Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 11640

Search results for: kernel methods

11640 Online Prediction of Nonlinear Signal Processing Problems Based Kernel Adaptive Filtering

Authors: Hamza Nejib, Okba Taouali

Abstract:

This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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11639 A Fuzzy Kernel K-Medoids Algorithm for Clustering Uncertain Data Objects

Authors: Behnam Tavakkol

Abstract:

Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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11638 Extraction and Characterization of Kernel Oil of Acrocomia Totai

Authors: Gredson Keif Souza, Nehemias Curvelo Pereira

Abstract:

Kernel oil from Macaúba is an important source of essential fatty acids. Thus, a new knowledge of the oil of this species could be used in new applications, such as pharmaceutical drugs based in the manufacture of cosmetics, and in various industrial processes. The aim of this study was to characterize the kernel oil of macaúba (Acrocomia Totai) at different times of their maturation. The physico-chemical characteristics were determined in accordance with the official analytical methods of oils and fats. It was determined the content of water and lipids in kernel, saponification value, acid value, water content in the oil, viscosity, density, composition in fatty acids by gas chromatography and molar mass. The results submitted to Tukey test for significant value to 5%. Found for the unripe fruits values superior to unsaturated fatty acids.

Keywords: extraction, characterization, kernel oil, acrocomia totai

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11637 Kernel-Based Double Nearest Proportion Feature Extraction for Hyperspectral Image Classification

Authors: Hung-Sheng Lin, Cheng-Hsuan Li

Abstract:

Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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11636 Improving the Quality and Nutrient Content of Palm Kernel Cake through Fermentation with Bacillus subtilis

Authors: Mirnawati, Gita Ciptaan, Ferawati

Abstract:

Background and Objective: Palm kernel cake (PKC) is a waste of the palm oil industry. Indonesia, as the largest palm oil producer in the world, produced 45-46% palm kernel cake. Palm kernel cake can potentially be used as animal ration but its utilization for poultry is limited. Thus, fermentation process was done in order to increase the utilization PKC in poultry ration. An experiment was conducted to study the effect between Inoculum Doses with Bacillus subtilis and fermentation time to improve the quality and nutrient content of fermented Palm Kernel Cake. Material and Methods: 1) Palm kernel cake derived from Palm Kernel Processing Manufacture of Andalas Agro Industry in Pasaman, West Sumatra. 2) Bacillus subtilis obtained from The Research Center of Applied Chemistry LIPI, Bogor. 3) Preparations nutrient agar medium (NA) produced by Difoo - Becton Dickinson. 4) Rice bran 5) Aquades and mineral standard. The experiment used completely randomize design (CRD) with 3 x 3 factorial and 3 replications. The first factors were three doses of inoculum Bacillus subtilis: (3%), (5%), and (7%). The second factor was fermentation time: (1) 2 day, (2) 4 day, and (3) 6 day. The parameters were crude protein, crude fiber, nitrogen retention, and crude fiber digestibility of fermented palm kernel cake (FPKC). Results: The result of the study showed that there was significant interaction (P<0.01) between factor A and factor B and each factor A and B also showed significant effect (P<0.01) on crude protein, crude fiber, nitrogen retention, and crude fiber digestibility. Conclusion: From this study, it can be concluded that fermented PKC with 7% doses of Bacillus subtilis and 6 days fermentation time provides the best result as seen from 24.65% crude protein, 17.35% crude fiber, 68.47% nitrogen retention, 53.25% crude fiber digestibility of fermented palm kernel cake (FPKC).

Keywords: fermentation, Bacillus Subtilis, inoculum, palm kernel cake, quality, nutrient

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11635 Composite Kernels for Public Emotion Recognition from Twitter

Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

Abstract:

The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

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11634 Kernel Parallelization Equation for Identifying Structures under Unknown and Periodic Loads

Authors: Seyed Sadegh Naseralavi

Abstract:

This paper presents a Kernel parallelization equation for damage identification in structures under unknown periodic excitations. Herein, the dynamic differential equation of the motion of structure is viewed as a mapping from displacements to external forces. Utilizing this viewpoint, a new method for damage detection in structures under periodic loads is presented. The developed method requires only two periods of load. The method detects the damages without finding the input loads. The method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering the concept, kernel parallelization equation (KPE) is derived for damage detection under unknown periodic loads. The method is verified for a case study under periodic loads.

