Precise Characterization and Multiobjective Optimization of Low Noise Amplifiers

Although practically all function blocks of the satellite navigation receivers are realized using the CMOS digital integrated circuits, it is appropriate to create a separate low noise antenna preamplifier based on a low noise pHEMT. Such an RF front end can be strongly optimized to attain a suitable tradeoff between the noise figure and transducer power gain. Further, as all the four principal navigation systems (GPS, GLONASS, Galileo, and COMPASS) work in similar frequency bands (roughly from 1.1 to 1.7 GHz), it is reasonable to create the low noise preamplifier for all of them. In the paper, a sophisticated method of the amplifier design is suggested based on multiobjective optimization. A substantial improvement of a standard optimization method is also outlined to satisfy a uniform coverage of Pareto front. Moreover, for enhancing efficiency of many times repeated solutions of large linear systems during the optimization, a new modification of the Markowitz criterion is suggested compatible with fast modes of the LU factorization. Extraordinary attention was also given to the accuracy of modeling. First, an extraction of pHEMT model parameters was performed including its noise part, and several models were compared. The extraction was carried out by an original identification procedure based on a combination of metaheuristic and direct methods. Second, the equations of the passive elements (including transmission lines and T-splitters) were carefully defined using frequency dispersion of their parameters as Q, ESR, etc. Third, an optimal selection of the operating point and essential passive elements was performed using the improved optimization method. Finally, the s-parameters and noise figure of the amplifier were measured, and stability and third-order intermodulation products were also checked

A short review on the application of computational intelligence and machine learning in the bioenvironmental sciences

This paper aims to provide a short review on the application of computational intelligence (CI) and machine learning (ML) in the bioenvironmental sciences. To clearly illustrate the current status, we limit our focus to some key approaches, namely fuzzy systems (FSs), artificial neural networks (ANNs) and genetic algorithms (GAs) as well as some ML methods. The trends in the application studies are categorized based on the targets of the model such as animal, fish, plant, soil and water. We give an overview of specific topics in the bioenvironmental sciences on the basis of the review papers on model comparisons in the field. The summary of the modelling approaches with respect to their aim and potential application fields can promote the use of CI and ML in the bioenvironmental sciences

A study of JAUS components for personal intelligent Electric Vehicle

研究成果の概要 (和文) : パーソナルEV知能化のためのJAUSコンポーネントの製作とその実装を行った.2010年度から2012年度までの間に行った研究は,主にJAUSを用いたローカルコンポーネントの開発および実験,既存のセンサをJAUSコンポーネントとして扱うことができるJAUSProtocolConverterの開発とJAUSシステムの統合に関する研究および実験を行った。そのJAUS準拠EVコンポーネントの実証実験およびデモンストレーションの場として,(1)米国で開催されたIGVC大会、(2)日本で開催されたつくばチャレンジへの参加出場、(3)第25回国際計量計測展のアカデミックプラザに出展し、JAUSProtocolConverterの試作モデルの展示を行った。研究成果の概要 (英文) : In this research, we develop a JAUS component for intelligent personal electric vehicle. Based on JAUS Reference Architecture AS5669A，AS5684， AS5710，AS6009, we carried out various types of JAUS components are developed during the period from 2010 to 2012. In order to build JAUS component rapidly, we develop general purpose JAUS protocol converter for existing conventional mobile robot sensor units. To evaluate effectiveness of the developed JAUS component, we demonstrate at (1) IGVC (Intelligent ground vehicle competition) 2010, 2011, 2012 in the U.S.A. and (2) Tsukuba Challenge 2010, 2011, 2012 in Japan and (3) Intermeasure 2012 in Japan

パーティクルフィルタによるリアルタイムアプリケーションたち

Open House, ISM in Tachikawa, 2014.6.13統計数理研究所オープンハウス（立川）、H26.6.13ポスター発

H∞ Controller with Graphical LMI Region Profile for Gantry Crane System

This paper presents investigations into the development of H∞ controller with pole clustering based on LMI techniques to control the payload positioning of INTECO 3D crane system with very minimal swing. The linear model of INTECO 3D crane system is obtained using the system identification process. Using LMI approach, the regional pole placement known as LMI region combined with design objective in H∞ controller guarantee a fast input tracking capability, precise payload positioning and very minimal sway motion. A graphical profile of the transient response of crane system with respect to pole placement is very useful in giving more flexibility to the researcher in choosing a specific LMI region. The results of the response with the controllers are presented in time domains. The performances of control schemes are examined in terms of level of input tracking capability, sway angle reduction and time response specification. Finally, the control techniques is discussed and presented

Negative imaginary theorem with an application to robust control of a crane system

This paper presents an integral sliding mode (ISM) control for a case of negative imaginary (NI) systems. A gantry crane system (GCS) is considered in this work. ISM is a nonlinear control method introducing significant properties of precision, robustness, stress-free tuning and implementation. The GCS model considered in this work is derived based on the x direction and sway motion of the payload. The GCS is a negative imaginary (NI) system with a single pole at the origin. ISM consist of two blocks; the inner block made up of a pole placement controller (NI controller), designed using linear matrix inequality for robustness and outer block made up of sliding mode control to reject disturbances. The ISM is designed to control position tracking and anti-swing payload motion. The robustness of the control scheme is tested with an input disturbance of a sine wave signal. The simulation results show the effectiveness of the control scheme

Extraction of Problem Events from Web Documents to Construct Cause-Effect Loop

This research aims to extract problem events, particularly cause-effect concept pair series with explanations by several simple sentences with causative/effect concepts, from web documents of drug addiction. The extracted problem events are used to construct cause-effect loop which benefits for the problem analysis in the solving system. The research has three problems; how to determine the cause/effect event concepts expressed by verb phrases having a problem of the overlap between causative-verb concepts and effect-verb concepts, how to determine the series of cause-effect concept pairs with the causative/effect concept boundary consideration, and how to determine the feedback-loop of cause-effect concept pair series. Therefore, we apply the event rate to solve the overlap problem. We then propose using N-WordCo to determine the cause-effect concept pair series and also use a cue-word set to solve the feedback-loop. The research results provide the high precision of the problem event extraction from the documents