Abstract: Deep cold rolling (DCR) is a cold working process, which easily produces a smooth and work-hardened surface by plastic deformation of surface irregularities. In the present study, the influence of main deep cold rolling process parameters on the surface roughness and the hardness of AISI 4140 steel were studied by using fractional factorial design of experiments. The assessment of the surface integrity aspects on work material was done, in terms of identifying the predominant factor amongst the selected parameters, their order of significance and setting the levels of the factors for minimizing surface roughness and/or maximizing surface hardness. It was found that the ball diameter, rolling force, initial surface roughness and number of tool passes are the most pronounced parameters, which have great effects on the work piece-s surface during the deep cold rolling process. A simple, inexpensive and newly developed DCR tool, with interchangeable collet for using different ball diameters, was used throughout the experimental work presented in this paper.
Abstract: Composite steel-concrete slabs using thin-walled
corrugated steel sheets with embossments represent a modern and
effective combination of steel and concrete. However, the design
of new types of sheeting is conditional on the execution of expensive
and time-consuming laboratory testing. The effort to develop
a cheaper and faster method has lead to many investigations all over
the world. In our paper we compare the results from our experiments
involving vacuum loading, four-point bending and small-scale shear
tests.
Abstract: Manufacturing components of fiber-reinforced
thermoplastics requires three steps: heating the matrix, forming and
consolidation of the composite and terminal cooling the matrix. For
the heating process a pre-determined temperature distribution through
the layers and the thickness of the pre-consolidated sheets is
recommended to enable forming mechanism. Thus, a design for the
heating process for forming composites with thermoplastic matrices
is necessary. To obtain a constant temperature through thickness and
width of the sheet, the heating process was analyzed by the help of
the finite element method. The simulation models were validated by
experiments with resistance thermometers as well as with an infrared
camera. Based on the finite element simulation, heating methods for
infrared radiators have been developed. Using the numeric
simulation many iteration loops are required to determine the process
parameters. Hence, the initiation of a model for calculating relevant
process parameters started applying regression functions.
Abstract: In this paper, an erosion-based model for abrasive
waterjet (AWJ) turning process is presented. By using modified
Hashish erosion model, the volume of material removed by impacting
of abrasive particles to surface of the rotating cylindrical specimen is
estimated and radius reduction at each rotation is calculated.
Different to previous works, the proposed model considers the
continuous change in local impact angle due to change in workpiece
diameter, axial traverse rate of the jet, the abrasive particle roundness
and density. The accuracy of the proposed model is examined by
experimental tests under various traverse rates. The final diameters
estimated by the proposed model are in good accordance with
experiments.
Abstract: Experiments have been carried out at the Latvia
University of Agriculture Department of Food Technology. The aim
of this work was to assess the effect of sous vide packaging during
the storage time of salad with meat in mayonnaise at different storage
temperature. Samples were evaluated at 0, 1, 3, 7, 10, 15, 18, 25, 29,
42, and 52 storage days at the storage temperature of +4±0.5 ºC and
+10±0.5 ºC. Experimentally the quality of the salad with meat in
mayonnaise was characterized by measuring colour, pH and
microbiological properties. The sous vide packaging was effective in
protecting the product from physical, chemical, and microbial quality
degradation. The sous vide packaging significantly reduces microbial
growth at storage temperature of +4±0.5 ºC and +10±0.5 ºC.
Moreover, it is possible to extend the product shelf life to 52 days
even when stored at +10±0.5 ºC.
Abstract: The effect of dry milling on the carbothermic
reduction of celestite was investigated. Mixtures of celestite
concentrate (98% SrSO4) and activated carbon (99% carbon) was
milled for 1 and 24 hours in a planetary ball mill. Un-milled and
milled mixtures and their products after carbothermic reduction were
studied by a combination of XRD and TGA/DTA experiments. The
thermogravimetric analyses and XRD results showed that by milling
celestite-carbon mixtures for one hour, the formation temperature of
strontium sulfide decreased from about 720°C (in un-milled sample)
to about 600°C, after 24 hours milling it decreased to 530°C. It was
concluded that milling induces increasingly thorough mixing of the
reactants to reduction occurring at lower temperatures
Abstract: The purpose of this article is to study the effects of
plants cover on overland flow and, therefore, its influences on the
amount of eroded and transported soil. In this investigation, all the
experiments were conducted in the LEGHYD laboratory using a
rainfall simulator and a soil tray. The experiments were conducted
using an experimental plot (soil tray) which is 2m long, 0.5 m wide
and 0.15 m deep. The soil used is an agricultural sandy soil (62,08%
coarse sand, 19,14% fine sand, 11,57% silt and 7,21% clay). Plastic
rods (4 mm in diameter) were used to simulate the plants at different
densities: 0 stem/m2 (bared soil), 126 stems/m², 203 stems/m², 461
stems/m² and 2500 stems/m²). The used rainfall intensity is 73mm/h
and the soil tray slope is fixed to 3°. The results have shown that the
overland flow velocities decreased with increasing stems density, and
the density cover has a great effect on sediment concentration.