Keywords: Kernel, unknown periodic load, damage detection, Kernel parallelization equation

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11633 A Time-Varying and Non-Stationary Convolution Spectral Mixture Kernel for Gaussian Process

Authors: Kai Chen, Shuguang Cui, Feng Yin

Abstract:

Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP.

Keywords: Gaussian process, spectral mixture, non-stationary, convolution

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11632 Analysis of the Relationship between the Unitary Impulse Response for the nth-Volterra Kernel of a Duffing Oscillator System

Authors: Guillermo Manuel Flores Figueroa, Juan Alejandro Vazquez Feijoo, Jose Navarro Antonio

Abstract:

A continuous nonlinear system response may be obtained by an infinite sum of the so-called Volterra operators. Each operator is obtained from multidimensional convolution of nth-order between the nth-order Volterra kernel and the system input. These operators can also be obtained from the Associated Linear Equations (ALEs) that are linear models of subsystems which inputs and outputs are of the same nth-order. Each ALEs produces a particular nth-Volterra operator. As linear models a unitary impulse response can be obtained from them. This work shows the relationship between this unitary impulse responses and the corresponding order Volterra kernel.

Keywords: Volterra series, frequency response functions FRF, associated linear equations ALEs, unitary response function, Voterra kernel

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11631 Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis

Authors: Amir Hajian, Sepehr Damavandinejadmonfared

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.

Keywords: biometrics, finger vein recognition, principal component analysis (PCA), kernel principal component analysis (KPCA)

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11630 Water Footprint for the Palm Oil Industry in Malaysia

Authors: Vijaya Subramaniam, Loh Soh Kheang, Astimar Abdul Aziz

Abstract:

Water footprint (WFP) has gained importance due to the increase in water scarcity in the world. This study analyses the WFP for an agriculture sector, i.e., the oil palm supply chain, which produces oil palm fresh fruit bunch (FFB), crude palm oil, palm kernel, and crude palm kernel oil. The water accounting and vulnerability evaluation (WAVE) method was used. This method analyses the water depletion index (WDI) based on the local blue water scarcity. The main contribution towards the WFP at the plantation was the production of FFB from the crop itself at 0.23m³/tonne FFB. At the mill, the burden shifts to the water added during the process, which consists of the boiler and process water, which accounted for 6.91m³/tonne crude palm oil. There was a 33% reduction in the WFP when there was no dilution or water addition after the screw press at the mill. When allocation was performed, the WFP reduced by 42% as the burden was shared with the palm kernel and palm kernel shell. At the kernel crushing plant (KCP), the main contributor towards the WFP 4.96 m³/tonne crude palm kernel oil which came from the palm kernel which carried the burden from upstream followed by electricity, 0.33 m³/tonne crude palm kernel oil used for the process and 0.08 m³/tonne crude palm kernel oil for transportation of the palm kernel. A comparison was carried out for mills with biogas capture versus no biogas capture, and the WFP had no difference for both scenarios. The comparison when the KCPs operate in the proximity of mills as compared to those operating in the proximity of ports only gave a reduction of 6% for the WFP. Both these scenarios showed no difference and insignificant difference, which differed from previous life cycle assessment studies on the carbon footprint, which showed significant differences. This shows that findings change when only certain impact categories are focused on. It can be concluded that the impact from the water used by the oil palm tree is low due to the practice of no irrigation at the plantations and the high availability of water from rainfall in Malaysia. This reiterates the importance of planting oil palm trees in regions with high rainfall all year long, like the tropics. The milling stage had the most significant impact on the WFP. Mills should avoid dilution to reduce this impact.

