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"This book presents a methodology based on inverse problems for use in solutions for fault diagnosis in control systems, combining tools from mathematics, physics, computational and mathematical modeling, optimization and computational intelligence. This methodology, known as fault diagnosis – inverse problem methodology or FD-IPM, unifies the results of several years of work of the authors in the fields of fault detection and isolation (FDI), inverse problems and optimization. The book clearly and systematically presents the main ideas, concepts and results obtained in recent years. By formulating fault diagnosis as an inverse problem, and by solving it using metaheuristics, the authors offer researchers and students a fresh, interdisciplinary perspective for problem solving in these fields. Graduate courses in engineering, applied mathematics and computing also benefit from this work."

"Fungsi hazard dapat dikategorikan menjadi dua, yaitu monoton (naik atau turun) dan non monoton (bathtub shape dan upside down bathtub shape). Untuk memodelkan datadengan fungsi hazard monoton, naik atau turun, dan non monoton bathtub shape umumnya digunakan distribusi Gamma atau Weibull. Pada skripsi ini, akan diperkenalkan sebuah distribusi yang dapat memodelkan data dengan fungsi hazard berbentuk upside down bathtub shape. Distribusi ini diturunkan dari distribusi Lindley dengan melakukan transformasi yang disebut distribusi generalized inverse Lindley. Distribusi ini lebih fleksibel dalam memodelkan data dengan fungsi hazard non-monoton upside down bathtub. Hal ini dikarenakan parameter shape pada distribusi tersebut menyebabkan fungsi hazard memiliki banyak variasi bentuk namun tetap mempertahankan bentuk upside down bathtub. Beberapa karakteristik dari distribusi seperti fungsi kepadatan peluang, fungsi distribusi, fungsi survival, fungsi hazard,dan momen ke-r akan dicari. Untuk mengestimasi parameter distribusinya akan digunakan metode maximum likelihood. Di akhir skripsi ini, akan dibangun data untuk mengestimasi parameter dari distribusi yang bersangkutan

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Hazard rate are categorized by their shape, either its monotone (decreasing or increasing) or non-monotone (upside down bathtub shaped and bathtub shaped). Modelling data from monotone hazard rate, either decreasing or increasing, and bathtub shaped hazard rate are possible with common distribution such as Gamma distribution or Weibull distribution. For data which has upside down bathtub shaped hazard rate is usually done by using inverse transformation of exponential distribution such as inverse Gamma, inverse Weibull, and inverse Lindley. In this paper, a distribution that can model a data with upside down bathtub shaped hazard rate is introduced. The distribution is derived from Lindley distribution with transformation and is called generalized inverse Lindley distribution. The distribution is more flexible because shape parameter which make wide variety of shape without changing its hazard rate from upside down bathtub shaped. Somestatistic properties of the distribution such as density function, cumulative function, survival function, hazard function, and moment will be discussed. For estimatingparameter of the distribution, maximum likelihood method will be used. In the end, simulation data will be generated to see the estimation of the distributions parameter."

"The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students in a practical implementation framework.Matlab codes and some benchmark structural optimization problems are provided. The aim is to provide an efficient context for experienced researchers or readers not familiar with theory, applications and computational developments of the considered metaheuristics.The information will also be of interest to readers interested in application of metaheuristics for hard optimization, comparing conceptually different metaheuristics and designing new metaheuristics."

"PEM fuel cells with bio-ethanol processor systems presents a control system design that provides basic regulation of the hydrogen production process with PEM fuel cells. It then goes on to construct a fault diagnosis system to improve plant safety above this control structure. PEM fuel cells with bio-ethanol processor systems is divided into two parts: the first covers fuel cells and the second discusses plants for hydrogen production from bio-ethanol to feed PEM fuel cells. Both parts give detailed analyses of modeling, simulation, advanced control, and fault diagnosis. They give an extensive, in-depth discussion of the problems that can occur in fuel cell systems and propose a way to control these systems through advanced control algorithms. A significant part of the book is also given over to computer-aided engineering software tools that can be used to evaluate the dynamic performance of the overall plant."

