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"The purpose of this handbook is to provide an accessible and comprehensive compendium of Monte Carlo techniques and related topics. It contains a mix of theory (summarized), algorithms (pseudo and actual), and applications. Since the audience is broad, the theory is kept to a minimum, this without sacrificing rigor. The book is intended to be used as an essential guide to Monte Carlo methods to quickly look up ideas, procedures, formulas, pictures, etc., rather than purely a monograph for researchers or a textbook for students. As the popularity of these methods continues to grow, and new methods are developed in rapid succession, the staggering number of related techniques, ideas, concepts and algorithms makes it difficult to maintain an overall picture of the Monte Carlo approach. This book attempts to encapsulate the emerging dynamics of this field of study."

"This book represents the refereed proceedings of the Ninth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Warsaw (Poland) in August 2010. These biennial conferences are major events for Monte Carlo and the premiere event for quasi-Monte Carlo research. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. The reader will be provided with information on latest developments in these very active areas. The book is an excellent reference for theoreticians and practitioners interested in solving high-dimensional computational problems arising, in particular, in finance and statistics."

"Tremendous progress has taken place in the related areas of uniform pseudorandom number generation and quasi-Monte Carlo methods in the last five years. This volume contains recent important work in these two areas, and stresses the interplay between them. Some developments contained here have never before appeared in book form. Includes the discussion of the integrated treatment of pseudorandom numbers and quasi-Monte Carlo methods; the systematic development of the theory of lattice rules and the theory of nets and (t,s)-sequences; the construction of new and better low-discrepancy point sets and sequences; Nonlinear congruential methods; the initiation of a systematic study of methods for pseudorandom vector generation; and shift-register pseudorandom numbers."

"Informasi yang akurat dari respon energi thermoluminesensi detector (TLD) sangat penting untuk memahami ketergantungan TLD terhadap respon energi. Respon energi Thermoluminescent Dosimeter menjadi penting karena selama pengukuran radiasi spektrum tidak persis sama dengan spektrum kalibrasi Penelitian ini bertujuan untuk menentukan persamaan estimasi respon dosis TLD pada spektrum sinar x dan untuk mengetahui respon dosis TLD terhadap perubahan energi dengan menggunakan simulasi Monte Carlo. Simulasi dilakukan dengan meletakkan TLD [LiF:MgTi] di tengah sinar x dengan jarak 100cm dari sumber dengan luas lapangan berukuran 10 cm x 10 cm, dan diradiasi menggunakan spektrum monoenergi dari 10 keV sampai 1500 keV dan spektrum sinar x kontinyu. Spektrum sinar x dihitung menggunakan program XCOMP5r pada RQR 2 sampai RQR 10. Dari simulasi, database respon monoenergi yang hasilnya akan digunakan untuk perhitungan estimasi respon dosis TLD. Ada 4 metode perhitungan untuk estimasi respon dosis TLD, berdasarkan perhitungan energi rata-rata, dua model perhitungan tidak memasukkan koreksi stopping power dan dua model perhitungan terakhir memasukkan koreksi stopping power. Perbandingan hasil simulasi Monte Carlo spektrum kontinyu dengan perhitungan metode no. 4, memberikan hasil yang lebih mendekati.

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An accurate information of thermoluminesensi detector (TLD) energy response is very important to understand TLD energy response dependence. Thermoluminescent Dosimeter energy response becomes important because during the measurement radiation spectrum is not exactly the same as calibration spectrum. This study aims to determine the dose-response estimation equation of TLD in the X ray spectrum and to understand the dose response of TLD due to energy changes using Monte Carlo simulation. Simulation is done by placing the TLD [LiF: MgTi] in the middle of the X ray beam at 100 cm from the source with 10 cm x 10 cm field size, and irradiated using monoenergy spectrum from 10 keV to 1500 keV and continuous X ray spectrum. The X ray spectrum is calculated using XCOMP5r using RQR 2 to RQR 10. From the simulation, monoenergy response database will obtained used for calculation of TLD dose response estimation. There are 4 calculation methods to estimate TLD dose response, which based on mean energy calculation, two calculation model does not include stopping power correction and the last two calculation model include stopping power correction. Comparison of Monte Carlo simulation result of continuous spectrum with calculation method no. 4, is giving the closest result."

