Magnetic suspension turbine flow meter

Measurement of liquid flow in certain area such as industrial plant is in critical. Inaccurate measurement can cause serious result. Most of the liquid flow are using Bernoulli principle‘s but in turbine flow meter the flow rate is determine differently by using kinetic energy. Turbine flow meter is one of flow rate transducer that widely used in metallurgical, petroleum, chemical and other industrial and agricultural areas, as shown in Figure 1.1. It is present as high precision of flow meter and when fluid flow troughs it the impeller that faces the fluid will rotate due to flow force exist. The rotation speed is directly proportional to the speed of fluid. During the process, the working states of impeller and bearing are very complicated due the interactive effects from the fluid axial thrust, impeller rotating, and static and dynamic components. In current turbine flow meter design, the common material use for meter bulk body is 1Cr18Ni9Ti, while for the blade 2Gr13 are used. Axis and bearing are made from stainless steel or carbide alloy. The space between the axis and bearing determines it minimum flow rate and life span, and also determines its measurement range (1:10~1:15 - maximum flow rate to minimum flow rate). Since the turbine has movable parts it can produce friction between the axis and ring during the operation. This will cause accuracy of the measurement decrease and can damage the impeller blade. In this research, the friction can be reduced by adopting the principle of magnetic suspension. Rotating shaft will levitate in the magnetic field due to the forces. Friction coefficient reduced because of rotating shaft rotates without abrasion and mechanical contact in space

Optimal Rotor Structure Design of Interior Permanent Magnet Synchronous Machine based on Efficient Genetic Algorithm Using Kriging Model

Abstract -In the recent past, genetic algorithm (GA) and evolutionary optimization scheme have become increasingly popular for the design of electromagnetic (EM) devices. However, the conventional GA suffers from computational drawback and parameter dependency when applied to a computationally expensive problem, such as practical EM optimization design. To overcome these issues, a hybrid optimization scheme using GA in conjunction with Kriging is proposed. The algorithm is validated by using two mathematical problems and by optimizing rotor structure of interior permanent magnet synchronous machine

Decision Making for Rapid Information Acquisition in the Reconnaissance of Random Fields

Research into several aspects of robot-enabled reconnaissance of random fields is reported. The work has two major components: the underlying theory of information acquisition in the exploration of unknown fields and the results of experiments on how humans use sensor-equipped robots to perform a simulated reconnaissance exercise. The theoretical framework reported herein extends work on robotic exploration that has been reported by ourselves and others. Several new figures of merit for evaluating exploration strategies are proposed and compared. Using concepts from differential topology and information theory, we develop the theoretical foundation of search strategies aimed at rapid discovery of topological features (locations of critical points and critical level sets) of a priori unknown differentiable random fields. The theory enables study of efficient reconnaissance strategies in which the tradeoff between speed and accuracy can be understood. The proposed approach to rapid discovery of topological features has led in a natural way to to the creation of parsimonious reconnaissance routines that do not rely on any prior knowledge of the environment. The design of topology-guided search protocols uses a mathematical framework that quantifies the relationship between what is discovered and what remains to be discovered. The quantification rests on an information theory inspired model whose properties allow us to treat search as a problem in optimal information acquisition. A central theme in this approach is that "conservative" and "aggressive" search strategies can be precisely defined, and search decisions regarding "exploration" vs. "exploitation" choices are informed by the rate at which the information metric is changing.Comment: 34 pages, 20 figure

Safe navigation and motion coordination control strategies for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

