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Showing papers in "Journal of Engineering Science and Technology Review in 2020"


Journal ArticleDOI
TL;DR: The detailed configurations and features of IoT application involved in the modern communication platform towards the development of smart cities by incorporating data mining using AI (Artificial Intelligence) are enumerated.
Abstract: The modern wireless sensor network is focused on integrating the emerging technology of IoT with the real world for enhanced data processing to improve automation and communication more smartly and safely. In the present industrial upgradation with IoT application greatly proved its positive influences on the operation and management phases along with progressive adoption of cloud computing and big data analytics. In Industry, smart monitoring and distributed control of entire architecture are hold up with the renovated technology of the Internet of Things (IoT). It lies on the top layer of the wireless communication network to afford enhanced connectivity among smart wireless or wired sensor with the embedded modern controller to the cloud server. In IoT application, real-time data acquisition is done securely and transmitted to the data analytics services to identify any catastrophic situation existence. It is reliable and moved industrial automation to the next step evolution of formulating to make proactive decisions to decide and react to the industrial variation constraints. Though modernization has shifted to analog to the digital generation, it is interoperable with available protocols and data standardizations in industrial sectors. Hence this paper enumerates the detailed configurations and features of IoT application involved in the modern communication platform towards the development of smart cities by incorporating data mining using AI (Artificial Intelligence). Further, the trending overview of Industrial IoT application in risk-based environments with complete knowledge-based architecture with incorporated wireless technology and protocols are outlined.

25 citations


Journal ArticleDOI
TL;DR: This paper attempts to provide a detailed study on the sentiment analysis methods applied on languages other than English, covering methods that analyze translated data as well as methods that analyzed available data in the target language.

14 citations


Journal ArticleDOI
TL;DR: In this article, power network structural topology (PNST) is proposed to optimally locate distributed generator within distribution system which results in minimal loss as well as maintaining voltage profile within constraint limits.
Abstract: Distribution system is very essential to load centre or service mains. This is because it is the final section of electric power system (EPS) to supply the consumers. Once this section is compromised, low voltage consumers will be denied of a reliable supply of electricity. One way to make supply to low voltage consumers reliable is by bringing generation close to them through distributed generators. However, location of distributed generator is very important with respect to the entire EPS security. In this study, power network structural topology (PNST) is proposed to optimally locate distributed generator within distribution system which results in minimal loss as well as maintaining voltage profile within constraint limits. 5- bus IEEE test system was used as case study to show the feasibility of the proposed method. Results obtained for both test systems were validated through the results from power world simulation tool.

10 citations






Journal ArticleDOI
TL;DR: An aspect-based recommendation framework is designed by performing three tasks: identifying the mentions associated with item aspects in user reviews, extracting the sentiment related opinions using Latent Semantic Analysis of such aspects in the reviews, and performing the opinion mining from all of the aspects to generate enhanced recommendations with Ensemble Multimodel Deep Learning (EMDL).
Abstract: Nowadays, mobile devices and apps are meant to fulfill the needs of various people in society. But, mobile app Stores are facing major challenges in recommending proper apps for users. Recommending mobile apps for users according to personal preference and various mobile device limitations is therefore important. In this scenario, there is a huge need for developing recommender systems (RS) for the user’s community in enabling critical mobile apps such as Health based Apps. Recommendation Systems perform an extensive survey on the collection of user reviews, preferences and opinions to discover recommendations of suitable applications to the users' community. In this paper, we have designed an aspect-based recommendation framework by performing three tasks: such as identifying the mentions associated with item aspects in user reviews, extracting the sentiment related opinions using Latent Semantic Analysis of such aspects in the reviews, and perform the opinion mining from all of the aspects to generate enhanced recommendations with Ensemble Multimodel Deep Learning (EMDL). EMDL comprises of two state-ofthe-art classifiers such as Deep Neural Networks (DNN) and Long Short Term Memory (LSTM). In contrast to the prior work, we conducted a series of experiments with several state-of-art deep learning models to extract useful recommendations. The achieved results show that classification with outperforms in all the aspects based on various evaluation metrics when compared to the rest of the models.

