scholarly journals An Automated Auroral Detection System Using Deep Learning: Real-time Operation in Tromsø, Norway

2021 ◽  
Author(s):  
Sota Nanjo ◽  
Satonori Nozawa ◽  
Masaki Yamamoto ◽  
Tetsuya Kawabata ◽  
Magnar G. Johnsen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Large Scale ◽  
Detection System ◽  
Optical Data ◽  
Occurrence Rate ◽  
Digital Cameras ◽  
Validation Data ◽  
Human Eye ◽  
Time Operation

Abstract The activity of citizen scientists who capture images of aurora borealis using digital cameras has recently been contributing to research regarding space physics by professional scientists. Auroral images captured using digital cameras not only fascinate us, but may also provide information about the energy of precipitating auroral electrons from space; this ability makes the use of digital cameras more meaningful. To support the application of digital cameras, we have developed artificial intelligence that monitors the auroral appearance in Tromsø, Norway, instead of relying on the human eye, and implemented a web application, “Tromsø AI”, which notifies the scientists of the appearance of auroras in real-time. This “AI” has a double meaning: artificial intelligence and eyes (instead of human eyes). Utilizing the Tromsø AI, we also classified large-scale optical data to derive annual, monthly, and UT variations of the auroral occurrence rate for the first time. The derived occurrence characteristics are fairly consistent with the results obtained using the naked eye, and the evaluation using the validation data also showed a high F1 score of over 93%, indicating that the classifier has a performance comparable to that of the human eye classifying observed images

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hyperspectral Imaging ◽  
High Speed ◽  
Detection System ◽  
Imaging Techniques ◽  
Foreign Object ◽  
Subtraction Method ◽  
Foreign Objects ◽  
Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

scholarly journals Improving weather forecasts by means of HPC solutions: the LEXIS approach in the 2020 Bitti flood event

2021 ◽  
Author(s):  
Paola Mazzoglio ◽  
Paolo Pasquali ◽  
Andrea Parodi ◽  
Antonio Parodi
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Early Warning ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Detection System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Flood Event ◽  
Heavy Rainfall Event

&lt;p&gt;In the framework of LEXIS (Large-scale EXecution for Industry &amp; Society) H2020 project, CIMA Research Foundation is running a 3 nested domain WRF (Weather Research and Forecasting) model with European coverage and weather radar data assimilation over Italy. Forecasts up to 48 hours characterized by a 7.5 km resolution are then processed by ITHACA ERDS (Extreme Rainfall Detection System), an early warning system for the heavy rainfall monitoring and forecasting. This type of information is currently managed by ERDS together with two global-scale datasets. The first one is provided by NASA/JAXA GPM (Global Precipitation Measurement) Mission through the IMERG (Integrated Multi-satellitE Retrievals for GPM) Early run data, a near real-time rainfall information with hourly updates, 0.1&amp;#176; spatial resolution and a 4 hours latency. The second one is instead provided by GFS (Global Forecast System) at a 0.25&amp;#176; spatial resolution.&lt;br&gt;The entire WRF-ERDS workflow has been tested and validated on the heavy rainfall event that affected the Sardinia region between 27 and 29 November 2020. This convective event significantly impacted the southern and eastern areas of the island, with a daily rainfall depth of 500.6 mm recorded at Oliena and 328.6 mm recorded at Bitti. During the 28th, the town of Bitti (Nuoro province) was hit by a severe flood event.&lt;br&gt;Near real-time information provided by GPM data allowed us to issue alerts starting from the late morning of the 28th. The first alert over Sardinia based on GFS data was provided in the late afternoon of the 27th, about 40 km far from Bitti. In the early morning of the 28th, a new and more precise alert was issued over Bitti. The first alert based on WRF data was instead provided in the morning of the 27th and the system continued to issue alerts until the evening of the 29th, confirming that, for this type of event, precise forecasts are needed to provide timely alerts.&lt;br&gt;Obtained results show how, taking advantage of HPC resources to perform finer weather forecast experiments, it is possible to significantly improve the capabilities of early warning systems. By using WRF data, ERDS was able to provide heavy rainfall alerts one day before than with the other data.&lt;br&gt;The integration within the LEXIS platform will help with the automatization by data-aware orchestration of our workflow together with easy control of data and workflow steps through a user-friendly web interface.&lt;/p&gt;

scholarly journals Real-time analysis of a mass vaccination effort via an Artificial Intelligence platform confirms the safety of FDA-authorized COVID-19 vaccines

2021 ◽  
Author(s):  
Reid McMurry ◽  
Patrick Lenehan ◽  
Samir Awasthi ◽  
Eli Silvert ◽  
Arjun Puranik ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Adverse Effects ◽  
Real Time ◽  
Odds Ratio ◽  
Large Scale ◽  
Vaccination Campaign ◽  
Vaccine Dose ◽  
Incidence Rates ◽  
Low Frequencies ◽  
Clinical Notes

