bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture
Authors: Pandey Sunil , Nagwani Naresh Kumar , Verma Shrish 
Published: December 2021

Abstract
The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neural network implementations have been observed to train to nearly 80% training accuracy in 200 epochs. In effect, therefore, the comparison is on the time taken to complete the training epochs.
Detection of Cotton Leaf Disease Using Image Processing Techniques
Authors: Tripathy Sushreeta 
Published: December 2021

Abstract
In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.
Face Transferring on Webcam images using StyleGAN
Authors: Prasanthi J. , Anuradha G. 
Published: December 2021

Abstract
In image processing technology, face transfer is broadly used for privacy protection, picture enhancement, and entertainment applications. Face transfer is the domain that maps one image into another image and extracts several features of the face from one person to morph that face to another person. This face transfer will carry the facial expressions also. This is also called face morph, face swap, etc. Here we propose StyleGAN technology using face transfer with the image to get high quality. In this StyleGAN contribute the bilinear interpolation and affine transformation. Bilinear interpolation is to remove the noise and increase the quality of images. Affine transformation is to supply the images with 2d warping to improve the image quantity. To upgrade the quality of the images with face transfer is adopted to increase the accuracy of the image quality after image transfer.
Wi-Fi Based Fire Extinguisher Robot
Authors: Laiq Abul Hassan , Kaushal Bheem Kant , Kumar Mohan , Choubey Chandan 
Published: December 2021

AbstractFire occurrences are the disaster that can cause many humans as well as animal death, property mutilation and disabilities to affected humans. The main motive to develop this robot is to overcome the loss of human lives and get best of this disaster. The idea is to develop a robot that will be used for fire extinguishing purpose and save a huge number of human lives. Keeping in mind that the human and animal lives are important to work upon. A low-cost robot will be developed for fire extinguisher. Arduino programming will be done wisely so that the flame sensor can work accordingly. Small and low-cost robot will definitely reduce the chances of fire to get to its extreme level.
Endpoint detection and response using machine learning
Authors: Kaur Harmionee , Tiwari Richa 
Published: December 2021

Abstract
The need for cybersecurity has increased manifold over the past decade due to an unprecedented shift towards digital. With the increase in the number and sophistication of threats, cybersecurity experts have been forced to seek out new and efficient ways to secure endpoints on a network. Machine learning provides one such solution. This paper discusses how IoT devices are threatened and the need for endpoint security. It overviews different Machine learning-based intrusion detection systems that are currently in use e.g., STAT, Haystack, etc., and other Endpoint Detection and Response Techniques.
Kathakali face expression detection using deep learning techniques
Authors: Selvi C , Anvitha Y , Asritha C H , Sayannah P B 
Published: December 2021

Abstract
To develop a Deep Learning algorithm that detects the Kathakali face expression (or Navarasas) from a given image of a person who performs Kathakali. One of India’s major classical dance forms is Kathakali. It is a “story play” genre of art, but one distinguished by the traditional male-actor-dancers costumes, face masks and makeup they wear. In the Southern region of India, Kathakali is a Hindu performance art in Malayalam speaking. Most of the plays are epic scenes of Mahabharata and Ramayana. A lot of foreigners visiting India are inspired by this art form and have been curious about the culture. It is still used for entertainment as a part of tourism and temple rituals. An understanding of facial expressions are essential so as to enjoy the play. The scope of the paper is to identify the facial expressions of Kathakali to have a better understanding of the art play. In this paper, Machine Learning and Image Processing techniques are used to decode the expressions. Kathakali face expressions are nine types namely-Adbhutam (wonder), Hasyam (comic), Sringaram(love), Bheebatsam(repulsion), Bhayanakam(fear), Roudram(anger), Veeram(pride), Karunam(sympathy) and Shantham (peace). These Expressions are mapped to real world human emotions for better classification through face detection and extraction to achieve the same. Similarly a lot of research in terms of Preprocessing and Classification is done to achieve the maximum accuracy. Using CNN algorithm 90% of the accuracy was achieved. In order to conserve the pixel distribution and as no preprocessing was used for better object recognition and analysis Fuzzy algorithm is taken into consideration. Using this preprocessing technique 93% accuracy was achieved.
Prediction of EV arrivals at Battery Swapping Station using Hybrid Neural Network models
Authors: Suri Manikanta , Raj Neha , Sireesha K 
Published: December 2021

