Abstract: The basis of examines is survey of 500 in the years
2002-2010, which was selected according to homogeneity of land
cover and where 1090 revenues were evaluated. For achieved yields
of winter wheat is obtained multicriterial regression function
depending on the major factors influencing the consumption of
nitrogen. The coefficient of discrimination of the established model is
0.722. The increase in efficiency of fertilization is involved in supply
of organic nutrients, tillage, soil pH, past weather, the humus content
in the subsoil and grain content to 0.001 mm. The decrease in
efficiency was mainly influenced by the total dose of mineral
nitrogen, although it was divided into multiple doses, the proportion
loamy particles up to 0.01 mm, rainy, or conversely dry weather
during the vegetation. The efficiency of nitrogen was found to be the
smallest on undeveloped soils and the highest on chernozem and
alluvial soils.
Abstract: This paper presented the technique of robot control by event-related potentials (ERPs) of brain waves. Based on the proposed technique, severe physical disabilities can free browse outside world. A specific component of ERPs, N2P3, was found and used to control the movement of robot and the view of camera on the designed brain-computer interface (BCI). Users only required watching the stimuli of attended button on the BCI, the evoked potentials of brain waves of the target button, N2P3, had the greatest amplitude among all control buttons. An experimental scene had been constructed that the robot required walking to a specific position and move the view of camera to see the instruction of the mission, and then completed the task. Twelve volunteers participated in this experiment, and experimental results showed that the correct rate of BCI control achieved 80% and the average of execution time was 353 seconds for completing the mission. Four main contributions included in this research: (1) find an efficient component of ERPs, N2P3, for BCI control, (2) embed robot's viewpoint image into user interface for robot control, (3) design an experimental scene and conduct the experiment, and (4) evaluate the performance of the proposed system for assessing the practicability.
Abstract: Gesture recognition is a challenging task for extracting
meaningful gesture from continuous hand motion. In this paper, we propose an automatic system that recognizes isolated gesture,
in addition meaningful gesture from continuous hand motion for Arabic numbers from 0 to 9 in real-time based on Hidden Markov Models (HMM). In order to handle isolated gesture, HMM using
Ergodic, Left-Right (LR) and Left-Right Banded (LRB) topologies is applied over the discrete vector feature that is extracted from stereo
color image sequences. These topologies are considered to different
number of states ranging from 3 to 10. A new system is developed to recognize the meaningful gesture based on zero-codeword detection
with static velocity motion for continuous gesture. Therefore, the
LRB topology in conjunction with Baum-Welch (BW) algorithm for
training and forward algorithm with Viterbi path for testing presents the best performance. Experimental results show that the proposed system can successfully recognize isolated and meaningful gesture and achieve average rate recognition 98.6% and 94.29% respectively.
Abstract: The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.
Abstract: The standard approach to image reconstruction is to stabilize the problem by including an edge-preserving roughness penalty in addition to faithfulness to the data. However, this methodology produces noisy object boundaries and creates a staircase effect. The existing attempts to favor the formation of smooth contour lines take the edge field explicitly into account; they either are computationally expensive or produce disappointing results. In this paper, we propose to incorporate the smoothness of the edge field in an implicit way by means of an additional penalty term defined in the wavelet domain. We also derive an efficient half-quadratic algorithm to solve the resulting optimization problem, including the case when the data fidelity term is non-quadratic and the cost function is nonconvex. Numerical experiments show that our technique preserves edge sharpness while smoothing contour lines; it produces visually pleasing reconstructions which are quantitatively better than those obtained without wavelet-domain constraints.
Abstract: The influence of humidity and low temperature on the α- amylase activity and isoenzyme composition of grains of different wheat varieties have been studied. The identified samples of varieties have significant difference in the level of enzyme induction under the impact of high humidity and low temperature. It is proposed to use this methodological approach for testing genotypes and wheat breeding lines for resistance to pre-harvest sprouting (PHS).
Abstract: The scale dependence of the strength of virtually homogeneous rock is usually considered to be insignificant but the spectrum of discontinuities plays a very important role for the strength of differently sized rock elements and also controls the rock creep strain. Large-scale load tests comprised recording of the creep strain rate that was found to be strongly retarded and negligible for stresses lower than about 1/3 of the failure load. For higher stresses creep took place according to a log time law representing secondary creep that ultimately changed to tertiary creep and failure.
Abstract: Drought is one of the most important natural disasters which is probable to occur in all regions with completely different climates and in addition to causing death. It results in many economic losses and social consequences. For this reason. Studying the effects and losses caused by drought which include limitation or shortage of agricultural and drinking water resources. Decreased rainfall and increased evapotranspiration. Limited plant growth and decreased agricultural products. Especially those of dry-farming. Lower levels of surface and ground waters and increased immigrations. Etc. in the country is statistical period (1988-2007) for six stations in Roudbar town were used for statistical analysis and calculating humid and dry years. The dependable rainfall index (DRI) was the main method used in this research. Results showed that during the said statistical period and also during the years 1996-1998 and 2007. more than half of the stations had faced drought. With consideration of the conducted studies. Drawing diagrams and comparing the available data with those of dry and humid years it was found that drought affected agricultural products (e.g.olive) in a way that during the year 1996 1996 drought. Olive groves of Roudbar suffered the greatest damages. Whereupon about 70% of the crops were lost.
