded to take good care of the liver, however liver disease is still quite prevalent in Taiwan. 13,000 deaths each year are due to some form of liver disease. Liver cancer is the 2nd leading cause of death in Taiwan and chronic liver disease & cirrhosis are the 9th leading cause of death.

div style="text-align: justify"According to the statistics of World Health Organization (WHO) in 2018, Chronic Obstruction Pulmonary Disease (COPD) has killed 3 million peoplebecome the 3rd place among the top 10 leading causes of death in the world, which means one person dies for this fatal disease every 10 seconds. In Taiwan,chronic lower respiratory disease is at the 7th place among the top 10 leading cause of death. There are over 5000 people died for obstructive pulmonary every year.

To develop an AI model that doctors trustthat generalizes to as many hospitals as possible, NVIDIAMass General Brigham embarked on an initiative called strongEXAM/strong (strongE/strongMR CstrongX/strongR strongA/strongI strongM/strongodel) the largest, most diverse federated learning initiative with 20 hospitalsresearch institutions from around the world. In just two weeks, the global collaboration achieved a model with .94 area under the curve (with an AUC goal of 1.0), resulting in excellent prediction for the level of oxygen required by incoming patients.  

This project aims to develops a completely manufacturing flexible decision framework by applying big data analytics and AI techniques that integrates data from different decision-making units such as APC, APS, capacity planning, and inventory management planning to increase the decision quality and resilient capability in uncertain risks based on the perspective of factory operation.

If you experience symptoms such as difficulty hearing, frequent headaches, loss of balance, deteriorating hearing, etc., it might be wise to check if you have acoustic neuroma. Because symptoms are similar to other common ear issues, it isn’t always detected. Vestibular schwannoma is a benign tumor with a slow growth rate, however as it grows and affects surrounding nerves it may have side effects that could possibly be fatal if not addressed.

Use NLP techniques to realize the automatic coding of ICD10. According to the input of the patient’s age, gender, medical order, admissions, progress note, surgical records, discharge, ICD-10 diagnostic code and ICD-10 disposal code, perform machine learning model training and code prediction.