Keywords: life cycle assessment, water footprint, crude palm oil, crude palm kernel oil, WAVE method

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11629 Effects of Neem (Azadirachta indica A. Juss) Kernel Inclusion in Broiler Diet on Growth Performance, Organ Weight and Gut Morphometry

Authors: Olatundun Bukola Ezekiel, Adejumo Olusoji

Abstract:

A feeding trial was conducted with 100 two-weeks old broiler chicken to evaluate the influence of inclusion in broiler diets at 0, 2.5, 5, 7.5 and 10% neem kernel (used to replace equal quantity of maize) on their performance, organ weight and gut morphometry. The birds were randomly allotted to five dietary treatments, each treatment having four replicates consisting of five broilers in a completely randomized design. The diets were formulated to be iso-nitrogenous (23% CP). Weekly feed intake and changes in body weight were calculated and feed efficiency determined. At the end of the 28-day feeding trial, four broilers per treatment were selected and sacrificed for carcass evaluation. Results were subjected to statistical analysis using the analysis of variance procedures of Statistical Analysis Software The treatment means were presented with group standard errors of means and where significant, were compared using the Duncan multiple range test of the same software. The results showed that broilers fed 2.5% neem kernel inclusion diets had growth performance statistically comparable to those fed the control diet. Birds on 5, 7.5 and 10% neem kernel diets showed significant (P<0.05) increase in relative weight of liver. The absolute weight of spleen also increased significantly (P<0.05) in birds on 10 % neem kernel diet. More than 5 % neem kernel diets gave significant (P<0.05) increase in the relative weight of the kidney. The length of the small intestine significantly increased in birds fed 7.5 and 10% neem kernel diets. Significant differences (P<0.05) did not occur in the length of the large intestine, right and left caeca. It is recommended that neem kernel can be included up to 2.5% in broiler chicken diet without any deleterious effects on the performance and physiological status of the birds.

Keywords: broiler chicken, growth performance, gut morphometry, neem kernel, organ weight

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11628 Flame Kernel Growth and Related Effects of Spark Plug Electrodes: Fluid Motion Interaction in an Optically Accessible DISI Engine

Authors: A. Schirru, A. Irimescu, S. Merola, A. d’Adamo, S. Fontanesi

Abstract:

One of the aspects that are usually neglected during the design phase of an engine is the effect of the spark plug on the flow field inside the combustion chamber. Because of the difficulties in the experimental investigation of the mutual interaction between flow alteration and early flame kernel convection effect inside the engine combustion chamber, CFD-3D simulation is usually exploited in such cases. Experimentally speaking, a particular type of engine has to be used in order to directly observe the flame propagation process. In this study, a double electrode spark plug was fitted into an optically accessible engine and a high-speed camera was used to capture the initial stages of the combustion process. Both the arc and the kernel phases were observed. Then, a morphologic analysis was carried out and the position of the center of mass of the flame, relative to the spark plug position, was calculated. The crossflow orientation was chosen for the spark plug and the kernel growth process was observed for different air-fuel ratios. It was observed that during a normal cycle the flow field between the electrodes tends to transport the arc deforming it. Because of that, the kernel growth phase takes place away from the electrodes and the flame propagates with a preferential direction dictated by the flow field.

Keywords: Combustion, Optically Accessible Engine, Spark-Ignition Engine, Sparl Orientation, Kernel Growth

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11627 Development of Non-Structural Crushed Palm Kernel Shell Fine Aggregate Concrete

Authors: Kazeem K. Adewole, Ismail A. Yahya

Abstract:

In the published literature, Palm Kernel Shell (PKS), an agricultural waste has largely been used as a large aggregate in PKS concrete production. In this paper, the development of Crushed Palm Kernel Shell Fine Aggregate Concrete (CPKSFAC) with crushed PKS (CPKS) as the fine aggregate and granite as the coarse aggregate is presented. 100mm x 100mm x 100mm 1:11/2:3 and 1:2:4 CPKSFAC and River Sand Fine Aggregate Concrete (RSFAC) cubes were molded, cured for 28 days and subjected to a compressive strength test. The average wet densities of the 1:11/2:3 and 1:2:4 CPKSFAC cubes are 2240kg/m3 and 2335kg/m3 respectively. The average wet densities of the 1:11/2:3 and 1:2:4 RSFAC cubes are 2606kg/m3 and 2553kg/m3 respectively. The average compressive strengths of the 1:11/2:3 and 1:2:4 CPKSFAC cubes are 15.40MPa and 14.30MPa respectively. This study demonstrates that CPKSFA is suitable for the production of non-structural C8/10 and C12/15 concrete specified in BS EN 206-1:2000.

