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"This book provides a concise introduction to the behavior of mechanical structures and testing their stochastic stability under the influence of noise. It explains the physical effects of noise and in particular the concept of Gaussian white noise. In closing, the book explains how to model the effects of noise on mechanical structures, and how to nullify/compensate for it by designing effective controllers."

"This volume sharpens our picture of the applications of conformal invariance, introducing non-local observables such as loops and interfaces before explaining how they arise in specific physical contexts. It then shows how to use conformal invariance to determine their properties. Moving on to cover key conceptual developments in conformal invariance, the book devotes much of its space to stochastic Loewner evolution (SLE), detailing SLE’s conceptual foundations as well as extensive numerical tests. The chapters then elucidate SLE’s use in geometric phase transitions such as percolation or polymer systems, paying particular attention to surface effects. "

"This book treats methods inspired by attempts to understand the seemingly non-mathematical question of bacterial chemotaxis and their application in other environments. The text presents significant generalizations on existing stochastic averaging theory developed from scratch and necessitated by the need to avoid violation of previous theoretical assumptions by algorithms which are otherwise effective in treating these systems. Coverage is given to four main topics. Stochastic averaging theorems are developed for the analysis of continuous-time nonlinear systems with random forcing, removing prior restrictions on nonlinearity growth and on the finiteness of the time interval. The new stochastic averaging theorems are usable not only as approximation tools but also for providing stability guarantees. Stochastic extremum-seeking algorithms are introduced for optimization of systems without available models. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms for non-cooperative/adversarial games is described. The analysis of their convergence to Nash equilibria is provided. The algorithms are illustrated on models of economic competition and on problems of the deployment of teams of robotic vehicles. Bacterial locomotion, such as chemotaxis in E. coli, is explored with the aim of identifying two simple feedback laws for climbing nutrient gradients. Stochastic extremum seeking is shown to be a biologically-plausible interpretation for chemotaxis. For the same chemotaxis-inspired stochastic feedback laws, the book also provides a detailed analysis of convergence for models of nonholonomic robotic vehicles operating in GPS-denied environments. The book contains block diagrams and several simulation examples, including examples arising from bacterial locomotion, multi-agent robotic systems, and economic market models. Stochastic averaging and extremum seeking will be informative for control engineers from backgrounds in electrical, mechanical, chemical and aerospace engineering and to applied mathematicians. Economics researchers, biologists, biophysicists and roboticists will find the applications examples instructive."

"Penelitian tentang model persediaan telah banyak dilakukan dan menjadi solusi bagi perusahaan dalam menghadapi ketidakpastiaan dari permintaan pelanggan. Beberapa permasalahan bagi perusahaan yang tidak menerapkan model persediaan pada proses pembelian bahan baku adalah terjadinya stockout serta tingginya total biaya inventori. Namun, pemecahan model persediaan yang memiliki permintaan bersifat stokastik tidak dapat dilakukan dengan menggunakan metode analisis tradisional biasa. Hal tersebut dikarenakan permintaan pelanggan tidak mengikuti distribusi normal. Sehingga penelitian ini dibuat dengan mengembangkan metode simulasi Monte Carlo untuk menyelesaikan masalah persediaan dengan permintaan stokastik dan intermiten. Simulasi dilakukan untuk mengevaluasi dua jenis sistem persediaan, yaitu kebijakan peninjauan persediaan berkelanjutan (Q,R), serta kebijakan peninjauan persediaan berkala (s,S). Model simulasi kemudian dioptimalkan menggunakan model persediaan (Q,R) dan (s,S) dengan metode integer linear programming. Model-model tersebut diterapkan berdasarkan data dari salah satu perusahaan manufaktur perhiasan di Indonesia dengan produk yang diuji bertipe diamond jewelry. Hasil penelitian menunjukkan bahwa kebijakan kuantitas peninjauan persediaan berkelanjutan (Q, R) lebih baik daripada kebijakan peninjauan persediaan berkala (s, S) dari segi total biaya persediaan untuk kelompok bahan baku komponen.

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Research on the inventory models have conducted intensively and become a solution for companies when dealing with the uncertainty of customer demand. Some problems that occur to companies that have not applied the inventory models in the process of purchasing raw materials are stockout material and high total inventory costs. However, solving the inventory models that have stochastic demand cannot be done using traditional analysis methods. That is because the customer demand does not follow the normal distribution. So, this research was made by developing a Monte Carlo simulation method to solve inventory problems with stochastic and intermittent demand. Simulations is conducted to evaluate two types of inventory systems, which is the continuous inventory review policy (Q, R), and the periodic inventory review policy (s, S). The simulation models are optimized using the inventory model (Q, R) and (s, S) with the integer linear programming method. The models are applied based on data from one of the jewelry manufacturing companies in Indonesia with the product being tested as diamond jewelry. The results showed that the continuous inventory review policy (Q, R) was better than the periodic inventory review policy (s, S) in terms of total inventory cost for the component raw material group."

