bluuebunny/crossref_metadata_2025_split huggingface dataset:
A thorough investigation of the sawdust’s angle of repose subjected to a spectrum of stimulating vibration frequencies, using a specially developed testing device.
Authors: Zalimidis Pavlos 
Published: February 2021

Abstract
The transportation and storage of powdered bulk materials is an important part of the manufacturing process of many products and requires equally important productive resources such as time, space and capital. Accurate estimation of these materials’ mechanical properties, especially those related to the above processes, such as the angle of repose provides information necessary for the design of optimized mechanical systems that minimize production costs. In the context of the present study, a mechanical device has been developed at the Machine Design Laboratory of the School of Pedagogical & Technological Education (ASPETE) for the determination of the angle of repose of bulk materials, a dimension necessary for many calculations related to machinery, such as maximum capacity e.g. of conveyor bolts, straps and lifts. This device allows studying this property very accurately under various vibration conditions, thus giving more realistic and much more useful results. Measurements were conducted on dry and wet sawdust, a raw material of many important product, and angle-of-repose diagrams were generated with respect to stimulating vibration frequency.
Equilibrium solutions of axially moving Timoshenko beam with a supercritical speed
Authors: Wang Ze , Ren Jianting 
Published: February 2021

Abstract
Analytical solution of the equilibrium configuration of an axially moving Timoshenko beam in supercritical regime is studied. Three kinds of classical boundary conditions are considered. An analytical solution of the equilibrium configuration in terms of the axial velocity in supercritical regime is determined. But most of all, for an axially moving Timoshenko beam with the fixed ends in supercritical regime, an anti-symmetric configuration is firstly detected. And, its analytical solution is solved. Besides, numerical example shows that the solution of the equilibrium configuration bifurcates with axially velocity. And the critical velocity derived from the Timoshenko beam theory is smaller than that derived from Euler-Bernoulli beam theory. The amplitude of the solution of equilibrium configuration derived from Timoshenko beam is larger.
SSA-LSTM neural network for hourly PM2.5 concentration prediction in Shenyang, China
Authors: Zhang Ye , Li Weide 
Published: February 2021

Abstract
Atmospheric environment has become the focus of global attention. Fine particulate matter has posed a serious threat to human respiratory system. In order to effectively control atmospheric environment and protect human health, pollutant prediction has become a necessary work for human survival and development. In order to improve the accuracy of PM2.5 prediction, this paper developed a new combined prediction model — SSA-LSTM. Firstly, the observed time series are decomposed into periodic component and noise component by SSA (Singular spectrum analysis). Then, LSTM (Long short-term memory) neural network was used to forecast the decomposed components. Finally, the predicted results of different components are integrated to generate the final predicted results. The results show that the proposed model has a significant improvement in the accuracy of prediction.
Parameter tuning of active disturbance rejection control in quad tilt rotor based on particle swarm optimization
Authors: Wang Zhigang , Lyu Zhichao , Li Jianbo 
Published: February 2021

Abstract
The quad tilt rotor (QTR) has complex dynamics characteristics, especially in transition mode. It is difficult to model the QTR dynamics and the environmental factors have a great influence on it. To solve the problem of control in transition mode of QTR, this paper carries out the design of the controller based on active disturbance rejection control (ADRC). ADRC has many parameters to be tuned, and the coupling effect is more serious, so it is very difficult to tune parameters. Particle swarm optimization (PSO) algorithm has faster computing speed and better global search ability in dynamic and multi-objective optimization. So, using PSO algorithm to realize self-tuning of ADRC parameters in ADRC parameters tuning. By comparing the control performance of ADRC before and after optimization, the rationality and effectiveness of ADRC parameters tuning algorithm in QTR are verified in both time domain and frequency domain.
Arbitrary polynomial chaos expansion and its application to power flow analysis-Fast approximation of probability distribution by arbitrary polynomial expansion
Authors: Katagiri Yuki , Iwamura Kazuaki , Nakanishi Yosuke , Takano Sachio , Suzuki Ryohei 
Published: February 2021

