• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.30-38
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• 2021

This research explored methods for improvements to be made within the field of drone usage within the construction industry based on an investigation of factors which limit their efficiency and productivity. Limiting factors and improvement measures were presented in terms of technology, service, law and policy for employing drones at construction sites. Our first suggestion is, from a technical point of view, that companies need to expand professional manpower and infrastructure for systematic management. Second, in terms of service expansion, it is necessary to have management capabilities for operation such as the use of drones with enhanced safety and reinforced on-site education and personal information management. Third, in terms of legal and institutional support measures, it is necessary to prepare a plan for reforming the legal system for revitalization and to expand the training of professional manpower. This study may contribute not only to the development of drone technology, but also to effectively respond to various problems that appear at construction sites.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.167-172
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• 2017

Mobile nodes are used for prolonging the life-time of the entire wireless sensor networks and many studies that use drones to collected data have been actively conducted with the development of drone related technology. In case of associating a drone and tactical wireless sensor networks, real-time feature and efficiency are improved. The previous studies so focus on reducing drone's flight distance that the energy consumption of sensor nodes is unbalanced. This unbalanced energy consumption accelerates the network partition and increases drone's flight distance. In this paper, we proposed a new selection scheme considered drone's flight distance and nodes' life-time to solve this problem when rendezvous nodes that collect data from their cluster and directly communicate with a drone are selected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.38-44
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• 2017

Currently, wired gables have been installed or portable storage devices have been installed for data acquisition of flying drone. In this paper, we propose a technology to transmit data wirelessly by sensing information such as battery discharge value, acceleration, and temperature by attaching RF sensor to a drone. The purpose of this paper is to design and develop the monitoring technology of agriculture drone battery usage in real time using RF sensor. In this paper, we propose a monitoring system that can check real time data of battery changed value, temperature, and acceleration during pesticide control activity of agricultural drone.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.67-73
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• 2021

In this paper, in order to prevent the fall of the drone, a study was conducted to collect vibration data from the motor connected to the propeller of the drone, and to predict the abnormal vibration of the drone using recurrent neural network (RNN) and long short term memory (LSTM). In order to collect the vibration data of the drone, a vibration sensor is attached to the motor connected to the propeller of the drone to collect vibration data on normal, bar damage, rotor damage, and shaft deflection, and abnormal vibration data are collected through LSTM and RNN. The root mean square error (RMSE) value of the vibration prediction result were compared and analyzed. As a result of the comparative simulation, it was confirmed that both the predicted result through RNN and LSTM predicted the abnormal vibration pattern very accurately. However, the vibration predicted by the LSTM was found to be 15.4% lower on average than the vibration predicted by the RNN.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.60-61
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• 2019

Drones are opening new horizon as a major Internet-of-Things (IoT) player which is a network of objects. Drone needs to charge itself during providing services from the charging stations. If there are lots of drones and one charging station, then it is a critical situation to decide which drone should get charged first and make order of priorities for drones to get charged sequentially. In this paper, we propose an efficient scheduling algorithm for drone power charging (ESADPC), in which charging station would have a scheduler to decide which drone can get charged earlier among many other drones. Simulation results have showed that our algorithm reduces the deadline miss ration and turnaround time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.173-178
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• 2016

Many studies have been carried out on monitoring the target by cooperating a drone with remote sensors recently. This monitoring system uses static sensors to measure environmental data and drones to collect measured data. In geotechnical engineering, inspectors go around measuring the safety of construction site and it is impractical to compose a network among numerous sensors in terms of the cost efficiency. In this paper, we propose a data collection system based on interaction between a drone and a few sensors that are installed around the target structure for geotechnical projects. Through experimental results, we also verify the availability and the time and cost efficiency of the proposed system comparing with using inspectors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.103-111
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• 2021

The purpose of this study is to study a method for preventing collisions of multiple drones in controlled airspace. As a result of the study, it was proved that it is appropriate as a method to control drone collisions after setting accurate information on the ROI (Region of Interest) area estimated based on the expected drone path and time in the control system as a method to avoid drone collision. As a result of the empirical analysis, the diameter of the flight path of the operating drone should be selected to reduce the risk of collision, and the change in the departure time and operating speed of the operating drone did not act as an influencing factor in the collision. In addition, it has been demonstrated that providing flight priority is one of the appropriate methods as a countermeasure to avoid collisions. For collision avoidance methods, not only drone sensor-based collision avoidance, but also collision avoidance can be doubled by monitoring and predicting collisions in the control system and performing real-time control. This study is meaningful in that it provided an idea for a method for preventing collisions of multiple drones in controlled airspace and conducted practical tests. This helps to solve the problem of collisions that occur when multiple drones of different types are operating based on the control system. This study will contribute to the development of related industries by preventing accidents caused by drone collisions and providing a safe drone operation environment.