We investigate the optimization problem of transmit power control and MMSE Receiver filter for multi access points environment. Previous work showed that increasing the number of access points decreases the transmit power consumption. Accordingly, transmit power control algorithm was developed in such a way that the transmit power is minimized, while each terminal meets Signal to Interference and Noise Ratio Requirement. In this work, we further reduce the transmit power consumption by optimizing the transmit power level and the MMSE receiver filter together. We showed that the proposed joint optimization algorithm satisfies the necessary and sufficient conditions to be standard interference function, which guarantees convergence and minimum transmit power consumption. We observed that the proposed algorithm outperforms the algorithm which only optimizes the transmit power.

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

Kubernetes is one of the most widely used tool in cloud computing environments. It m anages POD as a basic operational unit, providing a isolated environments through container tech nology. Basically, its scheduler properly allocates POD, considering the utilizations of CPUs, mem ories and volumes on hosts. In our work, we develop a customized scheduling framework additio nally considering network resources. In the framework, we can monitor the dynamic variations of resources and make it possible to utilize the resources for the scheduler. This framework offers not only observing necessary information but also visualizing the data to scheduler for providing convenience.

This paper is concerned with the H₂ attitude controller design for 3 degree of freedom (DOF) Hover systems with an event-triggered mechanism. The 3 DOF Hover system is an embedded platform for unmanned aerial vehicle (UAV) provided by Quanser. The mathematical model of this system is obtained by a linearization around operating points and it is represented as a linear parameter-varying (LPV) model. To save communication network resources, the event-triggered mechanism (ETM) is considered and the performance of the system is guaranteed by the H₂ controller. The stabilization condition is obtained by using Lyapunov-Krasovskii functionals (LKFs) and some useful lemmas. The effectiveness of the proposed method is shown by simulation and experimental results.

Regarding previously-developed drone simulators, it was easy to check their flight stability or controlling functions based on the condition that their weight was fixed from the design. However, the drone is largely classified into two types that is the one with the fixed weight whose purpose is recording video with camera and racing and another is whole weight-variable during flight with loading the articles for delivery and spraying pesticide though the weight of airframe is fixed. The purpose of this thesis is to analyze the structure of drone and its flight principle, suggest dynamics-model-based simulator that is capable of simulating weight-variable drone and develop the simulator that can be used for designing main control board, motor and transmission along the application of weight-variable drone. Weight-variable simulator was developed by using various calculation to apply flying method of drone to the simulator. First, ground coordinate system and airframe-fixing coordinate system were established and switching matrix of those two coordinates were made. Then, dynamics model of drone was established using the law of Newton and moment balance principle. Dynamics model was established in Simulink platform and simulation experiment was carried out by changing the weight of drone. In order to evaluate the validity of developed weight-variable simulator, it was compared to the results of clean flight public simulator against existing weight-fixed drone. Lastly, simulation test was performed with the developed weight-variable simulation by changing the weight of drone. It was found out that dynamics model controlled various flying positions of drone well from simulation and the possibility of securing the optimum condition of weight-variable drone that has flying stability and easiness of controlling.