Darcy–Weisbach and Manning friction coefficients of overland flow
increased when the stems density increased. Froude and Reynolds
numbers decreased with increasing stems density and, consequently,
the flow regime of all treatments was laminar and subcritical. From
these findings, we conclude that increasing the plants cover can
efficiently reduce soil loss and avoid denuding the roots plants.
Abstract: Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time
Abstract: This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.
Abstract: Stock portfolio selection is a classic problem in finance,
and it involves deciding how to allocate an institution-s or an individual-s
wealth to a number of stocks, with certain investment objectives
(return and risk). In this paper, we adopt the classical Markowitz
mean-variance model and consider an additional common realistic
constraint, namely, the cardinality constraint. Thus, stock portfolio
optimization becomes a mixed-integer quadratic programming problem
and it is difficult to be solved by exact optimization algorithms.
Chemical Reaction Optimization (CRO), which mimics the molecular
interactions in a chemical reaction process, is a population-based
metaheuristic method. Two different types of CRO, named canonical
CRO and Super Molecule-based CRO (S-CRO), are proposed to solve
the stock portfolio selection problem. We test both canonical CRO
and S-CRO on a benchmark and compare their performance under
two criteria: Markowitz efficient frontier (Pareto frontier) and Sharpe
ratio. Computational experiments suggest that S-CRO is promising
in handling the stock portfolio optimization problem.
Abstract: Experiments were carried out at the Faculty of Food
Technology of Latvia University of Agriculture (LLU). Soft cheese
Kleo produced in Latvia was packed in a biodegradable PLA without
barrierproperties and VC999 BioPack lidding film PLA, coated with
a barrier of pure silicon oxide (SiOx) and in combination with
modified atmosphere (MAP) the influence on the shelf life was
investigated and compared with some conventional (OPP, PE/PA,
PE/OPA and Multibarrier 60) polymer film impact. Modified
atmosphere consisted of carbon dioxide CO2 (E 290) 30% and
nitrogen N2 (E 941) 70%. The analyzable samples were stored at the
temperature of +4.0±0.5 °C up to 32 days- and analyzed before
packaging and in the 0, 5th, 11th, 15th, 18th, 22nd, 25th, 29th and 32nd
day of storage. The shelf life was extended along to 32 days, good
outside appearance and lactic acid aroma was observed.
Abstract: Intelligent tutoring systems constitute an evolution of computer-aided educational software. We present here the modules of an intelligent tutoring system for Automatic Control, developed in our department. Through the software application developed,students can perform complete automatic control laboratory experiments, either over the departmental local area network or over the Internet. Monitoring of access to the system (local as well as international), along with student performance statistics, has yielded strongly encouraging results (as of fall 2004), despite the advanced technical content of the presented paradigm, thus showing the potential of the system developed for education and for training.
Abstract: The work presents a development of EN338 strength classes for Strombosia pustulata, Pterygotama crocarpa, Nauclea diderrichii and Entandrophragma cyclindricum Nigerian timber species. The specimens for experimental measurements were obtained from the timber-shed at the famous Panteka market in Kaduna in the northern part of Nigeria. Laboratory experiments were conducted to determine the physical and mechanical properties of the selected timber species in accordance with EN 13183-1 and ASTM D193. The mechanical properties were determined using three point bending test. The generated properties were used to obtain the characteristic values of the material properties in accordance with EN384. The selected timber species were then classified according to EN 338. Strombosia pustulata, Pterygotama crocarpa, Nauclea diderrichii and Entandrophragma cyclindricum were assigned to strength classes D40, C14, D40 and D24 respectively. Other properties such as tensile and compressive strengths parallel and perpendicular to grains, shear strength as well as shear modulus were obtained in accordance with EN 338.
Abstract: Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.
