Interacting Blends of Novel Acrylated Poly(Ester-Amide) Resins - I with Methyl Methacrylate Monomer—Part 1
Bisphthalamic acids were prepared by reaction of phthalic anhydride and aliphatic diamines. Novel poly(ester-amide)s (PEAs) were prepared by reaction of epoxy resin with bisphthalamic acids. Acrylation of PEAs were carried out using acryloyl chloride; products are called acrylated poly(ester-amide)s (APEAs). Elemental analysis, Number average molecular weight and IR spectra recorded for both PEAs and APEAs. Blending of APEAs was carried out with methyl methacrylate monomer. Curing of APEAs-MMA blends were monitored on differential scanning calorimeter. Composites of these blends have been laminated and characterized. Unreinforced cured samples of these blends were analyzed thermogravimetrically.
Studies on Bismaleamic Acids Cured Tetrafunctional Epoxy Resin of Bisphenol‐C and Glass‐Epoxy Composites
The tetrafunctional epoxy resin of bisphenol-C was cured using bismaleamic acids of maleicanhydride and 4,4'-diaminodiphenylsulfone, 4,4'-diaminodiphenylether, and 4,4'-diaminodiphenylmethane and characterized by infrared, differential scanning calorimetric, and thermogravimetric analysis. Cured resins showed endothermic transition at about 229–248°C, which supplemented decomposition reactions. Cured resins are thermally stable up to about 200–230°C and followed two-step decomposition reactions. A 34–56% residue at 600°C supplemented the formation of highly thermally stable cross-linked products. Glass composites showed 133–153 MPa tensile strength, 223–290 MPa flexural strength, 7520–8015 MPa flexural modulus, 33–38 kg m−2 impact strength, 37–40 Barcol hardness, 8.3–12.3 kV mm−1 electric strength, 8.9 × 1012 to 4.2 × 1014 ohm cm volume resistivity, and 13–14% water absorption. Good thermal, mechanical and electrical properties and excellent hydrolytic stability of the glass composites signify their industrial importance for low load bearing housing, electrical, and marine applications.
Automatic classification of TV news articles based on telop character recognition
The purpose of this study is to develop a multimedia database system for TV news video data. TV news video data consist of speech, characters and images. In this study, telop characters are recognized and given to the news articles as indices for classification. At first, telop frames which include telop characters are detected and then the telop characters are extracted and recognized. Through morphological analysis of the recognized telop characters, keywords are extracted which consist of more than two characters. Their keywords are used as indices to classify the TV news articles. We carried out experiments for 30 days of NHK 5 minutes news and obtained 95.4% telop character extraction rate, 81.4% character recognition rate and 83.8% article classification rate. We improved the article classification rate by 43.4% through character recognition improvement from 44.1% to 81.4%.