Search results for: Deep and surface approaches

Authors: Kuan-Liang Liu, Eric Lee, Jung-Jyh Lee, Jyh-Ping Hsu

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The electrical interaction between two axisymmetric spheroidal particles in an electrolyte solution is examined numerically. A Galerkin finite element method combined with a Newton-Raphson iteration scheme is proposed to evaluate the spatial variation in the electrical potential, and the result obtained used to estimate the interaction energy between two particles. We show that if the surface charge density is fixed, the potential gradient is larger at a point, which has a larger curvature, and if surface potential is fixed, surface charge density is proportional to the curvature. Also, if the total interaction energy against closest surface-to-surface curve exhibits a primary maximum, the maximum follows the order (oblate-oblate) > (sphere-sphere)>(oblate-prolate)>(prolate-prolate), and if the curve has a secondary minimum, the absolute value of the minimum follows the same order.

Keywords: interaction energy, interaction force, Poisson-Boltzmann equation, spheroid.

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Authors: Muhammad Naseem Baig, Abdul Qudoos Khan, Jamal Ali

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Ground deformations in deep excavations are affected by wall stiffness and pile embedment ratio. This paper presents the findings of a parametric study of a 64-ft deep excavation in mixed stiff soil conditions supported by cantilever pile wall. A series of finite element analysis has been carried out in Plaxis 2D by varying the pile embedment ratio and wall stiffness. It has been observed that maximum wall deflections decrease by increasing the embedment ratio up to 1.50; however, any further increase in pile length does not improve the performance of the wall. Similarly, increasing wall stiffness reduces the wall deformations and affects the deflection patterns of the wall. The finite element analysis results are compared with the field data of 25 case studies of cantilever walls. Analysis results fall within the range of normalized wall deflections of the 25 case studies. It has been concluded that deep excavations can be supported by cantilever walls provided the system stiffness is increased significantly.

Keywords: Excavations, support systems, wall stiffness, cantilever walls.

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: Visual search, deep learning, convolutional neural network, machine learning.

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Authors: Togzhan D. Mukasheva, Anel A. Omirbekova, Raikhan S. Sydykbekova, Ramza Zh. Berzhanova, Lyudmila V. Ignatova

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In studying the possibility of using plants as rhizoremediators, barley and grass mixture which showed resistance to various concentrations of oil were selected. The minimum inhibitory effect of oil on these plants by morphological parameters such as survival of plants, length and biomass of shoot and root compared with the control was showed. In determining physiological parameters, a slight decrease in the number of chlorophyll a and b in the leaves of plants was noted. The differences in the ratio of the total surface of the roots to the work surface with the growth of plants in soil with oil in the study of adsorption of the root surface were showed.

Keywords: Length of shoot and root, biomass, chlorophyll a and b, adsorption surface, barley, grass mixture.

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Authors: A. Mostafa

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This paper presents a numerical study on determination of ballistic limit velocity (V₅₀) of stainless steel 304 (SS 304) used in manufacturing security screens. The simulated ballistic impact tests were conducted on clamped sheets with different thicknesses using ABAQUS/Explicit nonlinear finite element (FE) package. The ballistic limit velocity was determined using three approaches, namely: numerical tests based on material properties, FE calculated residual velocities and FE calculated residual energies. Johnson-Cook plasticity and failure criterion were utilized to simulate the dynamic behaviour of the SS 304 under various strain rates, while the well-known Lambert-Jonas equation was used for the data regression for the residual velocity and energy model. Good agreement between the investigated numerical methods was achieved. Additionally, the dependence of the ballistic limit velocity on the sheet thickness was observed. The proposed approaches present viable and cost-effective assessment methods of the ballistic performance of SS 304, which will support the development of robust security screen systems.

Keywords: Ballistic velocity, stainless steel, numerical approaches, security screen.

