Search

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

With the penetration of renewable energy sources based distributed generation (RES-DG) in the distribution network (DN), the operating time of protection relays becomes a major concern to avoid the mis-coordination and nuisance tripping of RES-DG. It is due to the bidirectional flow of current, variance in the fault current level, i.e. high fault current in case of Squirrel cage induction generator (SCIG) based RES-DG, and low fault current for an inverter-based RES-DGs. This paper proposes a novel User-defined dual setting direction overcurrent relay with hybrid time current-voltage (UDDOR-TCV) characteristics to intensify the proposed scheme’s flexibility without any communication assistance for radial DN. The proposed model is formulated in a constrained non-linear optimization fashion and solved with the MINLP solver of general algebraic modelling system(GAMS) software to determine the optimal relay settings. The propounded protection scheme is simulated on IEEE-33 bus radial distribution system and a local 40-bus system hosting the multiple SCIG and PV based RES-DGs at optimal locations. Detailed numerical studies are carried out to show the performance of the proposed scheme. 66.848% and 68.26% reduction in relay operation time with zero mis-coordination is achieved with SCIG-WTG case and PV-units case for the IEEE-33 bus system and a similar reduction in operation time is 68.937% and 66.563% for Local-40 bus system. Moreover, the relays numbers are reduced by 32.692% and 39.7% for IEEE-33 and Local-40 bus systems, respectively, due to the forward/reverse characteristics capability of the proposed UDDOR-TCV. The performance of the proposed scheme is also evaluated on IEEE-8 bus meshed DN. The results are compared with the existing protection schemes in recent literature.

Information is exploding on the web at exponential pace, and online movie review over the web is a substantial source of information for online users. However, users write millions of movie reviews on regular basis, and it is not possible for users to condense the reviews. Classification and summarization of reviews is a difficult task in computational linguistics. Hence, an automatic method is demanded to summarize the vast amount of movie reviews, and this method will permit the users to speedily distinguish between positive and negative features of a movie. This work has proposed a classification and summarization method for movie reviews. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents and represent the review documents as a vector. Next, the Na¨ıve Bayes algorithm is employed to categorize the movie reviews (signified as a feature vector) into negative and positive reviews. For the task of movie review summarization, word2vec model is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are employed to compute the salience score of all review sentences in clusters. Finally, the best-ranked review sentences are picked based on top salience scores to form a summary of movie reviews. Empirical results indicate that the suggested machine learning approach performed better than benchmark summarization approaches.

Nowadays, data are flooding into online web forums, and it is highly desirable to turn gigantic amount of data into actionable knowledge. Online web forums have become an integral part of the web and are main sources of knowledge. People use this platform to post their questions and get answers from other forum members. Usually, an initial post (question) gets more than one reply posts (answers) that make it difficult for a user to scan all of them for most relevant and quality answer. Thus, how to automatically extract the most relevant answer for a question within a thread is an important issue. In this research, we treat the task of answer extraction as classification problem. A reply post can be classified as relevant, partially relevant, or irrelevant to the initial post. To find the relevancy/similarity of a reply to the question, both lexical and nonlexical features are used. We proposed to use LinearSVC, a variant of support vector machine (SVM), for answer classification. Two selection techniques such as chi-square and univariate are employed to reduce the feature space size. The experimental results showed that LinearSVC classifier outperformed the other state-of-the-art classifiers in the context of classification accuracy for both Ubuntu and TripAdvisor (NYC) discussion forum datasets.

Human pose recognition is a new field of study that promises to have widespread practical applications. While there have been efforts to improve human position estimation with radio frequency identification (RFID), no major research has addressed the problem of predicting full-body poses. Therefore, a system that can determine the human pose by analyzing the entire human body, from the head to the toes, is required. This paper presents a 3D human pose recognition framework based on ANN for learning error estimation. A workable laboratory-based multisensory testbed has been developed to verify the concept and validation of results. A case study was discussed to determine the conditions under which an acceptable estimation rate can be achieved in pose analysis. Using the Butterworth filtering technique, environmental factors are de-noised to reduce the system’s computational cost. The acquired signal is then segmented using an adaptive moving average technique to determine the beginning and ending points of an activity, and significant features are extracted to estimate the activity of each human pose. Experiments demonstrate that RFID transceiver-based solutions can be used effectively to estimate a person’s pose in real time using the proposed method.