Keywords: crushed palm kernel shell, fine aggregate, lightweight concrete, non-structural concrete

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11626 The Reach of Shopping Center Layout Form on Subway Based on Kernel Density Estimate

Authors: Wen Liu

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 use the 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, policy support, 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.

Keywords: Shanghai, shopping center on the subway, layout form, Kernel density estimate

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11625 Preliminary Results on a Maximum Mean Discrepancy Approach for Seizure Detection

Authors: Boumediene Hamzi, Turky N. AlOtaiby, Saleh AlShebeili, Arwa AlAnqary

Abstract:

We introduce a data-driven method for seizure detection drawing on recent progress in Machine Learning. The method is based on embedding probability measures in a high (or infinite) dimensional reproducing kernel Hilbert space (RKHS) where the Maximum Mean Discrepancy (MMD) is computed. The MMD is metric between probability measures that are computed as the difference between the means of probability measures after being embedded in an RKHS. Working in RKHS provides a convenient, general functional-analytical framework for theoretical understanding of data. We apply this approach to the problem of seizure detection.

Keywords: kernel methods, maximum mean discrepancy, seizure detection, machine learning

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11624 Clustering Based Level Set Evaluation for Low Contrast Images

Authors: Bikshalu Kalagadda, Srikanth Rangu

Abstract:

The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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11623 Extraction and Antibacterial Studies of Oil from Three Mango Kernel Obtained from Makurdi, Nigeria

Authors: K. Asemave, D. O. Abakpa, T. T. Ligom

Abstract:

The ability of bacteria to develop resistance to many antibiotics cannot be undermined, given the multifaceted health challenges in the present times. For this reason, a lot of attention is on botanicals and their products in search of new antibacterial agents. On the other hand, mango kernel oils (MKO) can be heavily valorized by taking advantage of the myriads bioactive phytochemicals it contains. Herein, we validated the use of MKO as bioactive agent against bacteria. The MKOs for the study were extracted by soxhlet means with ethanol and hexane for 4 h from 3 different mango kernels, namely; 'local' (sample A), 'julie' (sample B), and 'john' (sample C). Prior to the extraction, ground fine particles of the kernels were obtained from the seed kernels dried in oven at 100 °C for 8 h. Hexane gave higher yield of the oils than ethanol. It was also qualitatively confirmed that the mango kernel oils contain some phytochemicals such as phenol, quinone, saponin, and terpenoid. The results of the antibacterial activities of the MKO against both gram positive (Staphylococcus aureus) and gram negative (Pseudomonas aeruginosa) at different concentrations showed that the oils extracted with ethanol gave better antibacterial properties than those of the hexane. More so, the bioactivities were best with the local mango kernel oil. Indeed this work has completely validated the previous claim that MKOs are effective antibacterial agents. Thus, these oils (especially the ethanol-derived ones) can be used as bacteriostatic and antibacterial agents in say food, cosmetics, and allied industries.

Keywords: bacteria, mango, kernel, oil, phytochemicals

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11622 Kinetics, Equilibrium and Thermodynamic Studies on Adsorption of Reactive Blue 29 from Aqueous Solution Using Activated Tamarind Kernel Powder

Authors: E. D. Paul, A. D. Adams, O. Sunmonu, U. S. Ishiaku

Abstract:

