Search results for: clustering on flowing data

Authors: Feda Negesse

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One of the most important indicators of research quality is a good data - collection instrument that can yield reliable and valid data. The Swadesh wordlist has been used for more than half a century for collecting data in comparative and historical linguistics though arbitrariness is observed in its application and size. This research compare s the classification results of the 100 Swadesh wordlist with those of its subsets to determine if reducing the size of the wordlist impact s its effectiveness. In the comparison, the 100, 50 and 40 wordlists were used to compute lexical distances of 29 Cushitic and Semitic languages spoken in Ethiopia and neighbouring countries. Gabmap, a based application, was employed to compute the lexical distances and to divide the languages into related clusters. The study shows that the subsets are not as effective as the 100 wordlist in clustering languages into smaller subgroups but they are equally effective in di viding languages into bigger groups such as subfamilies. It is noted that the subsets may lead to an erroneous classification whereby unrelated languages by chance form a cluster which is not attested by a comparative study. The chance to get a wrong result is higher when the subsets are used to classify languages which are not closely related. Though a further study is still needed to settle the issues around the size of the Swadesh wordlist, this study indicates that the 50 and 40 wordlists cannot be recommended as reliable substitute s for the 100 wordlist under all circumstances. The choice seems to be determined by the objective of a researcher and the degree of affiliation among the languages to be classified.

Keywords: classification, Cushitic, Swadesh, wordlist

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Authors: Kayode O. Afolabi, Benson C. Iweriebor, Anthony I. Okoh, Larry C. Obi

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Porcine circovirus type 2 (PCV2) is the major etiological viral agent of porcine multisystemic wasting syndrome (PWMS) and other porcine circovirus-associated diseases (PCVAD) of great economic importance in pig industry globally. In an effort to determine the status of swine herds in the Province as regarding the ‘small but powerful’ viral pathogen; a total of 375 blood, faecal and nasal swab samples were obtained from seven pig farms (commercial and communal) in Amathole, O.R. Tambo and Chris-Hani District Municipalities of Eastern Cape Province between the year 2015 and 2016. Three hundred and thirty nine (339) samples out of the total sample were subjected to molecular screening using PCV2 specific primers by conventional polymerase chain reaction (PCR). Selected sequences were further analyzed and confirmed through genome sequencing and phylogenetic analyses. The data obtained revealed that 15.93% of the screened samples (54/339) from the swine herds of the studied areas were positive for PCV2; while the severity of occurrence of the viral pathogen as observed at farm level ranges from approximately 5.6% to 60% in the studied farms. The Majority, precisely 15 out of 17 (88%) analyzed sequences were found clustering with other PCV2b reference strains in the phylogenetic analysis. More interestingly, two other sequences obtained were also found clustering within PCV2d genogroup, which is presently another fast-spreading genotype with observable higher virulence in global swine herds. This finding confirmed the presence of this all-important viral pathogen in pigs of the region; which could result in a serious outbreak of PCVAD and huge economic loss at the instances of triggering factors if no appropriate measures are taken to curb its spread effectively.

Keywords: pigs, polymerase chain reaction, porcine circovirus type 2, South Africa

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Authors: Orhun Aydin, Mark V. Janikas, Rodrigo Alves, Renato Assuncao

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In this paper, a tree-based perturbation methodology for regionalization inference is presented. Regionalization is a constrained optimization problem that aims to create groups with similar attributes while satisfying spatial contiguity constraints. Similar to any constrained optimization problem, the spatial constraint may hinder convergence to some global minima, resulting in spatially contiguous members of a group with dissimilar attributes. This paper presents a general methodology for rigorously perturbing spatial constraints through the use of random spanning trees. The general framework presented can be used to quantify the effect of the spatial constraints in the overall regionalization result. We compare several types of stochastic spanning trees used in inference problems such as fuzzy regionalization and determining the number of regions. Performance of stochastic spanning trees is juxtaposed against the traditional permutation-based hypothesis testing frequently used in spatial statistics. Inference results for fuzzy regionalization and determining the number of regions is presented on the Local Area Personal Incomes for Texas Counties provided by the Bureau of Economic Analysis.

