Abstract: The paper presents new results concerning selection of
optimal information fusion formula for ensembles of C-OTDR
channels. The goal of information fusion is to create an integral
classificator designed for effective classification of seismoacoustic
target events. The LPBoost (LP-β and LP-B variants), the Multiple
Kernel Learning, and Weighing of Inversely as Lipschitz Constants
(WILC) approaches were compared. The WILC is a brand new
approach to optimal fusion of Lipschitz Classifiers Ensembles.
Results of practical usage are presented.
Abstract: This work is the first dowel in a rather wide research
activity in collaboration with Euro Mediterranean Center for Climate
Changes, aimed at introducing scalable approaches in Ocean
Circulation Models. We discuss designing and implementation of
a parallel algorithm for solving the Variational Data Assimilation
(DA) problem on Graphics Processing Units (GPUs). The algorithm
is based on the fully scalable 3DVar DA model, previously proposed
by the authors, which uses a Domain Decomposition approach
(we refer to this model as the DD-DA model). We proceed with
an incremental porting process consisting of 3 distinct stages:
requirements and source code analysis, incremental development of
CUDA kernels, testing and optimization. Experiments confirm the
theoretic performance analysis based on the so-called scale up factor
demonstrating that the DD-DA model can be suitably mapped on
GPU architectures.
Abstract: Customer churn prediction is one of the most useful
areas of study in customer analytics. Due to the enormous amount
of data available for such predictions, machine learning and data
mining have been heavily used in this domain. There exist many
machine learning algorithms directly applicable for the problem of
customer churn prediction, and here, we attempt to experiment on
a novel approach by using a cognitive learning based technique in
an attempt to improve the results obtained by using a combination
of supervised learning methods, with cognitive unsupervised learning
methods.
Abstract: This work reports the potential of using Palm Kernel
(PK) ash and shell as a partial substitute for Portland Cement (PC)
and coarse aggregate in the development of mortar and concrete. PK
ash and shell are agro-waste materials from palm oil mills, the
disposal of PK ash and shell is an environmental problem of concern.
The PK ash has pozzolanic properties that enables it as a partial
replacement for cement and also plays an important role in the
strength and durability of concrete, its use in concrete will alleviate
the increasing challenges of scarcity and high cost of cement. In order
to investigate the PC replacement potential of PK ash, three types of
PK ash were produced at varying temperature (350-750C) and they
were used to replace up to 50% PC. The PK shell was used to replace
up to 100% coarse aggregate in order to study its aggregate
replacement potential. The testing programme included material
characterisation, the determination of compressive strength, tensile
splitting strength and chemical durability in aggressive sulfatebearing
exposure conditions. The 90 day compressive results showed
a significant strength gain (up to 26.2 N/mm2). The Portland cement
and conventional coarse aggregate has significantly higher influence
in the strength gain compared to the equivalent PK ash and PK shell.
The chemical durability results demonstrated that after a prolonged
period of exposure, significant strength losses in all the concretes
were observed. This phenomenon is explained, due to lower change
in concrete morphology and inhibition of reaction species and the
final disruption of the aggregate cement paste matrix.
Abstract: Novel bio-based polymer electrolyte was synthesized
with LiClO4 as the main source of charge carrier. Initially,
polyurethane-LiClO4 polymer electrolytes were synthesized via
prepolymerization method with different NCO/OH ratios and labelled
them as PU1, PU2, PU3 and PU4. Fourier transform infrared (FTIR)
analysis indicates the co-ordination between Li+ ion and polyurethane
in PU1. Differential scanning calorimetry (DSC) analysis indicates
PU1 has the highest glass transition temperature (Tg) corresponds to
the most abundant urethane group which is the hard segment in PU1.
Scanning electron microscopy (SEM) shows the good miscibility
between lithium salt and the polymer. The study found that PU1
possessed the greatest ionic conductivity and the lowest activation
energy, Ea. All the polyurethanes exhibited linear Arrhenius
variations indicating ion transport via simple lithium ion hopping in
polyurethane. This research proves the NCO content in polyurethane
plays an important role in affecting the ionic conductivity of this
polymer electrolyte.
Abstract: With the rapid progress of modern cities, the railway
construction must be developing quickly in China.As a typical
high-density country, shopping center on the subway should be one
important factor during the process of urban development. The paper
discusses the influence of the layout of shopping center on the subway,
and put it in the time and space’s axis of Shanghai urban development.
