Jonghyuck Park

Korea University, Sŏul, Seoul, South Korea

Are you Jonghyuck Park?

Claim your profile

Publications (3)6.54 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Quality control is attracting more attention in semiconductor market due to harsh competition. This paper considers Fault Detection (FD), a well-known philosophy in quality control. Conventional methods, such as non-stationary SPC chart, PCA, PLS, and Hotelling’s T2, are widely used to detect faults. However, even for identical processes, the process time differs. Missing data may hinder fault detection. Artificial intelligence (AI) techniques are used to deal with these problems. In this paper, a new fault detection method using spline regression and Support Vector Machine (SVM) is proposed. For a given process signal, spline regression is applied regarding step changing points as knot points. The coefficients multiplied to the basis of the spline function are considered as the features for the signal. SVM uses those extracted features as input variables to construct the classifier for fault detection. Numerical experiments are conducted in the case of artificial data that replicates semiconductor manufacturing signals to evaluate the performance of the proposed method.
    Expert Systems with Applications 05/2011; 38:5711-5718. · 1.97 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: An autoregressive error term model was applied to examine the dynamic oscillation of ammonia-oxidizing bacterial (AOB) lineages found in an activated sludge bioreactor. The current abundance of AOB lineages was affected by the past abundance of AOB lineages and past environmental and operational factors as well as current influencing factors.
    Journal of Bioscience and Bioengineering 04/2011; 112(2):166-9. · 1.74 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Spinal cord injury leads to the permanent loss of motor and sensory function in the body. To enhance spinal cord regeneration, we used a hyaluronic acid-based hydrogel as a three-dimensional biomimetic scaffold for peptides and growth factors. Three components were used to provide guidance cues: a matrix metalloproteinase peptide crosslinker, an IKVAV (Ile- Lys-Val-Ala-Val) peptide derived from laminin, and brain-derived neurotrophic factor (BDNF). Human mesenchymal stem cells (hMSCs) were cultured in hydrogels in vitro for 10 days to induce neuronal differentiation of hMSCs. Based on gene-expression data, the matrix metalloproteinase-sensitive peptide, IKVAV peptide, and BDNF were critical in the differentiation of hMSCs. Remodeling activity was found to be a key factor in guiding neural differentiation of stem cells. To test this approach in vivo, we used the spinal cord injured rat model and five different hydrogel compositions. Samples were injected into the intrathecal space, and animals were monitored for 6 weeks. Compared to all other groups, animals injected with BDNF-containing hydrogels showed the greatest improvement on locomotive tests (Basso-Beattie-Bresnahan score) during the initial stage after injury. These results suggest that hyaluronic acid-based hydrogels containing IKVAV and BDNF create microenvironments that foster differentiation of stem cells along the neural cell lineage, and they could be used to facilitate nerve regeneration after spinal cord injury.
    Journal of Biomedical Materials Research Part A 09/2009; 93(3):1091-9. · 2.83 Impact Factor

Publication Stats

50 Citations
6.54 Total Impact Points


  • 2009–2011
    • Korea University
      • • Department of Civil, Environmental and Architectural Engineering
      • • Medical Science Research Center
      Sŏul, Seoul, South Korea