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Authors: K. -H. Yang

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Supplier selection problem is one of the important issues of supply chain problems. Two categories of methodologies include qualitative and quantitative approaches which can be applied to supplier selection problems. However, due to the complexities of the problem and lacking of reliable and quantitative data, qualitative approaches are more than quantitative approaches. This study considers operational cost and supplier’s reliability factor and solves the problem by using a quantitative approach. A mixed integer programming model is the primary analytic tool. Analyses of different scenarios with variable cost and reliability structures show that the effectiveness of this approach to the supplier selection problem.

Keywords: Mixed integer programming, quantitative approach, supplier’s reliability, supplier selection.

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Authors: Surya Prakash, Sunil Kumar Sinha

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This paper deals with the application of artificial neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy Inference System(ANFIS) approach to Load Frequency Control (LFC) of multi unequal area hydro-thermal interconnected power system. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. Area-1 and area-2 consists of thermal reheat power plant whereas area-3 and area-4 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent controller like ANFIS, ANN and Fuzzy controllers and conventional PI and PID control approaches. To enhance the performance of intelligent and conventional controller sliding surface is included. The performances of the controllers are simulated using MATLAB/SIMULINK package. A comparison of ANFIS, ANN, Fuzzy, PI and PID based approaches shows the superiority of proposed ANFIS over ANN & fuzzy, PI and PID controller for 1% step load variation.

Keywords: Load Frequency Control (LFC), ANFIS, ANN & Fuzzy, PI, PID Controllers, Area Control Error (ACE), Tie-line, MATLAB / SIMULINK.

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Authors: Harish Kumar Ponnappan, Joseph C. Chen

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The main objective of this research is to optimize the surface roughness of a milling operation on AISI 1018 steel using live tooling on a HAAS ST-20 lathe. In this study, Taguchi analysis is used to optimize the milling process by investigating the effect of different machining parameters on surface roughness. The L₉ orthogonal array is designed with four controllable factors with three different levels each and an uncontrollable factor, resulting in 18 experimental runs. The optimal parameters determined from Taguchi analysis were feed rate – 76.2 mm/min, spindle speed 1150 rpm, depth of cut – 0.762 mm and 2-flute TiN coated high-speed steel as tool material. The process capability Cp and process capability index Cpk values were improved from 0.62 and -0.44 to 1.39 and 1.24 respectively. The average surface roughness values from the confirmation runs were 1.30 µ, decreasing the defect rate from 87.72% to 0.01%. The purpose of this study is to efficiently utilize the Taguchi design to optimize the surface roughness in a milling operation using live tooling.

Keywords: Live tooling, surface roughness, Taguchi analysis, Computer Numerical Control (CNC) milling operation, CNC turning operation.

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Authors: Ahmed A. D. Sarhan, M. Sayuti, M. Hamdi

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Nowadays, the demand for high product quality focuses extensive attention to the quality of machined surface. The (CNC) milling machine facilities provides a wide variety of parameters set-up, making the machining process on the glass excellent in manufacturing complicated special products compared to other machining processes. However, the application of grinding process on the CNC milling machine could be an ideal solution to improve the product quality, but adopting the right machining parameters is required. In glass milling operation, several machining parameters are considered to be significant in affecting surface roughness. These parameters include the lubrication pressure, spindle speed, feed rate and depth of cut. In this research work, a fuzzy logic model is offered to predict the surface roughness of a machined surface in glass milling operation using CBN grinding tool. Four membership functions are allocated to be connected with each input of the model. The predicted results achieved via fuzzy logic model are compared to the experimental result. The result demonstrated settlement between the fuzzy model and experimental results with the 93.103% accuracy.

Keywords: CNC-machine, Glass milling, Grinding, Surface roughness, Cutting force, Fuzzy logic model.

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Authors: Dayananda Pai, Shrikantha S. Rao, Savitha G.Kini

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In this study, the effects of machining parameters on specific energy during surface grinding of 6061Al-SiC35P composites are investigated. Vol% of SiC, feed and depth of cut were chosen as process variables. The power needed for the calculation of the specific energy is measured from the two watt meter method. Experiments are conducted using standard RSM design called Central composite design (CCD). A second order response surface model was developed for specific energy. The results identify the significant influence factors to minimize the specific energy. The confirmation results demonstrate the practicability and effectiveness of the proposed approach.

