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"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."

"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."

"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."

"Penelitian ini dilakukan untuk mengevaluasi distribusi dosis radioterapi pada kasus kanker payudara dengan Teknik EDW menggunakan simulasi monte carlo. Menggunakan fantom Rando female sebagai objek untuk mendapatkan nilai CT dengan pendekatan jaringan tubuh manusia. Penelitian dilakukan dengan 2 tahap. Tahap 1 commissioning Monte Carlo pada lapangan 10 × 10 dengan sudut wedge 25. Tahap 2 simulasi Monte Carlo menyesuaikan perencanaan pada TPS untuk kasus kanker payudara pada fantom rando. Evaluasi pada dosis titik dilakukan dengan membandingkan nilai dosis pada simulasi Monte Carlo dengan TPS dan pengukuran TLD. Hasil dari commissioning menunjukkan seluruh nilai profile pada kedalaman 10 cm berada dalam batas toleransi IAEA TRS 430. Hasil perbandingan pada fantom rando dengan pengukuran TPS dan TLD untuk organ Breast atas berturut-turut adalah 2,08% dan 5,45% sedangkan untuk Breast bawah adalah 4,59% dan 5,98%, untuk jantung adalah 12,76% dan 13,68%, dan untuk paru-paru adalah 12,76% dan 13,68%. Berdasarkan hasil tersebut hasil simulasi memiliki akurasi data yang cukup baik jika dibandingkan dengan pengukuran pada TPS dan pengukuran yang dilakukan dengan menggunakan TLD.

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The research was conducted to evaluate the radiation dose distribution in breast cancer cases using the Electron Dynamic Wedge (EDW) technique through Monte Carlo simulations. The research comprised two phases: Phase 1 involved commissioning Monte Carlo for 10 x 10 field with a 25-degree wedge angle, while phase 2 entailed Monte Carlo simulations to adapt planning on the Treatment Planning System (TPS) for breast cancer cases in the Rando phantom. Point dose evaluation involved comparing dose values in Monte Carlo simulations with those in the TPS and Thermoluminescent Dosimeters (TLD) measurements. Commissioning results demonstrated that all profile values at a depth of 10 cm fell within the tolerance limits of IAEA TRS 430. Comparisons on the Rando phantom between TPS and TLD measurements for the upper breast organ yielded percentages of 2.08% and 5.45%, respectively. For the lower breast, the percentages were 4.59% and 5.98%. Comparisons for the heart resulted in percentages of 12.76% and 13.68%, while for the lungs, they were 12.76% and 13.68%. Based on these findings, the simulation results indicated reasonably good accuracy when compared to both TPS measurements and measurements conducted using TLD."

"Secara umum, mobile robot merupakan salah satu tipe platform robot yang memiliki tugas yang kompleks karena robot tersebut akan berada pada lingkungan yang juga bersifat kompleks. Secara khusus, mobile robot harus bisa melakukan lokalisasi agar bisa melakukan tugas-tugas pokok selanjutnya. Oleh karenanya diperlukan sistem lokalisasi yang bisa menyelesaikan permasalahan tersebut. Sistem vision merupakan salah satu jawaban yang paling mungkin untuk menyelesaikan masalah pada platform mobile robot. Beranjak dari hasil penelitian sebelumnya mengenai lokalisasi pada map topologi, maka pada penelitian ini akan dikembangkan sistem lokalisasi berbasis map metric dimana nantinya akan didapatkan pose xr,yr,?r dari mobile robot. Untuk menyelesaikan sistem lokalisasi ini akan digunakan metode pose estimation oleh stereovision untuk mendapatkan pose dalam bentuk translasi x,y,z dan rotasi ?, ?, ? yang akan dimaksimalkan dengan penggunaan FAST sebagai algoritma fitur detection dengan kecepatan tinggi. Akhirnya dengan proses integrasi dengan penelitian sebelumnya akan didapatkan global position yang berguna untuk lokalisasi mobile robot.

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Generally, mobile robot is one of robot that has a complex task because the robot will also work in the complex environment. Particularly, service robot should be able to do localization in order to continue its task. Therefore it will need a localization system that could solve the problem. Vision system is one of the most likely answer to solve the problem in mobile robot platform. Based from the results of previous work on the localization of the topological map, this work will developed localization system for building metrics map which will obtain pose in term of xr, yr, ?r of the mobile robot. In order to complete this localization system, pose estimation method base stereovision will be used to get translational pose x, y, z and rotation pose ?, ?, ? which will be maximized by the use of FAST as the high speed feature detection algorithms. Finally the integration process with prior work will obtain global position that is useful for mobile robot localization.

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"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. "

"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."

"This paper presents application of various Soft-computing control strategies for offline control of one wheel mobile robot (OWMR). The techniques considered for controlling were Fuzzy logic reasoning, Adaptive neuro-fuzzy inference system (ANFIS), and Neural networks (NNs). The study compares the performance of proposed techniques in terms of settling time, maximum overshoot, and steady state error. A Matlab-Simulink model of OWMR system has also been developed. The results obtained from simulation of fuzzy controller were used to train ANFIS and NNs controller. The simulation results showed better performance and learning ability of ANFIS controller compared to other two controllers. The results are shown with the help of graphs and tables to validate the proposed study."

"ABSTRAKElektron biasanya digunakan untuk pengobatan kanker payudara sebagai dosistambahan. Pengukuran dosis yang diterima pasien pada rentang energi 6 MeV, 10MeV dan 12 MeV dari kepala linac, lapangan aplikator 14 x 14 cm2, SSD 95 cmdisimulasikan. Dosis pada paru disimulasikan dengan sistem EGS monte carlo.Distribusi dosis yang dikalkulasi dengan teknik monte carlo berbeda dengan hasilTPS. Hal ini karena adanya koreksi dari densitas jaringan (inhomogenitas)disekitar paru pada simulasi monte carlo sedangkan pada kalkulasi TPS ISIS tidakmemperhitungkan hal tersebut. Dosis 10% di paru hasil kalkulasi simulasi montecarlo diperoleh pada kedalaman 4.22 cm sedangkan pada TPS 2.98 cm untukenergi 6 MeV. Sedangkan untuk 10 MeV dan 12 MeV dosis 10% untuk simulasimonte carlo dan TPS berutur-turut adalah 4.69 cm, 5.72 cm dan 5,79 cm dan 6.95cm.

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ABSTRACTTreatment option by using electron beam is always done after surgery as boosterdoses. Dose measurement in patient lung in energy range 6 MeV, 10 MeV and 12MeV, filed size 14 x 14 cm2 and SSD 95 cm was simulated. The modelings inMonte Carlo simulation are modeling treatment head and water phantom by usingBEAMnrc and DOSXYZnrc based on EGSnrc codes. The result frommeasurement and simulation is diffrent because correction factors ofinhomogenity lung not included in the TPS ISIS. Depth Dose 10% in lung fromcalculation with monte carlo simulation is 4.22 cm and TPS is 2.98 cm withenergy of 6 MeV. For energy of electron 10 MeV and 12 MeV, depth dose 10%from simulation monte carlo and TPS 4.69 cm, 5.72 cm and 5,79 cm, 6.95 cm."