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Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.
Dinh-Dung Nguyen; Hong Son Tran; Thi Thuy Tran; Dat Dang Quoc; Hong Tien Nguyen. Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors. International Journal of Aviation Science and Technology 2021, vm02, 15 -21.
AMA StyleDinh-Dung Nguyen, Hong Son Tran, Thi Thuy Tran, Dat Dang Quoc, Hong Tien Nguyen. Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors. International Journal of Aviation Science and Technology. 2021; vm02 (is01):15-21.
Chicago/Turabian StyleDinh-Dung Nguyen; Hong Son Tran; Thi Thuy Tran; Dat Dang Quoc; Hong Tien Nguyen. 2021. "Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors." International Journal of Aviation Science and Technology vm02, no. is01: 15-21.
With the exponential growth of numerous drone operations ranging from infrastructure monitoring to even package delivery services, the integration of UAS in the smart city transportation systems is an actual task that requires radically new, sustainable (safe, secure, with minimum environmental impact and life cycle cost) solutions. The primary objective of this proposed option is the definition of routes as desired and commanded trajectories and their autonomous execution. The airspace structure and fixed routes are given in the global GPS reference system with supporting GIS mapping. The concept application requires a series of further studies and solutions as drone trajectory (or corridor) following by an autonomous trajectory tracking control system, coupled with autonomous conflict detection, resolution, safe drone following, and formation flight options. The second part of the paper introduces such possible models and shows some results of their verification tests. Drones will be connected with the agency, designed trajectories to support them with factual information on trajectories and corridors. While the agency will use trajectory elements to design fixed or desired trajectories, drones may use the conventional GPS, infrared, acoustic, and visual sensors for positioning and advanced navigation. The accuracy can be improved by unique markers integrated into the infrastructure.
Dinh Nguyen; Jozsef Rohacs; Daniel Rohacs. Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management. ISPRS International Journal of Geo-Information 2021, 10, 338 .
AMA StyleDinh Nguyen, Jozsef Rohacs, Daniel Rohacs. Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management. ISPRS International Journal of Geo-Information. 2021; 10 (5):338.
Chicago/Turabian StyleDinh Nguyen; Jozsef Rohacs; Daniel Rohacs. 2021. "Autonomous Flight Trajectory Control System for Drones in Smart City Traffic Management." ISPRS International Journal of Geo-Information 10, no. 5: 338.
Recently, unmanned aerial vehicles (UAVs), or drones, can be used to complete several different military tasks to the industry with numerous studies available in the literature. With the accelerated development of technologies, especially computing, sensing, the Internet of things (IoT), and Information and Communication Technologies (ICT), the demand for using drones has been increased in real-world applications. However, there will be more accidents when more drones are active in the sky. Therefore, it is essential to manage drones in operation areas, especially the urban environment. This research introduces an approach, called a cloud-based approach for managing drones in a smart city. This approach is based on the cloud devices and services such as computation, storage, and web services. A ground control station controls and monitors drones, allowing users to define path planning and achieve the information from drone’s sensors. Users, or remoted pilots, can create paths or missions for drones, saved, and transferred to a connected drone. This approach lets users control and monitor drones as connected objects in a real-time environment. An experimental study of monitoring and controlling drones via the Internet (4G D-com Viettel) has been carried out, aiming to evaluate the real-time performance of monitoring and controlling drones. The experimental results have illustrated that the proposed method is a cloud solution that enables to manage and control drones in a real-time environment.
Dinh-Dung Nguyen. Cloud-Based Drone Management System in Smart Cities. Developments in Advanced Control and Intelligent Automation for Complex Systems 2021, 211 -230.
AMA StyleDinh-Dung Nguyen. Cloud-Based Drone Management System in Smart Cities. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2021; ():211-230.
Chicago/Turabian StyleDinh-Dung Nguyen. 2021. "Cloud-Based Drone Management System in Smart Cities." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 211-230.
Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.
Dinh Dung Nguyen; József Rohács; Dániel Rohács; Anita Boros. Intelligent Total Transportation Management System for Future Smart Cities. Applied Sciences 2020, 10, 8933 .
AMA StyleDinh Dung Nguyen, József Rohács, Dániel Rohács, Anita Boros. Intelligent Total Transportation Management System for Future Smart Cities. Applied Sciences. 2020; 10 (24):8933.
Chicago/Turabian StyleDinh Dung Nguyen; József Rohács; Dániel Rohács; Anita Boros. 2020. "Intelligent Total Transportation Management System for Future Smart Cities." Applied Sciences 10, no. 24: 8933.
Unmanned aerial vehicles (UAVs), especially drones, have advantages of having applications in different areas, including agriculture, transportation, such as land use surveys and traffic surveillance, and weather research. Many network protocols are architected for the communication between multiple drones. The present study proposes drone-following models for managing drones in the transportation management system in smart cities. These models are based on the initial idea that drones flight towards a leading drone in the traffic flow. Such models are described by the relative distance and velocity functions. Two types of drone-following models are presented in the study. The first model is a safe distance model (SD model), in which a safe distance between a drone and its ahead is maintained. By applying the stochastic diffusion process, an improved model, called Markov model, is deduced. These drone-following models are simulated in a 2D environment using numerical simulation techniques. With the simulation results, it could be noted that: i) there is no accident and no unrealistic deceleration; ii) the velocity of the followed drone is changed according to the speed of the drone ahead; iii) the followed drones keep a safe distance to drone ahead even the velocities are changed; iv) the performance of the Markov model is better than that of the SD model.
Nguyen Dinh Dung. Developing Models for Managing Drones in the Transportation System in Smart Cities. Electrical, Control and Communication Engineering 2019, 15, 71 -78.
AMA StyleNguyen Dinh Dung. Developing Models for Managing Drones in the Transportation System in Smart Cities. Electrical, Control and Communication Engineering. 2019; 15 (2):71-78.
Chicago/Turabian StyleNguyen Dinh Dung. 2019. "Developing Models for Managing Drones in the Transportation System in Smart Cities." Electrical, Control and Communication Engineering 15, no. 2: 71-78.
Today’s nations are facing numerous challenges in transforming living environments in a way better-serving people’s demands of the future. The principal point in this transformation is reinventing cities as smart cities that combine their data, their resources, their infrastructure and their people to continually focus on improving livability while minimizing the use of resources. The usage data and sensor network is the primary characteristics of any smart city. However, just having data is not enough, data points themselves are only information. It is good to have, but hardly useful by themselves. This paper gives a short overview of the concepts for transport management system in the smart city and proposes a new transport management approach that is contract-based and priority transport management. These methods allow to estimate and control traffic efficiently. Based on these concepts, the authors propose a new transport management system that is working as a single system. This proposed system has three layers: physical, info-communication and control generation. The system deals with four different classes of tasks: (i) handling the non-cooperative vehicle, (ii) traffic management based on the cooperative vehicle information, (iii) contract-based traffic management, (iv) priority transport management. Some benefits of implementing this system are also expected in this paper.
Nguyen Dinh Dung; Jozsef Rohacs. Smart City Total Transport-Managing System. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019, 74 -85.
AMA StyleNguyen Dinh Dung, Jozsef Rohacs. Smart City Total Transport-Managing System. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2019; ():74-85.
Chicago/Turabian StyleNguyen Dinh Dung; Jozsef Rohacs. 2019. "Smart City Total Transport-Managing System." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 74-85.
