Additive manufactured portable vertical axis windmill

ABSTRACT

The vertical axis windmill is a type of wind turbine in which the main rotor shaft is set vertically. The main aim of this is to make it more compatible and accessible. According to the research, there is plenty of enough wind available globally to meet the human energy requirements – if it is harvested on a large scale effectively. If the wind turbine efficiency is increased, then there is a chance for more power generation and to drastically decrease the use of power generators which are rather expensive and also cause pollution. In comparison to horizontal axis wind turbines, the vertical axis wind turbines may be efficient as them and are also cheaper to build and maintain. In this paper, we designed and fabricated a vertical axis wind turbine and fabricated the same using the ‘3D printing technique’ which is one of the additive manufacturing processes used now.     

Nonsaturation throughput enhancement of IEEE 802.11b distributed coordination function for heterogeneous traffic under noisy environment

In this paper, we propose a mechanism named modified backoff (MB) mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function (DCF). In the noisy channel, when signal-to-noise ratio (SNR) is low, applying this mechanism in DCF greatly improves the throughput and lowers the channel idle time. This paper presents an analytical model for the performance study of IEEE 802.11 MB-DCF for nonsaturated heterogeneous traffic in the presence of transmission errors. First, we introduce the MB-DCF and compare its performance to IEEE 802.11 DCF with binary exponential backoff (BEB). The IEEE 802.11 DCF with BEB mechanism suffers from more channel idle time under low SNR. The MB-DCF ensures high throughput and low packet delay by reducing the channel idle time under the low traffic in the network. However, to the best of the authors' knowledge, there are no previous works that enhance the performance of the DCF under imperfect wireless channel. We show through analysis that the proposed mechanism greatly outperforms the original IEEE 802.11 DCF in the imperfect channel condition. The effectiveness of physical and link layer parameters on throughput performance is explored. We also present a throughput investigation of the heterogeneous traffic for different radio conditions.     

Delay-dependent robust stabilization and H ∞ control for uncertain stochastic T-S fuzzy systems with discrete interval and distributed time-varying delays

In this paper, delay-dependent robust stabilization and H∞ control for uncertain stochastic Takagi-Sugeno (T-S) fuzzy systems with discrete interval and distributed time-varying delays are discussed. The purpose of the robust stochastic stabilization problem is to design a memoryless state feedback controller such that the closed-loop system is mean-square asymptotically stable for all admissible uncertainties. In the robust H∞ control problem, in addition to the mean-square asymptotic stability requirement, a prescribed H∞ performance is required to be achieved. Sufficient conditions for the solvability of these problems are proposed in terms of a set of linear matrix inequalities (LMIs) and solving these LMIs, a desired controller can be obtained. Finally, two numerical examples are given to illustrate the effectiveness and less conservativeness of our results over the existing ones.     

Deep Learning Enabled Social Media Recommendation Based on User Comments

Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect the data based on the transactional reviews of social media. The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset. Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.). In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features. Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system. The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.     

A Novel Approach for Network Vulnerability Analysis in IIoT

Industrial Internet of Things (IIoT) offers efficient communication among business partners and customers. With an enlargement of IoT tools connected through the internet, the ability of web traffic gets increased. Due to the raise in the size of network traffic, discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues. A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification (MPDQDJREBC) is introduced for accurate attack detection with minimum time consumption in IIoT. The proposed MPDQDJREBC technique includes feature selection and categorization. First, the network traffic features are collected from the dataset. Then applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time consumption. After the significant features selection, classification is performed using the Jaccardized Rocchio Emphasis Boost technique. Jaccardized Rocchio Emphasis Boost Classification technique combines the weak learner result into strong output. Jaccardized Rocchio classification technique is considered as the weak learners to identify the normal and attack. Thus, proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic error. This assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time usage. Experimental assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy, precision, recall, F-measure and attack detection time. The observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.     

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region. The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs. Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight, easy to implement, and of cutting-edge accuracy. Finally, the region-based black box method for determining pupil center coordinates was introduced. The proposed architecture was tested using various IRIS databases, including the CASIA (Chinese academy of the scientific research institute of automation) Iris V4 dataset, which has 99.5% sensitivity and 99.75% accuracy, and the IIT (Indian Institute of Technology) Delhi dataset, which has 99.35% specificity and MMU (Multimedia University) 99.45% accuracy, which is higher than the existing architectures.     

High Energetic High Density Materials: A BASIC Program (FCALC) for the design and selection of efficient high energetic materials containing nitro and azido groups

A program named “FCALC” has been developed in BASIC language to calculate the detonation volumes (through a factor F) for various explosives as well as for any new organic structure. The number and kind of substiments that need to be incorporated into an unsubstituted organic compound in order to achieve maximum F factor, and thus maximum detonation velocity, can also be predicted. The program calculates F factors for a user-defined set of combinations of substituents.     

Miniaturized frequency-selective surface with reduced number of metallic vias for electromagnetic shielding

A miniaturized frequency-selective surface (FSS) for radio frequency (RF) filtering applications is reported in this paper. The proposed FSS is designed to operate with band stop property at 1.57 GHz. The FSS element is developed using highly convoluted lines running on either side of the substrate interconnected using via lines. This specific arrangement increases the electrical length of the resonator without increasing the physical size. Furthermore, the stability towards polarization variation and independence to different angles of incidence are other major concerns in the design of FSS. The FSS unit cell has rotational symmetry thereby providing polarization-independent operation. The angular independence is tested for various incidence angles, and the results are presented. The performance of the FSS is validated in real time, and the results are presented. The maximum realized transmission loss is 27 dB at 1.57 GHz, and the attenuation bandwidth with reference 10 dB shielding level is estimated to be 400 MHz centered around 1.57 GHz. The convolution technique along with shorting vias has offered this extreme miniaturization ranging from 7% to 95.7% with reference to most of the FSS reported for L-band services such as radio, telecommunications, military, and aircraft surveillance.     

Experimental Investigation and Optimization of EDM Performance Measures of MoSi2-SiC Intermetallic Ceramic Composite using RSM with Regression Equations

Silicon - Electric Discharge Machining (EDM) is a good potential composite ceramic design and manufacturing technique. It's the first study of its kind to investigate EDM on an...     

Polycaprolactone/Graphene Oxide–Silver Nanocomposite: A Multifunctional Agent for Biomedical Applications

Journal of Inorganic and Organometallic Polymers and Materials - In recent times, the global demand for multi-biofunctional tissue scaffolds is rising gradually. The present study deals with the...