Day 1 Mon, June 28, 2021最新文献

{"title":"Nakagami-m Fading Detection with Eigen Value Spectrum Algorithms","authors":"B. Vivekanandam","doi":"10.36548/JEI.2021.2.006","DOIUrl":"https://doi.org/10.36548/JEI.2021.2.006","url":null,"abstract":"One of the most crucial roles of the cognitive radio (CR) is detection of spectrum ‘holes’. The ‘no a-priori knowledge required’ prospective of blind detection techniques has attracted the attention of researchers and industries, using simple Eigen values. Over the years, a number of study and research has been carried out to determine the impact of thermal noise in the performance of the detector. However, there has not been much work on the impact of man-made noise, which also hinders the performance of the detector. As a result, both man-made impulse noise and thermal Gaussian noise are examined in this proposed study to determine the performance of blind Eigen value-based spectrum sensing. Many studies have been conducted over long sample length by oversampling or increasing the duration of sensing. As a result, a research progress has been made on shorter sample lengths by using a novel algorithm. The proposed system utilizes three algorithms; they are contra-harmonic-mean minimum Eigen value, contra-harmonic mean Maximum Eigen value and maximum Eigenvalue harmonic mean. For smaller sample lengths, there is a substantial rise in the number of cooperative secondary users, as well as a low signal-to-noise ratio when employing the maximum Eigen value Harmonic mean. The experimental analysis of the proposed work with respect to impulse noise and Gaussian signal using Nakagami-m fading channel is observed and the results identified are tabulated.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81949047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach","authors":"Akey Sungheetha, R. RajeshSharma","doi":"10.36548/JITDW.2021.2.006","DOIUrl":"https://doi.org/10.36548/JITDW.2021.2.006","url":null,"abstract":"Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a lessweighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87252012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network","authors":"K. P.","doi":"10.36548/jei.2021.2.003","DOIUrl":"https://doi.org/10.36548/jei.2021.2.003","url":null,"abstract":"In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85177350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network","authors":"P. Karuppusamy","doi":"10.36548/10.36548/JEI.2021.2.003","DOIUrl":"https://doi.org/10.36548/10.36548/JEI.2021.2.003","url":null,"abstract":"In several industrial applications, rotating machinery is widely utilized in various forms. A growing amount of study, in the academic and industrial fields, as a potential sector for the confidentiality of modern industrial labor systems, has been drawing early fault diagnosis (EFD) techniques. However, EFD plays an essential role in providing sufficient information for performing maintenance activities, preventing and reducing financial loss and disastrous defaults. Many of the existing techniques for identifying rotations were ineffective. For the identification of spinning machine faults, many in-depth learning methods have recently been developed. This research report has included and analysed a number of research publications that have higher precision than standard algorithms for detecting early failures in rotating machinery. In addition to the artificial intelligence monitoring (AIM) model, detecting the defects in rotating machine was also realized through the simulation output. AIM framework model is also testing the rotating machinery in three different stages, which is based on the vibration signal obtained from the bearing system and further it has been trained with the neural network preceding. Compared to other traditional algorithms, the AIM model has achieved greater precision and also the other performance measures are tabulated in the result and discussion section.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89488099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industrial Quality Prediction System through Data Mining Algorithm","authors":"P. Karthigaikumar","doi":"10.36548/JEI.2021.2.005","DOIUrl":"https://doi.org/10.36548/JEI.2021.2.005","url":null,"abstract":"Based on an assessment of production capabilities, manufacturing sectors' core competency is increased. The importance of product quality in this aspect cannot be overstated. Several academics have introduced Deming's 14 principles, Shewhart cycle, total quality management, and other approaches to decrease the external failure costs and enhance product yield rates. Analysis of industrial data and process monitoring is becoming increasingly important as a part of the Industry 4.0 paradigm. In order to reduce the internal failure cost and inspection overhead, quality control (QC) schemes are utilized by industries. The final product quality has an interactive and