Abstract
This paper introduces an arbitrary polynomial chaos expansion method for performing probabilistic power flow analysis in power systems. The proposed method is used for uncertainty analysis, expressing the uncertainty of a system as random variables with an arbitrary output distribution based on orthogonal polynomial expansion. This method is advantageous because of its calculation speed and accuracy. This study expresses probabilistic power flow in a power system with many uncertain power sources using linear combination polynomial expansion. The orthogonal polynomial system employed is generated by moment analysis from renewable energy output data, with the polynomial coefficients derived from a collocation method. Simulation of probabilistic power flow using the proposed method is applied to a 29-bus transmission network model including three renewable energies, and the calculation speed and accuracy are evaluated by changing the expansion order of the polynomial. In addition, the influence on the polynomial coefficient is assessed when the system topology is changed due to a line fault. Therefore, since the arbitrary polynomial chaos expansion method can represent complex networks by linear combination of orthogonal polynomial sets, calculation based on it is several hundred times faster than the conventional Monte Carlo method. The results demonstrate that the proposed method is very useful for analyzing the probabilistic power distribution and that third-order expansion is practically appropriate.
Pedestrian identification in infrared and visible images based on pose keypoints matching
Authors: Brehar Raluca , Marita Tiberiu , Negru Mihai , Nedevschi Sergiu 
Published: February 2021

Abstract
The identification of persons in multi-spectral images that are not spatially aligned is a challenging process. A correct identification can improve the pedestrian detection task for machine vision applications. In this context we propose a person identification mechanism able to correctly find the same person in infrared and visible images. The main contribution of the paper is the keypoint based matcher that uses a deep learning based solution for finding relevant human pose keypoints and a local neighbour search for extracting the best candidates for person identification. For each of the two images, infrared and color we first perform pedestrian detection. Next, on each detected instance we extract the relevant keypoints for shoulders, hands and legs. A matching algorithm between the extracted keypoints is proposed in order to perform the identification of persons in the two images. We obtain an identification accuracy of 76% for pedestrians that have a medium height with respect to the image dimensions, and have a small occlusion degree.
Processing of myoelectric signals in a microcomputer for identification of movements intention and the cost reduction in the purchase of prosthesis in Peru
Authors: Chávez Eduardo , Sifuentes Billy , Chauca Ricardo Vidaland Mario 
Published: February 2021

Abstract
In the present document a processing technique is applied to identify the intention of movement in the myoelectric signals, by means of algorithms entered in a microcomputer with a software of free programming that presents a great speed when executing the instructions, with the necessary resources, to replace the PC and thus achieve low costs while maintaining the high quality standard, making the system accessible and reliable for people with low economic resources, while generating greater portability compared to the current prostheses that are handled in Peru, that are mostly made abroad, which increases the cost
Redesign website of engineering faculty in university of Indonesia using fuzzy delphi method and fuzzy cognitive maps
Authors: Suzianti Amalia , Mualim Agasi , Danisworo Ademas Muhammad 
Published: February 2021

Abstract
Utilizing website in business process has become essential. But lot of web designer have no respect of using methodology in developing a website cause the quality that website do not meet the needs of its users. On the other hand, usability level of the website could not attract a lot of visitation from any user. On this paper, researcher using Engineering Faculty in University of Indonesia’s website to be studied. To increase the usability of this website, research conducted by redesign the website using Fuzzy Delphi Method to determine the media type to presenting each service and also to determine the services layout of each web page. In addition, this research also uses Fuzzy Cognitive Maps to models the interrelationship between each services and media type. Resulting the prototype of 12 web page for website owned by Engineering Faculty in University of Indonesia with total 36 services provided in this website to gives an optimal information for the user.
Prediction of genetically-evaluated tumour responses to chemotherapy from breast MRI using machine learning with model selection
Authors: Yuan Taiguang , Jin Ze , Tokuda Yukiko , Naoi Yasuto , Tomiyama Noriyuki , Obi Takashi , Suzuki Kenji 
Published: February 2021

Abstract
Genetic tests can provide prognostic information in breast cancer for both diagnosis and treatment planning. However, the cost of a genetic test is still high. In this study, we developed a radiogenomics method to predict genetically-evaluated responses to chemotherapy for breast cancer using our machine-learning technology coupled with model selection. Our proposed method consists of feature extraction, model selection, and prediction by the selected model. In the feature extraction, 318 morphological and texture features were extracted from a tumour region. In the model selection module, there are two major components: (1) selection of imaging biomarkers based on our original sequential forward floating selection (SFFS) feature selection and (2) building of a support vector machine (SVM) classifier including kernel function selection and hyperparameter optimization. The optimized feature set, i.e. imaging biomarkers, coupled with an SVM classifier were chosen by maximizing the area under curve (AUC) of corresponding receiver-operating-characteristic curve (ROC). After the model selection, the optimized SVM classifier operated on the selected imaging biomarkers for prediction. We applied our proposed method to 118 breast MRI studies from 118 patients for predicting genetically-evaluated responses to chemotherapy for breast cancer that evaluated by the genetic test of IRSN-23. We achieved an AUC value of 0.96 using the optimized SVM classifier model coupled with 24 selected imaging biomarkers in predicting the results of IRSN-23 in a five-fold cross-validation procedure.
Implementation of PROMETHEE Method for Employee Performance Assessment System
Authors: Zulita Leni Natalia , Diansyah Tengku Mohd , Aulia Rachmat 
Published: February 2021