9 citations


Journal ArticleDOI
Bin Meng, Na Lu, Xinyao Guo, Qingmin Si, Owen Bai 
TL;DR: The PSR-BN model for emergency scenario analysis can comprehensively and systematically analyse the evolution of emergency scenarios in civil aviation airports, prove the feasibility and effectiveness of the analysis method, and effectively compensate for the shortcomings in the static analysis of emergency events.
Abstract: The emergency response capability of civil aviation airports is the core to ensure the efficient handling of civil aviation emergencies. The dynamic characterization of the multi-scenario evolution paths of emergencies in civil aviation airports and quantitative targeted evaluation have become the study foci urgently needed to be solved by the current academic circles and airport departments for emergency management. To clarify the emergency mechanism, evolution mechanism, and multi-scenario evolution paths, this study first constructed the pressure-state-response (PSR) network expression of emergency scenario evolution in civil aviation airports. Then, the evolution path of airport emergency scenarios and the probability of different evolution scenarios were evaluated on the basis of the PSR model and Bayesian network (BN). Lastly, the specific process of the analysis method for emergency scenarios based on PSR and BN was demonstrated in consideration of emergency rescue drills in civil aviation airports as example. Results show that different emergency response measures are adopted for the critical scenarios of emergencies, and the development and evolution paths of emergency scenarios completely differ. The PSR-BN model for emergency scenario analysis can realize the reasoning process of combining the qualitative and quantitative scenarios of the dynamic evolution of civil aviation emergencies. It can comprehensively and systematically analyse the evolution of emergency scenarios in civil aviation airports, prove the feasibility and effectiveness of the analysis method, and effectively compensate for the shortcomings in the static analysis of emergency events. The model provides reference for the emergency analysis of civil aviation airports.

8 citations


Journal ArticleDOI
TL;DR: A new hybrid model using Long ShortTerm Memory (LSTM), a Recurrent Neural Network (RNN) technique and Auto Regressive Integrated Moving Average (ARIMA), a time series forecasting technique to capture the live stock market data of S&P 500 using preexisting Application Programming Interface (API).
Abstract: The stock market is a highly volatile industry with ever changing bull (rise) and bear (fall) trends. This paper proposes a new hybrid model using Long ShortTerm Memory (LSTM), a Recurrent Neural Network (RNN) technique and Auto Regressive Integrated Moving Average (ARIMA), a time series forecasting technique to capture the live stock market data of S&P 500 using preexisting Application Programming Interface (API). Rise and fall in stock values in the previous years is analyzed. A novel LSTMARIMA hybrid is designed for capturing the linear and nonlinear portions of the time series. The Prophet forecasting library by Facebook has also been used that requires less preprocessing. Finally, both the approaches are compared and the one with better performance is accepted for the final stock market prediction system. In this case, Prophet has a high Root Mean Square Error (RMSE) of 27.59 and Mean Square Error (MSE) of 761.33 whereas the ARIMALSTM hybrid gives an MSE of 3.03 and RMSE of 1.74 along with a 99% fit of the model. Hence the hybrid performs much better than Prophet and is accepted as the final algorithm for implementation.

7 citations


Journal ArticleDOI
TL;DR: In this paper, the reliability, availability and maintainability (RAM) measures to improve the operational performance of a soft water treatment and supply plant (SWTS-Plant) through the illustrative case study was performed.
Abstract: The purpose of this paper is to provide results for reliability, availability and maintainability (RAM) measures to improve the operational performance of a soft water treatment and supply plant (SWTS-Plant) through the illustrative case study. RAM analysis of SWTS plant installed in a high-rise society ABC, Jaipur was performed. The descriptive analysis of time to failure and repair has been made along with trend analysis and goodness-of-fit test. The best fitted distributed and parameters have been identified from the existing theoretical distributions using the maintenance data of ABC plant. Reliability and maintainability measures also calculated for the entire plant. It is observed that (i) the plant availability decline from 97.96% to 92.67% (ii) failures of five subsystems dominant with 81.5 % failures and (iii) average failure rate is 937.4 minutes. This study will be supportive to identify the occurring complications in the plant.