cumulative effect of various parameters like operators and equipment in multistage manufacturing processes (MMP). In other cases, the final product is inspected in a single workstation with QC. It's challenging to do a cause analysis in MMP whenever a failure occurs. Several industries are looking for the optimal quality prediction model in order to achieve flawless production. The majority of current approaches solely handles single-stage manufacturing and is inadequate in dealing with MMP quality concerns. To overcome this issue, this paper proposes an industrial quality prediction system with a combination of multiple Program Component Analysis (PCA) and Decision Stump (DS) algorithm for MMP quality prediction. A SECOM (SEmiCOnductor Manufacturing) dataset is used for verification and validation of the proposed model. Based on the findings, it is clear that this model is capable of performing accurate classification and prediction in the field of industrial quality.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87248115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Nanopackaging using Cellulose Fibers Composition with Feasibility in SEM Environment","authors":"S. Shakya","doi":"10.36548/JEI.2021.2.004","DOIUrl":"https://doi.org/10.36548/JEI.2021.2.004","url":null,"abstract":"By contributing to the system enhancement, the integration of Nano systems for nanosensors with biomaterials proves to be a unique element in the development of novel innovative systems. The techniques by which manipulation, handling, and preparation of the device are accomplished with respect to industrial use are a critical component that must be considered before the system is developed. The approach must be able to be used in a scanning electron microscope (SEM), resistant to environmental changes, and designed to be automated. Based on this deduction, the main objective of this research work is to develop a novel design of Nano electronic parts, which address the issue of packaging at a nanoscale. The proposed research work has used wood fibres and DNA as the bio material to develop nanoscale packaging. The use of a certain atomic force microscope (ATM) for handling DNA in dry circumstances is demonstrated with SCM wood fibrils/fibers manipulation in a scanning electron microscope (SEM).Keywords: Nano electronics, bioelectronics, scanning electron microscope (SEM), packaging, atomic force microscope (ATM)","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79122868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Light Weight CNN based Robust Image Watermarking Scheme for Security","authors":"D. R.","doi":"10.36548/JITDW.2021.2.005","DOIUrl":"https://doi.org/10.36548/JITDW.2021.2.005","url":null,"abstract":"In recent years, digital watermarking has improved the accuracy and resistance of watermarked images against many assaults, such as various noises and random dosage characteristics. Because, based on the most recent assault, all existing watermarking research techniques have an acceptable level of resistance. The deep learning approach is one of the most remarkable methods for guaranteeing maximal resistance in the watermarking system's digital image processing. In the digital watermarking technique, a smaller amount of calculation time with high robustness has recently become a difficult challenge. In this research study, the light weight convolution neural network (LW-CNN) technique is introduced and implemented for the digital watermarking scheme, which has more resilience than any other standard approaches. Because of the LW-CNN framework's feature selection, the calculation time has been reduced. Furthermore, we have demonstrated the robustness of two distinct assaults, collusion and geometric type. This research work has reduced the calculation time and made the system more resistant to current assaults.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74635925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rheological behavior of magnetic pulp fiber suspensions","authors":"Jorge H. Sánchez, Xiomara Pineda, German C. Quintana, A. Herrera","doi":"10.32964/tj20.6.393","DOIUrl":"https://doi.org/10.32964/tj20.6.393","url":null,"abstract":"This paper is focused on the rheology of magnetic pulp suspensions in absence and presence of an external magnetic field. Magnetic fibers were prepared by the lumen loading method using bleached eucalyptus \u0000fibers and cobalt ferrite (CoFe2O4) nanoparticles. The effect of mass consistency, temperature, concentration of magnetic fibers, and magnetic field strength on yield stress and apparent viscosity of the suspensions were investigated. In the absence of an applied field, a dependence of yield stress with consistency, as well as with the percentage of magnetic fibers present in the suspension, was found. In flow tests, all the suspensions exhibited shear-thinning behavior, showing that the viscosity is only affected by the consistency of the suspension. On the other hand, magnetorheological measurements show a negative effect of the applied magnetic field on the viscosity of the suspension.