Abstract
There is an enormous increase in demand for Electric Vehicles (EV) in the present era, as they are environment-friendly when compared to conventional vehicles. Battery Swapping Stations (BSS) are gaining a lot of attention from the EV sector as it is like the gasoline stations. Forecasting of EV arrivals at BSS helps in optimally scheduling the depleted batteries to different charging piles without affecting the State of Health of the battery. Back Propagation Neural Network (BPNN) is widely used in the prediction of real-time data. Training of BPNN using metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) helps to overcome the local optima problem in BPNN. Thus, in the present work forecasting on the EV arrivals is carried out using GA-BPNN and PSO-BPNN hybrid models. Finally, a comparative study is carried out among BPNN, GA-BPNN, and PSO-BPNN models using the performance metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC). From the results, it was obtained that GA-BPNN model is preferred in forecasting the EV arrivals at BSS as the model has less overfitting. The hybrid models have been simulated in MATLAB/Simulink software.
Improved Hybrid Salp swarm and Sine-cosine Optimization based MPPT Control for PV Systems Under Partial Shading Conditions
Authors: Mishra Chandrakanta , Tiwari Sheela 
Published: December 2021

Abstract
A novel improved meta heuristic method, Hybrid salp swarm using sine cosine algorithm (Hybrid SS-SC) for maximum power point tracking (MPPT) under partial shading condition is proposed in this paper. The proposed algorithm envisages finding the best GMPP. The simulation studies clearly demonstrate that the method compares favourably with the conventional P&O method as well as with some other nature inspired algorithms in tracking GMPP in less time.
Investigation of 1-Hexanol Diesel Blends on Performance, Combustion and Emission Characteristics of a Diesel Engine
Authors: Singh Ashish Kumar , Khan Mohd.Mohsin , Pali Harveer Singh 
Published: December 2021

Abstract
The most potential long-term and renewable substitute of mineral diesel are biofuels. The growth and degradation of energy resources have an enormous influence on the long-term viability of the human community. Alcohols are gaining prominence in the current renewable energy scenario due to their ease of manufacturing and fuel characteristics. In this investigation, hexanol-diesel blend ratios (up to 20% v/v) is taken into account for this investigation in a single cylinder, water cooled, unmodified 4-stroke DI diesel engine. The increase in 1-hexanol volume content correlates to an improvement in combustion thereby promoting brake thermal efficiency. The greater concentration of oxygen in 1-hexanol reduces emission viz. HC and CO and increases value of NOx. Current investigation recommends a feasible option to substitute ULSD for the capabilities of 1-hexanol.
Application of Pyrolysis and Hydrothermal Liquefaction Process to Convert Bio-Fuel from Biomass
Authors: Kumar Amit , Pali Harveer Singh , Kumar Manoj 
Published: December 2021

Abstract
Rapid increase in demand of fuels creates the challenge for scientific community. The fossil fuel resources are declining day by day. So, biomass derived fuel starts to feel an exquisite alternative option, by reason of it develop from residual cooking oil, animal fats, or vegetable oils, etc. In present work biomass is converted into bio-fuel by the process of pyrolysis and hydrothermal liquefaction. But if it is compared with fossil fuel some undesirable properties have been found such like high oxygen content high water content, high corrosiveness and high viscosity etc. Directly biofuels cannot be used in IC engine due to their unwanted content in the fuel. Therefore upgrading of bio-fuel is needed to improve its properties for liquid fuels by the pre-treatment process of biomass. The purpose of biomass pre-treatment is the degradation of cellulose phibrils, lowering their crystallization and polymerization levels, hemicelluloses separation and degradation of cellulose. However application to this bio-fuel have been needed to enhance its properties for CI engine fuel using blending for direct use in engine application.
Study of hygroscopic behaviour of spruce during adsorption and desorption
Authors: Zhou X , Desmarais G , Mannes D , Derome D , Carmeliet J 
Published: December 2021

