Technical category
    • Applying Machine Learning to User Mobility Type Identification for 5th Generation Mobile Networks

      AI & IOT Application FutureTech Applying Machine Learning to User Mobility Type Identification for 5th Generation Mobile Networks

      Due to the fast development of 5G networks, it is critical to identify users service types to allocate resources intelligently. Our technology focuses on users mobility type identification by extracting practical features from users cellular information. We proposed a system architecturehave collected 700-hour data with 150 GB. By using our dataother datasets in the world, we show that our technology can achieve 95 accuracy,reduce 16 energy consumption compared to traditional methods.
    • 利用機器學習分析惡意流量特徵

      FutureTech 利用機器學習分析惡意流量特徵

      The network technology plays an important role in the industrial control systems (ICS)then has become the target of cyber-attackers. For industrial internet-of-things (IIoT) applications with limited computing resources, designing an effective NIDS is challenging. A compacteffective NIDS for IIoT is proposedvalidated by using the more recent UNSW-NB 15 dataset to improve the detection capability against new types of attacks in the real world. Experimental results show that the proposed method achieves better performance than previous methods.
    • 以智慧型手表的生理徵象監測以建立急診醫護人員的過勞示警

      FutureTech 以智慧型手表的生理徵象監測以建立急診醫護人員的過勞示警

      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.
    • Building A Deep Learning-based Chest X-ray CADe Platform MedCheX

      Precision Health Ecosystem FutureTech Building A Deep Learning-based Chest X-ray CADe Platform MedCheX

      As we continue to face the rapid increase in confirmed Coronavirus cases around the world, we created an AI-based pneumonia detection platform for COVID-19. The system is able to automatically detect high-risk patients with pneumonia that will then send information to doctors. With that information, the doctors are then able to make follow-up decisions and provide a treatment plan after the diagnosis. In specific, doctors from the Department of Medical Imaging provided us thousands of positive and negative chest x-rays for pneumonia as a training set. Our system has already been tested with and adopted by doctors at the NCKU Hospital. The system achieved 95% accuracy to detect the pneumonia symptom, based on 1400 test images.
    • 智慧醫院 ICD10 病歷分類自動編碼系統

      FutureTech 智慧醫院 ICD10 病歷分類自動編碼系統

      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 codeICD-10 disposal code, perform machine learning model trainingcode prediction. In addition, the combination codemedical order-related coding rules in practice are used to establish corresponding rules to optimize the accuracy of AI prediction.
    • 運用人工智慧技術建構胸腔X光影像偵測早期肺癌病灶模型

      FutureTech 運用人工智慧技術建構胸腔X光影像偵測早期肺癌病灶模型

      With the rise in computing power, deep-learning based computer-aided diagnosis systems have gained interest in the research community. Our system process the images to assist doctors to determine whether the patients have nodules in lungs. Meanwhile, we utilized the Feature Pyramid Network to extend the receptive field on the convolutional kernel, which improved the performance on the nodule detection with various locations in CXR. The semi-supervised learning mechanism also achieves the way of soft-annotation to reduce human effort in medical image annotation.
    • 以統合分析與機器學習技術建構集成式大腦生物年齡估算平台

      FutureTech 以統合分析與機器學習技術建構集成式大腦生物年齡估算平台

      Based on our long-term accumulated healthy brain image database, we used meta-analysismultivariate analytical algorithms to extract the cognitive biological characteristics of individual subjects. By using machine learning / artificial intelligence algorithms, these features were further calculated for capturing individual cognition according to related brain networks. This biological index could be a potential marker for evaluating individual brain health, cognitive functions,risk for neurodegenerative diseases.
    • 肝癌治療成效追蹤與術後復發預測輔助系統

      FutureTech 肝癌治療成效追蹤與術後復發預測輔助系統

      "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."
    • AI_Variant Prioritizer

