Deep Convolutional Neural Network based Ship Images Classification

Ships are an integral part of maritime traffic where they play both militaries as well as non-combatant roles. This vast maritime traffic needs to be managed and monitored by identifying and recognising vessels to ensure the maritime safety and security. As an approach to find an automated and efficient solution, a deep learning model exploiting convolutional neural network (CNN) as a basic building block, has been proposed in this paper. CNN has been predominantly used in image recognition due to its automatic high-level features extraction capabilities and exceptional performance. We have used transfer learning approach using pre-trained CNNs based on VGG16 architecture to develop an algorithm that performs the different ship types classification. This paper adopts data augmentation and fine-tuning to further improve and optimize the baseline VGG16 model. The proposed model attains an average classification accuracy of 97.08% compared to the average classification accuracy of 88.54% obtained from the baseline model

Designing Integration Unit to Integrate Navigational and Tactical Equipment Onboard Naval Platforms

A typical warship consists of weapons and sensors that are of diverse origins and are generally based on different design standards and philosophies. But to enhance the operation capability of the platform, it is very important that many of the equipment work in tandem. This paper discusses the design of an integration unit that integrates various sensors and equipment onboard a naval platform. It takes a strictly modular approach and is therefore, adaptable to any size and mission requirement. The proposed solution uses commercial off-the-shelf (COTS) hardware and relevant software, to provide the required Quality of Service (QoS) data to the end equipment and systems. This solution provides an efficient and seamless integration of various sensors, weapons and equipment onboard a naval platform.Defence Science Journal, Vol. 64, No. 5, September 2014, pp.445-450, DOI:http://dx.doi.org/10.14429/dsj.64.503

Machinability Investigation on Novel Incoloy 330 Super Alloy using Coconut Oil Based SiO2 Nano fluid

Over the years, the quality of the finished surface has become the foremost prevalent owing to better output performance, reliability and life span of a machined part.  Moreover, the effects of cooling and lubrication approach during the machining process play a vital role. Incoloy 330 generally used in petrochemical, chemical, power generations, thermal processing applications. This exploration focuses on the appropriate utilization of the Minimum Quantity Lubrication (MQL) based cooling approach using diverse concentrations of coconut oil based SiO2 nanofluids in the turning practice of Incoloy 330 alloy. The input variables are nanofluids concentration (Nc), feed (f) and cutting speed (Vc). The cutting insert TiAlN PVD coated cemented carbide tool is utilized to study the output responses like tool flank wear (VBc), surface roughness (Ra), material removal rate (MRR), and chip morphology. SiO2 nanofluids work effectively as tool flank wear is found to be less (VBc varies in between 0.057 mm to 0.077 mm). From ANOVA, cutting speed is found to be topmost influencing input (83.24%) for tool flank wear. Machining on the highest feed value (0.35 mm/rev) is not recommended for this work as Ra is found to be greater than 1.6 µm. With increasing cutting speed and feed rate, MRR increases. In each run, coiled continuous helical chips are obtained. Deformed chip thickness is found to be lower ( 0.3 to 0.74 mm) due to the application of SiO2 nanofluid through MQL which enhanced the heat dissipation thus eliminated the tendency of chip welding on the top surface of the tool. Chip reduction coefficient decreases with feed and cutting speed. Further, the TOPSIS optimization technique has been implemented to get an optimum set of cutting parameters for multiple responses and it is found to be Nc3 (0.3 % wt)-f1 (0.15 mm/rev)-Vc3 (160 m/min)

Predicting prognosis in large cohort of decompensated cirrhosis of liver (DCLD)- a machine learning (ML) approach