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work.

Laser milling is a competent precision process especially when the work material is hard-to-machine such as titanium alloys. While performing the laser milling, a slight change in one of the laser parameters results in an abrupt change in the machining outcomes. A close match between the designed and the machined geometries is the essence of precision machining. A precise control over the material removal rate per laser scan is highly desirable but difficult to achieve. The difficulty level becomes higher if high surface finish is desired alongside the precision machining. In this research, the objective was set to perform the laser milling on titanium alloy (Ti-6Al-4V) with 100% control over material removal rate (MRR) per laser scan and minimum surface roughness (SR). Influence of the five laser parameters (laser intensity, pulse frequency, scan speed, layer thickness, and track displacement) on MRR and SR has been deeply investigated. Significance of each laser parameter is evaluated through ANOVA. Mathematical models for both the responses are developed to estimate the resulting responses at any parametric setting. Models have also been validated through confirmatory tests. Optimization of laser parameters is of great importance to remove the material exactly equal to the desired depth with minimum surface roughness. Therefore, the optimized combinations of laser parameters have been proposed which ensure the conformance of 100% MRR and minimum surface roughness with composite desirability > 0.9. Confirmatory experiments revealed that the optimized parameters are capable to produce the laser milling results as per the models’ predicted results. Additionally, the microstructure of the subsequent layers below the milled area has also been examined and compared with the microstructure of the bulk Ti-6Al-4V. By the use of optimized parameters, microstructure of the sub-layers remains unchanged as compared with the microstructure of the base metal. No evidence has been found altering the microstructure of the sub-layers.

Achieving the maximum material removal rate (MRRmax) is not always desired in machining especially during laser milling. Actual volume of the material removed during laser beam machining (LBM) is not always precisely equal to the designed volume. Dimensional accuracy of the laser milled feature requires the controlled layer of the substrate removal after each scanning cycle so that the cumulative material removal after full length of canning cycle conforms to the designed depth or geometry. In this research, laser milling of aluminum alloy has been carried out. Percentage of material removal rate (MRR%) and the roughness of the machined surface (SR) are taken as the response indicators. Optimal parametric combinations resulting in MRR% close to 100% with minimum SR have been pursued. Strength of the effects of five significant variables (in terms of one-way, square, and two-way interactions) is also identified. Furthermore, mathematical models are developed to predict the machining responses prior to proceed for actual machining. The research outcomes may be utilized to perform laser milling of AA 2024 (aluminum alloy used in various fields including aerospace industry) with precise control over MRR which ultimately will strengthen the dimensional accuracy of the machined profiles.

Clean water scarcity is still an intense, prolonged global issue that needs to be resolved urgently. The solar steam generation has shown great potential with a high energy conversion efficiency for clean water production from seawater and wastewater. However, the high evaporation rate of water cannot be preserved due to the inevitable fouling of solar absorbers. Herein, a self-floatable and super hydrophilic solar-driven steam generator composed of activated carbon coated melamine foam (ACM). The deposited ACM photothermal layer exhibits outstanding solar absorption (92%) and an efficient evaporation rate of 1.27 kg m

The apparent advantages of Multilevel Inverter (MLI) topologies in handling medium and high power with less loss in switching and lower harmonic distortion in an output voltage waveform makes it better than the conventional inverter. However, the MLI topologies utilize a large number of DC power supplies and power semiconductor devices. They also have a higher value of total standing voltage (TSV). Moreover, capacitor voltage balancing problems, self-voltage boosting inability, and complex control techniques require a relook and improvement in their structure. More recently, Switched-Capacitor Multilevel Inverter (SCMLI) topologies have been proposed to overcome the shortcomings of MLIs. In this paper, a generalized structure for a single-phase switched capacitor multilevel inverter (SCMLI) with self-voltage boosting and self-voltage balancing capability is proposed. A detailed analysis of a general structure of SCMLI is presented. The comparative analysis of the structures is carried out with recently reported topologies to demonstrate superiority. An optimized low-frequency modulation controls the output voltage waveform. The simulation and experimental results are included in the paper for single-unit symmetric (9-level voltage) and asymmetric (17-level voltage) configurations.

