Search results for: mineral clustering

Authors: Francky Fouedjio

Abstract:

Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods.

Keywords: clustering, geostatistics, multivariate data, non-parametric

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Authors: Lukáš Plošek, Jaroslav Hynšt, Jaroslav Záhora, Jakub Elbl, Antonín Kintl, Ivana Charousová, Silvia Kovácsová

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Compost can influence soil fertility and plant health. At the same time compost can play an important role in the nitrogen cycle and it can influence leaching of mineral nitrogen from soil to underground water. This paper deals with the influence of compost addition and mineral nitrogen fertilizer on leaching of mineral nitrogen, nitrogen availability in microbial biomass and plant biomass production in the lysimetric experiment. Twenty-one lysimeters were filed with topsoil and subsoil collected in the area of protection zone of underground source of drinking water - Březová nad Svitavou. The highest leaching of mineral nitrogen was detected in the variant fertilized only mineral nitrogen fertilizer (624.58 mg m-2), the lowest leaching was recorded in the variant with high addition of compost (315.51 mg m-2). On the other hand, losses of mineral nitrogen are not in connection with the losses of available form of nitrogen in microbial biomass. Because loss of mineral nitrogen was detected in variant with the least change in the availability of N in microbial biomass. The leaching of mineral nitrogen, yields as well as the results concerning nitrogen availability from the first year of long term experiment suggest that compost can positive influence the leaching of nitrogen into underground water.

Keywords: nitrogen, compost, biomass production, lysimeter

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Authors: Taysir Soliman

Abstract:

One of the current popular clustering techniques is Spectral Clustering (SC) because of its advantages over conventional approaches such as hierarchical clustering, k-means, etc. and other techniques as well. However, one of the disadvantages of SC is the time consuming process because it requires computing the eigenvectors. In the past to overcome this disadvantage, a number of attempts have been proposed such as the Power Iteration Clustering (PIC) technique, which is one of versions from SC; some of PIC advantages are: 1) its scalability and efficiency, 2) finding one pseudo-eigenvectors instead of computing eigenvectors, and 3) linear combination of the eigenvectors in linear time. However, its worst disadvantage is an inter-class collision problem because it used only one pseudo-eigenvectors which is not enough. Previous researchers developed Deflation-based Power Iteration Clustering (DPIC) to overcome problems of PIC technique on inter-class collision with the same efficiency of PIC. In this paper, we developed Parallel DPIC (PDPIC) to improve the time and memory complexity which is run on apache spark framework using sparse matrix. To test the performance of PDPIC, we compared it to SC, ESCG, ESCALG algorithms on four small graph benchmark datasets and nine large graph benchmark datasets, where PDPIC proved higher accuracy and better time consuming than other compared algorithms.

Keywords: spectral clustering, power iteration clustering, deflation-based power iteration clustering, Apache spark, large graph

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Authors: Salima Kouici, Abdelkader Khelladi

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In this work, we begin with the presentation of the Tθ family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally, the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied.

Keywords: binary data, similarity measure, Tθ measures, agglomerative hierarchical clustering

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Authors: Alhadi Bustaman, Soeganda Formalidin, Titin Siswantining

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DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data.

Keywords: agglomerative hierarchical clustering (AHC), biclustering, gene expression data, lymphoma, singular value decomposition (SVD)

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Authors: Seyyedeh Faezeh Hassani Ziabari, Sadegh Eskandari, Maziar Salahi

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Infinite Feature Selection (IFS) algorithm is an efficient feature selection algorithm that selects a subset of features of all sizes (including infinity). In this paper, we present an improved version of it, called clustering IFS (CIFS), by clustering the dataset in advance. To do so, first, we apply the K-means algorithm to cluster the dataset, then we apply IFS. In the CIFS method, the spatial and temporal complexities are reduced compared to the IFS method. Experimental results on 6 datasets show the superiority of CIFS compared to IFS in terms of accuracy, running time, and memory consumption.