"Material cerdas paduan ingat bentuk merupakan material yang memiliki kemampuan kembali ke bentuk semula setelah deformasi dengan perlakuan panas. Paduan ingat bentuk Cu-Zn-Al adalah salah satu paduan ingat bentuk dengan harga yang lebih murah dan mudah difabrikasi dibanding paduan ingat bentuk yang umum digunakan sepeti Ni-Ti. Pada Cu-Zn-Al sebagai paduan ingat bentuk memiliki kelemahan seperti stabilisa fasa martensit yang dapat dihindari dengan perlakuan panas dan metode pencelupan. Pada penelitian ini dipelajari pengaruh metode pencelupan terhadap sifat ingat bentuk pada paduan Cu-19,55Zn-7,04Al wt.% yang difabrikasi dengan proses pengecoran gravitasi. Hasil pengecoran gravitasi selanjutnya dihomogenisasi pada temperatur 850 oC selama 2 jam lalu didinginkan pada temperatur ruang. Setelah itu, paduan diberi perlakuan panas betatizing pada temperatur 850 oC selama 30 menit diikuti tiga metode pencelupan berbeda yaitu pencelupan langsung (direct quenching / DQ) ke dalam media air ditambah es kering, pencelupan naik (up quenching / UQ) ke dalam media air ditambah es kering selama 30 menit lalu dicelupkan lagi ke air mendidih 100 ℃ selama 30 menit, dan terakhir pencelupan bertahap (step quenching / SQ) dimana sampel dicelupkan pada air temperatur 100 ℃ selama 30 menit lalu diikuti pencelupan ke air ditambah es kering selama 30 menit. Karakterisasi paduan dilakukan menggunakan OES untuk uji komposisi, mikroskop optik dan SEM-EDS untuk mengamati struktur mikro, XRD untuk mengetahui struktur kristal, DSC untuk menganalisis transformasi fasa, Microvickers untuk pengujian keras dan uji pemuilahan regangan ingat bentuk menggunakan metode bending. Paduan as-cast dan as-homgenized memiliki struktur mikro yang didominasi oleh fasa β sebagai matriks dan fasa kedua seperti α yang berbentuk lath dan γ yang berbentuk seperti presipitat hitamdengan rasio fraksi fasa β:(α+ γ) sebesar 92:8. Untuk sampel hasil perlakuan panas, struktur mikro pencelupan DQ terdiri atas fasa martensit β’ yang berbentuk needle-like dan twin V. Struktur mikro pencelupan UQ memiliki struktur mikro martensit β’ berbentuk needle-like dan twin V dengan sedikit fasa kedua seperti fasa α dan γ. Struktur mikro pencelupan SQ memiliki martensit β’ berbentuk needle-like dan twin V dan fasa β. Kekerasan paduan untuk pencelupan langsung sebesar 155,61 HVN, pencelupan naik sebesar 179,76 HVN dan pencelupan bertahap sebesar 93,74 HVN. Pemulihan regangan untuk pencelupan langsung sebesar 72,05%, pencelupan naik sebesar 74,15% dan pencelupan bertahap sebesar 81,95%.

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The smart material shape memory alloy is a material that could revert to its starting form after deformation with heat treatment. Shape memory alloy Cu-Zn-Al is a cheaper and more easily fabricated shape memory alloy than the commonly used ones like Ni-Ti. The Cu-Zn-Al alloy as a shape memory alloy has a weakness in the form of phase stability, which could be avoided by heat treatment and quenching methods. This research studies the effect of the quenching method on the shape-memory properties in the Cu-19.55Zn-7.04Al alloy fabricated via gravity casting. The result of gravity casting was then homogenized at 850°C for 2 hours then cooled down at room temperature. Afterwards, the alloy was heat treated via betatization at 850°C for 30 minutes, followed by three different quenching methods of direct quenching (DQ) into a water medium with dry ice, up quenching (UQ) into a water medium with dry ice for 30 minutes before being quenched again into 100°C boiling water for 30 minutes, and step quenching (SQ) where the sample was quenched into 100°C water for 30 minutes then quenched into a water medium with dry ice for 30 minutes. Characterization of the alloy was conducted with OES to observe the composition, optical microscope and SEM-EDS to observe the microstructure, XRD to know the crystal structure, DSC to analyze phase transformation, Micro-Vickers to know the hardness, and shape-memory strain recovery testing using bending method. The microstructure of as-cast and as-homogenized is consist of β and another phase like α and γ with β:(α+ γ) phase fraction ratio of 92:8. After the heat treatment process, the microstructure of DQ showed needle-like and v-shaped structure that belongs to β’ martensite phase. Meanwhile, the microstructure of UQ showed needle-like v-shaped structure that belongs to β’ martensite phase and few phase like α and γ. , the microstructure of UQ showed needl-like and v-shaped β’ martensite phase and β phase. Alloy hardness for direct quenching was 155.61 HVN, up quenching was 179.76 HVN, and step quenching was 93.74 HVN. Strain recovery for direct quenching was 72.05%, up quenching was 74.15%, and step quenching was 81.95%."