Currently, the study of unmanned aerial vehicle (UAV) in complex environments is relatively well developed and has many achievements. However, the fact that the landing process of UAVs in complex or unknown environments is a significant challenge for scientists. In this paper, the problem on how to determine the desired landing orbit will be solved. The approach and landing process might be constructed from the straight lines and orbit curves. This study indicates that the UAV has four landing trajectories because the UAV can turn left or right to reach the left or right circle in the landing process. The calculation is based on solving aircraft equations of motion and analytical method. This paper also shows the methodologies to determine the landing routes that the UAV can be landing in any direction or given direction. The simulation results utilising MATLAB software show that the calculated landing orbit is the shortest.
Nguyen Dinh Dung; Jozsef Rohacs. Robust planning the landing process of unmanned aerial vehicles. International Journal of Sustainable Aviation 2019, 5, 1 .
AMA StyleNguyen Dinh Dung, Jozsef Rohacs. Robust planning the landing process of unmanned aerial vehicles. International Journal of Sustainable Aviation. 2019; 5 (1):1.
Chicago/Turabian StyleNguyen Dinh Dung; Jozsef Rohacs. 2019. "Robust planning the landing process of unmanned aerial vehicles." International Journal of Sustainable Aviation 5, no. 1: 1.
Drones are estimated to play a critical role in the smart city, assisting with a variety of use cases: medical, transportation and agriculture. The applications of drones in the smart city will involve multiple drone platforms that operate simultaneously to run missions. Therefore, the safe and secure environment for drones' operational quality and stability is necessary. It is also essential for governments to implement regulations to enforce safe security standards and disallow the implementation of weak cybersecurity measures in live environments. The Federal Aviation Administration (FAA) predicted that 30,000 drones could be flying in U.S. skies in less than 20 years. The investigation of the drone traffic safety and development of the intelligent transportation system needs drone-following models, which describes the one-by-one following process of drones in the traffic flow. There are two types of drone-following models that are proposed and discussed in this paper. The first models based on the principle that keeps a safe distance according to relative velocity, which based on the determining of the drone acceleration depending on the differences in speeds and gaps between the given drone and its leading drone. Another model is the Markov drone-following model which is improved model based on the approximation of the stochastic diffusion process of speed decision. The simulation results show that the changes in velocities of the following drones are nearly the same as leading one.
Nguyen Dinh Dung; Jozsef Rohacs. The drone-following models in smart cities. 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) 2018, 1 -6.
AMA StyleNguyen Dinh Dung, Jozsef Rohacs. The drone-following models in smart cities. 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). 2018; ():1-6.
Chicago/Turabian StyleNguyen Dinh Dung; Jozsef Rohacs. 2018. "The drone-following models in smart cities." 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) , no. : 1-6.
Bài báo giới thiệu phương pháp xác định các tham số trong mô hình toán cho đối tượng điều khiển là thiết bị bay không người lái dạng quadrotor, trong đó đi sâu phân tích thuật toán nhận dạng, đánh giá khả năng áp dụng và chứng minh tính tính khả thi của thuật toán bằng cách so sánh phản ứng của hệ thống với mô hình toán nhận dạng được và với mô hình toán mẫu thông qua mô phỏng.
Đỗ Quốc Tuấn; Nguyễn Đình Dũng; Phạm Hữu Uông. Xác định các tham số trong mô hình toán của quadrotor. Tuyển tập công trình HNKH toàn quốc lần thứ 3 về điều khiển & Tự động hoá VCCA - 2015 2016, 1, 1 .
AMA StyleĐỗ Quốc Tuấn, Nguyễn Đình Dũng, Phạm Hữu Uông. Xác định các tham số trong mô hình toán của quadrotor. Tuyển tập công trình HNKH toàn quốc lần thứ 3 về điều khiển & Tự động hoá VCCA - 2015. 2016; 1 (1):1.
Chicago/Turabian StyleĐỗ Quốc Tuấn; Nguyễn Đình Dũng; Phạm Hữu Uông. 2016. "Xác định các tham số trong mô hình toán của quadrotor." Tuyển tập công trình HNKH toàn quốc lần thứ 3 về điều khiển & Tự động hoá VCCA - 2015 1, no. 1: 1.