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This paper aims to propose a bidirectional hidden converter (BHC)-based three-phase DC–AC conversion for energy storage application. BHC is the new concept to vary an energy storage device voltage into wide range. Hidden converter power loss and power rating are reduced by using zero-sequence injection-based carrier-based pulse-width modulation (CBPWM) strategy.

By using this control strategy, a BHC processes only little amount of power during double-stage conversion, mostly during direct or single-stage conversion of the three-phase three-port converter (TPTPC) only processing the maximum power.

TPTPC consists of two sets of positive group switches for inversion process, one set of switches is regular inverter switches called vertical positive group switches, and the second set is anti-series switches, which are horizontally connected for direct or single-stage conversion.

Characteristics, principles and implementations of proposed DC–AC 3Ø conversion system and its PWM strategy are analyzed. Through experimental outputs, the effectiveness and viability of the proposed solutions are validated.

This paper aims to propose Hidden Converter (H-Converter) combined with dual port 3Ø inverter for energy storage application to produce wide range of voltage. Some of the application required wide range of voltages, but problem from E-chopper is either boost or buck mode of operations, both modes are not possible. To overcome this drawback, H-Converter is combined with dual port 3Ø inverter controlled by carrier-based pulse width modulation (CB-PWM) technique is added with zero sequence injection.

Hidden converter is a bidirectional DC-DC chopper used to convert fixed DC to variable DC and vice versa in both buck and boost modes of operations. Dual port inverter is combined with hidden DC-DC converter can produce wide range of voltages.

The bidirectional DC-AC converter requires less power for processing and consumes less power losses by using modest carrier built- pulse width modulation scheme through proposed zero structure addition.

By using this proposed strategy H-Converter can produce wide range of voltage in both the sides and mostly power is processed in the 3Ø inverter with a one stage conversion with less power loss. As a result, with one stage power conversion has more efficiency because of less power loss. This proposed converter has designed by analysis, and the real time result is tested in an experiment.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.

The data communications protocol supporting the internet protocol version 4 (IPv4) is almost 40 years old, and its 32-bit address space is too small for the internet. A “next-generation” internet protocol version 6 (IPv6), has a much larger, 128-bit address space. However, IPv6 is not backward compatible with the existing internet. For 20 years, the internet technical community has attempted to migrate the entire internet to the new standard. This study aims to address important but overlooked questions about the internet’s technical evolution: will the world converge on IPv6? Will IPv6 die out? or will we live in a mixed world for the foreseeable future?

The research offers an economically-grounded study of IPv6’s progress and prospects. Many promoters of IPv6 sincerely believe that the new standard must succeed if the internet is to grow, and assume that the transition is inevitable because of the presumed depletion of the IPv4 address resources. However, by examining the associated network effects, developing the economic parameters for transition, and modeling the underlying economic forces, which impact network operator decisions, the study paints a more complex, nuanced picture.

The report concludes that legacy IPv4 will coexist with IPv6 indefinitely. IPv6 is unlikely to become an orphan. For some network operators that need to grow, particularly mobile networks where the software and hardware ecosystem is mostly converted, IPv6 deployment can make economic sense. However, the lack of backward compatibility with non-deployers eliminates many network effects that would create pressure to convert to IPv6. A variety of conversion technologies, and more efficient use of IPv4 addresses using network address translation, will support a “mixed world” of the two standards for the foreseeable future.

The authors’ conceptualization and observations provide a clearer understanding of the economic factors affecting the transition to IPv6.

Classifying emails as ham or spam based on their content is essential. Determining the semantic and syntactic meaning of words and putting them in a high-dimensional feature vector form for processing is the most difficult challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model.

In this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets, SpamAssassin and Enron, are used in the experimentation.

In the first set of experiments, machine learning classifiers, the support vector machine (SVM) model, perform better than other machine learning methodologies. The second set of experiments compares the deep learning model performance without embedding, GloVe and BERT embedding. The experiments show that GloVe embedding can be helpful for faster execution with better performance on large-sized datasets.

The experiment reveals that the CNN model with GloVe embedding gives slightly better accuracy than the model with BERT embedding and traditional machine learning algorithms to classify an email as ham or spam. It is concluded that the word embedding models improve email classifiers accuracy.

This paper aims to present a new fault detection and classification scheme of both DC faults and AC faults on a DC microgrid network.

To achieve reliable protection, the derivative of DC current signal is decomposed into several intrinsic modes using variational mode decomposition (VMD), which are then used as inputs to the Hilbert–Haung transform technique to obtain the instantaneous amplitude and frequency of the decomposed modes of the signal. A weighted Kurtosis index is used to obtain the most sensitive mode, which is used to compute sudden change in discrete Teager energy (DTE), indicating the occurrence of the fault. A stacked autoencoder-based neural network is applied for classifying the pole to ground (PG), pole to pole (PP), line to ground (LG), line to line (LL) and three-phase line to ground (LLLG) faults. The effectiveness of the proposed protection technique is validated in MATLAB/SIMULINK by considering different test cases.

