Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.publishedVersio

Fabrication of Fe doped reduced graphene oxide (rGO) decorated WO3 based low temperature ppm level acetone sensor: Unveiling sensing mechanism by impedance spectroscopy

Chemiresistive MOS-based acetone sensing device is a futuristic pathway for non-invasive diagnosis of diabetes. Although their potential deployment is restricted till now due to lack of selective, low temperature operated ppm-level sensors. In this work, we demonstrated synthesis of iron doped reduced graphene oxide (rGO) decorated WO3 nanocomposites in a facile, environment friendly wet chemical sol-gel process. The as synthesized nanocomposites were comprehensively characterized by using different characterization techniques. A maximum-78% sensing response was obtained for the optimized composition of-10 wt% Fe doped 3 wt% rGO decorated WO3 based thin film (thickness-700 nm) sensor towards-10 ppm acetone gas. This sensing performance was observed at comparatively low working temperature of -130 ? with fast response (-20 s) and recovery (-75 s) time. The efficacy of the fabricated sensors was established by their capabilities to sense a very low concentration of -1 ppm acetone under similar working environment. Further illustration of versatility of the sensors revealed that, the sensors could be able to manifest a repeatable and reproducible sensing performance with prolong stability and superior selectivity for acetone over other interfering gases. The acetone sensing mechanism was illustrated with the help of electron depletion model and impedance spectroscopy study. Impedance spectroscopy quantifies different electrical properties and enlightens the smooth electronic transition mechanism between analyte and sensing material

Performance and diagnostic usefulness of commercially available enzyme linked immunosorbent assay and rapid kits for detection of HIV, HBV and HCV in India

Abstract Background HIV, HBV and HCV pose a major public health problem throughout the world. Detection of infection markers for these agents is a major challenge for testing laboratories in a resource poor setting. As blood transfusion is an important activity saving millions of live every year, it also carries a risk of transfusion transmissible infections caused by these fatal blood borne pathogens if the quality of testing is compromised. Conventional ELISA is regarded as the mostly used screening technique but due to limitations like high cost, unavailability in many blood banks and testing sites, involvement of costly instruments, time taking nature and requirement of highly skilled personnel for interpretation, rapid tests are gaining more importance and warrants comparison of performance. Results A comparative study between these two techniques has been performed using commercially available diagnostic kits to assess their efficacy for detection of HIV, HBV and HCV infections. Rapid kits were more efficient in specificity with synthetic antigens along with high PPV than ELISA in most cases. Comparison between different ELISA kits revealed that Microlisa HIV and Hepalisa (J. Mitra & Co. Pvt. Ltd.); ERBA LISA HIV1 + 2, ERBA LISA Hepatitis B and ERBA LISA HCV (Transasia Bio-medicals Ltd.) gives uniform result with good performance in terms of sensitivity, specificity, PPV, NPV and efficiency, whereas, Microlisa HCV (J. Mitra & Co. Pvt. Ltd.), Microscreen HBsAg ELISA and INNOVA HCV (Span Diagnostics Ltd.) did not perform well. Rapid kits were also having high degree of sensitivity and specificity (100%) except in HIV Comb and HCV Comb (J. Mitra & Co. Pvt. Ltd.). The kit efficiency didn’t vary significantly among different companies and lots in all the cases except for HCV ELISA showing statistically significant variation (p  Conclusions ELISA is a good screening assay for markers of HIV, HBV and HCV infections. Rapid tests are useful for further detection of false positive samples. ELISA seems the appropriate assay in blood bank. For availability of quality commercial diagnostic assays, evaluation of kit may be helpful.</p

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring

Optimization of Amylase Production from B. amyloliquefaciens

Demand for microbial amylase production persists because of its immense importance in wide spectrum industries. The present work has been initiated with a goal of optimization of solid state fermentation condition for amylase using agroindustrial waste and microbial strain like B. amyloliquefaciens (MTCC 1270). In an aim to improve the productivity of amylase, fermentation has been carried out in the presence of calcium (Ca+2), Nitrate (NO3−), and chloride ions (Cl−) as well as in the presence of D-inositol and mannitol. Amylase needs calcium ion for the preservation of its structure, activity and stability that proves beneficial also for amylase production using solid state fermentation. The inclusion of ions and sugars in the SSF media is promising which can be explained by the protection offered by them against thermal decay of amylase at various incubation periods at 37°C

A New Approach to Design of Cost-Efficient Reversible Quantum Dual-Full Adder and Subtractor

This paper proposed the design and development of reversible cost-efficient innovative quantum dual-full adder and subtractor or QD-FAS circuit using quantum gate. The proposed circuit can be used as full adder and full subtractor simultaneously, which is designed using double Peres gate or DPG and Feynman gate or FG. The quantum cost, garbage output and constant input of the QD-FAS is 8, 1 and 1. Which is better w.r.t previously reported work. The QD-FAS circuit, as proposed, includes shared sum and difference terminals, as well as a carry-out and a borrow output terminal. Notably, this innovation showcases a remarkable 27.27% reduction in quantum cost. The improvement in garbage output is even more striking, showing a 50% enhancement. When assessing the overall advancement in quantum cost, it falls within the range of 27.27% to 66.66%. To confirm the viability of this design, extensive testing is carried out using the IBM Qiskit simulator. This design holds significant importance in a variety of applications, including quantum computing, cryptography, and the realm of reversible Arithmetic Logic Units (ALU)