"Penyakit filariasis adalah penyakit yang menyerang saluran dan kelenjar getah bening. Penyakit ini disebabkan oleh cacing filaria dan ditularkan melalui nyamuk. Spesies cacing yang menjadi penyebab filariasis, yaitu Wuchereria bancrofti, Brugia malayi, dan Brugia timori. Sementara, genus nyamuk yang menjadi perantara penularan filariasis, yaitu Mansonia, Anopheles, Culex, Aedes, dan Armigeres. Terdapat beberapa hal yang dapat dilakukan untuk mengendalikan penyebaran penyakit fiariasis, diantaranya melakukan proses screening dan pengobatan. Pada skripsi ini digunakan model matematika untuk membahas bagaimana pengendalian penyakit filariasis dengan mempertimbangkan efek kesalahan diagnosis dalam proses screening. Model dikonstruksi dengan menggunakan pendekatan sistem persamaan diferensial nonlinier berdimensi sembilan dengan dua populasi, yaitu populasi manusia dan populasi nyamuk. Populasi manusia dibagi ke dalam tujuh kompartemen dan populasi nyamuk dibagi ke dalam dua kompartemen. Kajian analitik yang dilakukan terhadap model yang telah dikonstruksi, yaitu menentukan eksistensi dan menganalisis kestabilan titik keseimbangan, menentukan bilangan reproduksi dasar, dan menyelidiki eksistensi bifurkasi dari model yang telah dikonstruksi. Simulasi numerik terdiri dari analisis elastisitas dan sensitivitas bilangan reproduksi dasar serta simulasi autonomous. Hasil analitik didukung oleh simulasi numerik terkait elastisitas dan sensitivitas bilangan reproduksi dasar serta simulasi autonomous. 

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Filariasis is a disease that attacks the ducts and lymph nodes. This disease is caused by filarial worms and transmitted by mosquitoes. Worms that cause filariasis are from the species Wuchereria bancrofti, Brugia malayi, and Brugia timori. The genus of mosquitoes that transmit filariasis, namely Mansonia, Anopheles, Culex, Aedes, and Armigeres. Several things can be done to control the spread of filariasis, such as implementing a screening process and treatment. This study uses a mathematical model to discuss controlling filariasis by considering the effect of misdiagnosing in the screening process. The model is constructed using a system of nine-dimensional nonlinear differential equations approach with two populations, namely the human population and the mosquito population. The human population is divided into seven compartments, and the mosquito population is divided into two compartments. The analytical study was carried out to analyze the existence and stability of the equilibrium points, analyze the basic reproduction number, and investigate the existence of bifurcations of the model that has been constructed. The numerical simulation consists of the analysis of the elasticity and sensitivity of basic reproduction number and autonomous simulation. The analytical results are supported by numerical simulations related to the elasticity and sensitivity of basic reproduction number and autonomous simulation."

"In this self-consistent monograph, the author gathers and describes different mathematical techniques and combines all together to form practical procedures for the inverse analyses. It puts together topics coming from mathematical programming, with soft computing and Proper Orthogonal Decomposition, in order to show, in the context of structural analyses, how the things work and what are the main problems one needs to tackle. Throughout the book a number of examples and exercises."

"Sistem manufaktur telah mengalami kemajuan menuju personalisasi massal dalam konteks Industri 4.0, yang memiliki implikasi besar terhadap efisiensi produksi dan kepuasan konsumen. Tujuan dari penelitian ini adalah untuk menentukan metaheuristik mana yang paling berhasil untuk mengatasi masalah penjadwalan job shop umum antara Genetic Algorithm (GA), Particle Swarm Optimization (PSO), dan Ant Colony Optimization (ACO). Masalah-masalah ini dikenal sebagai NP-hard, yang menuntut penggunaan pendekatan metaheuristik. Penelitian ini menilai kinerja setiap metaheuristik pada kumpulan data kecil, menengah, dan besar, dengan fokus pada indikator utama makespan. Hasilnya menunjukkan bahwa GA secara konsisten menawarkan solusi yang mendekati optimal, mengungguli PSO dan ACO. PSO menunjukkan potensi dalam kumpulan data yang lebih besar namun kurang konsisten, sedangkan ACO adalah yang paling tidak berhasil, sering kali menghasilkan nilai makespan yang lebih tinggi. Kesimpulannya, GA direkomendasikan untuk aplikasi masalah penjadwalan job shop karena keandalan dan efektivitasnya.

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Manufacturing systems have progressed toward mass personalization in the context of Industry 4.0, with substantial implications for production efficiency and consumer satisfaction. The goal of this study is to determine which metaheuristic is most successful for addressing general Job Shop Scheduling Problems (JSSP) among Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). These issues are known to be NP-hard, demanding the use of metaheuristic approaches. The research assesses the performance of each metaheuristic on small, medium, and big datasets, with a focus on the key indicator of makespan. The results show that GA consistently offers near-optimal solutions, outperforming PSO and ACO. PSO demonstrated potential in larger datasets but lacked consistency, whereas ACO was the least successful, frequently producing higher makespan values. Consequently, GA is recommended for actual JSSP applications because of its dependability and effectiveness."