Keywords: regionalization, constrained clustering, probabilistic inference, fuzzy clustering

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Pahirangan Sivapatham, Barbara Esser, Andreas Grimmel

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In times of limited public funds and, in particular, an increased social, environmental awareness, as well as the limited availability of construction materials, sustainable and resource-saving pavement management system, is becoming more and more important. Therefore, the knowledge about the condition of the structural substances, particularly bearing capacity and its consideration while planning the maintenance measures of the subordinate network, i.e., state and municipal roads unavoidable. According to the experience, the recommended ride speed of the Traffic Speed Deflectometer (TSD) shall be higher than 40 km/h. Holding of this speed on the intra-urban roads is nearly not possible because of intersections and traffic lights as well as the speed limit. A sufficient background of experience for the evaluation of bearing capacity measurements with TSD in the range of lower speeds is not available yet. The aim of this study is to determine the possible lowest ride speed of the TSD while the bearing capacity measurement on the intra-urban roads. The manufacturer of the TSD used in this study states that the measurements can be conducted at a ride speed of higher than 5 km/h. It is well known that with decreasing ride speed, the viscous fractions in the response of the asphalt pavement increase. This must be taken into account when evaluating the bearing capacity data. In the scope of this study, several measurements were carried out at different speeds between 10 km/h and 60 km/h on the selected intra-urban roads with Pavement-Scanner of the University of Wuppertal, which is equipped with TSD. Pavement-Scanner is able to continuously determine the deflections of asphalt roads in flowing traffic at speeds of up to 80 km/h. The raw data is then aggregated to 10 m mean values so that, as a rule, a bearing capacity characteristic value can be determined for each 10 m road section. By means of analysing of obtained test results, the quality and validity of the determined data rate subject to the riding speed of TSD have been determined. Moreover, the data and pictures of the additional measuring systems of Pavement-Scanners such as High-Speed Road Monitor, Ground Penetration Radar and front cameras can be used to determine and eliminate irregularities in the pavement, which could influence the bearing capacity.

Keywords: bearing capacity measurement, traffic speed deflectometer, inter-urban roads, Pavement-Scanner, structural substance

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Authors: Tsu-Yun Chiu, Tsung-Hsueh Lu, Tain-Junn Cheng

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Background: Traditional chronic disease management did not pay attention to effects of geographic factors on the compliance of treatment regime, which resulted in geographic inequality in outcomes of chronic disease management. This study aims to examine the geographic distribution and clustering of quality indicators of diabetes care. Method: We first extracted address, demographic information and quality of care indicators (number of visits, complications, prescription and laboratory records) of patients with diabetes for 2014 from medical information system in a medical center in Tainan City, Taiwan, and the patients’ addresses were transformed into district- and village-level data. We then compared the differences of geographic distribution and clustering of quality of care indicators between districts and villages. Despite the descriptive results, rate ratios and 95% confidence intervals (CI) were estimated for indices of care in order to compare the quality of diabetes care among different areas. Results: A total of 23,588 patients with diabetes were extracted from the hospital data system; whereas 12,716 patients’ information and medical records were included to the following analysis. More than half of the subjects in this study were male and between 60-79 years old. Furthermore, the quality of diabetes care did indeed vary by geographical levels. Thru the smaller level, we could point out clustered areas more specifically. Fuguo Village (of Yongkang District) and Zhiyi Village (of Sinhua District) were found to be “hotspots” for nephropathy and cerebrovascular disease; while Wangliau Village and Erwang Village (of Yongkang District) would be “coldspots” for lowest proportion of ≥80% compliance to blood lipids examination. On the other hand, Yuping Village (in Anping District) was the area with the lowest proportion of ≥80% compliance to all laboratory examination. Conclusion: In spite of examining the geographic distribution, calculating rate ratios and their 95% CI could also be a useful and consistent method to test the association. This information is useful for health planners, diabetes case managers and other affiliate practitioners to organize care resources to the areas most needed.

