scholarly journals Penerapan Sensor Ultrasonic SRF05 Berbasis Mikrocontroller ATMega 8535 Untuk Sistem Pengereman Otomatis

2021 ◽  
Vol 2 (4) ◽  
pp. 268-275
Author(s):  
Eri Riana
Keyword(s):  
Artificial Intelligence ◽  
Automatic Control ◽  
Electric Energy ◽  
Signal Propagation ◽  
Ultrasonic Sensor ◽  
Fuel Oil ◽  
Concept Design ◽  
Electric Cars ◽  
Intelligence System ◽  
Human Driver

Transportation technology using electric energy is growing. Electric cars are increasingly being used to reduce fuel oil usage. In addition to the increasingly sophisticated performance of electric cars, the vehicle safety system must also be improved. The existence of Artificial Intelligence is one of the developments in technological sophistication in a world that is increasingly unstoppable and unavoidable. One of the most popular luxury cars in Indonesia, one of which is the Tesla, which is a sophisticated car with the support of an artificial intelligence system, making Tesla an electric car with an automatic control vehicle feature that can run without using a human driver and can provide safety protection for the driver by using automatic braking system in case of an unexpected accident. This journal proposes a future concept design for the forerunner of automatic braking for electric cars using the ATMEGA 8535 microcontroller as the main automatic control and ultrasonic distance sensor. The PWM signal from the microcontroller is used to adjust the speed of the DC motor as the main driving force of the vehicle. The display of the distance between the vehicle and the obstacle can be seen on the LCD and the buzzer is used as an indicator of the safe distance of the car. The experiment was carried out using an RC car with the area to be protected> 500cm (5m), if the SRF05 ultrasonic sensor detects an obstacle at that distance, the SRF05 ultrasonic sensor will send a signal to the microcontroller and turn on an alarm in the form of a buzzer so that the SRF05 ultrasonic sensor will stop signal propagation.

330 DEVELOPMENT AND VALIDATION OF AN ARTIFICIAL INTELLIGENCE SYSTEM TO OPTIMIZE CLINICIAN REVIEW OF PATIENT RECORDS

2021 ◽  
Vol 160 (6) ◽  
pp. S-64-S-65
Author(s):  
Ethan A. Chi ◽  
Gordon Chi ◽  
Cheuk To Tsui ◽  
Yan Jiang ◽  
Karolin Jarr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Patient Records ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Development And Validation

scholarly journals COVID-19-induced vascular angiopathy: CTPA signs in critically ill patients other than acute pulmonary embolism and high-lung opacity scores

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mohamed Hossameldin khalifa ◽  
Ahmed Samir ◽  
Ayman Ibrahim Baess ◽  
Sara Samy Hendawi
Keyword(s):  
Artificial Intelligence ◽  
Pulmonary Embolism ◽  
Lung Volume ◽  
Acute Pulmonary Embolism ◽  
Pulmonary Angiography ◽  
Vascular Tree ◽  
Ct Pulmonary Angiography ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Severity Scores

Abstract Background Vascular angiopathy is suggested to be the major cause of silent hypoxia among COVID-19 patients without severe parenchymal involvement. However, pulmonologists and clinicians in intensive care units become confused when they encounter acute respiratory deterioration with neither severe parenchymal lung involvement nor acute pulmonary embolism. Other radiological vascular signs might solve this confusion. This study investigated other indirect vascular angiopathy signs on CT pulmonary angiography (CTPA) and involved a novel statistical analysis that was performed to determine the significance of associations between these signs and the CT opacity score of the pathological lung volume, which is calculated by an artificial intelligence system. Results The study was conducted retrospectively, during September and October 2020, on 73 patients with critical COVID-19 who were admitted to the ICU with progressive dyspnea and low O2 saturation on room air (PaO2 < 93%). They included 53 males and 20 females (73%:27%), and their age ranged from 18 to 88 years (mean ± SD=53.3 ± 13.5). CT-pulmonary angiography was performed for all patients, and an artificial intelligence system was utilized to quantitatively assess the diseased lung volume. The radiological data were analyzed by three expert consultant radiologists to reach consensus. A low CT opacity score (≤10) was found in 18 patients (24.7%), while a high CT opacity score (>10) was found in 55 patients (75.3%). Pulmonary embolism was found in 24 patients (32.9%); three of them had low CT opacity scores. Four other indirect vasculopathy CTPA signs were identified: (1) pulmonary vascular enlargement (57 patients—78.1%), (2) pulmonary hypertension (14 patients—19.2%), (3) vascular tree-in-bud pattern (10 patients—13.7%), and (4) pulmonary infarction (three patients—4.1%). There were no significant associations between these signs and the CT opacity score (0.3205–0.7551, all >0.05). Furthermore, both pulmonary vascular enlargement and the vascular tree-in-bud sign were found in patients without pulmonary embolism and low CT-severity scores (13/15–86.7% and 2/15–13.3%, respectively). Conclusion Pulmonary vascular enlargement or, less commonly, vascular tree-in-bud pattern are both indirect vascular angiopathy signs on CTPA that can explain the respiratory deterioration which complicates COVID-19 in the absence of severe parenchymal involvement or acute pulmonary embolism.

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