Abstract: Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.
Abstract: The dilute acid pretreatment and enzymatic
saccharification of lignocellulosic substrate, cogon grass (Imperata
cylindrical, L.) was optimized prior ethanol fermentation using
simultaneous saccharification and fermentation (SSF) method. The
optimum pretreatment conditions, temperature, sulfuric acid
concentration, and reaction time were evaluated by determining the
maximum sugar yield at constant enzyme loading. Cogon grass, at
10% w/v substrate loading, has optimum pretreatment conditions of
126°C, 0.6% v/v H2SO4, and 20min reaction time. These
pretreatment conditions were used to optimize enzymatic
saccharification using different enzyme combinations. The maximum
saccharification yield of 36.68mg/mL (71.29% reducing sugar) was
obtained using 25FPU/g-cellulose cellulase complex combined with
1.1% w/w of cellobiase, ß-glucosidase, and 0.225% w/w of
hemicellulase complex, after 96 hours of saccharification. Using the
optimum pretreatment and saccharification conditions, SSF of treated
substrates was done at 37°C for 120 hours using industrial yeast
strain HBY3, Saccharomyces cerevisiae. The ethanol yield for cogon
grass at 4% w/w loading was 9.11g/L with 5.74mg/mL total residual
sugar.
Abstract: This paper presents the results of preliminary
assessment of water quality along the coastal areas in the vicinity of
Left Bank Outfall Drainage (LBOD) and Tidal Link Drain (TLD) in
Sindh province after the cyclone 2A occurred in 1999. The water
samples were collected from various RDs of Tidal Link Drain and
lakes during September 2001 to April 2002 and were analysed for
salinity, nitrite, phosphate, ammonia, silicate and suspended material
in water. The results of the study showed considerable variations in
water quality depending upon the location along the coast in the
vicinity of LBOD and RDs. The salinity ranged between 4.39–65.25
ppt in Tidal Link Drain samples whereas 2.4–38.05 ppt in samples
collected from lakes. The values of suspended material at various
RDs of Tidal Link Drain ranged between 56.6–2134 ppm and at the
lakes between 68–297 ppm. The data of continuous monitoring at
RD–93 showed the range of PO4 (8.6–25.2 μg/l), SiO3 (554.96–1462
μg/l), NO2 (0.557.2–25.2 μg/l) and NH3 (9.38–23.62 μg/l). The
concentration of nutrients in water samples collected from different
RDs was found in the range of PO4 (10.85 to 11.47 μg/l), SiO3 (1624
to 2635.08 μg/l), NO2 (20.38 to 44.8 μg/l) and NH3 (24.08 to 26.6
μg/l). Sindh coastal areas which situated at the north-western
boundary the Arabian Sea are highly vulnerable to flood damages
due to flash floods during SW monsoon or impact of sea level rise
and storm surges coupled with cyclones passing through Arabian Sea
along Pakistan coast. It is hoped that the obtained data in this study
would act as a database for future investigations and monitoring of
LBOD and Tidal Link Drain coastal waters.
Abstract: MOC (method of cell) is a new method of investigating
wave propagating in material with periodic microstructure, and can
reflect the effect of microstructure. Wave propagation in periodically
laminated medium consisting of linearly elastic layers can be treated
as a special application of this method. In this paper, it was used to
simulate the dynamic response of carbon-phenolic to impulsive
loading under certain boundary conditions. From the comparison
between the results obtained from this method and the exact results
based on propagator matrix theory, excellent agreement is achieved.
Conclusion can be made that the oscillation periodicity is decided by
the thickness of sub-cells. In the end, the NHDMOC method, which
permits studying stress wave propagation with one dimensional strain,
was applied to study the one-dimensional stress wave propagation. In
this paper, the ZWT nonlinear visco-elastic constitutive relationship
with 7 parameters, NHDMOC, and corresponding equations were
deduced. The equations were verified, comparing the elastic stress
wave propagation in SHPB with, respectively, the elastic and the
visco-elastic bar. Finally the dispersion and attenuation of stress wave
in SHPB with visco-elastic bar was studied.
Abstract: A seismic isolation pad produced by utilizing the scrap
tire rubber which contains interleaved steel reinforcing cords has been
proposed. The steel cords are expected to function similar to the steel
plates used in conventional laminated rubber bearings. The scrap tire
rubber pad (STRP) isolator is intended to be used in low rise
residential buildings of highly seismic areas of the developing
countries. Experimental investigation was conducted on unbonded
STRP isolators, and test results provided useful information including
stiffness, damping values and an eventual instability of the isolation
unit. Finite element analysis (FE analysis) of STRP isolator was
carried out on properly bonded samples. These types of isolators
provide positive incremental force resisting capacity up to shear strain
level of 155%. This paper briefly discusses the force deformation
behavior of bonded STRP isolators including stability of the isolation
unit.