Keywords: catchment area of healthcare, chronic disease management, Geographic information system, quality of diabetes care

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Authors: Ganesh B. Shinde, Vijaya B. Musande

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Cotton crop identification based on the timely information has significant advantage to the different implications of food, economic and environment. Due to the significant advantages, the accurate detection of cotton crop regions using supervised learning procedure is challenging problem in remote sensing. Here, classifiers on the direct image are played a major role but the results are not much satisfactorily. In order to further improve the effectiveness, variety of vegetation indices are proposed in the literature. But, recently, the major challenge is to find the better vegetation indices for the cotton crop identification through the proposed methodology. Accordingly, fuzzy c-means clustering is combined with neural network algorithm, trained by Levenberg-Marquardt for cotton crop classification. To experiment the proposed method, five LISS-III satellite images was taken and the experimentation was done with six vegetation indices such as Simple Ratio, Normalized Difference Vegetation Index, Enhanced Vegetation Index, Green Atmospherically Resistant Vegetation Index, Wide-Dynamic Range Vegetation Index, Green Chlorophyll Index. Along with these indices, Green Leaf Area Index is also considered for investigation. From the research outcome, Green Atmospherically Resistant Vegetation Index outperformed with all other indices by reaching the average accuracy value of 95.21%.

Keywords: Fuzzy C-Means clustering (FCM), neural network, Levenberg-Marquardt (LM) algorithm, vegetation indices

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Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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Authors: Sresha Yadav, Ishwar Kumar

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Second language learning is an important area of research in the language and linguistic domains. Literature suggests that several factors impact second language learning, including age, motivation, objectives, teacher, instructional material, classroom interaction, intelligence and previous background, previous linguistic experience, other student characteristics. Previous researchers have also highlighted that classroom atmospherics has a significant impact on learning as well as on the performance of students. However, the impact of classroom atmospherics on second language learning is still not known in the existing literature. Therefore, the purpose of the present study is to explore whether classroom atmospherics has an impact on second language learning or not? And if it does, it would be worthwhile to explore the nature of such relationship. The present study aims to explore the impact of classroom atmospherics on second language learning by dwelling into the existing literature to explore factors which impact second language learning, classroom atmospherics which impact language learning and the metrics through which such learning impacts could be measured. Based on the findings of literature review, the researchers have adopted a clustering approach for categorization and positioning of various measures of second language learning. Based on the clustering approach, the researchers have approach for measuring the impact of classroom atmospherics on second language learning by drawing a student sample consisting of 80 respondents. The results of the study uncover various basic premises of second language learning, especially with regard to classroom atmospherics. The present study is important not only from the point of view of language learning but implications could be drawn with regard to the design of classroom atmospherics, environmental psychology, anthropometrics, etc as well.

Keywords: classroom atmospherics, cluster analysis, linguistics, second language learning

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Authors: J. S. Saini, P. P. K. Sandhu

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The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

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Authors: Khadija Raissi, Bechir Ben Gouissem

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In this paper, we perform the investigation of some routing protocols in Vehicular Ad-Hoc Network (VANET) context. Indeed, we study the efficiency of protocols like Dynamic Source Routing (DSR), Ad hoc On-demand Distance Vector Routing (AODV), Destination Sequenced Distance Vector (DSDV), Optimized Link State Routing convention (OLSR) and Vehicular Multi-hop algorithm for Stable Clustering (VMASC) in terms of packet delivery ratio (PDR) and throughput. The performance evaluation and comparison between the studied protocols shows that the VMASC is the best protocols regarding fast data transmission and link stability in VANETs. The validation of all results is done by the NS3 simulator.

Keywords: VANET, smart city, AODV, OLSR, DSR, OLSR, VMASC, routing protocols, NS3

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Authors: Shreya Banerjee, Roshmi Sen, Subrata Chattopadhyay

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Urbanization during the second half of the last century has created many serious environment related issues leading to global warming and climate change. India is not an exception as the country is also facing the problems of global warming and urban heat islands (UHI) in all the major metropolises. This paper discusses the effect of different housing cluster layouts, site geometry, and geometry of urban street canyons on the urban heat island profile. The study is carried out using the three dimensional microclimatic computational fluid dynamics model ENVI-met version 3.1. Simulation models are done for a typical summer day of 21st June, 2015 in four different residential neighborhoods in the city of Kolkata which predominantly belongs to Warm-Humid Monsoon Climate. The results show the changing pattern of urban heat island profile with respect to different clustering layouts, geometry, and morphology of urban street canyons. The comparison between the four neighborhoods shows that different microclimatic variables are strongly dependant on the neighborhood layout pattern and geometry. The inferences obtained from this study can be indicative towards the formulation of neighborhood design by-laws that will attenuate the urban heat island effect.

