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Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry
Get PDFSurface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on low-frequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them
Dielectric Relaxation Studies of Cellulose-Water Mixtures Using Time and Frequency Domain Technique
Get PDFThe complex dielectric permittivity of hydroxypropyl methyl cellulose (HPMC)-water mixture was measured by using Time Domain Reflectometry (TDR) and Frequency Domain (LCR) Technique at 25 oC. The complex dielectric permittivity e*(w), complex electrical modulus M*(w), complex electrical conductivity s*(w), loss tangent (tan d), static dielectric constant (ε0) and relaxation time (τ) have been determined for the cellulose-water system
Robust Logic Circuits Design Using SOI Shorted-Gate FinFETs
Get PDFThe scaling of planar Metal Oxide Semiconductor Field Effect Transistor (MOSFET) technology has reached to its extremity. Double Gate (DG) device was introduced to derive the benefits of scaling gate lengths. Fin-shaped Field Effect Transistors (FinFETs) proved to be the best architecture to realize a double gate structure. In this paper, a static leakage control technique is proposed and a ring-oscillator of five inverters based on shorted gate (SG) FinFETs is simulated using the technique. The basic logic gates like Inverter, 2-input NAND gate, and 2-input NOR gate are simulated using the proposed technique. Leakage power and Power Delay Product (PDP) optimization of 93.46% and 97.78% has been found in 2-input SG FinFET-based proposed NAND gate compared to that of 2-input SG FinFET-based conventional NAND gate.Also, SG FinFET-based proposed 2-input NOR gate shows 98.03% and 98% optimization of leakage power and PDP,respectively compared to the SG FinFET-based conventional 2-input NOR gate. The proposed SG FinFET-based ring-oscillator shows a leakage power and PDP optimization of 62.12% and 35.56%, respectively in comparison to theconventional SG FinFET-based ring-oscillator. The reliability of the proposed circuit is calculated, which came out to be thehighest at 0.7V supply and 16nm process node for a 10% deviation in operating parameters. Also, the process parametervariation of leakage power, delay, and PDP of the proposed circuit came out to be proper and stable thus maintaining thefunctionality of the proposed circuit
Methyl orange adsorption by modified montmorillonite nanomaterials: Characterization, kinetic, isotherms and thermodynamic studies
Get PDFClays intercalated with cetyltrimethylammonium bromide (CTAB-Mt) and hydroxyl aluminium polycation have been prepared and analysed by X-ray fluorescence spectrometry, X-ray diffraction, fourier transform infrared spectroscopy, nitrogen adsorption-desorption at 77 K and thermal gravimetric analysis. The adsorption capacities of modified montmorillonite nanomaterials to remove methyl orange (MO) from aqueous solutions have been studied as a function of contact time, solution pH, adsorbent dosage and initial MO concentration at room temperature. The maximum removal efficiency of MO has been found in acidic medium, with 60 min equilibrium time and 1 g/L adsorbent dosage. The adsorption kinetics and isotherms have been well fitted by pseudo-second order and Langmuir models. The montmorillonites intercalated with both cetyltrimethylammonium bromide and hydroxyl aluminium polycation (CTAB-Al-Mt) have shown a high affinity for MO molecules. Thermodynamic results have indicated an exothermic, spontaneous and physical adsorption process. The characterization and adsorption performance of CTAB-Mt and CTAB-Al-Mt toward MO has also been compared with that of the hydroxyl-aluminium pillared montmorillonite (OH-Al-Mt) and purified montmorillonite (Na-Mt)
Molecular docking and cytotoxicity interactions of naringenin and its nano-structured lipid carriers in ERα positive breast cancer
No full textPhytoestrogens are known to have beneficial properties in various carcinomas. They exhibit its efficacy at cellular levels. Naringenin a flavonoidal phytoestrogen is been explored for its antioxidant, cardio protective and cytotoxic function. The low absorbtion and poor bioavailability of naringenin makes it less efficient in targeting tumours at cellular levels. Due to the structural similarity of naringenin with estradiol and considering the affinity of naringenin with estrogen receptor, this study explores the interactions of naringenin on important signaling proteins involved in ER positive breast cancer through molecular docking studies and the prepared naringenin solid lipid nano particles were characterized and studied for its preventive potential against breast cancer cell lines. The lipidoid form of phytoestrogen shows promising cytotoxic potential compared with naringenin
Deep Learning Hybrid Approaches to Detect Fake Reviews and Ratings
