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"This book addresses the experimental calibration of best-estimate numerical simulation models. The results of measurements and computations are never exact. Therefore, knowing only the nominal values of experimentally measured or computed quantities is insufficient for applications, particularly since the respective experimental and computed nominal values seldom coincide. In the authors view, the objective of predictive modeling is to extract best estimate values for model parameters and predicted results, together with best estimate uncertainties for these parameters and results. To achieve this goal, predictive modeling combines imprecisely known experimental and computational data, which calls for reasoning on the basis of incomplete, error-rich, and occasionally discrepant information."

"ABSTRAKPermasalahan kondisi tata guna lahan DAS Ciliwung dalam kurun waktu 10 tahun terakhir mengalami degradasi lingkungan sebesar 7,14 0,7 per tahun Bhakti, 2015 . Implikasi perubahan tata guna lahan suatu DAS mengakibatkan sumber daya air terganggu, yaitu dapat menurunkan resapan air ke dalam tanah dan meningkatkan limpasan permukaan. Tujuan penelitian ini menganalisa pengaruh perubahan tata guna lahan terhadap hidrograf banjir pada Sub-DAS Ciliwung Tengah hingga Pintu Air Manggarai dengan memperhitungkan karakteristik sempadan sungai dan diskretisasi spasial dengan menggunakan model hidrologi HEC-GeoHMS. Data inflow dari debit Bendung Katulampa tahun 2017, sedangkan data outflow menggunakan debit Pintu Air Manggarai tahun 2017. Metode analisa dengan 3 skenario yaitu skenario 1 stream threshold area 174,39 km2 menghasilkan 1 sub-DAS, skenario 2 stream threshold area 25 km2 menghasilkan 3 Sub-DAS dan skenario 3 stream threshold area 15 km2 menghasilkan 9 Sub-DAS. Debit puncak hasil simulasi pada skenario 1 sebesar 142,80 m3/dt, skenario 2 sebesar 142,50 m3/dt dan skenario 3 sebesar 135,6 m3/dt. Dari ketiga skenario, skenario 3 yang lebih mendekati data observasi dengan nilai koefisien Efisiensi Nash-Sutcliffe NSE 0,764. Selanjutnya skenario 3 digunakan untuk menghitung hidrograf banjir dengan menggunakan peta RTRW, dihasilkan debit puncak di Pintu Air Manggarai kala ulang 2 tahun sebesar 465,5 m3/dt, kala ulang 5 tahun sebesar 612,7 m3/dt dan kala ulang 10 tahun sebesar 722,6 m3/dt. Semakin kecil diskretisasi spasial, semakin banyak Sub-DAS yang di delineasi dan semakin banyak reach yang dianalisa, sehingga semakin kecil bentangan dan detail karakristik sempadan sungai yang diamati yang dapat mempengaruhi nilai koefisien kekasaran saluran n Manning . Oleh karena itu, semakin kecil diskretisasi spasial Sub DAS, maka akan semakin menurunkan debit puncak banjir dan memperpanjang waktu puncak banjir.

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ABSTRACT The problem of Ciliwung Watershed Landuse condition in the last 10 years has environmental degradation of 7.14 0.7 per year Bhakti, 2015 . The implications of land use change in a watershed result in disturbed water resources, which can decrease water absorption into the soil and increase surface runoff. The aims of this study are to analyze the effect of land use change on flood hydrograph in Middle Ciliwung Sub watershed to Manggarai Weir by taking into account the characteristics of riparian and spatial discretization using HEC GeoHMS hydrological model. Inflow data from discharge of Katulampa Weir in 2017, while outflow data using Manggarai Weir discharge in 2017. The analysis method with 3 scenarios namely scenario 1 stream threshold area 174,39 km2 yield 1 sub watershed, scenario 2 stream threshold area 25 km2 yield 3 Sub watersheds and scenario 3 stream threshold area 15 km2 yielding 9 Sub watershed. The peak discharge simulation result in scenario 1 is 142,80 m3 s, scenario 2 is 142,50 m3 s and scenario 3 is 135,6 m3 s. From the three scenarios, scenario 3 is closer to the observation data with the value of the Nash Sutcliffe Efficiency coefficient NSE 0,764. Further scenario 3 is used to calculate the flood hydrograph using the Land Use Plan map, resulting in peak discharge at the Manggarai Weir when the 2 year return period is 465.5 m3 s, 5 year return period is 612,7 m3 s and 10 years return period is 722,6 m3 s. The smaller spatial discretization, the more delineated sub watersheds and the more reaches being analyzed, the smaller the expanse and the observed limits of riparian that can affect the value of the roughness coefficient n Manning . Therefore, the smaller spatial discretization of sub watershed, the more it will decrease the peak flood discharge and extend peak time. "