Abstract: This study investigated the removal efficiency of electrokinetic remediation of copper-contaminated soil at different combinations of enhancement reagents used as anolyte and catholyte. Sodium hydroxide (at 0.1, 0.5, and 1.0 M concentrations) and distilled water were used as anolyte, while lactic acid (at 0.01, 0.1, and 0.5 M concentrations), ammonium citrate (also at 0.01, 0.1, and 0.5 M concentrations) and distilled water were used as catholyte. A continuous voltage application (1.0 VDC/cm) was employed for 240 hours for each experiment. The copper content of the catholyte was determined at the end of the 240-hour period. Optimization was carried out with a Response Surface Methodology - Optimal Design, including F test, and multiple comparison method, to determine which pair of anolyte-catholyte was the most significant for the removal efficiency. "1.0 M NaOH" was found to be the most significant anolyte while it was established that lactic acid was the most significant type of catholyte to be used for the most successful electrokinetic experiments. Concentrations of lactic acid should be at the range of 0.1 M to 0.5 M to achieve maximum percent removal values.
Abstract: In the present work, we propose a new method for
solving the matrix equation AXB=F . The new method can
be considered as a generalized form of the well-known global full
orthogonalization method (Gl-FOM) for solving multiple linear
systems. Hence, the method will be called extended Gl-FOM (EGl-
FOM). For implementing EGl-FOM, generalized forms of block
Krylov subspace and global Arnoldi process are presented. Finally,
some numerical experiments are given to illustrate the efficiency of
our new method.
Abstract: The Minimum Weighted Vertex Cover (MWVC) problem is a classic graph optimization NP - complete problem. Given an undirected graph G = (V, E) and weighting function defined on the vertex set, the minimum weighted vertex cover problem is to find a vertex set S V whose total weight is minimum subject to every edge of G has at least one end point in S. In this paper an effective algorithm, called Support Ratio Algorithm (SRA), is designed to find the minimum weighted vertex cover of a graph. Computational experiments are designed and conducted to study the performance of our proposed algorithm. Extensive simulation results show that the SRA can yield better solutions than other existing algorithms found in the literature for solving the minimum vertex cover problem.
Abstract: We present new finite element methods for Helmholtz and Maxwell equations on general three-dimensional polyhedral meshes, based on domain decomposition with boundary elements on the surfaces of the polyhedral volume elements. The methods use the lowest-order polynomial spaces and produce sparse, symmetric linear systems despite the use of boundary elements. Moreover, piecewise constant coefficients are admissible. The resulting approximation on the element surfaces can be extended throughout the domain via representation formulas. Numerical experiments confirm that the convergence behavior on tetrahedral meshes is comparable to that of standard finite element methods, and equally good performance is attained on more general meshes.
Abstract: Sugarcane bagasses are one of the most extensively used agricultural residues. Using acid hydrolysis and fermentation, conversion of sugarcane bagasses to lactic acid was technically and economically feasible. This research was concerned with the solubility of lignin in ammonium hydroxide, acid hydrolysis and lactic acid fermentation by Lactococcus lactis, Lactobacillus delbrueckii, Lactobacillus plantarum, and Lactobacillus casei. The lignin extraction results for different ammonium hydroxide concentrations showed that 10 % (v/v) NH4OH was favorable to lignin dissolution. Acid hydrolysis can be enhanced with increasing acid concentration and reaction temperature. The optimum glucose and xylose concentrations occurred at 121 ○C for 1 hour hydrolysis time in 10% sulphuric acid solution were 32 and 11 g/l, respectively. In order to investigate the significance of medium composition on lactic acid production, experiments were undertaken whereby a culture of Lactococcus lactis was grown under various glucose, peptone, yeast extract and xylose concentrations. The optimum medium was composed of 5 g/l glucose, 2.5 g/l xylose, 10 g/l peptone and 5 g/l yeast extract. Lactococcus lactis represents the most efficient for lactic acid production amongst those considered. The lactic acid fermentation by Lactococcus lactis after 72 hours gave the highest yield of 1.4 (g lactic acid per g reducing sugar).
Abstract: Random and natural textures classification is still
one of the biggest challenges in the field of image processing and
pattern recognition. In this paper, texture feature extraction using
Slant Hadamard Transform was studied and compared to other
signal processing-based texture classification schemes. A
parametric SHT was also introduced and employed for natural
textures feature extraction. We showed that a subtly modified
parametric SHT can outperform ordinary Walsh-Hadamard
transform and discrete cosine transform. Experiments were carried
out on a subset of Vistex random natural texture images using a
kNN classifier.