scholarly journals Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 126
Author(s):  
Md. Kalim Amzad Chy ◽  
Abdul Kadar Muhammad Masum ◽  
Kazi Abdullah Mohammad Sayeed ◽  
Md Zia Uddin
Keyword(s):  
Deep Learning ◽  
Power Supply ◽  
Geographical Location ◽  
Buck Converter ◽  
Rapid Expansion ◽  
Raspberry Pi ◽  
Delivery Vehicle ◽  
Steering Angle ◽  
The Road ◽  
Product Delivery

The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.

Deep learning based Blockchain solution for preserving privacy in future vehicles

2020 ◽  
pp. 1-14
Author(s):  
Varsha R ◽  
Meghna Manoj Nair ◽  
Siddharth M. Nair ◽  
Amit Kumar Tyagi
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Smart Devices ◽  
The Road ◽  
Blockchain Technology ◽  
Technological Advances ◽  
Previous Decade ◽  
Smart Things

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

The Study on Preventing Users Electic Larceny

2014 ◽  
Vol 602-605 ◽  
pp. 2939-2942
Author(s):  
Ying Ying Yang ◽  
Yi Shan ◽  
Zhi Tong Liu ◽  
Jian Feng Li ◽  
Jing Jin ◽  
...  
Keyword(s):  
Electric Power ◽  
Power Supply ◽  
Economic Loss ◽  
The Road ◽  
Energy Meter ◽  
Power Means ◽  
Long Time

For a long time,users electic larceny has been a headache topic of the electric power department.The users long-term electic larceny has brought great economic loss to the power supply department.In recent years,the stealing power means are emerging in an endless stream,and they have been broght mant difficulties to prevent electic larceny.But the road is high one feet evil spirite is high one a unit of lengh, according to the study on electic energy meter,we will understand the electic larceny means at the sours.Therefore, further study of stealing power means is also our current priority,only the better anti-stealing electic power means, can be completely blocked leak.

Optimization of Active Magnetic Bearings’ Power Supply System for Main Helium Fan in High Temperature Gas-Cooled Reactor

2021 ◽  
Author(s):  
Huan Luo ◽  
Zhengang Shi ◽  
Yan Zhou ◽  
Ni Mo
Keyword(s):  
High Temperature ◽  
Power Supply ◽  
Power Supply System ◽  
Magnetic Bearing ◽  
Supply System ◽  
Buck Converter ◽  
Normal Operation ◽  
Rotating Equipment ◽  
Amb System ◽  
High Temperature Gas

Abstract High temperature gas-cooled reactor (HTR) is a kind of reactor with inherent safety developed by Institute of Nuclear Energy and New Energy Technology of Tsinghua University. In the first circuit, pure helium is used as coolant and the main helium fan is used to promote the coolant circulation. In order to meet the requirements of service environment and performance, the main helium fan adopts the non-lubricant active magnetic bearing (AMB) system as its support system. For the high-speed rotating equipment supported by AMBs, losing power would lead to bearing failure and cause serious damage to the equipment. In this paper, the power supply system of AMBs is optimized. The power supply of AMB system is connected with the DC-link of the motor converter through DC/DC converter. During normal operation, the AMB system is supplied by external power supply, and the DC/DC converter is used as the backup redundant power supply. In the event of a power failure accident, the DC/DC converter is put into operation, converting the remanet kinetic energy of the motor into stable power to maintain the normal operation of the AMB system. The DC/DC converter adopts two-stage topology structure of the former BUCK converter and the later LLC converter, and completes the voltage stabilization control of the latter LLC converter through the digital signal processor (DSP). Experimental results show that this scheme can realize the power loss protection function of the rotating equipment supported by AMBs.

scholarly journals Towards Energetic Autonomy of UAV

2020 ◽  
Author(s):  
Krzysztof Mateja ◽  
Wojciech Skarka
Keyword(s):  
Solar Cells ◽  
Power Supply ◽  
Power Supply System ◽  
Geographical Location ◽  
Supply System ◽  
Battery Management ◽  
A Charge ◽  
Favorable Weather ◽  
Energy Autonomy ◽  
Battery Cells

This article presents the results of work of power supply system of an unmanned aerial vehicle (UAV) powered by solar cells. The UAV power supply system consists of solar cells, a charge controller, battery cells and a BMS (Battery Management System). During the designing process various options for energy acquisition and recovery was considered, in particular ATG (Advanced Thermoelectric Generator). The MBD (Model-Based Design) methodology was used to develop the UAV power supply system. The system was developed in simulation model and next it was studied to find the space of possible solutions using this model. Solar cells are the most efficient if the sun rays fall on them perpendicular. During the simulation various angles of inclination of solar cells in relation to sun rays were studied. These values depend on latitude, azimuth, season (length of day), weatheri.e. if there are any clouds and even air pollution. The power supply system had to be constructed in such a way to ensure during the day excess to energy enabling the operation of the engines, peripheral devices (sensors, measuring devices, GPS module) as well as charging the batteries to maximum capacity. The next step was related to the proper selection of battery cells to ensure the operation of the devices and flight at night. The whole research was additionally extended by minimizing the mass of power supply elements while increasing the ability to achieve energy autonomy. The developed system allows to increase the UAV flight duration, and with appropriate construction, geographical location and favorable weather conditions it is able to provide full energy autonomy of the UAV. The UAV powered by solar cells enables for example monitoring of pollution, boundaries, power lines, crops and measuring selected physical quantities over any area e.g. smog.

scholarly journals Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi

2021 ◽  
Vol 5 (1) ◽  
pp. 107-113
Author(s):  
Kahlil Muchtar ◽  
Chairuman ◽  
Yudha Nurdin ◽  
Afdhal Afdhal
Keyword(s):  
Deep Learning ◽  
Leaf Spot ◽  
Raspberry Pi ◽  
Leaf Spot Disease ◽  
Detection Accuracy ◽  
Crop Failure ◽  
Tomato Production ◽  
Spot Disease ◽  
Average Accuracy ◽  
Septoria Leaf Spot

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.  

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