Ilkyu Ha

Drones are widely used in urban air pollution monitoring. Although studies have focused on single-drone applications, collaborative applications for air pollution detection are relatively underexplored. This paper presents a 3D cube-based adaptive cooperative search algorithm that allows two drones to collaborate to explore air pollution. The search space is divided into cubic regions, and each drone explores the upper or lower halves of the cubes and collects data from their vertices. The vertex with the highest measurement is selected by comparing the collected data, and an adjacent cube-shaped search area is generated for exploration. The search continues iteratively until any vertex measurement reaches a predefined threshold. An improved algorithm is also proposed to address the divergence and oscillation that occur during the search. In simulations, the proposed method consumed 21 times less CPU time and required 23 times less search distance compared to linear search. Additionally, the cooperative search method using multiple drones was more efficient than single-drone exploration in terms of the same parameters. Specifically, compared to single-drone exploration, the collaborative drone search reduced CPU time by a factor of 2.6 and search distance by approximately a factor of 2. In experiments in real-world scenarios, multiple drones equipped with the proposed algorithm successfully detected cubes containing air pollution above the threshold level. The findings serve as an important reference for research on drone-assisted target exploration, including air pollution detection.

Drones are widely used in urban air pollution monitoring. Although studies have focused on single-drone applications, collaborative applications for air pollution detection are relatively underexplored. This paper presents a 3D cube-based adaptive cooperative search algorithm that allows two drones to collaborate to explore air pollution. The search space is divided into cubic regions, and each drone explores the upper or lower halves of the cubes and collects data from their vertices. The vertex with the highest measurement is selected by comparing the collected data, and an adjacent cube-shaped search area is generated for exploration. The search continues iteratively until any vertex measurement reaches a predefined threshold. An improved algorithm is also proposed to address the divergence and oscillation that occur during the search. In simulations, the proposed method outperformed linear search in terms of CPU time and search distance. Additionally, the cooperative search method using multiple drones was more efficient than was single-drone exploration in terms of the same parameters. In experiments in real-world scenarios, multiple drones equipped with the proposed algorithm successfully detected cubes containing air pollution above the threshold level. The findings serve as an important reference for research on drone-assisted target exploration, including air pollution detection.

Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods.

Smoking is an extremely important health problem in modern society. This study focuses on a method for preventing smoking in non-smoking areas, such as public places, as well as the development of an artificial neural network based smoking motion recognition system for more accurately recognizing smokers in such areas. In particular, we attempted to increase the rate of recognition of smoking behaviors using an OpenPose based algorithm and the accuracy of such recognition by additionally applying a hardware device for recognizing cigarette smoke. In addition, a preprocessing method for inputting a dataset into the proposed system is proposed. To improve the recognition performance, four types of dataset models were created, and the most suitable dataset model was selected experimentally. Based on this dataset model, test data were created and input into the proposed neural network based smoking behavior recognition system. In addition, the nearest neighbor interpolation method was selected experimentally as an image interpolation approach and applied to the image preprocessing. When applying experimental data based on learned data, the developed system showed a recognition rate of 70-75%, and the smoking recognition accuracy was increased through the addition of the hardware device.

When searching for targets using unmanned aerial vehicles, speed is important for many applications such as the discovery of patients in a medical emergency. The speed of operation of actual unmanned aerial vehicles is strongly related to the performance of the camera sensor used for target recognition, search altitude, and the search algorithm employed by the unmanned aerial vehicle. In this study, the major factors affecting the speed of a probabilistic unmanned aerial vehicle target search are analyzed. In particular, simulations are performed to analyze the influence of the search altitude, sensor false alarm rate, and sensor missed detection rate on the required travel distance and the time required for a search. Furthermore, the search performance of an unmanned aerial vehicle is analyzed by varying the search altitude with fixed false alarm and missed detection probabilities. The simulation results show that the search performance is significantly affected by changes in the false alarm and missed detection probabilities of the sensor, and it confirms that the effect of the missed detection probability is greater than that of the false alarm probability. The second simulation proves that the altitude of an unmanned aerial vehicle is a very important factor for the speed of a target search. In particular, the result shows that, for a real data set, the search distance and time at 10 and 5 m are about 2.8 times and 14.3 times larger, respectively, than those at 20 m.

Finding a target quickly is one of the most important tasks in drone operations. In particular, rapid target detection is a critical issue for tasks such as finding rescue victims during the golden period, environmental monitoring, locating military facilities, and monitoring natural disasters. Therefore, in this study, an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes is proposed. This is a method for reducing the search time and search distance by improving the information transfer methods between high-altitude and low-altitude drones. Specifically, to improve the speed of target detection, a high-altitude drone first performs a search of a wide area. Then, when the probability of existence of the target is higher than a certain threshold, the search information is transmitted to a low-altitude drone which then performs a more detailed search in the identified area. This method takes full advantage of fast searching capabilities at high altitudes. In other words, it reduces the total time and travel distance required for searching by quickly searching a wide search area. Several drone collaboration scenarios that can be performed by two drones at different altitudes are described and compared to the proposed algorithm. Through simulations, the performances of the proposed algorithm and the cooperation scenarios are analyzed. It is demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm is approximately 13% more effective than a previous method and much better compared to other scenarios.