We usethe digital technology to establish the database of relevant
information. And then get the change role about shopping center on
subway in Shanghaiby the Kernel density estimate.The result shows
the development of shopping center on subway has a relationship with
local economic strength, population size, policysupport, and city
construction. And the suburbanization trend of shopping center would
be increasingly significant.By this case research, we could see the
Kernel density estimate is an efficient analysis method on the spatial
layout. It could reveal the characters of layout form of shopping center
on subway in essence. And it can also be applied to the other research
of space form.
Abstract: In this paper the issue of dimensionality reduction is
investigated in finger vein recognition systems using kernel Principal
Component Analysis (KPCA). One aspect of KPCA is to find the
most appropriate kernel function on finger vein recognition as there
are several kernel functions which can be used within PCA-based
algorithms. In this paper, however, another side of PCA-based
algorithms -particularly KPCA- is investigated. The aspect of
dimension of feature vector in PCA-based algorithms is of
importance especially when it comes to the real-world applications
and usage of such algorithms. It means that a fixed dimension of
feature vector has to be set to reduce the dimension of the input and
output data and extract the features from them. Then a classifier is
performed to classify the data and make the final decision. We
analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in
this paper and investigate the optimal feature extraction dimension in
finger vein recognition using KPCA.
Abstract: Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. Noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.
Abstract: The effects of varying air temperature (full, ¾ full, ½ full aircon adjustment, no aircon) in polishing component of Single-Pass Mill on the quality of Philippine inbred rice variety, was investigated. Parameters measured were milling recovery (MR), headrice recovery (HR), and percentage with bran streaks. Cooling method (with aircon) increased MR, HR, and percentage with bran streaks of milled rice. Highest MR and HR (67.62%; 47.33%) were obtained from ¾ full adjustment whereas no aircon were lowest (66.27%; 39.76%). Temperature in polishing component at ¾ full adjustment was 33oC whereas no aircon was 45oC. There was increase of 1.35% in MR and 7.57% in HR. Additional cost of milling per kg due to aircon cooling was P0.04 at 300 tons/yr volume, with 0.15 yr payback period. Net income was estimated at ₱98,100.00. Percentage of kernels with bran streaks increased from 5%–14%, indicating more nutrients of milled rice.
Abstract: This paper presents a nonparametric method to obtain the hazard rate “Bathtub curve” for power system components. The model is a mixture of the three known phases of a component life, the decreasing failure rate (DFR), the constant failure rate (CFR) and the increasing failure rate (IFR) represented by three parametric Weibull models. The parameters are obtained from a simultaneous fitting process of the model to the Kernel nonparametric hazard rate curve. From the Weibull parameters and failure rate curves the useful lifetime and the characteristic lifetime were defined. To demonstrate the model the historic time-to-failure of distribution transformers were used as an example. The resulted “Bathtub curve” shows the failure rate for the equipment lifetime which can be applied in economic and replacement decision models.
Abstract: Palm kernel shell is an important bioenergy resource
in Thailand. However, due to elevated alkali content in biomass ash,
this oil palm residue shows high tendency to bed agglomeration in a
fluidized-bed combustion system using conventional bed material
(silica sand). In this study, palm kernel shell was burned in the
conical fluidized-bed combustor (FBC) using alumina and dolomite
as alternative bed materials to prevent bed agglomeration. For each
bed material, the combustion tests were performed at 45kg/h fuel feed
rate with excess air within 20–80%. Experimental results revealed
rather weak effects of the bed material type but substantial influence
of excess air on the behavior of temperature, O2, CO, CxHy, and NO
inside the reactor, as well as on the combustion efficiency and major
gaseous emissions of the conical FBC. The optimal level of excess air
ensuring high combustion efficiency (about 98.5%) and acceptable
level of the emissions was found to be about 40% when using
alumina and 60% with dolomite. By using these alternative bed
materials, bed agglomeration can be prevented when burning the
shell in the proposed conical FBC. However, both bed materials
exhibited significant changes in their morphological, physical and
chemical properties in the course of the time.
Abstract: The corn earworm, Helicoverpa zea Boddie, is a serious pest of corn. Larval feeding in ear tips destroys kernels and allows growth of fungi and production of mycotoxins. Infested sweet corn is not marketable. Development of improved transgenic hybrids expressing insecticidal toxins from Bacillus thuringiensis (Bt) may limit or prevent crop losses. The effectiveness of Attribute® II Bt resistance and applications of Voliam Xpress insecticide were evaluated for effectiveness in controlling corn earworm in plots near Urbana, IL, USA, in 2013. Where no insecticides were applied, ear infestations and kernel damage in Attribute® II ‘Protector’ plots were consistently lower (near zero) than in plots of the non-Bt isoline ‘Garrison.’ Multiple applications of Voliam Xpress significantly reduced the number of corn earworm larvae and kernel damage in the Garrison plots, but infestations and damage in these plots were greater than in Protectorplots that did not receive insecticide applications. Our results indicate that Attribute® II Bt resistance is more effective than multiple applications of an insecticide for preventing losses caused by corn earworm in sweet corn.