Background and aims: Onset of decompensation in cirrhosis is associated with poor outcome. The current clinico-biochemical tools have limited accuracy in predicting outcomes reliably. Identifying the predictors with precision model on the big data using artificial intelligence may improve predictability. We aimed to develop a machine learning (ML) based prognostic model for predicting 90 day survival in patients of cirrhosis presenting with decompensation. Method: We analysed electronic medical records retrospectively of hospitalised cirrhosis patients at the ILBS, with a complete 90-day follow-up. Clinical data, laboratory parameters and organ involvement were serially noted. AI-modelling was done after appropriate mining, feature engineering, splitted randomly into train and testsets (20:80). The class imbalance problem was handled by random over-sampling technique, to make balanced 50:50 ratios. After 10- fold cross validation, 3 repetitions and grid search for optimal hyper parameters, the XGB-CV model was chosen. AUC was the primary selection criteria and confusion matrix was used to compare AUCs between AI-models and existing indices; CTP and MELD-score. Results: Total of 6326 patients [mean age 48.2 ± 11.5 years, 84% male, Mean CTP 10.4 ± 2.2 and MELD Na-30.4 ± 11.9, alcohol 49.4%] were included. Ninety day mortality was 29.2%. Acute insult was identified in 80% cases; of which extra-hepatic 49%, hepatic 46% and unknown 5% cases respectively. The XGB-CV model had the best accuracy for prediction of 90 days event in the train set 0.90 (0.90–0.93), validation set 0.80 (0.79–0.81) and for overall dataset 0.80 (0.79– 0.81). The AUC of the XGB-CV model was better than CTP and MELD Na-score by 16% and 15% respectively. The prediction model considered 43 variables; 18 of which predicted the outcome, and 10 maximum contributors are shown in concordance classifier. The most contributors to poor outcome included, index presentation as HE, diagnosis of AD/ACLF/ESLD, PT-INR, serum creatinine, total bilirubin, acute insult etiology, prior decompensation, acute hepatic or extrahepatic insult, leukocyte count and present duration of illness. In the Decision Tree Model, the presence of HE, PT-INR and syndromic diagnosis of AD or ACLF/ESLD was able to stratify the patients into low (22%), intermediate (23–46%) and high risk (\u3e75%) of mortality at 90 days. Conclusion: The AI based current model developed using a large data base of CLD patients presenting with decompensation immensely adds to the current indices of liver disease severity and can stratify patients at admission. Simple ML algorithms using HE and INR besides syndromic presentation, could help treatment decisions and prognostication

Acute-on-chronic liver failure: Consensus recommendations of the Asian pacific association for the study of the liver (APASL): An update

The first consensus report of the working party of the Asian Pacific Association for the Study of the Liver (APASL) set up in 2004 on acute-on-chronic liver failure (ACLF) was published in 2009. With international groups volunteering to join, the APASL ACLF Research Consortium (AARC) was formed in 2012, which continued to collect prospective ACLF patient data. Based on the prospective data analysis of nearly 1400 patients, the AARC consensus was published in 2014. In the past nearly four-and-a-half years, the AARC database has been enriched to about 5200 cases by major hepatology centers across Asia. The data published during the interim period were carefully analyzed and areas of contention and new developments in the field of ACLF were prioritized in a systematic manner. The AARC database was also approached for answering some of the issues where published data were limited, such as liver failure grading, its impact on the \u27Golden Therapeutic Window\u27, extrahepatic organ dysfunction and failure, development of sepsis, distinctive features of acute decompensation from ACLF and pediatric ACLF and the issues were analyzed. These initiatives concluded in a two-day meeting in October 2018 at New Delhi with finalization of the new AARC consensus. Only those statements, which were based on evidence using the Grade System and were unanimously recommended, were accepted. Finalized statements were again circulated to all the experts and subsequently presented at the AARC investigators meeting at the AASLD in November 2018. The suggestions from the experts were used to revise and finalize the consensus. After detailed deliberations and data analysis, the original definition of ACLF was found to withstand the test of time and be able to identify a homogenous group of patients presenting with liver failure. New management options including the algorithms for the management of coagulation disorders, renal replacement therapy, sepsis, variceal bleed, antivirals and criteria for liver transplantation for ACLF patients were proposed. The final consensus statements along with the relevant background information and areas requiring future studies are presented here

Constraints on Astro-unparticle Physics from SN 1987A

SN 1987A observations have been used to place constraints on the interactions between standard model particles and unparticles. In this study we calculate the energy loss from the supernovae core through scalar, pseudo scalar, vector, pseudo vector unparticle emission from nuclear bremsstrahlung for degenerate nuclear matter interacting through one pion exchange. In order to examine the constraints on $d_{\cal U}=1$ we considered the emission of scalar, pseudo scalar, vector, pseudo vector and tensor through the pair annihilation process $e^+e^-\to {\cal U} \gamma$. In addition we have re-examined other pair annihilation processes. The most stringent bounds on the dimensionless coupling constants for $d_{\cal U} =1$ and $\Lambda_{\cal U}= m_Z$ are obtained from nuclear bremsstrahlung process for the pseudo scalar and pseudo-vector couplings $\bigl|\lambda^{\cal P}_{0,1}\bigr|\leq 4\times 10^{-11}$ and for tensor interaction, the best limit on dimensionless coupling is obtained from $e^+ e^-\to {\cal U} \gamma$ and we get $\bigl|\lambda^{\cal T}\bigr| \leq 6\times 10^{-6}$.Comment: 12 pages, 2 postscript figure