AbstractAs the COVID-19 vaccination campaign unfolds as one of the most rapid and widespread in history,it is important to continuously assess the real world safety of the FDA-authorized vaccines. Curation from large-scale electronic health records (EHRs) allows for near real-time safety evaluations that were not previously possible. Here, we advance context- and sentiment-aware deep neural networks over the multi-state Mayo Clinic enterprise (Minnesota, Arizona, Florida, Wisconsin) for automatically curating the adverse effects mentioned by physicians in over 108,000 EHR clinical notes between December 1st 2020 to February 8th 2021. We retrospectively compared the clinical notes of 31,069 individuals who received at least one dose of the Pfizer/BioNTech or Moderna vaccine to those of 31,069 unvaccinated individuals who were propensity matched by demographics, residential location, and history of prior SARS-CoV-2 testing. We find that vaccinated and unvaccinated individuals were seen in the the clinic at similar rates within 21 days of the first or second actual or assigned vaccination dose (first dose Odds Ratio = 1.13, 95% CI: 1.09-1.16; second dose Odds Ratio = 0.89, 95% CI: 0.84-0.93). Further, the incidence rates of all surveyed adverse effects were similar or lower in vaccinated individuals compared to unvaccinated individuals after either vaccine dose. Finally, the most frequently documented adverse effects within 7 days of each vaccine dose were fatigue (Dose 1: 1.77%, Dose 2: 1.2%),nausea (Dose 1: 1.05%, Dose 2: 0.84%), myalgia (Dose 1: 0.67%; Dose 2: 0.66%), diarrhea (Dose 1: 0.67%; Dose 2: 0.46%), arthralgia (Dose 1: 0.64%; Dose 2: 0.57%), erythema (Dose 1: 0.59%; Dose 2: 0.46%), vomiting (Dose 1: 0.45%, Dose 2: 0.29%) and fever (Dose 1: 0.29%; Dose 2: 0.23%). These remarkably low frequencies of adverse effects recorded in EHRs versus those derived from active solicitation during clinical trials (arthralgia: 24-46%; erythema: 9.5-14.7%; myalgia: 38-62%; fever: 14.2-15.5%) emphasize the rarity of vaccine-associated adverse effects requiring clinical attention. This rapid and timely analysis of vaccine-related adverse effects from contextually rich EHR notes of 62,138 individuals, which was enabled through a large scale Artificial Intelligence (AI)-powered platform, reaffirms the safety and tolerability of the FDA-authorized COVID-19 vaccines in practice.

scholarly journals Multi Comm_Plus: A Community Detection System for Identification of Community in Multi-Dimensional Networks

2016 ◽  
Vol 4 (05) ◽  
pp. 9879-9884
Author(s):  
Dhanya Sudhakaran ◽  
Shini Renjith
Keyword(s):  
Big Data ◽  
Real Time ◽  
Community Detection ◽  
Data Analytics ◽  
Selection Criteria ◽  
Large Scale ◽  
Detection System ◽  
Big Data Analytics ◽  
Detection Algorithms ◽  
Large Scale Networks

Community detection is a common problem in graph and big data analytics. It consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. Community detection algorithms in literature proves to be less efficient, as it leads to generation of communities with noisy interactions. To address this limitation, there is a need to develop a system which identifies the best community among multi-dimensional networks based on relevant selection criteria and dimensionality of entities, thereby eliminating the noisy interactions in a real-time environment.

Artificial Intelligence Techniques for Cancer Detection in Medical Image Processing: A Review

2021 ◽  
Vol 12 (2) ◽  
pp. 2667-2673
Author(s):  
Er. Charnpreet kaur, Et. al.
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Image Processing ◽  
Large Scale ◽  
Detection System ◽  
Processing System ◽  
Body Parts ◽  
Cancer Breast ◽  
Broad Term ◽  
Abnormal Cells

Cancer is the uncontrolled growth of abnormal cells in any part of a body.  Cancer is a broad term for a group of diseases caused when abnormal cells grows in different body parts. There are more than hundred types of Cancer such as Lung cancer, Breast cancer, Skin cancer, Oral cancer, Colon cancer and Prostate cancer. Delay in treatment can cause serious health issues, even cause loss of life. This paper gives the review on methods of detection of lung cancer and brain cancer and liver using image processing. The methods used for detection are Automated and computer-aided detection system (CAD) with artificial intelligence and these methods are good to process a large datasets to provide accurate and efficient results in the detection of cancer. However, these processing system have to face many challenges to implement on large scale including imageacquisition,  pre-processing, segmentation, and data management and classification strategies to be compatible with AI. This paper reviews the various image acquisition and segmentation techniques. These techniques become the need of an hour to cater the growing patient population and for the improvement in the Healthcare system.

Commissioning a Real-Time Leak Detection System on a Large Scale Crude Oil Pipeline During Start Up

2006 ◽  
Author(s):  
Joanna Mabe ◽  
Keefe Murphy ◽  
Gareth Williams ◽  
Andrew Welsh
Keyword(s):  
Crude Oil ◽  
Real Time ◽  
Large Scale ◽  
Detection System ◽  
Leak Detection ◽  
Oil Pipeline ◽  
Start Up ◽  
Environmental Requirements ◽  
The Moment ◽  
Operational Data

This paper describes the process of incremental pipeline filling and the phased commissioning of a real-time leak detection system for the 1768 km long BTC crude oil pipeline. Due to stringent environmental requirements, it is essential for the leak detection system to work from the moment that crude oil is introduced into the pipeline. Without any prior operational data and with the pipeline partially filled, it is challenging for the leak detection system to monitor the integrity of the pipeline throughout the whole filling process. The application of the pig tracking software to track the oil front as the crude displaces nitrogen is also discussed.

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