• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.8
• /
• pp.1080-1088
• /
• 2018

General methods of controlling a drone are divided into manual control and automatic control, which means a drone moves along the route. In case of manual control, a man should be able to figure out the location and status of a drone and have a controller to control it remotely. When people control a drone, they collect information about the location and position of a drone with the eyes and have its internal information such as the battery voltage and atmospheric pressure delivered through telemetry. They make a decision about the movement of a drone based on the gathered information and control it with a radio device. The automatic control method of a drone finding its route itself is not much different from manual control by man. The information about the position of a drone is collected with the gyro and accelerator sensor, and the internal information is delivered to the CPU digitally. The location information of a drone is collected with GPS, atmospheric pressure sensors, camera sensors, and ultrasound sensors. This paper presents an investigation into drone control by a remote computer. Instead of using the automatic control function of a drone, this approach involves a computer observing a drone, determining its movement based on the observation results, and controlling it with a radio device. The computer with a Depth camera collects information, makes a decision, and controls a drone in a similar way to human beings, which makes it applicable to various fields. Its usability is enhanced further since it can control common commercial drones instead of specially manufactured drones for swarm flight. It can also be used to prevent drones clashing each other, control access to a drone, and control drones with no permit.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.248-268
• /
• 2018

The performance of smartphone applications are usually constrained in user interactions due to resource limitation and it promises great opportunities to improve the performance by exploring the smartphone built-in and embedded sensing techniques. However, heterogeneity in techniques, semantic gap between sensor data and usable context, and complexity of contextual situations keep the techniques from seamless integration. Relevant studies mainly focus on feasibility demonstration of emerging sensing techniques, which rarely address both general architectures and comprehensive technical solutions. Based on a proposed functional model, this investigation provides a general architecture to deal with the dynamic context for context-aware automation and decision support. In order to take advantage of the built-in sensors to improve the performance of mobile applications, an ontology-based method is employed for context modelling, linguistic variables are used for heterogeneous context presentation, and semantic distance-based rule matching is employed to customise functions to the contextual situations. A case study on mobile application authentication is conducted with smartphone built-in hardware modules. The results demonstrate the feasibility of the proposed solutions and their effectiveness in improving operational efficiency.

• Journal of Korea Multimedia Society
• /
• v.20 no.6
• /
• pp.927-934
• /
• 2017

As the technology of drone develops, the use of drone is increasing, In addition, the types of sensors that are inside of smart phones are becoming various and the accuracy is enhancing day by day. Various of researches are being progressed. Therefore, we need to control drone by using smart phone's sensors. In this paper, we propose the most suitable machine learning model that matches the gyro sensor data with drone's moving. First, we classified drone by it's moving of the gyro sensor value of 4 and 8 degree of freedom. After that, we made it to study machine learning. For the method of machine learning, we applied the One-Rule, Neural Network, Decision Tree, and Navie Bayesian. According to the result of experiment that we designated the value from gyro sensor as the attribute, we had the 97.3 percent of highest accuracy that came out from Naive Bayesian method using 2 attributes in 4 degree of freedom. On and the same, in 8 degree of freedom, Naive Bayesian method using 2 attributes showed the highest accuracy of 93.1 percent.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.12
• /
• pp.1159-1166
• /
• 2010

Today, detecting the location of moving object has been traced as various methods in our world. In this paper, we preset the system to improve the estimation accuracy utilizing detail localization using radar sensor based on WSN and situational awareness for a calibration (context aware) database, Rail concept. A variety of existing location tracking method has a problem with receiving of data and accuracy as tracking methodology, and since these located data are the only data to be collected for location tracing, the context aware or monitering as the surrounding environment is limited. So, in this paper, we enhanced the distance aware accuracy using radar sensor utilizing the Doppler effect among the distance measuring method, estimated the location using the Triangulation algorithm. Also, since we composed the environment data(temperature, illuminancem, humidity, noise) to entry of the database, it can be utilized in location-based service according to the later action information inference and positive context decision. In order to verify the validity of the suggested method, we give a few random situation and built test bed of designed node, and over the various test we proved the utilizing the context information through route tracking of moving and data processing.

• Proceedings of the KIEE Conference
• /
• 1999.07a
• /
• pp.64-66
• /
• 1999

Switched reluctance motor(SRM) has the advantages of simple structure, low rotor inertia and high efficiency. However, position sensor is essential in SRM in order to synchronize the phase excitation to the rotor position. The position sensors increase the cost of drive system and tend to reduce system reliability. This paper investigates the speed control of sensorless SRM in which the phase current and change rate are utilized in position decision, and the period of dwell angle is variable by compensating the rotor angle. The proposed system consists of position decision, phase locked loop controller, switching angle controller and inverter. The performances in the proposed system are verified through experiments.

• Journal of Korea Multimedia Society
• /
• v.15 no.2
• /
• pp.195-203
• /
• 2012

This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.11
• /
• pp.1095-1101
• /
• 2006

The clean tube system was developed as a means of transferring air-floated wafers inside a closed tube filled with super clean air. This paper presents a wafer position sensing method in the clean tube system, where the photo proximity sensors are used. The first presented method uses the two positions sensed lately in order to compute the wafer center position. The next method uses the latest sensed position and the next latest position compensated with the information of the wafer velocity. The third method uses the kalman filter, which enable us to use all the previous sensing information. The simulation results are compared to show results of the presented method. In addition, the paper presents a control method to stop the wafer at the center of the unit in the clean tube system. The experimental clean tube system worked successfully with the applying the both presented methods of sensing and control.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.9
• /
• pp.1575-1583
• /
• 2008

Wireless sensor network has merit of low-power, low-cost and self-organization network, so there are many researches substituted for existing wire network. As structure reinforcement work need a high accuracy, many sensors are installed in structure and connected with data logger for monitoring. However this wire data logger method takes a long time to install wires and installed wires obstruct to work. Additionally, wire data logger method represent sensor data by only numeric and graph, it is not able to support a rapid decision-making for working. To resolve wiring problem and support decision-making, we designed and implemented the monitoring system based in wireless sensor network. For verifying performance, accuracy and availability, we simulated and tested our system in real field. Consequently, wireless sensor network method is easier to install and deploy than wire data logger method, user is able to monitor instinctively and overall by 3D representation of structure and sensors, and it show not only accuracy but also performance for many sensors.

• Nuclear Engineering and Technology
• /
• v.53 no.8
• /
• pp.2534-2546
• /
• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

• The Journal of Korea Robotics Society
• /
• v.3 no.3
• /
• pp.186-193
• /
• 2008

We propose a novel real-time obstacle avoidance method for rescue robots. This method, named the ELA(Emergency Level Around), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward safe position. In the ELA, we consider two sensor modules, PSD(Position Sensitive Detector) infrared sensors taking charge of obstacle detection in short distance and LMS(Laser Measurement System) in long distance respectively. Hence if a robot recognizes an obstacle ahead by PSD infrared sensors first, and judges impossibility to overcome the obstacle based on driving mode decision process, the order of priority is transferred to LMS which collects data of radial distance centered on the robot to avoid the confronted obstacle. After gathering radial information, the ELA algorithm estimates emergency level around a robot and generates a polar histogram based on the emergency level to judge where the optimal free space is. Finally, steering angle is determined to guarantee rotation to randomly direction as well as robot width for safe avoidance. Simulation results from wandering in closed local area which includes various obstacles and different conditions demonstrate the power of the ELA.