Abstract
PT Trimba Engineering is a company which turns on field of the repair and maintenance modules for various telecommunications devices and transmissions to aviation navigation systems. The firms, does an annual audit to assess employees performance that the outcomes of their performance will determine whether they acquire an annual bonus or not. Performance assessment is divided by five achievement categories which are Special, Excellent, Good, Enough, and Poor. But, those who get a bonus, if the outcomes of the performance assessment obtained is Special (two time salary), Excellent (one time salary), and Good (half salary). The performance assessment over five that achievement categories, still have any error in manage of its data. There is no ranking for outcomes of performance assessment obtained. And, it only focuses on the first criteria, but the employee is not necessarily excellent to several other criteria. In this research, the assessment uses a Promethee method, which it done based on three criteria such as technical performance, task execution, and personality. Each criterion has twenty one sub-criteria. The final result according to employee assessment performance obtains by using Promethee with comparize between an alternative with others, then looking for a deviation in order to compute a value of leaving flow, entering flow, and net.
Rainbow Coloring of Three New Graph Classes
Authors: Fredlina Ketut Queena , Salman A.N.M. , Julihara I Gede Putu Krisna , Werthi Komang Tri , Astawa Ni Luh Putu Ning Septyarini Putri 
Published: February 2021

Abstract
Let G be a simple, finite, and connected graph. A path in an edge colored graph is said a rainbow path, if no two edges on the path have the same color. The graph G is called a rainbow-connected, if any two vertices are connected by a rainbow path. An edge-coloring of such G is rainbow coloring. The rainbow connection number rc(G) of G is the smallest number of colors needed in order to make G rainbow connected. In this paper, we introduce three new graph classes, namely tunjung graphs, sandat graphs, and jempiring graphs. We determine the rainbow connection number of the graphs.
Detection of lung cancer with electronic nose using a novel ensemble learning framework
Authors: Liu Lei , Li Wang , He ZiChun , Chen Weimin , Liu Hongying , Chen Ke , Pi Xitian 
Published: February 2021

Abstract
Breath analysis based on electronic nose (e-nose) is a promising new technology for the detection of lung cancer that is non-invasive, simple to operate and cost-effective. Lung cancer screening by e-nose relies on predictive models established using machine learning methods. However, using only a single machine learning method to detect lung cancer has some disadvantages, including low detection accuracy and high false negative rate. To address these problems, groups of individual learning models with excellent performance were selected from classic models, including support vector machine, decision tree, random forest, logistic regression and K-nearest neighbor regression, to build an ensemble learning framework (PCA-SVE). The output result of the PCA-SVE framework was obtained by voting. To test this approach, we analyzed 214 breath samples measured by e-nose with 11 gas sensors of four types using the proposed PCA-SVE framework. Experimental results indicated that the accuracy, sensitivity, and specificity of the proposed framework were 95.75%, 94.78%, and 96.96%, respectively. This framework overcomes the disadvantages of a single model, thereby providing an improved, practical alternative for exhaled breath analysis by e-nose.
3D printing. An alternative for small projects?
Authors: Mihalache A M , Nagit G , Ripanu M I , Slatineanu L 
Published: February 2021

Abstract
3D printing is one of the big top up-to-date technologies. Our aim is to show that up to a point 3D prints can successfully replace traditionally manufactured assemblies in the case of educational projects or lab experiments. We have considered a 3D printed simple-frame robotic arm upon which we have performed FEA analyses. They have shown promising results against its steel-based peer up to certain loads under the same working conditions in terms of safety factor and safety margin.
On machining Ti6Al4V ELI with EDM by using copper and graphite electrodes: a comparison study
Authors: Karmiris-Obratański P. , Zagórski K. , Papazoglou E.L. , Karkalos N.E. , Markopoulos A.P. 
Published: February 2021

Abstract
Electrical Discharge Machining (EDM) is a non-conventional machining process, in which, the material removal occurs by repetitive sparks. By utilizing EDM, conductive materials can be machined in complex shapes and geometries, with high dimensional accuracy, regardless of their mechanical properties. Titanium alloys, due to their unique inherent properties, find extensive use in the modern industrial environment. Nevertheless, they are still classified as hard-to-cut materials, facing significant difficulties in their machining with conventional methods. The current paper presents a comparative study regarding the machining of Ti6Al4V ELI with EDM, by using copper and graphite electrodes. The control parameters were the pulse-on current and time, while the machining performance was estimated in terms of Material Removal Rate (MRR) and Tool Wear Ratio (TWR). Additionally, the surface roughness was evaluated according to the Ra. For the aforementioned indexes, Analysis Of Variance (ANOVA) was performed.
On the micro-modelling of surface roughness in pulsed laser machining
Authors: Kaselouris E , Orphanos Y , Kosma K , Skoulakis A , Fitilis I , Bakarezos M , Papadogiannis N , Tatarakis M , Dimitriou V 
Published: February 2021