오버행 효과를 고려하는 영구자석 전동기의 특성 해석 및 최적 설계

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 정현교.본 논문에서는 오버행 구조가 방사자속 영구자석(radial flux permanent magnet : RFPM) 전동기와 축자속 영구자석(axial flux permanent magnet : AFPM) 전동기의 전자기적 특성에 미치는 영향을 살펴보았다. 아울러, 오버행 구조를 고려하는 기존의 연구들을 검토하고 문제점들을 분석하였으며, 이를 개선하기 위한 방법 및 오버행 구조를 갖는 영구자석 전동기의 최적화 문제에 적합한 새로운 알고리즘을 제안하였다. 먼저, 2차원(two dimensional : 2D) 유한요소법(finite element method : FEM)에서 오버행 구조를 갖는 RFPM 전동기를 해석하기 위한 기존 연구들의 문제점이 제시되었으며, 이를 개선하기 위한 오버행 파라미터(overhang parameter)가 제안되었다. 아울러, 초기 설계 단계에서 오버행 길이를 선정하기 위한 등가 자기모델을 제안하였으며, 제시된 방법으로부터의 결과는 3차원(three dimensional : 3D) FEM 결과와 비교함으로써 해석 기법의 타당성을 확인 하였다. 또한, 오버행 구조를 갖는 표면 부착형 영구자석(surface-mounted permanent magnet : SPM) 전동기에 약자속 제어가 적용될 때, 영구자석의 불가역 감자(irreversible demagnetization)가 발생할 수 있는 문제점을 제시하고, 이를 극복할 수 있는 새로운 오버행 구조가 제안되었다. 한편, AFPM 전동기의 설계 시 사용되어 왔던 quasi-3D FEM이 영구자석에 비해 고정자와 회전자가 돌출(overhang)될 때 적용될 수 없는 문제점을 제시하고, 이를 극복할 수 있는 개선된 quasi-3D FEM을 제안하였다. 또한, RFPM 전동기에 적용되어 왔던 오버행 구조가 AFPM 전동기에 적용될 때 전자기적 특성에 미치는 영향에 대해 살펴 보고 최적의 성능을 발휘할 수 있는 구조가 제시되었다. 마지막으로 여러 개의 국소 최적해를 갖는 복잡한 목적함수와 계산시간이 오래 걸리는 오버행 구조를 갖는 영구자석 전동기의 최적 설계 문제를 효과적으로 해결할 수 있는 multimodal 최적화 알고리즘이 제안되었다. 본 논문을 통해 제안된 기법의 검증을 위하여 2대의 시험용 전동기를 설계, 제작 및 실험하였다. 다양한 실험 결과로부터 본 연구에서 제시된 방법들의 타당성이 검증 되었으며, 향후 오버행 구조를 갖는 영구자석 전동기의 설계 및 해석에 많은 도움을 주고자 한다.국문초록 i 목 차 iii 그림 목차 v 표 목차 viii 기호 설명 ix 약어 설명 xi 제 1 장 서 론 1.1 연구 배경 1.2 논문 구성 제 2 장 영구자석 전동기 2.10 SPM 전동기와 IPM 전동기 2.20 RFPM 전동기와 AFPM 전동기 제 3 장 RFPM 전동기의 오버행 효과 3.1 등가 자기회로법을 이용한 해석 3.1.1 기존의 등가 자기회로법 3.1.2 개선된 등가 자기회로법 3.20 FEM을 이용한 해석 3.3 오버행 효과를 고려한 설계 제 4 장 AFPM 전동기의 오버행 효과 4.10 Quasi-3D FEM 4.2 개선된 quasi-3D FEM 4.30 AFPM 전동기의 오버행 효과 제 5 장 영구자석 전동기의 최적 설계 5.1 기존의 최적화 기법 5.20 Climb method 5.3 다변수 최적화 문제에서의 Climb method 5.4 시범 함수를 이용한 제안된 방법의 검증 5.50 AFPM 전동기의 최적 설계 5.5.10 AFPM 전동기의 코깅토크 최적화 5.5.20 AFPM 전동기의 오버행 길이 최적화 제 6 장 시험용 전동기의 설계, 제작 및 실험 6.1 시험용 RFPM 전동기 6.1.1 해석결과와 실험결과의 비교 6.2 시험용 AFPM 전동기 6.2.1 해석결과와 실험결과의 비교 제 7 장 결론 및 향후 연구 7.1 결론 7.2 향후 연구 계획 참고 문헌 AbstractDocto

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data