Abstract: In this paper, we propose a texture feature-based
language identification using wavelet-domain BDIP (block difference
of inverse probabilities) and BVLC (block variance of local
correlation coefficients) features and FFT (fast Fourier transform)
feature. In the proposed method, wavelet subbands are first obtained
by wavelet transform from a test image and denoised by Donoho-s
soft-thresholding. BDIP and BVLC operators are next applied to the
wavelet subbands. FFT blocks are also obtained by 2D (twodimensional)
FFT from the blocks into which the test image is
partitioned. Some significant FFT coefficients in each block are
selected and magnitude operator is applied to them. Moments for each
subband of BDIP and BVLC and for each magnitude of significant
FFT coefficients are then computed and fused into a feature vector. In
classification, a stabilized Bayesian classifier, which adopts variance
thresholding, searches the training feature vector most similar to the
test feature vector. Experimental results show that the proposed
method with the three operations yields excellent language
identification even with rather low feature dimension.
Abstract: This research were investigated, determined, and
analyzed of the climate characteristically change in the provincial
Udon Thani in the period of 60 surrounding years from 1951 to 2010
A.D. that it-s transferred to effects of climatologically data for
determining global warming. Statistically significant were not found
for the 60 years- data (R2
Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.
Abstract: The majority of existing predictors for time series are
model-dependent and therefore require some prior knowledge for the
identification of complex systems, usually involving system
identification, extensive training, or online adaptation in the case of
time-varying systems. Additionally, since a time series is usually
generated by complex processes such as the stock market or other
chaotic systems, identification, modeling or the online updating of
parameters can be problematic. In this paper a model-free predictor
(MFP) for a time series produced by an unknown nonlinear system or
process is derived using tracking theory. An identical derivation of the
MFP using the property of the Newton form of the interpolating
polynomial is also presented. The MFP is able to accurately predict
future values of a time series, is stable, has few tuning parameters and
is desirable for engineering applications due to its simplicity, fast
prediction speed and extremely low computational load. The
performance of the proposed MFP is demonstrated using the
prediction of the Dow Jones Industrial Average stock index.
Abstract: Software crisis refers to the situation in which the developers are not able to complete the projects within time and budget constraints and moreover these overscheduled and over budget projects are of low quality as well. Several methodologies have been adopted form time to time to overcome this situation and now in the focus is component based software engineering. In this approach, emphasis is on reuse of already existing software artifacts. But the results can not be achieved just by preaching the principles; they need to be practiced as well. This paper highlights some of the very basic elements of this approach, which has to be in place to get the desired goals of high quality, low cost with shorter time-to-market software products.
Abstract: The purpose of this investigation is to relate the rain
power and the overland flow power to soil erodibility to assess the
effects of both parameters on soil erosion using variable rainfall
intensity on remoulded agricultural soil. Six rainfall intensities were
used to simulate the natural rainfall and are as follows: 12.4mm/h,
20.3mm/h, 28.6mm/h, 52mm/h, 73.5mm/h and 103mm/h. The results
have shown that the relationship between overland flow power and
rain power is best represented by a linear function (R2=0.99). As
regards the relationships between soil erodibility factor and rain and
overland flow powers, the evolution of both parameters with the
erodibility factor follow a polynomial function with high coefficient
of determination. From their coefficients of determination (R2=0.95)
for rain power and (R2=0.96) for overland flow power, we can
conclude that the flow has more power to detach particles than rain.
This could be explained by the fact that the presence of particles,
already detached by rain and transported by the flow, give the flow
more weight and then contribute to the detachment of particles by
collision.
Abstract: Application of pesticides in the paddy fields has
deleterious effects on non-target organisms including cyanobacteria
which are photosynthesizing and nitrogen fixing micro-organisms
contributing significantly towards soil fertility and crop yield.
Pesticide contamination in the paddy fields has manifested into a
serious global environmental concern. To study the effect of one such
pesticide, three cyanobacterial strains; Anabaena fertilissima,
Aulosira fertilissima and Westiellopsis prolifica were selected for
their stress responses to an Organochlorine insecticide - 6, 7, 8, 9, 10,
10-hexachloro-1, 5, 5a, 6, 9, 9a-hexahydro-6, 9-methano-2, 4, 3-
benzodioxathiepine-3-oxide, with reference to their photosynthesic
pigments-chlorophyll-a and carotenoids as well as accessory
pigments-phycobiliproteins (phycocyanin, allophycocyanin and
phycoerythrin), stress induced biochemical metabolites like
carbohydrates, proteins, amino acids, phenols and enzymes-nitrate
reductase, glutamine synthetase and succinate dehydrogenase. All
the three cyanobacterial strains were adversely affected by the
insecticide doses and inhibition was dose dependent. Reduction in
photosynthetic and accessory pigments, metabolites, nitrogen fixing
and respiratory enzymes of the test organisms were accompanied
with an initial increase in their total protein at lower Organochlorine
doses. On the other hand, increased amount of phenols in all the
insecticide treated concentrations was indicative of stressed activities
of the organisms.