Activated tamarind kernel powder (ATKP) was prepared from tamarind fruit (Tamarindus indica), and utilized for the removal of Reactive Blue 29 (RB29) from its aqueous solution. The powder was activated using 4N nitric acid (HNO₃). The adsorbent was characterised using infrared spectroscopy, bulk density, ash content, pH, moisture content and dry matter content measurements. The effect of various parameters which include; temperature, pH, adsorbent dosage, ion concentration, and contact time were studied. Four different equilibrium isotherm models were tested on the experimental data, but the Temkin isotherm model was best-fitted into the experimental data. The pseudo-first order and pseudo-second-order kinetic models were also fitted into the graphs, but pseudo-second order was best fitted to the experimental data. The thermodynamic parameters showed that the adsorption of Reactive Blue 29 onto activated tamarind kernel powder is a physical process, feasible and spontaneous, exothermic in nature and there is decreased randomness at the solid/solution interphase during the adsorption process. Therefore, activated tamarind kernel powder has proven to be a very good adsorbent for the removal of Reactive Blue 29 dyes from industrial waste water.

Keywords: tamarind kernel powder, reactive blue 29, isotherms, kinetics

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11621 Development of Palm Kernel Shell Lightweight Masonry Mortar

Authors: Kazeem K. Adewole

Abstract:

There need to construct building walls with lightweight masonry bricks/blocks and mortar to reduce the weight and cost of cooling/heating of buildings in hot/cold climates is growing partly due to legislations on energy use and global warming. In this paper, the development of Palm Kernel Shell masonry mortar (PKSMM) prepared with Portland cement and crushed PKS fine aggregate (an agricultural waste) is demonstrated. We show that PKSMM can be used as a lightweight mortar for the construction of lightweight masonry walls with good thermal insulation efficiency than the natural river sand commonly used for masonry mortar production.

Keywords: building walls, fine aggregate, lightweight masonry mortar, palm kernel shell, wall thermal insulation efficacy

Procedia PDF Downloads 239
11620 Comparison of Receiver Operating Characteristic Curve Smoothing Methods

Authors: D. Sigirli

Abstract:

The Receiver Operating Characteristic (ROC) curve is a commonly used statistical tool for evaluating the diagnostic performance of screening and diagnostic test with continuous or ordinal scale results which aims to predict the presence or absence probability of a condition, usually a disease. When the test results were measured as numeric values, sensitivity and specificity can be computed across all possible threshold values which discriminate the subjects as diseased and non-diseased. There are infinite numbers of possible decision thresholds along the continuum of the test results. The ROC curve presents the trade-off between sensitivity and the 1-specificity as the threshold changes. The empirical ROC curve which is a non-parametric estimator of the ROC curve is robust and it represents data accurately. However, especially for small sample sizes, it has a problem of variability and as it is a step function there can be different false positive rates for a true positive rate value and vice versa. Besides, the estimated ROC curve being in a jagged form, since the true ROC curve is a smooth curve, it underestimates the true ROC curve. Since the true ROC curve is assumed to be smooth, several smoothing methods have been explored to smooth a ROC curve. These include using kernel estimates, using log-concave densities, to fit parameters for the specified density function to the data with the maximum-likelihood fitting of univariate distributions or to create a probability distribution by fitting the specified distribution to the data nd using smooth versions of the empirical distribution functions. In the present paper, we aimed to propose a smooth ROC curve estimation based on the boundary corrected kernel function and to compare the performances of ROC curve smoothing methods for the diagnostic test results coming from different distributions in different sample sizes. We performed simulation study to compare the performances of different methods for different scenarios with 1000 repetitions. It is seen that the performance of the proposed method was typically better than that of the empirical ROC curve and only slightly worse compared to the binormal model when in fact the underlying samples were generated from the normal distribution.

Keywords: empirical estimator, kernel function, smoothing, receiver operating characteristic curve

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11619 Hydrogen Storage in Carbonized Coconut Meat (Kernel)

Authors: Viney Dixit, Rohit R. Shahi, Ashish Bhatnagar, P. Jain, T. P. Yadav, O. N. Srivastava

Abstract:

Carbons are being widely investigated as hydrogen storage material owing to their light weight, fast hydrogen absorption kinetics and low cost. However, these materials suffer from low hydrogen storage capacity at room temperature. The aim of the present study is to synthesize carbon based material which shows moderate hydrogen storage at room temperature. For this purpose, hydrogenation characteristics of natural precursor coconut kernel is studied in this work. The hydrogen storage measurement reveals that the as-synthesized materials have good hydrogen adsorption and desorption capacity with fast kinetics. The synthesized material absorbs 8 wt.% of hydrogen at liquid nitrogen temperature and 2.3 wt.% at room temperature. This could be due to the presence of certain elements (KCl, Mg, Ca) which are confirmed by TEM.