Keywords: feature selection, infinite feature selection, clustering, graph

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Authors: W. J. Wang, D. F. Lin, L. Y. Chen, K. Y. Liu

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In this study, the ladle furnace slag was used as a mineral filler in asphalt concrete with electric arc furnace slag (EAF asphalt concrete) to investigate the effect on the engineering and thermal properties of asphalt cement mastics and EAF asphalt concrete, the lime was used as a comparison for mineral filler, and the usage percentage of mineral filler was set at 2%, 4%, 6%, and 8%. First of all, the engineering properties of the ladle furnace slag and lime were compared, and then the mineral filler was mixed with bitumen to form the asphalt cement mastics in order to analyze the influence of the ladle furnace slag on the properties of asphalt cement mastics, and lastly, the mineral filler was used in the EAF asphalt concrete to analyze its feasibility of using ladle furnace slag as a mineral filler. The study result shows that the ladle furnace slag and the lime have no obvious difference in their physical properties, and from the energy dispersive spectrometer (EDS) test results, we know that the lime and the ladle furnace slag have similar elemental composition, but the Ca found in the ladle furnace slag belongs to CaO, and the lime belongs to CaCO3, therefore the ladle furnace slag has the property of expansion. According to the test results, the viscosity of asphalt cement mastics will increase with the increase in the use of mineral filler. Since the ladle furnace slag has more CaO content, the viscosity of the asphalt cement mastics with ladle furnace slag will increase more than using lime as mineral filler in the asphalt cement mastics, and the use of ladle furnace slag only needs to be 2% in order to achieve the effect of anti-peeling which is 6% for lime. From the related test results of EAF asphalt concrete, it is known that the maximum stability value can be obtained when the use of mineral filler is about 5%. When the ladle furnace slag is used as the mineral filler, it can improve the stiffness, indirect tension strength, spalling resistance, and thermal insulation of EAF asphalt concrete, which also indicates that using the ladle furnace slag as the mineral filler of bitumen can help to improve the durability of the asphalt pavement.

Keywords: ladle furnace slag, mineral filler, asphalt cement mastics, EAF asphalt concrete

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Authors: Billel Kenidra, Mohamed Benmohammed

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Data mining technique used in the field of clustering is a subject of active research and assists in biological pattern recognition and extraction of new knowledge from raw data. Clustering means the act of partitioning an unlabeled dataset into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Several clustering methods are based on partitional clustering. This category attempts to directly decompose the dataset into a set of disjoint clusters leading to an integer number of clusters that optimizes a given criterion function. The criterion function may emphasize a local or a global structure of the data, and its optimization is an iterative relocation procedure. The K-Means algorithm is one of the most widely used partitional clustering techniques. Since K-Means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum, we propose a strategy to initiate K-Means centers. The improved K-Means algorithm is compared with the original K-Means, and the results prove how the efficiency has been significantly improved.

Keywords: microarray data mining, biological pattern recognition, partitional clustering, k-means algorithm, centroid initialization

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.

Keywords: clustering, unsupervised learning, pattern recognition, categorical datasets, knowledge discovery, k-means

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Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

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A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Qurbanov Huseyn Nuraddin

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The results of scientific research on a machine that pours an equal amount of mineral fertilizer under the soil to increase the productivity of grain in mountain farming and obtain quality grain are substantiated. The average yield of the crop depends on the nature of the distribution of fertilizers in the soil. Therefore, the study of effective energy-saving methods for the application of mineral fertilizers is the actual task of modern agriculture. Depending on the type and variety of plants in mountain farming, there is an optimal norm of mineral fertilizers. Applying an equal amount of fertilizer to the soil is one of the conditions that increase the efficiency of the field. One of the main agro-technical indicators of the work of mineral fertilizing machines is to ensure equal distribution of mineral fertilizers in the field. Taking into account the above-mentioned issues, a combined plough has been improved in our laboratory.