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88831843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking the paper cup — beginning with extrusion process optimization for compostability and recyclability","authors":"N. Whiteman, A. Auchter, A. Christie, Michael Prue","doi":"10.32964/tj20.6.353","DOIUrl":"https://doi.org/10.32964/tj20.6.353","url":null,"abstract":"More than 50 billion disposable paper cups used for cold and hot beverages are sold within the United States each year. Most of the cups are coated with a thin layer of plastic — low density polyethylene (LDPE) — to prevent leaking and staining. While the paper in these cups is both recyclable and compostable, the LDPE coating is neither. In recycling a paper cup, the paper is separated from the plastic lining. The paper is sent to be recycled and the plastic lining is typically sent to landfill. In an industrial composting environment, the paper and lining can be composted together if the lining is made from compostable materials. Coating paper cups with a compostable performance material uniquely allows for used cups to be processed by either recycling or composting, thus creating multiple pathways for these products to flow through a circular economy.\u0000A segment of the paper converting industry frequently uses an extrusion grade of polylactic acid (PLA) for zero-waste venues and for municipalities with ordinances for local composting and food service items. The results among these early adopters reveal process inefficiencies that elevate manufacturing costs while increasing scrap and generally lowering output when using PLA for extrusion coating. \u0000NatureWorks and Sung An Machinery (SAM) North America researched the extrusion coating process utilizing the incumbent polymer (LDPE) and PLA. The trademarked Ingeo 1102 is a new, compostable, and bio-based PLA grade that is specifically designed for the extrusion coating process. The research team identified the optimum process parameters for new, dedicated PLA extrusion coating lines. The team also identified changes to existing LDPE extrusion lines that processors can make today to improve output.\u0000The key finding is that LDPE and PLA are significantly different polymers and that processing them on the same equipment without modification of systems and/or setpoints can be the root cause of inefficiencies. These polymers each have unique processing requirements with inverse responses. Fine tuning existing systems may improve over-all output for the biopolymer without capital investment, and this study showed an increase in line speed of 130% by making these adjustments. However, the researchers found that highest productivity can be achieved by specifying new systems for PLA. A line speed increase to more than 180% and a reduction in coat weight to 8.6 µm \u0000(10.6 g/m2 or 6.5 lb/3000 ft2) was achieved in this study. These results show that Ingeo 1102 could be used as a paper coating beyond cups.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80890876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probing the molecular weights of sweetgum and pine kraft lignin fractions","authors":"Juliana M. Jardim, P. Hart, L. Lucia, H. Jameel","doi":"10.32964/tj20.6.381","DOIUrl":"https://doi.org/10.32964/tj20.6.381","url":null,"abstract":"The present investigation undertook a systematic investigation of the molecular weight (MW) of kraft lignins throughout the pulping process to establish a correlation between MW and lignin recovery at different extents of the kraft pulping process. The evaluation of MW is crucial for lignin characterization and utilization, since it is known to influence the kinetics of lignin reactivity and its resultant physicochemical properties. \u0000Sweetgum and pine lignins precipitated from black liquor at different pHs (9.5 and 2.5) and different extents of kraft pulping (30–150 min) were the subject of this effort. Gel permeation chromatography (GPC) was used to deter-\u0000mine the number average molecular weight (Mn), mass average molecular weight (Mw), and polydispersity of the lignin samples. It was shown that the MW of lignins from both feedstocks follow gel degradation theory; that is, at the onset of the kraft pulping process low molecular weightlignins were obtained, and as pulping progressed, the molecular weight peaked and subsequently decreased. An important finding was that acetobromination was shown to be a more effective derivatization technique for carbohydrates containing lignins than acetylation, the technique typically used for derivatization of lignin.","PeriodicalId":11075,"journal":{"name":"Day 1 Mon, June 28, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83410451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}