Keywords: coconut kernel, carbonization, hydrogenation, KCl, Mg, Ca

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11618 The Effects of Neurospora crassa-Fermented Palm Kernel Cake in the Diet on the Production Performance and Egg-Yolk Quality of Arab Laying-Hens

Authors: Yose Rizal, Nuraini, Mirnawati, Maria Endo Mahata, Rio Darman, Dendi Kurniawan

Abstract:

An experiment had been conducted to determine the effects of several levels of Neurospora crassa- fermented palm kernel cake in the diet on the production performance and egg-yolk quality of Arab laying-hens, and to obtain the appropriate level of this fermented palm kernel cake for reducing the utilization of concentrated feed in the diet. Three hundred Arab laying-hens of 72 weeks old were employed in this experiment, and randomly assigned to four treatments (0, 7.25, 10.15, and 13.05% fermented palm kernel cake in diets) in a completely randomized design with five replicates. Measured variables were production performance (feed consumption, egg-mass production, feed conversion, egg weight and hen-day egg production), and egg-yolk quality (ether extract and cholesterol contents, and egg-yolk color index). Results of experiment indicated that feed consumption, egg-mass production, feed conversion, egg weight, hen-day egg production and egg-yolk color index were not influenced (P>0.05) by diets. However, the ether extract and cholesterol contents of egg-yolk were very significantly reduced (P<0.01) by diets. In conclusion, Neurospora crassa-fermented palm kernel cake could be included up to 13.05% to effectively replace 45% concentrated feed in Arab laying-hens diet without adverse effect on the production performance.

Keywords: neurospora crassa-fermented palm kernel cake, Arab laying-hens, production performance, ether extract, cholesterol, egg-yolk color index

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11617 Pyrolysis and Combustion Kinetics of Palm Kernel Shell Using Thermogravimetric Analysis

Authors: Kanit Manatura

Abstract:

The combustion and pyrolysis behavior of Palm Kernel Shell (PKS) were investigated in a thermogravimetric analyzer. A 10 mg sample of each biomass was heated from 30 °C to 800 °C at four heating rates (within 5, 10, 15 and 30 °C/min) in nitrogen and dry air flow of 20 ml/min instead of pyrolysis and combustion process respectively. During pyrolysis, thermal decomposition occurred on three different stages include dehydration, hemicellulose-cellulose and lignin decomposition on each temperature range. The TG/DTG curves showed the degradation behavior and the pyrolysis/combustion characteristics of the PKS samples which led to apply in thermogravimetric analysis. The kinetic factors including activation energy and pre-exponential factor were determined by the Coats-Redfern method. The obtained kinetic factors are used to simulate the thermal decomposition and compare with experimental data. Rising heating rate leads to shift the mass loss towards higher temperature.

Keywords: combustion, palm kernel shell, pyrolysis, thermogravimetric analyzer

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11616 Spatial Point Process Analysis of Dengue Fever in Tainan, Taiwan

Authors: Ya-Mei Chang

Abstract:

This research is intended to apply spatio-temporal point process methods to the dengue fever data in Tainan. The spatio-temporal intensity function of the dataset is assumed to be separable. The kernel estimation is a widely used approach to estimate intensity functions. The intensity function is very helpful to study the relation of the spatio-temporal point process and some covariates. The covariate effects might be nonlinear. An nonparametric smoothing estimator is used to detect the nonlinearity of the covariate effects. A fitted parametric model could describe the influence of the covariates to the dengue fever. The correlation between the data points is detected by the K-function. The result of this research could provide useful information to help the government or the stakeholders making decisions.

