Hypothesis derivation and its verification by a wholly automated many-objective evolutionary optimization system

This study has constructed a fully automated multidisciplinary and many-objective evolutionary design optimization system independent of computer environments to evaluate objective functions; the research applied it to a geometric design problem of a flyback booster for next-generation space transportation. In optimization involving objective functions to appraise the aero-/structural-dynamic performance with high fidelity, spatial discretization hinders the overall automation. This research has facilitated an efficient optimal design by wholly automating high-fidelity assessments, which designers had to implement manually, and has accomplished optimizations that directly contribute to real-world design problems. Moreover, this study would accumulate design knowledge for space transportation that the market is reviving. The total automated system yielded the embedding of geometric trait lines to ensure the discretization even for large curvature surfaces; the system innovated a robust automatic error-checking mechanism in the system’s preprocess. Consequently, the entirely automatized optimization procured nondominated solution sets for more precise data analyses in a pragmatic execution period. Design informatics, a framework combining optimization and data analysis, functioned usefully in real-world design on flyback-booster geometry by materializing smooth deriving and verifying a design hypothesis; eventually, the research gained a new design principle.

Rancang Bangun Pembuat Layout PCB Otomatis Berbasis Android

ABSTRACT The purpose of this research is to produce an Android-based automatic PCB layout path maker tool. This tool can automatically create a PCB layout path without etching the PCB so that it can provide efficiency in making the PCB layout path. The research method used in this study used an experimental method with a quantitative approach. This research was conducted at the Jakarta State University Jakarta mechanical workshop laboratory in January 2020, testing carried out namely manual and automatic testing, manual testing including the making of lines and flat shapes using paper media and PCB boards, automatic testing includes making pcb lines automatically using media PCB board. The conclusion of this research is that the PCB Layout path making tool can automatically work as planned. The tool can make the PCB Layout path according to the design that is sent via Bluetooth communication serial. Automatic PCB layout maker tool has an average error percentage of under 5%. Testing is done in 2 ways, namely manual and automatic testing, in manual testing the average error is 0.297% and the automatic error testing is 0.136%. ABSTRAK Tujuan penelitian ini adalah menghasilkan alat pembuat jalur layout PCB otomatis berbasis android. Alat ini dapat membuat jalur layout PCB secara otomatis tanpa harus meng-etching PCB sehingga dapat memberikan efisiensi pada pembuatan jalur layout PCB. Metode penelitian yang digunakan dalam penelitian ini menggunakan metode eksperimen dengan pendekatan kuantitatif. Penelitian ini dilakukan di laboratorium bengkel mekanik Universitas Negeri Jakarta pada bulan Januari 2020, pengujian yang dilakukan yaitu pengujian secara manual dan otomatis, pengujian manual meliputi pembuatan garis dan bangun datar menggunakan media kertas dan papan PCB, pengujian otomatis meliputi pembuatan jalur pcb secara otomatis menggunakan media papan PCB. Kesimpulan dari penelitian ini adalah alat pembuat jalur Layout PCB secara otomatis dapat bekerja sesuai dengan yang di rencanakan. Alat dapat membuat jalur Layout PCB sesuai dengan desain yang dikirimkan melalui serial komunikasi Bluetooth. Alat pembuat layout PCB otomatis memiliki persentase error rata-rata di bawah 5%. Pengujian dilakukan dengan 2 cara yaitu pengujian manual dan otomatis, pada pengujian manual rata-rata error didapatkan sebesar 0,297% dan pengujian otomatis rata-rata error didapatkan sebesar 0.136%.

TAPER: Pinpointing errors in multiple sequence alignments despite varying rates of evolution

Erroneous data can creep into sequence datasets for reasons ranging from contamination to annotation and alignment mistakes. These errors can reduce the accuracy of downstream analyses such as tree inference and will diminish the confidence of the community in the results even when they do not impact the analysis. As datasets keep getting larger, it has become difficult to visually check for errors, and thus, automatic error detection methods are needed more than ever before. Alignment masking methods, which are widely used, completely remove entire aligned sites. Therefore, they may reduce signal as much as or more than they reduce the noise. An alternative is designing targeted methods that look for errors in small species-specific stretches of the alignment by detecting outliers. Crucially, such a method should attempt to distinguish the real heterogeneity, which includes signal, from errors. This type of error filtering is surprisingly under-explored. In this paper, we introduce TAPER, an automatic algorithm that looks for small stretches of error in sequence alignments. Our results show that TAPER removes very little data yet finds much of the error and cleans up the alignments.