As the maximum fault detection time is only 5 ms, the proposed detection technique is very fast. A stacked autoencoder-based neural network is applied for classifying the PG, PP, LG, LL and LLLG faults with classification accuracy of 99.1%.

The proposed technique provides a very fast, reliable and accurate protection scheme for DC microgrid system.

The usability aspect of the construction operations building information exchange (COBie) datasheet has been largely overlooked. Users find it difficult to find relevant data inside COBie and understand the dependencies of information. This research study is a part of a more comprehensive research study to identify the usability issues associated with COBie and propose solutions to deal with them. This paper aims to discuss the challenges associated with the visualization aspect of COBie and proposes a solution to mitigate them.

This paper is based on design thinking and waterfall methodology. While the design thinking methodology is used to explore the issues associated with the visualization aspect of COBie, the waterfall methodology is used to develop a working prototype of the visualizer for the COBie datasheet using a spreadsheet format.

The paper demonstrates that the property graph model based on a node-link diagram can be effectively used to represent the COBie datasheet. This will help in storing data in a visually connected manner and looking at links more dynamically. Moreover, converting and storing data into an appropriate database will help reach data directly rather than navigate multiple workbooks. This database can also help get the history of data inside the COBie datasheet as it develops throughout the project.

This research proposes a novel approach to visualize the COBie datasheet interactively using the property graph model, a type of node-link diagram. Using the property graph model will help users see data in a connected way, which is currently missing in the spreadsheet representation of COBie data. Moreover, this research also highlights that storing historical changes in COBie data can help understand how data has evolved throughout the construction. Additionally, structured storage of data in relationship format can help users to access the end of connected data directly through the efficient search.

This paper aims to present the design and implementation of a new general-purpose single-phase buck-type inverter.

The operation of the proposed inverter is based on the general-purpose buck converter. The proposed buck-type inverter topology is designed with reduced numbers of passive and active elements to minimize design cost and complexity. Also, an efficient hybrid control technique based on the proportional‐integral‐derivative (PID) supported by open-loop control signal is offered for the control of the proposed inverter. The proposed hybrid control method improves the performance of the PID controller during the change of inverter operation parameters. A close to single-phase sine wave output voltage with low total harmonic distortion (THD) can be produced by the proposed inverter in a wide range of voltage and frequency lower than the inverter input voltage value.

Simulation and experimental test studies are applied to the proposed inverter. The experimental laboratory setup is built for 0–50 Hz, 0–100 Vp, 0.5 kW. Both the simulation and the experimental test results show that the single-phase inverter can produce close to sine wave output voltage with THD level under 5% in a wide range of frequency for various operating conditions and for different loads.

In this paper, a new topology and a new hybrid control technique that are patented by the corresponding author are implemented for a single-phase buck-type inverter through a scientific project. The operating results of the study reveal the efficient operating capability with a simple topology structure.

A low cost single‐turn conductive plastic trimming and precision potentiometer 138 is available in 1 5/16 in. diameter and covers a resistance range from 1K ohm to 100K ohms. Elements are screened on but can be co‐moulded. It has a centre tap facility and continuous rotation and mechanical stops as standard. Power rating at 40°C ambient is 2W.

This study aims to conduct the aircraft electrical wiring interconnection system (EWIS) safety risk assessment process abundantly and hierarchically and establish the assessment index system considering the weights and interrelationships of different levels of indices.

Due to the failure of EWIS being multifactorial, hidden and diverse, this paper divides the factors influencing the failure of EWIS into 3 primary indices, 13 secondary indices and 38 tertiary indices. Taking open circuit failure (OCF) and short circuit failure (SCF) as examples, calculate the weights of assessment indices based on the triangular fuzzy number analytic hierarchy process (TFNAHP) and triangular fuzzy number decision-making trial and evaluation laboratory (TFNDEMATEL). The cloud model (CM) divides the risk levels and obtains the safety risk assessment results. The comparative analyses of different weight calculation methods, different failure modes and different aircraft EWIS zones verify the effectiveness and practicability of the proposed method.

The results show that the proposed method aligns more with the actual situation than other methods. Also, the results identify key focus objects in EWIS safety risk assessment, such as the surrounding environmental factors among the primary indices having the most significant influence on OCF and SCF, the risk level of SCF being higher than that of OCF, etc.

This paper proposes a safety risk assessment index system for aircraft EWIS based on the cable parameters, surrounding environmental factors, installation and protection methods. The weight assignment is added to the assessment index system, and the safety risk assessment model is constructed by combining TFNAHP, TFNDEMATEL and CM.