Keywords: ANOVA, Metal matrix composites, Response surface methodology, Specific energy, Two watt meter method.

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Authors: Boonorm Chomtee, John J. Borkowski

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The objective of the research is to study and compare response surface designs: Central composite designs (CCD), Box- Behnken designs (BBD), Small composite designs (SCD), Hybrid designs, and Uniform shell designs (USD) over sets of reduced models when the design is in a spherical region for 3 and 4 design variables. The two optimality criteria ( D and G ) are considered which larger values imply a better design. The comparison of design optimality criteria of the response surface designs across the full second order model and sets of reduced models for 3 and 4 factors based on the two criteria are presented.

Keywords: design optimality criteria, reduced models, response surface design, spherical design region

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Authors: B. Marungsri, W. Onchantuek, A. Oonsivilai, T. Kulworawanichpong

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This paper presents the simulation results of electric field and potential distributions along surface of silicone rubber polymer insulators under clean and various contamination conditions with/without water droplets. Straight sheds insulator having leakage distance 290 mm was used in this study. Two type of contaminants, playwood dust and cement dust, have been studied the effect of contamination on the insulator surface. The objective of this work is to comparison the effect of contamination on potential and electric field distributions along the insulator surface when water droplets exist on the insulator surface. Finite element method (FEM) is adopted for this work. The simulation results show that contaminations have no effect on potential distribution along the insulator surface while electric field distributions are obviously depended on contamination conditions.

Keywords: electric field distribution, potential distribution, silicone rubber polymer insulator, finite element method

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Authors: Wichai Pattanapol, Sarah J. Wakes, Michael J. Hilton, Katharine J.M. Dickinson

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Turbulence modeling of large-scale flow over a vegetated surface is complex. Such problems involve large scale computational domains, while the characteristics of flow near the surface are also involved. In modeling large scale flow, surface roughness including vegetation is generally taken into account by mean of roughness parameters in the modified law of the wall. However, the turbulence structure within the canopy region cannot be captured with this method, another method which applies source/sink terms to model plant drag can be used. These models have been developed and tested intensively but with a simple surface geometry. This paper aims to compare the use of roughness parameter, and additional source/sink terms in modeling the effect of plant drag on wind flow over a complex vegetated surface. The RNG k-ε turbulence model with the non-equilibrium wall function was tested with both cases. In addition, the k-ω turbulence model, which is claimed to be computationally stable, was also investigated with the source/sink terms. All numerical results were compared to the experimental results obtained at the study site Mason Bay, Stewart Island, New Zealand. In the near-surface region, it is found that the results obtained by using the source/sink term are more accurate than those using roughness parameters. The k-ω turbulence model with source/sink term is more appropriate as it is more accurate and more computationally stable than the RNG k-ε turbulence model. At higher region, there is no significant difference amongst the results obtained from all simulations.

Keywords: CFD, canopy flow, surface roughness, turbulence models.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: Deep learning, long-short-term memory, energy, renewable energy load forecasting.

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Authors: Anuar Mohd Salleh, Mohd Effendi Daud

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This study presents a kinematic positioning approach that uses a global positioning system (GPS) buoy for precise ocean surface monitoring. The GPS buoy data from the two experiments are processed using an accurate, medium-range differential kinematic technique. In each case, the data from a nearby coastal site are collected at a high rate (1 Hz) for more than 24 hours, and measurements are conducted in neighboring tidal stations to verify the estimated sea surface heights. The GPS buoy kinematic coordinates are estimated using epoch-wise pre-elimination and a backward substitution algorithm. Test results show that centimeterlevel accuracy can be successfully achieved in determining sea surface height using the proposed technique. The centimeter-level agreement between the two methods also suggests the possibility of using this inexpensive and more flexible GPS buoy equipment to enhance (or even replace) current tidal gauge stations.

Keywords: Global positioning system, kinematic GPS, sea surface height, GPS buoy, tide gauge.

