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Abstract

In this paper, advanced DC-Link (DCL) based reversing voltage type Multilevel Inverter (MLI) topologies by compensating the difficulties in the conventional MLIs are reviewed. These topologies consist of less switching components and driver circuits when compared with conventional MLIs predominantly in higher levels. Consequently, installation area, total cost and hardware difficulties are reduced by increasing the voltage levels. The unipolar based Pulse Width Modulation Schemes (PWMS) will improve DCL inverters performance. This paper presents unipolar Multi-Reference (MR) based sine and space vector PWMS with single triangular carrier wave for generating required levels in output voltage. Comparison between UMR sine and space vector PWMS for DCL inverter topologies is presented in terms of Fundamental Output Voltage (FOV) and Total Harmonic Distortion (THD). The research tries to establish the survey analysis for single-phase 7-level DCL based reversing voltage type MLI topologies with UMR based sine and space vector PWMs. Finally, to confirm the feasibility of proposed DCL-MLIs in terms of FOV and THD the simulation results are incorporated. Further, the prototype model is developed for single-phase 7-level DCL inverter with Field Programmable Gate Array (FPGA) based UMR sine and space vector PWMS to authenticate simulation results. The efficiency of the proposed cascaded MLI achieves the value of 99.003%.

Open access

Abstract

In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

Open access
International Review of Applied Sciences and Engineering
Authors:
K. S. Ajay Venkadesh
,
K. Harish Kumar
,
B. Hariharan
,
A. Arumugam
,
A. Nithish Kumar
,
P. Karthigai Priya
, and
S. Vanitha

Abstract

Raw materials requirement is foremost necessary in construction sector. Due to the increase in construction activities, the raw material utilization is also increased, which may lead to depletion of the resources. The usage of M sand also increases day by day. On the other side, waste disposal is posing a major threat to environment and human health. This paper shows the investigation carried out in manufacturing fly ash bricks made by utilizing vermicompost as an alternative material for M sand, the physical and chemical properties of M sand and vermicompost are studied and they seem to be the same. In this study, an attempt is made to check the feasibility of replacement of vermicompost for M sand in brick making. The brick specimens are casted as per the mix proportions and they are tested for strength and durability at the age of 28 days. It has been identified that the vermicompost replacement at 5% and 10%, the compressive strength of the brick is 7.90 and 7.31% respectively, which is found to be nearer to the strength of the control specimen and the water absorption for all the mixes of the brick casted were below 20% as per IS code. Inclusion of vermicompost in the fly ash bricks will tend to reduce the use of M sand.

Open access
International Review of Applied Sciences and Engineering
Authors:
N. ArikaraVelan
,
V. Deepak
,
N. Dhinesh Kumar
,
G. Muthulingam
,
S. Vanitha
,
P. Karthigai Priya
, and
Sachin Sabariraj

Abstract

In this study, vermicompost is replaced for fine aggregate in geopolymer concrete (GPC). Initially mix design is made for GPC and mix proportion is proposed. The vermicompost is replaced at 5%, 10%, 15% and 20% with M sand in GPC. Result indicates the 5% replacement with vermicompost based geopolymer concrete (GPVC) has the compressive strength of 32 N mm−2 (M30 grade) whereas the compressive strength of control specimen made with GPC is 37 N mm−2. Other replacement shows 21 N mm−2, 14 N mm−2 and 11 N mm−2 respectively. The 5% replaced concrete cubes and control specimen are tested at an elevated temperature of 200°C, 400°C, 600°C and 800°C and compared with the control specimen. There is no significant difference observed in weight lost at control (GPC) and GPVC specimen. An elevated temperature, the weight loss is almost 4% at 200°C because of expulsion of water from the concrete. Afterwards only 2% weight loss is observed in remaining elevated temperature. The compressive strength loss is observed at an elevated temperature in GPC and GPVC specimen because of thermal incompatibility between aggregate and the binder. EDX results show M sand and compost contains Si, Al, C, Fe, Ca, Mg, Na and K and it is similar in the elemental composition and SEM image confirms vermicompost contains fine particles.

Open access