Keywords: combined plough, mineral fertilizers, sprinkle fluently, fertilizer rate, cereals

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Authors: D. E. Egirani, J. E. Andrews, A. R. Baker

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This study investigates sorption of Cu and Zn contained in natural mine wastewater, using mixed mineral systems in sulfidic-anoxic condition. The mine wastewater was obtained from disused mine workings at Cwmheidol in Wales, United Kingdom. These contaminants flow into water courses. These water courses include River Rheidol. In this River fishing activities exist. In an attempt to reduce Cu-Zn levels of fish intake in the watercourses, single mineral systems and 1:1 mixed mineral systems of clay and goethite were tested with the mine waste water for copper and zinc removal at variable pH. Modelling of hydroxyl complexes was carried out using phreeqc method. Reactions using batch mode technique was conducted at room temperature. There was significant differences in the behaviour of copper and zinc removal using mixed mineral systems when compared  to single mineral systems. All mixed mineral systems sorb more Cu than Zn when tested with mine wastewater.

Keywords: Cu- Zn, hydroxyl complexes, kinetics, mixed mineral systems, reactivity

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Authors: Celal Kocatepe, Eyup Taslak, Celal Fadil Kumru, Oktay Arikan

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While choosing insulating oil, characteristic features such as thermal cooling, endurance, efficiency and being environment-friendly should be considered. Mineral oils are referred as petroleum-based oil. In this study, vegetable oils investigated as an alternative insulating liquid to mineral oil. Dissipation factor, breakdown voltage, relative dielectric constant and resistivity changes with the frequency and voltage of mineral, rapeseed and nut oils were measured. Experimental studies were performed according to ASTM D924 and IEC 60156 standards.

Keywords: breakdown voltage, dielectric dissipation factor, mineral oil, vegetable oils

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

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Authors: Sara Ali Alokley, Mansour Saleh Albarrak

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This paper investigates the dependency or clustering of extremes in the financial returns data by estimating the extremal index value θ∈[0,1]. The smaller the value of θ the more clustering we have. Here we apply the method of Ferro and Segers (2003) to estimate the extremal index for a range of threshold values. We compare the dependency structure of extremes in the developed and emerging markets. We use the financial returns of the stock market index in the developed markets of US, UK, France, Germany and Japan and the emerging markets of Brazil, Russia, India, China and Saudi Arabia. We expect that more clustering occurs in the emerging markets. This study will help to understand the dependency structure of the financial returns data.

Keywords: clustring, extremes, returns, dependency, extermal index

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Authors: Mohammad Reza Taherkhani‎

Abstract:

Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: underwater sensor networks, clustering, learning automata, energy consumption

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Abdelaziz Sheba Abdelrahman

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Compost enriched with actinomyces and Bacillus polymyxa algae as a Partial Substitute for Mineral N in Ewaise Mango Orchards Organic fertiliser, compost enriched with actinomyces, and the biofertilizer Bacillus polymyxa algae were used as a partial replacement for mineral N fertiliser in Ewaise mango orchards during the 2019 and 2020 seasons. When compared to using mineral N alone, the results showed that reducing the percentage of mineral N fertiliser from 100 to 50% and using compost enriched with actinomyces at 25 to 50% and Bacillus polymyxa had an announced promotion on leaf area, total chlorophylls, leaf N, P, and K, yield, and fruit quality. The use of compost enriched with actinomyces and Bacillus polymyxa, as well as mineral N, resulted in a significant decrease in nitrite in the pulp. Reducing mineral N to 25% of the suitable N had a negative impact on yield. The application of appropriate N via 50% inorganic N + compost enriched with actinomyces at 50% + Bacillus polymyxa algae increased yield quantitatively and qualitatively in Ewaise mango orchards. This promised treatment significantly reduced nitrite levels in the pulp fruit.

