• Journal of the Korean Society for Precision Engineering
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• v.18 no.8
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• pp.64-70
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• 2001

In all the Rapid Prototyping (RP) techniques, the computer-aided design (CAD) model of a three-dimensional part is sliced into horizontal layers of uniform, but not necessarily constant, thickness in the building direction. Each cross- sectional layer is successively deposited and, at the same time, bonded onto the previous layer. The stacked layers form a physical part of the model. The objective of this study is to develop a software for automatic generation of unit shape part(USP) for a new RP process, Variable Lamination Manufacturing using the linear hotwire cutting technique and expandable polystyrene foam sheet as part material(VLM-S). In order to examine the applicability of the developed software to VLM-S, USPs of general three-dimensional shapes, such as an auto-shift lever knob and a pyramid shape were generated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3594-3607
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• 2017

The traditional feature extraction methods such as principal component analysis (PCA) cannot obtain the local structure of the samples, and locally linear embedding (LLE) cannot obtain the global structure of the samples. However, a common drawback of existing PCA and LLE algorithm is that they cannot deal well with the sparse problem of the samples. Therefore, by integrating the globality of PCA and the locality of LLE with a sparse constraint, we developed an improved and unsupervised difference algorithm called Sparse Difference Embedding (SDE), for dimensionality reduction of high-dimensional data in small sample size problems. Significantly differing from the existing PCA and LLE algorithms, SDE seeks to find a set of perfect projections that can not only impact the locality of intraclass and maximize the globality of interclass, but can also simultaneously use the Lasso regression to obtain a sparse transformation matrix. This characteristic makes SDE more intuitive and more powerful than PCA and LLE. At last, the proposed algorithm was estimated through experiments using the Yale and AR face image databases and the USPS handwriting digital databases. The experimental results show that SDE outperforms PCA LLE and UDP attributed to its sparse discriminating characteristics, which also indicates that the SDE is an effective method for face recognition.

• Journal of Life Science
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• v.30 no.5
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• pp.483-490
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• 2020

The ubiquitin system uses ligases and deubiquitinases (DUBs) to regulate ubiquitin position on protein substrates and is involved in many biological processes which determine stability, activity, and interaction of the target substrate. DUBs are classified in six groups according to catalytic domain, namely ubiquitin-specific proteases (USPs); ubiquitin C-terminal hydrolases (UCHs); ovarian tumor proteases (OTUs); Machado Joseph Disease proteases (MJDs); motif interacting with Ub (MIU)-containing novel DUB family (MINDY); and Jab1/MPN/MOV34 metalloenzymes (JAMMs). Otubain 1 (OTUB1) is a DUB in the OTU family which possesses both canonical and non-canonical activity and can regulate multiple cellular signaling pathways. In this review, we describe the function of OTUB1 through regulation of its canonical and non-canonical activities in multiple specifically cancer-associated pathways. The canonical activity of OTUB1 inhibits protein ubiquitination by cleaving Lys48 linkages while its non-canonical activity prevents ubiquitin transfer onto target proteins through binding to E2-conjugating enzymes, resulting in the induction of protein deubiquitination. OTUB1 can therefore canonically and non-canonically promote tumor cell proliferation, invasion, and drug resistance through regulating FOXM1, ERα, KRAS, p53, and mTORC1. Moreover, clinical research has demonstrated that OTUB1 overexpresses with high metastasis in many tumor types including breast, ovarian, esophageal squamous, and glioma. Therefore, OTUB1 has been suggested as a diagnosis marker and potential therapeutic target for oncotherapy.