Journal ArticleDOI
TL;DR: A new, low cost, compact and modular Internet of Things platform for air quality monitoring in urban areas is presented that is suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction.
Abstract: In the present paper, a new, low cost, compact and modular Internet of Things (IoT) platform for air quality monitoring in urban areas is presented This platform comprises dedicated low cost, low power, hardware and the associated embedded software that enables measurement of particles (PM2 5 and PM10), NO, CO, CO2 and O3 concentration in the air, along with relative temperature and humidity This integrated platform acts as part of a greater air pollution data collecting wireless network that is able to monitor the air quality in various regions and neighborhoods of an urban area, by providing sensors measurements at a high rate that reaches up to one sample per second It is therefore suitable for Big Data analysis applications such as air quality forecasts, weather forecasts and traffic prediction The first real world test for the newly developed platform took place in Thessaloniki, Greece, where multiple devices were installed in various buildings in the city Preliminary results from the pilot testing period are provided with focus on COVID-19 impact on air quality © 2020 School of Science, IHU All rights reserved



Journal ArticleDOI
TL;DR: In this article, a digital educational game, Wordsearch, was proposed for learning a foreign language vocabulary, which was evaluated by 11 students and showed that the user interface of the application is simple and easy to use, and that the proposed Wordsearch Puzzle is likely to help long term vocabulary recall, while it makes learning fun by creating internal motivation to the students.
Abstract: In recent years, digital games have taken an important place in the lives of children. Through play, children acquire digital literacy informally, even before they go to school. Games can also be used as a language learning tool nowadays. In this paper we propose a digital educational game, Wordsearch, for learning a foreign language vocabulary. In order to evaluate the game and to test its effectiveness in learning vocabulary, Wordsearch was evaluated by 11 students. Results show that the user interface of the application is simple and easy to use, and that the proposed Wordsearch Puzzle is likely to help long term vocabulary recall, while it makes learning fun by creating internal motivation to the students.

Journal ArticleDOI
TL;DR: This paper presents a comprehensive review on the important aspects of experimental analysis, estimation, modeling, and avoiding of interference for WSN and offers some insight in dealing with aforementioned problem.


Journal ArticleDOI
TL;DR: In this paper, a cold modeling experiment of a 1 t/h biomass boiler under different staged air distribution ratios when the rear arch coverage varies was conducted using Fluent software in this study after thermal performance computation and initial structural design of grate and furnace.
Abstract: The arch is an important component of a biomass boiler. Initial arch design of most boilers is generally gained through manual computation, thus resulting in uncertain reasonability of flue gas flow. Moreover, biomass fuels in the market have instable characteristics, which influence the utilization of biomass energies considerably. To address the problems concerning reasonable flue gas flow caused by the collaborative design of arch and air staging and the combustion adaptability of fuels, a cold modeling experiment of a 1 t/h biomass boiler under different staged air distribution ratios when the rear arch coverage varies was conducted using Fluent software in this study after thermal performance computation and initial structural design of grate and furnace. Furthermore, a boiler performance test based on main fuels and a combustion adaptation test of auxiliary fuels were also performed. The experiments show that the best flue gas flow in the furnace is achieved when the rear arch coverage is 60% and the primary–secondary air distribution ratio is 4:6. The mean boiler efficiency and the mean boiler heat output are 81.26% and 715.76 kW/h by using Pinus koraiensis pellets, wood–straw mixed pellets, and cotton straw briquettes as main fuels; and the tested pollutant emissions are in compliance with the limits of the national standard. The results of the combustion adaptation test reveal that the excessive particle size, the high ash content and the relatively low calorific value of biomass molded fuels are all against the combustion of biomass boilers. Fuel upgrading based on washing process and other methods is suggested. This study can provide references to the performance optimization of traditional small-scale biomass chain heating boilers.


Journal ArticleDOI
TL;DR: In this article, a wave front-time analysis model was proposed to reveal the relationship between the wave front time and electrical energy and hydrostatic pressure, and the relations between these conditions and shockwave front time were derived and combined with theoretical information.
Abstract: Strong shockwaves can be formed by pulse discharge in water, and are applicable in fields such as control blasting, oil production, and national defence construction. Wave front-time is an important parameter in shockwave theory and effect. In order to reveal the relationship between the wave front-time and electrical energy and hydrostatic pressure, this study proposed a wave front-time analysis model. The relations between these conditions and shockwave front-time were derived and combined with theoretical information. The shockwave front-time was obtained at several sites under different conditions of hydrostatic pressure, breakdown energy, and propagation distance. Results indicate that shockwave front-time decreases with increasing breakdown energy, when electrical energies are in the range of 15003500 J. Wave front-time increases with increasing hydrostatic pressure, when hydrostatic pressures are in the 0-4 MPa range. Wave front-time increases with increasing propagation distance within 4 m. The breakdown energy and hydrostatic pressure have significant effects on the increase of the front-time as the propagation distance increases. The findings provide references for experiments on pulse discharge in water and evaluation of shockwave performance.