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Authors: S.Solaymani, T.Ghodselahi, N.B.Nezafat, H.Zahrabi, A.Gelali

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The morphological parameter of a thin film surface can be characterized by power spectral density (PSD) functions which provides a better description to the topography than the RMS roughness and imparts several useful information of the surface including fractal and superstructure contributions. Through the present study Nanoparticle copper/carbon composite films were prepared by co-deposition of RF-Sputtering and RF-PECVD method from acetylene gas and copper target. Surface morphology of thin films is characterized by using atomic force microscopy (AFM). The Carbon content of our films was obtained by Rutherford Back Scattering (RBS) and it varied from .4% to 78%. The power values of power spectral density (PSD) for the AFM data were determined by the fast Fourier transform (FFT) algorithms. We investigate the effect of carbon on the roughness of thin films surface. Using such information, roughness contributions of the surface have been successfully extracted.

Keywords: Atomic force microscopy, Fast Fourier transform, Power spectral density, RBS.

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Authors: Tamara Nestorović, Jean Lefèvre, Stefan Ringwelski, Ulrich Gabbert

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Two approaches for model development of a smart acoustic box are suggested in this paper: the finite element (FE) approach and the subspace identification. Both approaches result in a state-space model, which can be used for obtaining the frequency responses and for the controller design. In order to validate the developed FE model and to perform the subspace identification, an experimental set-up with the acoustic box and dSPACE system was used. Experimentally obtained frequency responses show good agreement with the frequency responses obtained from the FE model and from the identified model.

Keywords: Acoustic box, experimental verification, finite element model, subspace identification.

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Authors: Wenbo Wang, Yi-fang Brook Wu

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The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state of the art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study presents a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive and authoritative data; 2) developing a search function to automatically select relevant, new and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that: 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graph in Wikidata to dynamically augment the representations of claims and references without introducing too much noises; II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: Fact checking, claim verification, Deep Learning, Natural Language Processing.

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Authors: Sarabjeet Singh Sidhu, Ajay Batish, Sanjeev Kumar

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This paper reports the optimal process conditions for machining of three different types of MMC’s 65vol%SiC/A356.2; 10vol%SiC-5vol%quartz/Al and 30vol%SiC/A359 using PMEDM process. MRR, TWR, SR and surface integrity were evaluated after each trial and contributing process parameters were identified. The four responses were then collectively optimized using TOPSIS and optimal process conditions were identified for each type of MMC. The density of reinforced particles shields the matrix material from spark energy hence the high MRR and SR was observed with lowest reinforced particle. TWR was highest with Cu-Gr electrode due to disintegration of the weakly bonded particles in the composite electrode. Each workpiece was examined for surface integrity and ranked as per severity of surface defects observed and their rankings were used for arriving at the most optimal process settings for each workpiece. 

Keywords: Metal matrix composites (MMCs), Metal removal rate (MRR), Surface roughness (SR), Surface integrity (SI), Tool wear rate (TWR), Technique for order preference by similarity to ideal solution (TOPSIS).

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Authors: Jean François Chatelain, Imed Zaghbani, Joseph Monier

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The machining of Carbon Fiber Reinforced Plastics has come to constitute a significant challenge for many fields of industry. The resulting surface finish of machined parts is of primary concern for several reasons, including contact quality and impact on the assembly. Therefore, the characterization and prediction of roughness based on machining parameters are crucial for costeffective operations. In this study, a PCD tool comprised of two straight flutes was used to trim 32-ply carbon fiber laminates in a bid to analyze the effects of the feed rate and the cutting speed on the surface roughness. The results show that while the speed has but a slight impact on the surface finish, the feed rate for its part affects it strongly. A detailed study was also conducted on the effect of fiber orientation on surface roughness, for quasi-isotropic laminates used in aerospace. The resulting roughness profiles for the four-ply orientation lay-up were compared, and it was found that fiber angle is a critical parameter relating to surface roughness. One of the four orientations studied led to very poor surface finishes, and characteristic roughness profiles were identified and found to only relate to the ply orientations of multilayer carbon fiber laminates.