Abstract
The surface roughness and the evolution of its morphology of a pulsed laser irradiated aluminum workpiece, is investigated via numerical simulations and experimental measurements. A three-dimensional transient thermo-structural finite element model is developed to simulate the machining process. The developed finite element material model considers the effects of plastic strain, strain rate and temperature, along with a fracture model. For the experiments, a single laser pulse of 6 ns duration and 0.7 mJ of energy at 532 nm is employed as the heat source, and the surface roughness is measured using white light interferometry set-up and related experimental diagnostics. A step-like linear approximation is used to model the surface roughness. A satisfactory agreement between the experimental data and the simulation results is found. This preliminary study aims to contribute to a better understanding of the initial physical processes involved in pulsed laser machining considering the influence of surface roughness and is beneficial for industrial applications such as laser polishing, engraving and cutting.
Rolling element bearings fault classification based on feature extraction from acceleration data and artificial neural networks
Authors: Kotsanidis K , Benardos P 
Published: February 2021

Abstract
This paper presents the development of an artificial neural network (ANN) model for rolling element bearings fault classification that uses features extracted from acceleration data collected during run-to-failure experiments. The presented approach initially employs a wavelet decomposition method for signal denoising and subsequently relies on a Fourier transform to analyse the acceleration signal in the frequency domain. Several features that correspond to the entire signal range as well as to specific frequency bands are then extracted and used as inputs in the ANN model, which is trained to identify three different operational states, namely, no fault, inner race fault and outer race fault. The developed ANN model is validated using experimental data from the publicly available dataset provided by the Center of Intelligent Maintenance Systems (IMS) of the University of Cincinnati. The results show that the trained ANN model has a classification accuracy of 90.2% in the training data and 100% in the test data.
FEM based investigation on thrust force and torque during Al7075-T6 drilling
Authors: Tzotzis Anastasios , Markopoulos Angelos P. , Karkalos Nikolaos E. , Tzetzis Dimitrios , Kyratsis Panagiotis 
Published: February 2021

Abstract
As modern industry advances, the demand for more time and cost effective machining is rising. In order to achieve high levels of standard during machining it is necessary to employ sophisticated techniques for precise prediction of various important parameters that relate to the machining processes. Such technique is the implementation of finite element modelling (FEM) which can become a valuable tool for researchers and industry engineers alike. In this work, the 3D modelling of Al7075-T6 drilling process with solid carbide tooling is being presented. DEFORM3D™ finite element analysis (FEA) software was utilized for simulating the drilling process based on frequently used cutting conditions; cutting speed of 100m/min and feed of 0.15mm/rev, 0.20mm/rev and 0.25mm/rev respectively. In order to approximate the complex phenomena that occur during drilling, the most critical factors were considered in the presented model such as the developed friction, heat transfer and damage interaction between the tool and the workpiece. Additionally, a validation of the generated results for thrust force and torque was performed by comparing the simulated results with experimental data. Three drilling experiments were carried out with the aid of a CNC machining center and a four component dynamometer in order to acquire the experimental values of thrust force and torque. Most of the simulations yielded results in accordance to the experimental ones with the agreement percentage reaching 95% in most cases for both the thrust force and torque, confirming the validity of the models and the accuracy of the simulated results.
Shrinkage Factor Analysis in the Dry Machining of UNS A92024 Alloy by FEM
Authors: Trujillo F J , Bermudo C , Martín-Béjar S , Svensson D , Andersson T , Sevilla L 
Published: February 2021

Abstract
Chip evacuation becomes a relevant factor in the dry machining of aeronautical aluminium alloys, either when they are machined as isotropic material or hybridized with other materials. Chip morphology and geometry highly depend on cutting parameters. Several works can be found in the literature focusing on the analysis of chip morphology of aluminium alloys at high cutting speeds but there is a lack of studies that apply low cutting speeds. FEM simulation may be useful to reduce the experimental time and cost of this kind of analysis. Therefore, in this work, the influence of feed-rate on several chip geometrical parameters (height of peaks, height of valleys and shrinkage factor) of dry turned UNS A92024 alloy has been analysed by FEM simulation. Three different energy fracture modes have been tested as damage evolution criterion, in order to get the mode that better fit the experimental chip morphology. In general, the mixed mode was the fracture energy mode that best suited the chip morphology behaviour, within wider feed-rate range.