Abstract
Sorption of water vapour in hygroscopic porous materials is associated with latent heat release and absorption. This phenomenon should be taken into account to achieve a better understanding of the coupled transfer of vapour and heat in hygroscopic porous materials. In this paper, water vapour adsorption and desorption in the longitudinal direction of spruce samples are studied. Neutron radiography is used to measure changes in moisture content and wireless thermocouples are used to measure temperature changes. During the adsorption and desorption experiments, large changes in moisture content and temperature are observed. A hygrothermal model is developed to simulate vapour and heat transfer during adsorption and desorption experiments. Generally, the numerical model predicts well the measured moisture and temperature changes. The large moisture change is due to the low vapour resistance factor in the longitudinal direction of the spruce samples. The latent heat associated with vapour adsorption is the cause of the large temperature changes. It was found that vapour permeability affects both vapour and heat transfer, while thermal conductivity only affects heat transfer.
Recent progress on hygroscopic materials for indoor moisture buffering
Authors: Zhang Xu , Qin Menghao , Zu Kan 
Published: December 2021

Abstract
Once in contact with the indoor air, hygroscopic materials can moderate the indoor humidity fluctuation by adsorbing or releasing water vapour, and then improve the moisture regulation and thermal management of buildings. It is desirable to explore the characterized properties of these materials about moisture buffering behaviour. In this regard, we review various hygroscopic materials used for the built environment control. The hygrothermal properties of hygroscopic materials often can be characterized by some parameters, such as water vapour adsorption/desorption capacity, water vapour adsorption/desorption rate, water vapour diffusion coefficient, and so on. To provide an insight on the existing research on humidity control materials, different research studies and the recent progress on humidity control materials have been summarized. The materials include traditional and conventional building materials, some natural materials, and novel humidity control materials. Besides, the relevant parameters are considered as well as the improvement suggestions to enhance the application of humidity control materials in building environments. Finally, new multifunctional materials and intelligent moisture control materials together with the corresponding systems are collated to summarize the latest research trends. The overview of the application of hygroscopic materials can provide current and future researchers guidelines for the science-oriented design of moisture control systems for new energy-efficient buildings.
Water vapour permeability of wood – remarks on cup method procedure and measured data of spruce wood
Authors: Richter Jan , Staněk Kamil 
Published: December 2021

Abstract
Although the water vapour permeability of wood has been determined many times, there can be found a wide spread of values even related to only one wood specie and its single anatomical direction. This spread can possibly be attributed to the measurement method itself, since the cup method often shows high inter-laboratory error. However, even if the measurement and evaluation processes are well performed and under control, there were found several factors that can still highly affect the resulting value – up to ca. 70 %. These factors are neither mentioned in standards, nor were found in other sources. This paper theoretically describes those factors and their possible impact to the final value of water vapour permeability. Paper also presents one particular measurement scheme and obtained data from four cup tests performed on spruce wood in transverse direction.
Moisture-induced deformations of wood and shape memory
Authors: Zhang Chi , Chen Mingyang , Derome Dominique , Carmeliet Jan 
Published: December 2021

Abstract
Wood is known to swell substantially during moisture adsorption and shrink during desorption. These deformations may lead to wood damage in the form of cracking and disjoining of wooden components in e.g. floor or windows. Two swelling mechanisms may be distinguished: reversible swelling/shrinkage and moisture-induced shape memory effect. In the latter, wood is deformed in the wet state and afterward dried under maintained deformation, in order that wood retains its deformed shape even after the removal of the mechanical loading, called fixation. When wood is wetted again, it loses its fixation, partially regains its original shape, called recovery. These two mechanisms have their origin at the nanoscale and are modelled here using atomistic simulation and after upscaled to continuum level allowing finite element modelling. Hysteretic sorption and swelling are explained at nanoscale by the opening and closing of sorption sites in ad-and desorption, where in desorption water molecules preferentially remained bonded at sorption sites. The moisture-induced shape memory is explained by the moisture-induced activation of the interfaces between the reinforcing crystalline cellulose fibres and its matrix at nanoscale, referred to as a molecular switch. Our work aims to highlight that the understanding of sorption-induced reversible deformation and moisture-induced shape memory may play an important role in wood engineering and in building physics applications.
Effects of different light wavelengths on mold growth in tomb
Authors: Zhou X H , Xie H R , Pei Y , Liu W J , Hokoi S , Li Y H 
Published: December 2021