Keywords: bacillus polymyxa algae, fertiliser, biofertilizer, ewaise mango

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Authors: Alicja Kot-Niewiadomska

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Securing sustainable access to raw materials is vital for the growth of the European economy and for the goals laid down in Strategy Europe 2020. One of the most important sources of mineral raw materials are primary deposits. The efficient management of them, including extraction, will ensure competitiveness of the European economy. A critical element of this approach is mineral deposits safeguarding and the most important tool - spatial planning. The safeguarding of deposits should be understood as safeguarding of land access, and safeguarding of area against development, which may (potential) prevent the use of the deposit and the necessary mining activities. Many European Union countries successfully integrated their mineral policy and spatial policy, which has ensured the proper place of mineral deposits in their spatial planning systems. These, in turn, are widely recognized as the most important mineral deposit safeguarding tool, the essence of which is to ensure long-term access to its resources. The examples of Austria, Portugal, Slovakia, Czech Republic, Sweden, and the United Kingdom, discussed in the paper, are often mentioned as examples of good practices in this area. Although none of these countries managed to avoid cases of social and environmental conflicts related to mining activities, the solutions they implement certainly deserve special attention. And for many countries, including Poland, they can be a potential source of solutions aimed at improving the protection of mineral deposits.

Keywords: mineral deposits, land use planning, mineral deposit safeguarding, European practices

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Authors: Motahareh Fadaei

Abstract:

Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Lee Myung-Won, Kwak Keun-Chang

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This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation

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Authors: Kemal Polat

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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: Ibrahim Isah Lakan, Nasiru Ibrahim

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These Phytochemical screening, Antimicrobial activities and mineral Determination of aqueous extract of Stachtarpheta indica were assessed. The result reveals the presence of flavonoids, tannins, saponins, alkaloids, glycosides and anthraquinones. The disc diffusion of aqueous extract showed Escherichia coli, 13 and antibiotic, 19 mm; Bacillus subtilis, 10 and anti –biotic, 17 mm; Klebsiller pnemuoniae , 14 and antibiotic, 24mm and Pseudmonas aeruginosa, 24 and antibiotic, 36 mm which are all comparable with the standard antibiotic cyprotomycin. The mineral content determination by flame photometer revealed that 1.25 (Na+), 0.85 (K +), 1.75 (Ca 2+) % which is a clear indication of the safety of the extract for the hypertensive patients and could be used to lower blood pressure.

Keywords: microbials, mineral, phytochemicals, stachtarpheta indica extracts

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Authors: Shen Jian, Hu Jie, Ma Jin, Peng Ying Hong, Fang Yi, Liu Wen Hai

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Biologically inspired design inspires inventions and new technologies in the field of engineering by mimicking functions, principles, and structures in the biological domain. To deal with the obstacles of cross-domain knowledge acquisition in the existing biologically inspired design process, functional semantic clustering based on functional feature semantic correlation and environmental constraint clustering composition based on environmental characteristic constraining adaptability are proposed. A knowledge cell clustering algorithm and the corresponding prototype system is developed. Finally, the effectiveness of the method is verified by the visual prosthetic device design.

Keywords: knowledge clustering, knowledge acquisition, knowledge based engineering, knowledge cell, biologically inspired design

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Authors: Seung Hwan Park, Cheng-Sool Park, Jun Seok Kim, Youngji Yoo, Daewoong An, Jun-Geol Baek

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Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application.

Keywords: die-map clustering, feature extraction, pattern recognition, semiconductor manufacturing process

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Authors: Vladyslav Povoroznyuk, Oksana Ivanyk, Nataliia Dzerovych

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In the diagnostics of osteoporosis, the gold standard is considered to be bone mineral density; however, X-ray densitometry is not an accurate indicator of osteoporotic fracture risk under all circumstances. In this regard, the search for new methods that could determine the indicators not only of the mineral density, but of the bone tissue quality, is a logical step for diagnostic optimization. One of these methods is the evaluation of trabecular bone quality. The aim of this study was to examine the quality and mineral density of spine bone tissue, femoral neck, and body composition of women depending on the duration of the postmenopausal period, to determine the correlation of body fat with indicators of bone mineral density and quality. The study examined 179 women in premenopausal and postmenopausal periods. The patients were divided into the following groups: Women in the premenopausal period and women in the postmenopausal period at various stages (early, middle, late postmenopause). A general examination and study of the above parameters were conducted with General Electric X-ray densitometer. The results show that bone quality and mineral density probably deteriorate with advancing of postmenopausal period. Total fat and lean mass ratio is not likely to change with age. In the middle and late postmenopausal periods, the bone tissue mineral density of the spine and femoral neck increases along with total fat mass.