Keywords: urban heat island, neighborhood morphology, site microclimate, ENVI-met, numerical analysis

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Authors: Iniye Irene Wodi, Kennedy B. Gibson

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Students’ poor performance in mathematics in both internal and external examinations has been a source of concern to researchers in Nigeria. The cause of this has been attributed to both teachers and students. To this end, this study sought to find out students’ perceptions of teachers’ attributes as a cause of mathematics phobia among secondary school students in Bayelsa State Nigeria. The population of the study comprised of all students of senior secondary schools in Yenagoa metropolis. A sample of 120 students was drawn from this population using clustering and simple random sampling techniques. The instrument for data collection was a researcher constructed questionnaire titled Mathematics Phobia Questionnaire (MPQ). Data were analysed, and the results revealed that students perceived teachers’ attributes such as methods and styles of teaching, difficulty in communication, etc. as causes of mathematics phobia among students in senior secondary schools in Bayelsa State. Based on the result, it was therefore recommended that mathematics teachers should be retrained periodically in order to learn new and innovative ways of teaching mathematics to prevent its phobia among students.

Keywords: mathematics phobia, teacher attributes, teaching method, teaching style

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Authors: Devin Simmons

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At the USDOT’s Bureau of Transportation Statistics, a biannual census of ferry operators in the U.S. is conducted, with results such as route mileage used to determine federal funding levels for operators. AIS data allows for the possibility of using GIS software and geographical methods to confirm operator-reported mileage for individual ferry routes. As part of the USDOT’s work on the ferry census, an algorithm was developed that uses AIS data for ferry vessels in conjunction with known ferry terminal locations to model the average route travelled for use as both a cartographic product and confirmation of operator-reported mileage. AIS data from each vessel is first analyzed to determine individual journeys based on the vessel’s velocity, and changes in velocity over time. These trips are then converted to geographic linestring objects. Using the terminal locations, the algorithm then determines whether the trip represented a known ferry route. Given a large enough dataset, routes will be represented by multiple trip linestrings, which are then filtered by DBSCAN spatial clustering to remove outliers. Finally, these remaining trips are ready to be averaged into one route. The algorithm interpolates the point on each trip linestring that represents the start point. From these start points, a centroid is calculated, and the first point of the average route is determined. Each trip is interpolated again to find the point that represents one percent of the journey’s completion, and the centroid of those points is used as the next point in the average route, and so on until 100 points have been calculated. Routes created using this algorithm have shown demonstrable improvement over previous methods, which included the implementation of a LOESS model. Additionally, the algorithm greatly reduces the amount of manual digitizing needed to visualize ferry activity.

Keywords: ferry vessels, transportation, modeling, AIS data

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Authors: Malik Abid Hussain Khokhar, Muhammad Naveed Tahir, Muhammad Amin

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Energy demand has been increased manifold due to increasing population, urban sprawl and rapid socio-economic improvements. Low water capacity in dams for continuation of hydrological power, land cover and land use are the key parameters which are creating problems for more energy production. Overall installed hydropower capacity of Pakistan is more than 35000 MW whereas Pakistan is producing up to 17000 MW and the requirement is more than 22000 that is resulting shortfall of 5000 - 7000 MW. Therefore, there is a dire need to develop small hydropower to fulfill the up-coming requirements. In this regards, excessive rainfall, snow nurtured fast flowing perennial tributaries and streams in northern mountain regions of Pakistan offer a gigantic scope of hydropower potential throughout the year. Rivers flowing in KP (Khyber Pakhtunkhwa) province, GB (Gilgit Baltistan) and AJK (Azad Jammu & Kashmir) possess sufficient water availability for rapid energy growth. In the backdrop of such scenario, small hydropower plants are believed very suitable measures for more green environment and power sustainable option for the development of such regions. Aim of this study is to estimate hydropower potential sites for small hydropower plants and stream distribution as per steam network available in the available basins in the study area. The proposed methodology will focus on features to meet the objectives i.e. site selection of maximum hydropower potential for hydroelectric generation using well emerging GIS tool SWAT as hydrological run-off model on the Neelum, Kunhar and the Dor Rivers’ basins. For validation of the results, NDWI will be computed to show water concentration in the study area while overlaying on geospatial enhanced DEM. This study will represent analysis of basins, watershed, stream links, and flow directions with slope elevation for hydropower potential to produce increasing demand of electricity by installing small hydropower stations. Later on, this study will be benefitted for other adjacent regions for further estimation of site selection for installation of such small power plants as well.