Natural resources are a boon for human beings, and their conservation for future uses is indispensable. Most importantly, energy-food-water security (EFWS) nexus management is the utmost need of our time. An effective managerial policy for the current distribution and conservation to meet future demand is necessary and challenging. Thus, this paper investigates an interconnected and dynamic EFWS nexus optimization model by considering the socio-economic and environmental objectives with the optimal energy supply, electricity conversion, food production, water resources allocation, and CO2 emissions control in the multi-period time horizons. Due to real-life complexity, various parameters are taken as intuitionistic fuzzy numbers. A novel method called interactive neutrosophic programming approach (INPA) is suggested to solve the EFWS nexus model. To verify and validate the proposed EFWS model, a synthetic computational study is performed. The obtained solution results are compared with other optimization approaches, and the outcomes are also evaluated with significant practical implications. The study reveals that the food production processes require more water resources than electricity production, although recycled water has not been used for food production purposes. The use of a coal-fired plant is not a prominent electricity conversion source. However, natural gas power plants&rsquo, service is also optimally executed with a marginal rate of production. Finally, conclusions and future research are addressed. This current study emphasizes how the proposed EFWS nexus model would be reliable and beneficial in real-world applications and help policy-makers identify, modify, and implement the optimal EFWS nexus policy and strategies for the future conservation of these resources.

Clean water scarcity is still an intense, prolonged global issue that needs to be resolved urgently. The solar steam generation has shown great potential with a high energy conversion efficiency for clean water production from seawater and wastewater. However, the high evaporation rate of water cannot be preserved due to the inevitable fouling of solar absorbers. Herein, a self-floatable and super hydrophilic solar-driven steam generator composed of activated carbon coated melamine foam (ACM). The deposited ACM photothermal layer exhibits outstanding solar absorption (92%) and an efficient evaporation rate of 1.27 kg m&minus, 2 h&minus, 1, along with excellent photothermal conversion efficiency (80%) as compared to commercially available primitive solar stills. The open porous assembly of melamine foam equipped with 80% flexibility (0.8 MPa) enabled smooth water transport and sustain heat accumulation within the matrix. The thermal insulation of ACM is 10 times greater than pure water. Moreover, open porous assembly of designed solar-powered steam generator rejects salt ions as well as volatile organic compounds efficiently. The low-cost and facile fabrication of photothermal based water production presents a potential solution to single step drinking water supply from various resources of the sea, the lakes and mixtures of emulsified oil and industrial wastewater.

Increasing energy demand globally has led to exploring ways of utilizing renewable resources for sustainable development. More recently, the integration of renewable distributed resources in small- and large-scale grid has been seriously researched. Development in renewable power sources and its integration with the grid require voltage level conversion to match the grid/micro-grid level. The voltage level conversion is brought about by employing Direct Current-Direct Current (DC-DC) converters with boosting features. The paper presents a wide gain range DC-DC boost converter with a low-stress on switching devices. The proposed converter&rsquo, s voltage gain is high compared with the conventional quadratic boost converter and other recently developed high gain boost converters. The topology has been compared with recently proposed topologies, and comparative analysis based on various performance parameters has shown that the topology is suitable for renewable and sustainable energy storage and grid integration. The power loss analysis has been done by incorporating the switching and conduction losses. A hardware prototype of 150 W has been developed to validate the converter&rsquo, s performance in steady-state as well as in dynamic conditions.

In this paper, a new transformerless high voltage gain dc-dc converter is proposed for low and medium power application. The proposed converter has high quadratic gain and utilizes only two inductors to achieve this gain. It has two switches that are operated simultaneously, making control of the converter easy. The proposed converter&rsquo, s output voltage gain is higher than the conventional quadratic boost converter and other recently proposed high gain quadratic converters. A voltage multiplier circuit (VMC) is integrated with the proposed converter, which significantly increases the converter&rsquo, s output voltage. Apart from a high output voltage, the proposed converter has low voltage stress across switches and capacitors, which is a major advantage of the proposed topology. A hardware prototype of 200 W of the proposed converter is developed in the laboratory to validate the converter&rsquo, s performance. The efficiency of the converter is obtained through PLECS software by incorporating the switching and conduction losses.