Keywords: osteoporosis, bone tissue mineral density, bone quality, fat mass, lean mass, postmenopausal osteoporosis

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Authors: Rejab Hajlaoui, Tarek Moulahi, Hervé Guyennet

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In a highway scenario, the vehicle speed can exceed 120 kmph. Therefore, any vehicle can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To ensure an effective stability in vehicular ad hoc networks with minimum broadcasting storm, we have developed a weighted algorithm based on the k-medoids clustering algorithm (WKCA). Indeed, the number of clusters and the initial cluster heads will not be selected randomly as usual, but considering the available transmission range and the environment size. Then, to ensure optimal assignment of nodes to clusters in both k-medoids phases, the combined weight of any node will be computed according to additional metrics including direction, relative speed and proximity. Empirical results prove that in addition to the convergence speed that characterizes the k-medoids algorithm, our proposed model performs well both AODV-Clustering and OLSR-Clustering protocols under different densities and velocities in term of end-to-end delay, packet delivery ratio, and throughput.

Keywords: communication, clustering algorithm, k-medoids, sensor, vehicular ad hoc network

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Authors: Lidija Jevrić, Denis Kučević, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Milica Karadžić

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In the present study, the Hierarchical Cluster Analysis (HCA) was applied in order to determine the differences between the milk samples originating from a conventional dairy farm (CF) and an organic dairy farm (OF) in AP Vojvodina, Republic of Serbia. The clustering was based on the basis of the average values of saturated fatty acids (SFA) content and unsaturated fatty acids (UFA) content obtained for every season. Therefore, the HCA included the annual SFA and UFA content values. The clustering procedure was carried out on the basis of Euclidean distances and Single linkage algorithm. The obtained dendrograms indicated that the clustering of UFA in OF was much more uniform compared to clustering of UFA in CF. In OF, spring stands out from the other months of the year. The same case can be noticed for CF, where winter is separated from the other months. The results could be expected because the composition of fatty acids content is greatly influenced by the season and nutrition of dairy cows during the year.

Keywords: chemometrics, clustering, food engineering, milk quality

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Authors: Mayowa Philips Ibitola, Akinade-Solomon Olorunfemi, Abe Oluwaseun Banji

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Heavy minerals studies were carried out on eighteen sediment samples from Ologe lagoon located at Lagos Barrier complex, with the aim of evaluating the heavy mineral deposits and determining the provenance of the sediments. The samples were subjected to grain analysis techniques in order to collect the finest grain size. Separation of heavy minerals from the samples was done with the aid of bromoform to enable petrographic analyses of the heavy mineral suite, under the polarising microscope. The data obtained from the heavy mineral analysis were used in preparing histograms and pie chart, from which the individual heavy mineral percentage distribution and ZTR index were derived. The percentage composition of the individual heavy mineral analyzed are opaque mineral 63.92%, Zircon 12.43%, Tourmaline 5.79%, Rutile 13.44%, Garnet 1.74% and Staurolite 3.52%. The calculated zircon, tourmaline, rutile index in percentage (ZTR) varied between 76.13 -92.15%, average garnet-zircon index (GZI), average rutile-zircon index (RuZI) and average staurolite-zircon index values in all the stations are 16.18%, 54.33%, 25.11% respectively. The mean ZTR index percentage value is 85.17% indicates that the sediments within the lagoon are mineralogically matured. The high percentage of zircon, rutile, and tourmaline indicates an acid igneous rock source for the sediments. However, the low percentage of staurolite, rutile and garnet occurrence indicates sediment of metamorphic rock source input.

Keywords: lagoon, provenance, heavy mineral, ZTR index

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