PSG College of Technology

About: PSG College of Technology is a based out in . It is known for research contribution in the topics: Machining & Thin film. The organization has 3174 authors who have published 3575 publications receiving 40690 citations.

Analysis of axially functionally graded nano-tapered Timoshenko beams by element-based Bernstein pseudospectral collocation (EBBPC)

Abstract: In this paper, static bending, buckling and free vibration of axially functionally graded (AFG) nano-tapered Timoshenko (NTTB) or Bernoulli Euler (NTEB) beams are examined based on the nonlocal Timoshenko beam theory (NTBT). This theory incorporates the length scale parameter (nonlocal parameter) to capture the small-scale effect. The material properties and geometry properties of the nanobeam are assumed to vary along the length direction. The governing equations and the associated boundary conditions are derived using Hamilton’s principle. The model is then applied on the studies of static, buckling and free vibration analysis of NTTB or NTEB using element-based Bernstein pseudo-spectral collocation approach (EBBPC). After the Bernstein pseudo-spectral collocation method is validated, detailed numerical analyses about the effect of boundary conditions, load types are carried out. Non-local parameter and axial load effects on the static and dynamic response of AFG-NTTB and AFG-NTEB are discussed. The approach is tested on benchmark problems of static, buckling and free vibration analyses, showing high accuracy.

Activation of persulfate ions by TiO2/carbon dots nanocomposite under visible light for photocatalytic degradations of organic contaminants

Abstract: In this study, we synthesized TiO2/carbon dots (TiO2/C-Dots) powders and their photocatalytic performances were exceptionally improved under visible light in removals of different organic pollutants using persulfate anions. The characteristic of as-synthesized photocatalysts were studied by X-ray diffraction, high resolution transmission electron microscopy, scanning electron microscopy, energy dispersive analysis of X-rays, UV–vis diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy, Fourier transform-infrared, photoluminescence spectroscopy, and Brunauer–Emmett–Teller surface analysis. Among the synthesized samples, the TiO2/C-Dots (0.50 mL) nanocomposite had the greatest photoactivity. In addition, the removal rate constant of RhB by this photocatalyst in the existence of 1.48 mM persulfate ions was 439 × 10−4 min−1, which is almost 33.3, 3.35, 2.60, and 2.41-folds premier than those of the pure TiO2, persulfate, TiO2/persulfate, and TiO2/C-Dots (0.50 mL) samples, respectively. Reactive species scavenging experiments revealed that ·SO4− was generated following the addition of persulfate ions, whereas ·O2− and h+ were predominantly responsible for the degradation of pollutants. At last step, the band alignments were obtained by electrochemical studies and the mechanism for boosted photocatalytic ability in the presence of persulfate anions was suggested. The photocatalyst could be regenerated for five repeated use.

Paraphrase Extraction using fuzzy hierarchical clustering

Abstract: Collecting paraphrases using a Paraphrase Recognizer Fuzzy hierarchical clusters are processed by SVM classifier to detect paraphrasesAgglomeration on verbs is by followed by division on nounsA novel fuzzy grouping strategy has been utilized for merging clustersBetter performance than existing k-means clustering and other approaches such as cosine similarity and FCM clusteringApplying Word Sense Disambiguation has been found to be beneficial Paraphrase Extraction involves the discovery of equivalent text segments from large corpora and finds application in tasks such as multi-document summarization and document clustering Semantic similarity identification is a challenging problem which is further compounded by the large size of the corpus In this paper a two-stage approach which involves clustering followed by Paraphrase Recognition has been proposed for extraction of sentence-level paraphrases from text collections In order to handle the ambiguity and inherent variability of natural language a fuzzy hierarchical clustering approach which combines agglomeration based on verbs and division on nouns has been used Sentences within each resultant cluster are then processed by a machine-learning based Paraphrase Recognizer to discover the paraphrases The two-stage approach has been applied on the Microsoft Research Paraphrase Corpus and a subset of the Microsoft Research Video Description Corpus The performance has been evaluated against an existing k-means clustering approach as well as cosine-similarity technique and Fuzzy C-Means clustering and the two-stage system has consistently demonstrated better performance

PAPR Reduction in OFDM Systems Using Modified SLM with Different Phase Sequences

Abstract: High Peak-to-average power ratio (PAPR) is one of the main issues in multicarrier modulation systems. PAPR is mainly caused due to the summation of various signals having high amplitude values. Several PAPR reduction techniques were employed, out of which Selective Mapping (SLM) proved to be one of the most effective schemes. Some drawbacks in SLM technique are high computational complexity and phase search complexity. Selection of phase sequences in SLM plays a major role in the reduction of PAPR as well as the computational complexity. Modified SLM was introduced to reduce computational complexity in SLM technique. In this paper, we apply various phase sequences such as Riemann, Centering, Centered Riemann and New Centered to Modified SLM technique and their effect on PAPR reduction are analyzed. From the simulation results, it can be inferred that the modified SLM with New Centered scheme achieves a significant PAPR reduction of the range 8.3–9.3 dB with respect to Conventional OFDM and 3–5.5 dB with respect to Conventional SLM technique. The Modified SLM-New Centered scheme is most suitable for 64-QAM applications as it provides good PAPR reduction performance at lower computational complexity.