      Precision Health Ecosystem FutureTech AI_Variant Prioritizer

      The interpretation of next generation sequencing is a big challenge. In order to improve the molecular diagnosis of the patient, we developed the AI_Variant prioritizer, a machine learning based variant prioterization system that can help to prioritize candidate variants for human disease. This module highlighted the possible disease causing variants to help clinician to increase the disease diagnostic yield and decrease the load of manpower.
    • Ted-ICU AI Platform

      AI & IOT Application FutureTech Ted-ICU AI Platform

      Ted-ICU AI Platform: (1)Provide a single view of patients' EMRsvital signs (2)Support remote ICU monitoring (3)Online labeling tool & Expandable AI algorithms repository (4)Disease-specific prediction models (5)Standardized EMR templates
    • Real-time identification of crop losses using UAV imagery

      AI & IOT Application FutureTech Real-time identification of crop losses using UAV imagery

      This technology integrates 1000+ times of UAV imaging experiences with labeled rice lodging images for training. A rice lodging recognition model using deep learning reaches 90 accuracy. The recognition model can be deployed in a microcomputer mounted on UAVs to implement edge computing. While taking aerial images, the inference can be completedreveal lodging areadamage level in-time.
    • 應用作物生理指標建立需水超前預警系統

      FutureTech 應用作物生理指標建立需水超前預警系統

      This technique uses Tainan 11 (TN11), which occupies about 70 of the rice planting area in Taiwan. Building the early watering warning system of crops by combining the physiological indicators of rice with environmental dataartificial intelligence technology. The system can analyzepredict the impending water shortage for rice in advanceissue drought warning immediately. Therefore, decision makers can give the timelyappropriate amount for rice irrigationimprove the efficiency of using waterreducing the cost of farmersfarming losses.
    • 圖機器學習高回報率金融商品推薦技術

      FutureTech 圖機器學習高回報率金融商品推薦技術

      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.
    • Intelligent Scalp Detection System

      Precision Health Ecosystem FutureTech Intelligent Scalp Detection System

      This system uses the innovative AIoT application to target annoying scalp maintenance problemsdevelops an intelligent scalp detectionmanagement system. The core of the technology is to use deep learning-based object detection models. Tens of thousands of microscopic image data sets that are labeledtrained for scalp symptoms. As a result, the scalp image recognition module is develop, such as dandruff, hair loss, oil, and inflammation, etc. Hence this system can provide scalp detection, and maintenance effectiveness tracking functions lead scalp maintenance services to a new level of intelligent management.
    • Artificial Intelligence for Customs Fraud Detection

      AI & IOT Application FutureTech Artificial Intelligence for Customs Fraud Detection

      With the astronomically growing trade flows, customs administrations need effective and explainable methods to detect suspicious transactions. This project presents a novel artificial intelligence-based model named DATE that ranks trade flows in the order of fraud risk and to maximize customs revenue. We confirm the superiority of DATE over state-of-the-art AI models, with a remarkable precision of 92.7% on illegal cases and a recall of 49.3% on revenue after inspecting only 1% of all trade flows. Predictions of DATE are also interpretable from the attention mechanism. We are deploying DATE in Nigeria and Malawi Customs Services, in collaboration with the World Customs Organization (WCO). DATE has been published in ACM KDD 2020, which is an AI top conference.
    • Web-based Diagnostic System for Assessing Psychiatric Disorders

      AI & IOT Application FutureTech Web-based Diagnostic System for Assessing Psychiatric Disorders

      The Al-based web diagnostic system provides an online assessment tool for diagnosing schizophrenia. The Explainable Deep Neural Network classifier is deployed to analyze gray matterwhite matter to derive diagnostic classification of schizophrenia. The structural brain abnormalities associated with schizophrenia is visualized on the AI-based web diagnostic system at individual level.
    • 智慧都市治理:融合AIOT與即時城市異質大數據之時空預測模型

      FutureTech 智慧都市治理:融合AIOT與即時城市異質大數據之時空預測模型

      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.
    • 智慧急診即時決策支援系統:住院安排、停留時間、暨相似病歷取回之妥善化技術