Keywords: Roughness, Detouring, Composites, Aerospace

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Authors: F. Rasi Marzabadi

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A series of experiments were carried out to study unsteady behavior of the flow field as well as the boundary layer of an airfoil oscillating in plunging motion in a subsonic wind tunnel. The measurements involved surface pressure distribution complimented with surface-mounted hot-films. The effect of leadingedge roughness that simulates surface irregularities on the wind turbine blades was also studied on variations of aerodynamic loads and boundary layer behavior.

Keywords: Boundary layer transition, plunging, reduced frequency, wind turbine.

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Authors: Ahmet Güven, Ö. Özgür Bozkurt, Oya Kalıpsız

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Number of documents being created increases at an increasing pace while most of them being in already known topics and little of them introducing new concepts. This fact has started a new era in information retrieval discipline where the requirements have their own specialties. That is digging into topics and concepts and finding out subtopics or relations between topics. Up to now IR researches were interested in retrieving documents about a general topic or clustering documents under generic subjects. However these conventional approaches can-t go deep into content of documents which makes it difficult for people to reach to right documents they were searching. So we need new ways of mining document sets where the critic point is to know much about the contents of the documents. As a solution we are proposing to enhance LSI, one of the proven IR techniques by supporting its vector space with n-gram forms of words. Positive results we have obtained are shown in two different application area of IR domain; querying a document database, clustering documents in the document database.

Keywords: Document clustering, Information Extraction, Information Retrieval, LSI, n-gram.

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Authors: M.I.S. Ismail, Z. Taha

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In this paper, the experimental design of using the Taguchi method is employed to optimize the processing parameters in the plasma arc surface hardening process. The processing parameters evaluated are arc current, scanning velocity and carbon content of steel. In addition, other significant effects such as the relation between processing parameters are also investigated. An orthogonal array, signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are employed to investigate the effects of these processing parameters. Through this study, not only the hardened depth increased and surface roughness improved, but also the parameters that significantly affect the hardening performance are identified. Experimental results are provided to verify the effectiveness of this approach.

Keywords: Plasma arc, hardened depth, surface roughness, Taguchi method, optimization.

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Authors: Ainon Syazana Ab. Hamid, Seripah Awang Kechil, Ahmad Sukri Abd. Aziz

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The Marangoni convective instability in a horizontal fluid layer with the insoluble surfactant and nondeformable free surface is investigated. The surface tension at the free surface is linearly dependent on the temperature and concentration gradients. At the bottom surface, the temperature conditions of uniform temperature and uniform heat flux are considered. By linear stability theory, the exact analytical solutions for the steady Marangoni convection are derived and the marginal curves are plotted. The effects of surfactant or elasticity number, Lewis number and Biot number on the marginal Marangoni instability are assessed. The surfactant concentration gradients and the heat transfer mechanism at the free surface have stabilizing effects while the Lewis number destabilizes fluid system. The fluid system with uniform temperature condition at the bottom boundary is more stable than the fluid layer that is subjected to uniform heat flux at the bottom boundary.

Keywords: Analytical solutions, Marangoni Instability, Nondeformable free surface, Surfactant.

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Authors: V. Sivakumar, R. Neelakantan

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Mineral mapping on the Moon surface provides the clue to understand the origin, evolution, stratigraphy and geological history of the Moon. Recently, reflectance imaging spectroscopy plays a significant role in identifying minerals on the planetary surface in the Visible to NIR region of the electromagnetic spectrum. The Moon Mineralogy Mapper (M³) onboard Chandrayaan-1 provides unprecedented spectral data of lunar surface to study about the Moon surface. Here we used the M³ sensor data (hyperspectral imaging spectroscopy) for analysing mineralogy of Orientale basin region on the Moon surface. Reflectance spectrums were sampled from different locations of the basin and continuum was removed using ENvironment for Visualizing Images (ENVI) software. Reflectance spectra of unknown mineral composition were compared with known Reflectance Experiment Laboratory (RELAB) spectra for discriminating mineralogy. Minerals like olivine, Low-Ca Pyroxene (LCP), High-Ca Pyroxene (HCP) and plagioclase were identified. In addition to these minerals, an unusual type of spectral signature was identified, which indicates the probable Fe-Mg-spinel lithology in the basin region.