"This book has two objectives. First, it is a primer on the kMC method (predominantly using the lattice-gas model) and thus much of the book will also be useful for applications other than to surface reactions. Second, it is intended to teach the reader what can be learned from kMC simulations of surface reaction kinetics. "

"Monte Carlo Localization (MCL) dikenal handal sebagai algoritma selflocalization. Akan tetapi, implementasinya pada tracked mobile robot (TMR) masih jarang. Bisa jadi hal itu karena odometry TMR yang selalu berkonotasi buruk. Selain itu, kinerja MCL pada robot dengan jumlah sensor exteroceptive yang minim masih belum dibuktikan. Lagipula, isu sensitif pada MCL yaitu kebutuhan akan sumber daya komputasi masih menantang untuk dicari solusinya yang mudah diaplikasikan tetapi optimal. Pada riset ini, mula-mula dirumuskan model gerakan dan persepsi probabilistik TMR yang berperan dalam tahap sampling dan weighting pada algoritma MCL. Lebih lanjut, model gerakan probabilistik yang dipakai berjenis Odometry Motion Model dengan pendekatan distribusi Normal (Gausssian), dimana odometry berfungsi sebagai pendeskripsi informasi kontrol. Sementara itu, model persepsi probabilistik dirumuskan dengan asumsi bahwa semua exteroceptive sensor bersifat independent satu sama lain. Lebih spesifik, untuk sonar, ada tiga tipe error yang diperhitungkan secara eksplisit dalam model probabilistiknya, yaitu local noise, failures, dan random measurements. Akhirnya, berhasil dikembangkan suatu varian baru dari MCL yang dinamakan Dynamic-MCL. Karakteristik khasnya adalah adanya variasi jumlah particle yang dilibatkan berdasarkan Spread-factor (S), yaitu parameter yang mengindikasikan persebaran particles. Lebih lanjut, integrasinya dengan algoritma Plain-MCL dilakukan pada tahap resampling. Dengan eksperimen yang ekstensif, dibuktikan bahwa Dynamic-MCL dapat memecahkan tantangan lokalisasi pada TMR, mulai dari local-localization (pose tracking), globallocalization, sampai kidnapped-robot.

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Monte Carlo Localization (MCL) is known as a robust self-localization algorithm. Nonetheless, its implementation on tracked mobile robots (TMRs) is rare. It is likely because odometry of a TMR always seems unworthy (erroneous). Besides, performance of MCL on robots lack of exteroceptive sensors has not been well proven yet. Moreover, a sensitive issue on MCL i.e. the need of computational resources still warrants further investigations looking for handy-yet-optimal solutions.

In this research, a probabilistic motion model of a TMR is developed, as well as its probabilistic perception model. The former has a vital role in the sampling step, while the latter in the weighting step of MCL algorithm. Furthermore, the type of the probabilistic motion model used is Odometry Motion Model fitted by Normal (Gaussian) distribution, at which odometry is employed as a descriptor for control information. Meanwhile, the probabilistic perception model is formulated based on an assumption that is all sensors are mutually independent. Specifically, for sonars, there are three kinds of error that are explicitly reckoned in its probabilistic model, namely local noise, failures and random measurements.

Finally, a new variant of MCL is introduced named Dynamic-MCL. Its unique characteristic is there is a variation on the number of particles involved based on Spread-factor (S) i.e. a parameter indicating the spread of particles. Furthermore, its integration to the Plain-MCL algorithm is carried out in the resampling step. Based on extensive experiments, it is explicable to claim that Dynamic-MCL is capable to solve localization challenges on TMRs including local-localization (pose tracking), global-localization and kidnapped-robot."