Keywords: dengue fever, spatial point process, kernel estimation, covariate effect

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11615 Physicochemical Properties of Palm Stearin (PS) and Palm Kernel Olein (PKOO) Blends as Potential Edible Coating Materials

Authors: I. Ruzaina, A. B. Rashid, M. S. Halimahton Zahrah, C. S. Cheow, M. S. Adi

Abstract:

This study was conducted to determine the potential of palm stearin (PS) as edible coating materials for fruits. The palm stearin was blended with 20-80% palm kernel olein (PKOo) and the properties of the blends were evaluated in terms of the slip melting point (SMP), solid fat content (SFC), fatty acid and triacylglycerol compositions (TAG), and polymorphism. Blending of PS with PKOo reduced the SMP, SFC, altered the FAC and TAG composition and changed the crystal polymorphism from β to mixture of β and β′. The changes in the physicochemical properties of PS were due to the replacement of the high melting TAG in PS with medium chain TAG in PKOo. From the analysis, 1:1 and 3:2 were the better PSPKOo blend formulations in slowing down the weight loss, respiration gases and gave better appearance when compared to other PSPKOo blends formulations.

Keywords: guava, palm stearin, palm kernel olein, physicochemical

Procedia PDF Downloads 393
11614 Comparative Performance and Emission Analysis of Diesel Engine Fueled with Diesel and Bitter Apricot Kernal Oil Biodiesel Blends

Authors: Virender Singh Gurau, Akash Deep, Sarbjot S. Sandhu

Abstract:

Vegetable oils are produced from numerous oil seed crops. While all vegetable oils have high energy content, most require some processing to assure safe use in internal combustion engines. Some of these oils already have been evaluated as substitutes for diesel fuels. In the present research work Bitter Apricot kernel oil was employed as a feedstock for the production of biodiesel. The physicochemical properties of the Bitter Apricot kernel oil methyl ester were investigated as per ASTM D6751. From the series of engine testing, it is concluded that the brake thermal efficiency (BTE) with biodiesel blend was little lower than that of diesel. BSEC is slightly higher for Bitter apricot kernel oil methyl ester blends than neat diesel. For biodiesel blends, CO emission was lower than diesel fuel as B 20 reduced CO emissions by 18.75%. Approximately 11% increase in NOx emission was observed with 20% biodiesel blend. It is observed that HC emissions tend to decrease for biodiesel based fuels and Smoke opacity was found lower for biodiesel blends in comparison to diesel fuel.

Keywords: biodiesel, transesterification, bitter apricot kernel oil, performance and emission testing

Procedia PDF Downloads 251
11613 Nonparametric Quantile Regression for Multivariate Spatial Data

Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

Abstract:

Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

Procedia PDF Downloads 61
11612 The Linear Combination of Kernels in the Estimation of the Cumulative Distribution Functions

Authors: Abdel-Razzaq Mugdadi, Ruqayyah Sani

Abstract:

The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data.

Keywords: estimation, bandwidth, mean square error, cumulative distribution function

Procedia PDF Downloads 489
11611 A Boundary Backstepping Control Design for 2-D, 3-D and N-D Heat Equation

Authors: Aziz Sezgin

Abstract:

We consider the problem of stabilization of an unstable heat equation in a 2-D, 3-D and generally n-D domain by deriving a generalized backstepping boundary control design methodology. To stabilize the systems, we design boundary backstepping controllers inspired by the 1-D unstable heat equation stabilization procedure. We assume that one side of the boundary is hinged and the other side is controlled for each direction of the domain. Thus, controllers act on two boundaries for 2-D domain, three boundaries for 3-D domain and ”n” boundaries for n-D domain. The main idea of the design is to derive ”n” controllers for each of the dimensions by using ”n” kernel functions. Thus, we obtain ”n” controllers for the ”n” dimensional case. We use a transformation to change the system into an exponentially stable ”n” dimensional heat equation. The transformation used in this paper is a generalized Volterra/Fredholm type with ”n” kernel functions for n-D domain instead of the one kernel function of 1-D design.

Keywords: backstepping, boundary control, 2-D, 3-D, n-D heat equation, distributed parameter systems

Procedia PDF Downloads 299