Abstract: In this study, Support Vector Machine (SVM) technique was applied to predict the dichotomized value of Dissolved oxygen (DO) from two freshwater lakes namely Chini and Bera Lake (Malaysia). Data sample contained 11 parameters for water quality features from year 2005 until 2009. All data parameters were used to predicate the dissolved oxygen concentration which was dichotomized into 3 different levels (High, Medium, and Low). The input parameters were ranked, and forward selection method was applied to determine the optimum parameters that yield the lowest errors, and highest accuracy. Initial results showed that pH, Water Temperature, and Conductivity are the most important parameters that significantly affect the predication of DO. Then, SVM model was applied using the Anova kernel with those parameters yielded 74% accuracy rate. We concluded that using SVM models to predicate the DO is feasible, and using dichotomized value of DO yields higher prediction accuracy than using precise DO value.
Abstract: Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.
Abstract: Sea level rise threatens to increase the impact of future
storms and hurricanes on coastal communities. Accurate sea level
change prediction and supplement is an important task in determining
constructions and human activities in coastal and oceanic areas. In
this study, support vector machines (SVM) is proposed to predict
daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal
parameter values of kernel function are determined using a genetic
algorithm. The SVM results are compared with the field data and
with back propagation (BP). Among the models, the SVM is superior
to BPNN and has better generalization performance.
Abstract: In order to fulfill world energy demand, several efforts have been done to look for new and renewable energy candidates to substitute oil and gas. Biomass is one of new and renewable energy sources, which is abundant in Indonesia. Palm kernel shell is a kind of biomass discharge from palm oil industries as a waste. On the other hand, Jatropha curcas that is easy to grow in Indonesia is also a typical energy source either for bio-diesel or biomass. In this study, biomass was used as co-fuel in briquetting of low-rank coal to suppress the release of emission (such as CO, NOx and SOx) during coal combustion. Desulfurizer, CaO-base, was also added to ensure the SOx capture is effectively occurred. Ratio of coal to palm kernel shell (w/w) in the bio-briquette were 50:50, 60:40, 70:30, 80:20 and 90:10, while ratio of calcium to sulfur (Ca/S) in mole/mole were 1:1; 1.25:1; 1.5:1; 1.75:1 and 2:1. The bio-briquette then subjected to physical characterization and combustion test. The results show that the maximum weight loss in the durability measurement was ±6%. In addition, the highest stove efficiency for each desulfurizer was observed at the coal/PKS ratio of 90:10 and Ca/S ratio of 1:1 (except for the scallop shell desulfurizer that appeared at two Ca/S ratios; 1.25:1 and 1.5:1, respectively), i.e. 13.8% for the lime; 15.86% for the oyster shell; 14.54% for the scallop shell and 15.84% for the green mussel shell desulfurizers.
Abstract: Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.
Abstract: Support vector clustering (SVC) is an important kernelbased clustering algorithm in multi applications. It has got two main bottle necks, the high computation price and labeling piece. In this paper, we presented a modified SVC method, named Grid–SVC, to improve the original algorithm computationally. First we normalized and then we parted the interval, where the SVC is processing, using a novel Grid–based clustering algorithm. The algorithm parts the intervals, based on the density function of the data set and then applying the cartesian multiply makes multi-dimensional grids. Eliminating many outliers and noise in the preprocess, we apply an improved SVC method to each parted grid in a parallel way. The experimental results show both improvement in time complexity order and the accuracy.
Abstract: The purpose of present work was to study the drying kinetics of whole acorn and its kernel at different drying air temperatures and their effective moisture diffusivity. The results indicated that the drying time of whole acorn was 442, 206 and 188 min at the air temperature of 65, 75 and 85ºC, respectively. At the same temperatures, the drying time of kernel was 131, 56 and 76min. The results showed that the effect of drying air temperature increasing on the drying time reduction could not be significant on acorn drying at all conditions. The effective moisture diffusivity of whole acorn and kernel increased with increasing air temperature from 65 to 75ºC. However more air temperature increasing, led to decreasing this property of acorn kernel. The critical temperature of acorn drying was about 75°C in which acorn kernel had the highest effective moisture diffusivity.