      FutureTech 智慧急診即時決策支援系統:住院安排、停留時間、暨相似病歷取回之妥善化技術

      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.
    • AIBig Data Analytics for Energy SavingChiller Configuration Optimization

      Smart machinerynovel materials Innotech Expo AIBig Data Analytics for Energy SavingChiller Configuration Optimization

      This technique employs AIbig data analytics to precisely forecast cooling load demandestimate efficiency of different chiller combinations. Time-of-Use Pricingoptimal chiller load interval are also considered for practical needs. Decision supports of optimal chiller configuration are provided to enhance energy conservation.
    • Efficiency Boosting System for Computer Numerical Control Milling Machine Based on AIBig Data Analytics

      AI & IOT Application FutureTech Efficiency Boosting System for Computer Numerical Control Milling Machine Based on AIBig Data Analytics

      This technique combines more than one AI models to precisely predict current under noisy data from current sensoroptimizes speed rate based on this AI forecasting model. At the same time, it considers the practical requirementslimitations of workpiece surface roughness, tool machine current load, etc. This technique provides decision supports for optimizing parameters of CNC milling machine to keep high efficiencyenhance energy conservation.
    • Zero Contact Detection-Facial Stroke, Heart Rate and Breath Detection Technology

      Precision Health Ecosystem FutureTech Zero Contact Detection-Facial Stroke, Heart Rate and Breath Detection Technology

      We use features such as asymmetric expression and crooked eyes to assess the risk of facial stroke. Observing the micro vibration of the head caused by the contraction of the heart, and develop a zero-contact facial heart rate and respiration rate detection technology in conjunction with the camera. The technology can accurately measure heart rate and respiration rate in real time, thereby reducing the risk of infection. This technology has obtained two ROC patents (M590433, I689285), two US patents (HEART RATE DETECTION METHOD AND DEVICE THEREOF,MOUTH AND NOSE OCCLUDED DETECTING METHOD AND SYSTEM THEREOF). The possibility of detecting strokes through AI machine learning methods is not only accurate, but also find out signs of stroke early to grasp the best time to seek medical treatment.
    • 4G/5G多媒體系統之資安弱點檢測與威脅防護

      FutureTech 4G/5G多媒體系統之資安弱點檢測與威脅防護

      We develop a vulnerability detection module to identify three important security vulnerabilities in the IMS system that supports 4G/5G multimedia services. We have validated them in 4 carriers from TaiwanU.S. using smartphones from 7 brands,shown that they can be exploited to launch a stealthy call DoS attack, a social engineering attack with ghost calls,a call ID spoofing attack. Moreover, attackers can detect attackable users using an AI technique with only the users' phone numbers. We finally propose solutionsdevelop an AI-based technique of call ID spoofing detection.
    • 基於深度強化學習,智慧化商用Wi-Fi裝置增強通訊效能

      FutureTech 基於深度強化學習,智慧化商用Wi-Fi裝置增強通訊效能

      We develop an asynchronous framework across userkernel spaces for deep learning applications on the improvement of Wi-Fi performanceimplement it in the driver of commodity Intel Wi-Fi cards. Under this framework, we apply deep reinforcement learning to developing an intelligent rate adaptation (RA) algorithm (DRL-RA), which can achieve the highest throughput in varying channel conditions given many rate options of current Wi-Fi technologies. Its on-learning capability can learn how to efficiently approach the best rate from the experiences of its common usage patternenvironment.
    • 考慮積灰效應及少故障標籤資料之智慧型高精度太陽光電故障診斷

      FutureTech 考慮積灰效應及少故障標籤資料之智慧型高精度太陽光電故障診斷

      As for the proposed technology by combining the artificial bee colony algorithmthe semi-supervised extreme learning machine (ABC-SSELM), characteristic parameter normalization equations of I-V curves are tuned via low-cost data under normal operation of PV strings. The proposed ABC-SSELM method only needs 1-3 labeled data of the total dataset to save humantime cost. The accuracy of diagnosing various mixed faults can reach more than 99.84,the monitoring of dust accumulation can provide effective cleaning to increase the revenue of the solar PV power generation system.