Journal ArticleDOI
TL;DR: In this paper, a model of stress distribution on the sidewalls of open holes was established, and a fracturing experiment was conducted on polymethyl methacrylate (PMMA), and the influential factors of fracture breakdown were summarized.
Abstract: Shale oil and gas reservoirs, as well as compact oil and gas reservoirs, are important oil exploration resources. However, their tight lithology and extremely low permeability and porosity hinder extraction. Production stimulation measures, such as reservoir fracturing transformation, are needed to increase production by low-permeability oil and gas reservoirs. This study explored a new type of fracturing technology, namely, pulsed-plasma rock fracturing, to increase rock fracturing efficiency and recovery. First, a model of stress distribution on the sidewalls of open holes was established. Then, a fracturing experiment was conducted on polymethyl methacrylate (PMMA). Morphological features were analyzed on the basis of the visual characteristics of PMMA, and the influential factors of fracture breakdown were summarized. Meanwhile, the stress-changing rule was analyzed by simulating pulsed-plasma shock-wave rock fracturing with LS-DYNA. Results show that pulsed-plasma fracturing can generate valid cracks with actiniform and wave-form features. High discharge voltages and loads on rocks associate with long crack lengths. In the simulation, applies load strengths form 9 MPa to 30 MPa, and the crack lengths increase from 16 mm to 67.5 mm. At the same time, the width and number of fractures show an increasing trend, and radioactivity and multi-branched cracks inside the rocks become increasingly complex. This study provides a practical and reliable reference for the technology of pulsed-plasma rock fracturing.

Journal ArticleDOI
TL;DR: In this article, a target tracking algorithm based on an improved Gaussian mixture model was proposed to improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios.
Abstract: In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

Journal ArticleDOI
TL;DR: This work has investigated viruses and other forms of malware that have affected the mobile market and how malware analysts have proposed various suitable solutions to detect them efficiently and how they were discovered alongside analysis of said threats.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper used a double-track suspension to realize the automation of plant protection and the uniformity of pesticide application in a Chinese solar greenhouse (CSG), where the sprayer could move freely along the east and west directions of the CSG and perform two spraying modes, namely, fixed boom spray and lifting spray.
Abstract: Implementation of manual plant protection in a Chinese solar greenhouse (CSG) is expensive and labor-intensive. Moreover, this system cannot realize the separation of people and drugs. In a CSG, plants are cultivated at different distances, directions, and heights. Consequently, pesticide application via air-spraying is not uniform because the volume of pesticide droplets that deposit on plants is disproportionate. In this study, a CSG inter-row sprayer was designed to realize the automation of plant protection and the uniformity of pesticide application. With a double-track suspension, the sprayer could move freely along the east and west directions of the CSG and perform two spraying modes, namely, fixed boom spray and lifting spray. These modes could be applied depending on different crop growth stages and protection requirements. Electromagnetic detection and positioning technology were used to detect crop rows and satisfy inter-row spraying requirements. Automatic lifting and spraying operations based on crop height as detected by the sensor were realized by a lifting motor. The effects of inter-row lifting and fixed boom sprays on droplet deposition and penetrability were tested using mature tomatoes cultivated in the CSG. Results demonstrate that the average volumes of droplets deposited on the obverse and reverse sides of tomato leaves are 1.77 and 0.817 μL per square centimeter in the inter-row lifting spray, and the average variation coefficients of droplet volume deposition are 7.3% and 19.53%, respectively. In the fixed boom spray, the average volumes of droplets deposited on the obverse and reverse sides of tomato leaves are 1.12 and 0.086 μL per square centimeter, respectively, and the average variation coefficients of droplet volume deposition are 33.2% and 74.7%, respectively. These findings indicate that inter-row lifting spray significantly increases droplet deposition on the reverse side of tomato leaves and improves the uniformity and penetrability of droplet deposition. This study improves the automation degree of plant protection and uniformity of pesticide droplet deposition, as well as provides supplementary options for plant protection in CSGs.