Keywords: Chandrayaan-1, moon mineralogy mapper, orientale basin, moon, spectroscopy, hyperspectral.

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Authors: Wei Zhang

Abstract:

With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: Deep learning, field programmable gate array, FPGA, hardware acceleration, convolutional neural networks, CNN.

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Authors: H. Higashiyama, M. Sano, F. Nakanishi, M. Sugiyama, M. Kawanishi, S. Tsukuma

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The heat island phenomenon and extremely hot summer climate are becoming environmental problems in Japan. Cool pavements reduce the surface temperature compared to conventional asphalt pavements in the hot summer climate and improve the thermal environment in the urban area. The authors have studied cement–based grouting materials poured into voids in porous asphalt pavements to reduce the road surface temperature. For the cement–based grouting material, cement, ceramic waste powder, and natural zeolite were used. This cement–based grouting material developed reduced the road surface temperature by 20 °C or more in the hot summer season. Considering the urban landscape, this study investigates the effect of surface temperature reduction of colored cement–based grouting materials containing pigments poured into voids in porous asphalt pavements by measuring the surface temperature of asphalt pavements outdoors. The yellow color performed the same as the original cement–based grouting material containing no pigment and was thermally better performance than the other color. However, all the tested cement–based grouting materials performed well for reducing the surface temperature and for creating the urban landscape.

Keywords: Ceramic waste powder, natural zeolite, road surface temperature, asphalt pavements, urban landscape.

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Authors: M. Maimunah, G.A. Froemming, H. Nawawi, M.I. Nafeeza, O. Effat, M.R. Rohayu Izanwati, M.S. Mohamed Saifulaman

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Attachment of the circulating monocytes to the endothelium is the earliest detectable events during formation of atherosclerosis. The adhesion molecules, chemokines and matrix proteases genes were identified to be expressed in atherogenesis. Expressions of these genes may influence structural integrity of the luminal endothelium. The aim of this study is to relate changes in the ultrastructural morphology of the aortic luminal surface and gene expressions of the endothelial surface, chemokine and MMP-12 in normal and hypercholesterolemic rabbits. Luminal endothelial surface from rabbit aortic tissue was examined by scanning electron microscopy (SEM) using low vacuum mode to ascertain ultrastructural changes in development of atherosclerotic lesion. Gene expression of adhesion molecules, MCP-1 and MMP-12 were studied by Real-time PCR. Ultrastructural observations of the aortic luminal surface exhibited changes from normal regular smooth intact endothelium to irregular luminal surface including marked globular appearance and ruptures of the membrane layer. Real-time PCR demonstrated differentially expressed of studied genes in atherosclerotic tissues. The appearance of ultrastructural changes in aortic tissue of hypercholesterolemic rabbits is suggested to have relation with underlying changes of endothelial surface molecules, chemokine and MMP-12 gene expressions.

Keywords: Ultrastructure of luminal endothelial surface, Macrophage metalloelastase (MMP-12), Real-time PCR.

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: Satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization.

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Authors: Sadílek M., Čep R.

Abstract:

This work deals with problems of tool axis inclination angles in ball-end milling. Tool axis inclination angle contributes to improvement of functional surface properties (surface integrity - surface roughness, residual stress, micro hardness, etc.), decreasing cutting forces and improving production. By milling with ball-end milling tool, using standard way of cutting, when work piece and cutting tool contain right angle, we have zero cutting speed on edge. At this point cutting tool only pushes material into the work piece. Here we can observe the following undesirable effects - chip contraction, increasing of cutting temperature, increasing vibrations or creation of built-up edge. These effects have negative results – low quality of surface and decreasing of tool life (in the worse case even it is pinching out). These effects can be eliminated with the tilt of cutting tool or tilt of work piece.

Keywords: CAD/CAM system, tool axis inclination angle, ballend milling, surface roughness, cutting forces.

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