A highly cost-effective recycled biomaterial extracted from lime peel has been made biocompatible and has been coated on a commercial fluorine-doped tin oxide (FTO) substrate of glass using the spin coating method. Structural, morphologic, electronic, and antibacterial measurements were thoroughly characterized as a green biomaterial thin film using X-rays (XRD), PL, FTIR, Raman, SEM, HRTEM, AFM, I–V, and antibacterial diffusion techniques. The comprehensive analysis of structures of recyclable waste in the form of lime peel extract (LPE) as thin films showed the crystalline cellulose structure that corresponds to the lattice fringe (0.342 nm) exposed by HRTEM. The K+1 interstitial active sites or vacancies in LPE/FTO thin films are confirmed by the PL spectra that show important evidence about conduction mechanisms, and hence facilitates Ag+1 ion migration from the top to the bottom electrode. The AFM investigations revealed the minor surface roughness (169.61 nm) of the LPE/FTO film, which controls the current leakage that is associated with surface defects. The designed memory cell (Ag/LPE/FTO) exhibits stable, reproducible electrical switching under low operational voltage and is equipped with excellent retention over 5 × 103 s. Furthermore, owing to presence of flavonoids and their superior antioxidant nature, lime peel extract powder shows tremendous antimicrobial activity against gram-positive and Gram-negative bacterial strains.

Unfortunately, an abrupt corona-virus disease (COVID-19) outbreak brought a drastic change in human lives. Almost every sector of human-beings and their related activities are severely infected and affected by this COVID-19 pandemic. As days are passing, the impact of the COVID-19 epidemic is going to be more severe. The fundamental needs for personal protective equipment (PPEs) are rising drastically all over the world. In India, many non-pharmaceutical companies or organizations such as automobile companies are engaged in producing the PPEs at a very marginal rate. Thus this paper proposes a modeling and optimization framework for sustainable production and waste management (SPWM) decision-making model for COVID-19 medical equipment under uncertainty. To quantify the uncertainties among parameter values, we have taken advantage of the intuitionistic fuzzy set theory. A robust ranking function is presented to obtain a crisp version of it. Furthermore, a novel interactive intuitionistic fuzzy programming approach is developed to solve the proposed SPWM model. An ample opportunity to generate the desired solution sets are also depicted. The performance analysis based on multiple criteria such as savings from baseline, co-efficient of variations, and desirability degrees is also introduced. Practical managerial implications are also discussed based on the significant findings after applying to the real case study data-set. Finally, conclusive remarks and the future research direction are also addressed on behalf of the current contributing study.

Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.

The recent advancement in the application of the internet of things in the smart grid has led to an industrial revolution in the power industry. The Industry 4.0 revolution has already set in, allowing computers to interact for an efficient and intelligent approach in solving smart grid issues. multilevel inverters (MLIs) are an integral part of the smart grid system for integrating the distributed generation sources and storage energy systems into the smart grid. It attracted attention in industrial applications as they can handle high power and high voltage with an inherent feature of superior output voltage waveform quality. Moreover, its variant, the switched-capacitor MLI (SCMLI), has the added benefit of lesser DC supply requirement. In this paper, a switched-capacitor multilevel inverter topology has been proposed, which can operate in symmetric and asymmetric mode. The proposed SCMLI generate thirteen and thirty-one level output voltages for symmetric and asymmetric selection of DC voltage sources, respectively. The proposed SCMLI has a smaller number of switching devices for a given output voltage level as compared to other recently proposed topologies. A thorough comparison is presented with the recently proposed topologies on several parameters, including cost function. To validate the proposed topology, symmetric and asymmetric cases were simulated using Mthlab&reg, 2018a and the results were verified using an experimental hardware setup.