Abstract
The Second Mausoleum of the Southern Tang Dynasty have a history of more than 1,000 years in China. Since its opening in 1984, the building materials and murals inside the tomb have been severely damaged by mold. Field investigation found that the mold growth on the wall illuminated by the light in the tomb was more flourishing than that in the area without the light. Lighting in the tomb is inevitable for the exhibition. Therefore, the purpose of this study is to clarify the effects of different light wavelengths on the growth of mold in the tomb, and provide the theoretical basis for the lighting design in the tomb chamber. This study is divided into two parts, including in-situ experiment and laboratory experiment. In the in-situ experiment, four kinds of light wavelengths (white, red, blue, and green) were set in the tomb chamber to observe the difference of mold growth on the mural wall. The concentration of phototrophs propagules on different auricular walls was estimated in the laboratory.
The results showed that different light sources had different effects on microbial community composition in the tomb site and the red light had better inhibition effect on fungi and actinobacteria.
Impact on a CLT structure concerning moisture and mould growth using weather protection
Authors: Svensson Tengberg C , Bolmsvik Å 
Published: December 2021

Abstract
Timber buildings, including cross laminated timber (CLT), are gaining market shares globally, mainly due to anticipated environmental benefits, but a new technical solution also raises new questions. Durability is critical to obtain real sustainable constructions built for the future. There are field studies concerning hygrothermal conditions of timber structures, however, there is a lack of documented experiences combining hygrothermal conditions, mould growth potential and weather protection during construction using CLT. The use of full weather protection is being debated in the building industry as well as in the research community, due to lack of knowledge of the combined effects. How does weather protection during the construction affect hygrothermal conditions and risk of mould growth in a CLT structure? A case study using a weather protected six-storey CLT building was performed. The hygrothermal conditions – indoors and outdoors – were monitored during construction and samples from CLT were analysed with respect to mould. The results were analysed together with simulations of mould growth using actual hygrothermal conditions. Theoretical conclusions show the weather protection gives significantly improved conditions resulting in lower potential of mould growth compared to outdoor conditions. The results also show lessons to be learned concerning planning of the construction site.
Moisture safety in ventilated cathedral roofs
Authors: Månhardt G , Odén G , Stockhaus M , Wallentén P 
Published: December 2021

Abstract
Cathedral roofs are commonly used when constructing small houses in Sweden. In contrast to roof constructions with a cold attic, where frequent moisture damage has been noted, the cathedral roof is difficult to access for inspection. Furthermore, Swedish building regulations sets high demands regarding moisture safety, although there are no clear guidelines for their compliance. Hence, designing a cathedral roof must be done with great care. Previous studies investigating moisture safety in cathedral roofs, applies a constant air exchange in the ventilated air cavity. In this study a cathedral roof, ventilated from eave to eave, was analysed by examining the relevance of considering the variation in cavity air flow when conducting coupled heat and moisture calculations. The varied cavity air flow was calculated in an air flow model, considering wind and thermal buoyancy as driving forces. The accuracy of moisture safety assessments using the MRD model via hygrothermal calculations in WUFI Pro were also studied. Comparing moisture calculations with measurements showed high similarity when using a model with constant cavity air flow, and even higher resemblance when using a model with varied air flow. When actual conditions are sought, the study indicated that pinpointing important parameters, such as initial moisture content and moisture related material properties, would further increase precision in moisture calculations.
The impact of using Closed Cavity Façades (CCF) on buildings’ thermal and visual performance
Authors: Michael M , Overend M 
Published: December 2021

Abstract
Glazing is a critical buildings element as it is the most vulnerable envelope part to heat gain and heat loss accounting for around 50% of a building’s energy consumption. However, conventional glazing technologies have relatively low-performance characteristics which cause significant heat losses during winter and undesired heat gain in summer. In this regard, this study investigates the thermal and visual performance of various design configurations of a novel glazing technology, named Closed Cavity Façade (CCF), in comparison with traditional glazing technologies. Several CCF configurations were examined using Energy Plus and IDA ICE and compared to the baseline Double Glazing Unit (DGU) (traditional or thermochromic). MATELab, an office-like test facility at the University of Cambridge was used as the model for the simulations, which was beforehand experimentally validated. The results showed extensive benefits of CCFs compared to DGU systems, in terms of thermal performance and comfort. A 22-41% or 21-37% decrease in annual total energy consumption, compared to traditional DGU or thermochromic respectively, are identified along with a positive effect on thermal comfort with a significant reduction in radiant discomfort. Further investigation showed that glass coatings and solar shading device’s characteristics play an important role in achieving further performance improvements.