"Walaupun machine learning semakin umum digunakan pada berbagai bidang, mempercayakan sebuah kotak hitam untuk mengambil keputusan yang krusial, seperti keputusan terkait bidang kesehatan dan hukum, merupakan hal yang beresiko. Karena hal ini, merupakan ide yang baik jika terdapat suatu model machine learning yang mekanisme pengambilan keputusannya dapat diinterpretasikan oleh penggunanya untuk menjelaskan keputusan yang diambil. Dengan motivasi ini, tugas akhir ini akan berfokus pada studi lanjut mengenai model interpretable machine learning berbasis MaxSAT, yaitu MLIC dan IMLI. MLIC merupakan sebuah model interpretable machine learning berbasis MaxSAT yang mekanisme di dalamnya dapat terlihat secara transparan melalui rule berbentuk CNF dan DNF yang dihasilkan. Akan tetapi, performa waktu training model ini sangat buruk. Untuk mengatasi hal ini, IMLI dikembangkan dengan cara memodifikasi MLIC menggunakan sifat incremental. Hal ini berhasil meningkatkan waktu training MLIC dengan pengorbanan akurasi yang cukup kecil. Melalui studi lanjut ini, tugas akhir ini kemudian akan memaparkan perbandingan akurasi IMLI dengan cara mengganti metode diskretisasi fitur kontinu di dalamnya, dari diskretisasi berbasis quantile 10 bin menjadi diskretisasi berbasis entropi. Dari eksperimen yang dilakukan, diperoleh hasil bahwa IMLI memiliki performa waktu training hingga 1000 kali lebih baik daripada MLIC dengan pengorbanan akurasi tes secara rata-rata sebesar 1.47%. Kemudian, penggunaan diskretisasi berbasis entropi menghasilkan akurasi tes 2.67% lebih baik secara rata-rata dibandingkan diskretisasi berbasis quantile 10 bin pada IMLI. Uji statistik menunjukkan bahwa pengorbanan akurasi yang terjadi pada IMLI secara umum tidak signifikan. Terkait ukuran rule yang dihasilkan, diperoleh perubahan yang bervariasi tergantung dataset yang digunakan, baik antara MLIC dan IMLI maupun antara diskretisasi berbasis quantile dan entropi. Terakhir, tugas akhir ini juga akan memaparkan koreksi pengaruh banyak partisi terhadap waktu training yang sebelumnya dipaparkan pada paper IMLI.

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Despite the wide adoption of machine learning in various domains, trusting a black-box machine learning model to make critical decisions, e.g. in medical and law, might be too risky. Thus, having a transparent machine learning model whose decision-making mechanism is easy to understand by humans is increasingly becoming a requirement. Motivated by this, this bachelor’s thesis conducts a thorough study about the MaxSAT- based interpretable machine learning model, namely MLIC and IMLI. MLIC is a MaxSAT-based interpretable machine learning model whose mechanism is transparent by its generated CNF and DNF rules. However, it suffers from poor training time performance. To overcome this, an incremental version of MLIC, namely IMLI, was developed. IMLI has a far better training time performance with a slight sacrifice on its accuracy. This bachelor’s thesis then compares IMLI accuracy by changing its discretization method from the 10-bin quantile-based discretization to the entropy-based discretization. The conducted empirical studies show that IMLI has better training time performance, up to 1000 times better than MLIC with 1.47% sacrifice of test accuracy on average. It also shows that the entropy-based discretization results in 2.67% higher test accuracy on average compared to the 10-bin quantile-based discretization in IMLI. Test statistic shows that the sacrifice of accuracy in IMLI is insignificant. For the rule size, it shows that the choice of model and its discretization has various effects across the datasets. Lastly, this bachelor’s thesis explains a correction on the effect of partitions to training accuracy that is reported in the IMLI paper."

"ABSTRACTThis paper presents the small-signal modeling of a Single-Ended Primary Inductor Converter power stage operating in discontinuous conduction mode using the sampled-data modeling technique. In addition, two algebraic manipulating features are revealed; the simpler periodic solution determination by the concept of Volt-Second and Capacitor-Charge balance, and the replacement of the expression involving the singular matrix by the equivalent function with the s-domain matrix. Four pulse transfer functions are derived from the model: the smallsignal input-to-output voltage pulse transfer function, the small-signal duty duration-to-output voltage pulse transfer function, the input-to-output voltage pulse transfer function, and the duty duration-to-output voltage pulse transfer function. The model verification is analyzed by the simulation results. The response sequences from the pulse transfer functions oscillate by the same phase and frequency to the one from the simulation with slightly peak amplitude differences, confirming the validity of the acquired pulse transfer functions."

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"This book explores various digital representation strategies that could change the future of wooden architectures by blending tradition and innovation. Composed of 61 chapters, written by 153 authors hailing from 5 continents, 24 countries and 69 research centers, it addresses advanced digital modeling, with a particular focus on solutions involving generative models and dynamic value, inherent to the relation between knowing how to draw and how to build. Thanks to the potential of computing, areas like parametric design and digital manufacturing are opening exciting new avenues for the future of construction. The books chapters are divided into five sections that connect digital wood design to integrated approaches and generative design; to model synthesis and morphological comprehension; to lessons learned from nature and material explorations; to constructive wisdom and implementation-related challenges; and to parametric transfigurations and morphological optimizations. "