Get PDFNowadays, online reviews and ratings are the most valuable source of word-of-mouth, voice-of-customer, and feedback, also customers can make purchasing decisions on what to buy, where to buy, and what to select. Genuine online reviews are becoming popular, but unfortunately, we have an issue that might only sometimes be unbiased or accurate. Because most of the reviews are fake reviews and ratings, these could mislead innocent customers and highly influence customers' purchasing decisions in the wrong manner. This paper's primary goal is to accurately detect fake reviews and what is the main difference between them. The secondary goal is to detect fake ratings and actual ratings-based reviews across the online platform, especially Amazon datasets. The Paper proposes two novel deep-learning Hybrid techniques: CNN-LSTM for detecting fake online reviews, and LSTM-RNN for detecting fake ratings in the e-commerce domain. Both Hybrid models can outperform and achieve better performance with the most advanced word embedding techniques, Glove, and One hot encoding techniques. As per the experimental results, the first technique efficiently detects fake online reviews with the highest prediction accuracy. The second hybrid model is better than the existing models that detect fake online ratings with the most excellent precision of 93.8%. The experimental research efficiently revealed that the CNN-LSTM and LSTM-RNN methods are more efficient and practicable and might be better suited for optimal results and maximizing the efficiency of fake online review detection
Synthesis, characterization and antimicrobial studies of (E)-N-((2-chloro-6-substituted quinolin-3-yl)methylene)-4-(substituted phenyl)-6-phenyl-2H-1,3-oxazin-2-amines
Get PDFResearch work is planned to synthesize novel (E)-N-((2-chloro-6-substituted quinolin-3-yl)methylene)-4-(substituted phenyl)-6-phenyl-2H-1,3-oxazin-2-amines by the reaction of 6-(3-substituted phenyl)-4-(4-substituted phenyl)-5,6-dihydro-4H-1,3-oxazin-2-amines with 6-substituted-2-chloro-quinoline-3-carbaldehydes in alcoholic medium and in the presence of acetic acid. The structures of synthesized compounds are assigned on the basis of FT-IR, 1H-NMR, 13C-NMR and Mass spectral data. The new compounds are also screened for their antibacterial and antifungal activities. These compounds are showing potent antimicrobial activities due to their chemical structure
Investigating Physics Behind the Rapid Intensification and Catastrophic Landfall of Cyclone ‘Titli’ (2018) in the Bay of Bengal
Get PDFThe present study delineates the role of ocean conditions in the genesis and rapid intensification (RI) of a very severe cyclonic storm (VSCS) ‘Titli’ (2018). The tropical cyclone (TC) formed over the warm waters of the east-central Bay of Bengal during 08-13 October 2018. According to the India Meteorological Department (IMD), the cyclone was the most damaging storm to hit any coast of India in the year 2018, making it a special case of analysis. In the present study, 10 m winds , Sea Surface Temperature (SST), Latent heat flux, and relative vorticity (RV) during the lifespan of the cyclone are studied using ECMWF reanalysis V5 (ERA5) prepared by European Centre for Medium Range Weather Forecasts (ECMWF). Further, the Tropical Cyclone Heat Potential (TCHP) data generated by the Indian National Centre for Ocean Information Services (INCOIS) in Hyderabad is used to study the important information about the oceanic conditions of the TC. The investigation of the TC’s sea surface temperature data from satellites reveals that a relatively warmer SST prevailed during the cyclone’s occurrence, which may have been the primary factor in the TC’s rapid intensification. Further, the latent Heat flux (LHF) and TCHP values were also found high in conjunction with SST values. Our in-depth analysis reveals that the 10 m winds embedded into the TC were extremely strong, exceeding 12 m/s prior to the landfall. A positive and large value of RV was found when the TC was about to hit the coast. This may be one of the reasons behind the ‘catastrophic landfall’ of the cyclone
A case study on expired drugs: The potential corrosion inhibitory activity of expired labetalol drug in 1M HCl for plain carbon steel
Get PDFIn this work expired drug namely, labetalol has been used as a corrosion inhibitor in a stirred acidic medium (1M HCl) with mass loss measurement and electro analytical techniques. . The efficiency of the inhibitor increases as the temperature increases from 30-60°C. The results reveal that maximum inhibition efficiency is observed as 91.66% for 500 ppm. Potentiodynamic polarization measurement implies that it perform as a mixed type inhibitor. FT-IR analysis prove that corrosion product is formed on the mild steel surface. When compared with metal in IM HCl, it includes a shielding layer on the surface of mild steel by controlling further attack of acid. SEM and SFM surface morphology study have also been performed to confirm the effect of labetalol on metal. In addition to that, DFT is also carried out to analyse HOMO, LUMO, ΔE and Mulliken charges
Moisture transport behaviour of Eli-Twist knitted fabric and its comparison with fabric made from yarns spun on different spinning systems
Get PDFIn this study, moisture management properties of knitted fabrics from Eli-Twist polyester and cotton yarns have been investigated. A comparative analysis with knitted fabrics from conventional ring-spun, siro-spun and compact yarns has also been made to assess its suitability. It is observed that polyester and cotton fabrics made from Eli-Twist yarn show very good permeability and moisture management characteristics.