Jeonghoon Kwak

Unmanned Aerial Vehicle Flight Point Classification Algorithm Based on Symmetric Big Data

Unmanned aerial vehicles (UAVs) with auto-pilot capabilities are often used for surveillance and patrol. Pilots set the flight points on a map in order to navigate to the imaging point where surveillance or patrolling is required. However, there is the limit denoting the information such as absolute altitudes and angles. Therefore, it is required to set the information accurately. This paper hereby proposes a method to construct environmental symmetric big data using an unmanned aerial vehicle (UAV) during flight by designating the imaging and non-imaging points for surveillance and patrols. The K-Means-based algorithm proposed in this paper is then employed to divide the imaging points, which is set by the pilot, into K clusters, and K imaging points are determined using these clusters. Flight data are then used to set the points to which the UAV will fly. In our experiment, flight records were gathered through an UAV in order to monitor a stadium and the imaging and non-imaging points were set using the proposed method and compared with the points determined by a traditional K-Means algorithm. Through the proposed method, the cluster centroids and cumulative distance of its members were reduced by 87.57% more than with the traditional K-Means algorithm. With the traditional K-Means algorithm, imaging points were not created in the five points desired by the pilot, and two incorrect points were obtained. However, with the proposed method, two incorrect imaging points were obtained. Due to these two incorrect imaging points, the two points desired by the pilot were not generated.

Graph-based motor primitive generation framework

Unmanned aerial vehicles (UAVs) have many potential applications, such as delivery, leisure, and surveillance. To enable these applications, making the UAVs fly autonomously is the key issue, and requires defining UAV motor primitives. Diverse attempts have been made to automatically generate motor primitives for UAVs and robots. However, given that UAVs usually do not fly as expected because of external environmental factors, a novel approach for UAVs needs to be designed. This paper proposes a demonstration-based method that generates a graph-based motor primitive. In the experiment, an AR.Drone 2.0 was utilized. By controlling the AR.Drone 2.0, four motor primitives are generated and combined as a graph-based motor primitive. The generated motor primitives can be performed by a planner or a learner, such as a hierarchical task network or Q-learning. By defining the executable conditions of the motor primitives based on measured properties, the movements of the graph-based motor primitive can be chosen depending on changes in the indoor environment.

Beacon Distance Measurement Method in Indoor Ubiquitous Computing Environment

In the indoor ubiquitous computing environment where Global Positioning System (GPS) cannot be utilized, the approach to calculate the locations of Unmanned Aerial Vehicles (UAVs) is the core technique to control multiple UAVs. To calculate the locations of UAVs, the distance between Access Points (APs) and UAVs should be measured accurately given that the location of UAVs is obtained on the basis of the distance between APs and UAVs. In this paper, we propose a method to measure the distance between a single beacon and a single AP in an indoor ubiquitous computing environment. We assume that the beacon is attached to the bottom of a UAV. In the indoor experiment, while transferring a beacon, the distances between the beacon and an AP were measured and tuned. Therefore, the accumulated difference between the real beacon location and the calculated beacon location was reduced by 31.1 %.

Indoor Location-Based Natural User Interface for Ubiquitous Computing Environment

The locations of residents are utilized not only for recognizing the situation of indoor ubiquitous computing environments but also for controlling devices and expressing intention. This paper proposes a novel user interface to utilize the locations of users as control signals in indoor ubiquitous computing environments. The locations of users are estimated by analyzing the distance between a beacon and APs.

Improved Location Estimation Method of Trilateration in Ubiquitous Computing Indoor Environment

Estimating the location of users, Unmanned Aerial Vehicles (UAVs), and devices is the key to provide the diverse kinds of services in ubiquitous computing environments. Given that Global Positioning System (GPS) cannot be utilized in indoor environments, beacon-based and vision-based location estimation approaches are utilized. However, the accuracy of the location does not reaches the demanded amount. This paper propose an indoor location estimation method of beacons based on trilateration. By utilizing three APs and three beacons, the location of one of beacons can be predicted accurately. In the experiment, the proposed method was compared with the traditional trilateration method. Comparing to the result of the traditional trilateration method, the proposed method reduced the distance errors to about 20%.

Behavior Network-Based Risk Recognition Method

This chapter proposes a two-behavior networks-based method to automatically detect whether a situation is risky or not. Behavior network is used to analyze measured values from the sensors of a smart phone. Bayesian probability is also used for implementing such a behavior network. An experiment was conducted to validate that the speed of recognizing risk situations was improved by learning such situations iteratively through behavior networks with Bayesian probability.

Risk Decision Method Based on Sensory Values of Smart Devices

Smartphone sensors can be useful in recognizing risk situations faced by users. However, the use of diverse kinds of sensors for risk estimation increases the complexity of such analyses. This chapter proposes a method to recognize the risk quotient in user situations by measuring and managing the data that smartphone sensors provide.

Structure Design of Surveillance Location-Based UAV Motor Primitives

ABSTRACT Recently, the surveillance system research has focused because Unmanned Aerial Vehicle(UAV) has the ability to monitor wide area. When the wide area are monitored, controlling UAVs repeatedly by pilots invokes the cost problem to operate UAVs. If monitoring path can be defined in advance, the cost problem can be solved by controlling UAVs autonomously based on the monitoring path. The traditional approach generates multiple motor primitives based on flied GPS locations. However, the monitoring points by UAVs are not considered by the generated motor primitives, the surveillance by UAVs is not performed properly. This paper proposes a motor primitive structure for surveillance UAVs to be flied autonomously. Motor primitives are generated automatically by setting surveillance points to denote surveillance targets accurately.Keywords:Unmanned Aerial Vehicle, Ground Control System, Motor Primitive, Global Positioning System, Demonstration-Based Learning 감시 위치 기반의 UAV 모터프리미티브의 구조 설계

Integration Method of Composite Pattern for Solving Structure Problems of Visitor Pattern

This paper proposes a method in which the Visitor pattern is used to apply the Composite pattern for solving the structure problems of the scenario generator, based on Bayesian probability. The traditional Visitor pattern is not ideal for adding new member functions due to its structure problems. By applying the Composite pattern, our approach provides flexibility to interfaces for adding and deleting new member functions.

Integration Method of Proxy and Producer–Consumer Patterns

This chapter proposes a novel design pattern that combines proxy and producer–consumer patterns to solve problems of a scenario generator for verifications of sensors in smart space. In addition, how to apply the novel design pattern to scenario generator is introduced. Proxy pattern hides the complexity of the process of generating scenarios and solves the storage problem of a scenario generator. By applying the proposed Proxy-Producer pattern including multiple threads, the generation time of the huge number of scenarios was reduced.