Keywords: energy, stream network, basins, SWAT, evapotranspiration

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Jialin Wu, Zhiwei Lian, Jieyu Tang, Jingyun Shen

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Tornado is a serious and unpredictable natural disaster, which has an important impact on people's production and life. The probability of being hit by tornadoes in China was analyzed considering the principles of tornado formation. Then some suggestions on layout and shapes for newly-built buildings were provided combined with the characteristics of tornado wind fields. Fuzzy clustering and inverse closeness methods were used to evaluate the probability levels of tornado risks in various provinces based on classification and ranking. GIS was adopted to display the results. Finally, wind field single-vortex tornado was studied to discuss the optimized design of rural low-rise houses in Yancheng, Jiangsu as an example. This paper may provide enough data to support building and urban design in some specific regions.

Keywords: tornado probability, computational fluid dynamics, fuzzy mathematics, optimal design

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Authors: Kirti Singh, Kesheo Prasad

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A 3D Acoustic Doppler velocimeter was used in the current investigation to quantify the mean and turbulence characteristics in non-uniform open-channel flows. Results are obtained from studies done in the laboratory, analysing the behavior of sand particles under turbulent open channel flow conditions flowing through rough, porous beds. Data obtained from ADV is used to calculate turbulent flow characteristics, Reynolds stresses and turbulent kinetic energy. Theoretical formulations for the distribution of Reynolds stress and the vertical velocity have been constructed using the Reynolds equation and the continuity equation of 2D open-channel flow. The measured Reynolds stress profile and the vertical velocity are comparable with the derived expressions. This study uses the Navier-Stokes equations for analysing the behavior of the vertical velocity profile in the dominant region of full-fledged turbulent flows in open channels, and it gives a new origination of the profile. For both wide and narrow open channels, this origination can estimate the time-averaged primary velocity in the turbulent boundary layer's outer region.

Keywords: turbulence, bed roughness, logarithmic law, shear stress correlations, ADV, Reynolds shear stress

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Dongju Kim, Youngjoo Suh, Hyojin Kim, Gyeongyeong Kim

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Acoustic Emission Testing (AET) is widely used to test the structural integrity of an operational hydrogen storage container, and clustering algorithms are frequently used in pattern recognition methods to interpret AET results. However, the interpretation of AET results can vary from user to user as the tuning of the relevant parameters relies on the user's experience and knowledge of AET. Therefore, it is necessary to use a deep learning model to identify patterns in acoustic emission (AE) signal data that can be used to classify defects instead. In this paper, a deep learning-based model for classifying the types of defects in hydrogen storage tanks, using AE sensor waveforms, is proposed. As hydrogen storage tanks are commonly constructed using carbon fiber reinforced polymer composite (CFRP), a defect classification dataset is collected through a tensile test on a specimen of CFRP with an AE sensor attached. The performance of the classification model, using one-dimensional convolutional neural network (1-D CNN) and synthetic minority oversampling technique (SMOTE) data augmentation, achieved 91.09% accuracy for each defect. It is expected that the deep learning classification model in this paper, used with AET, will help in evaluating the operational safety of hydrogen storage containers.

Keywords: acoustic emission testing, carbon fiber reinforced polymer composite, one-dimensional convolutional neural network, smote data augmentation

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Authors: Shazi Farooqui, Anupam Sharma

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The Mahi River (~600km long) is an important west flowing the river of Central India. It originates in Madhya Pradesh and starts flowing in NW direction and enters into the state of Rajasthan. It flows across southern Rajasthan and then enters into Gujarat and finally debouches in the Gulf of Cambay. In Gujarat state, it flows through all four geomorphic zones i.e. eastern upland zone, shallow buried piedmont zone, alluvial zone and coastal zone. In lower reaches and particularly when it is flowing under the coastal regime, it provides an opportunity to study – 1. Land–Sea interaction and role of relative sea level changes, 2. Coastal/estuarine geological process, 3. Landscape evolution in marginal areas and so on. The Late Quaternary deposits of Mainland Gujarat is appreciably studied by Chamyal and his group of MS University of Baroda, and they have established that the 30-35m thick sediment package of the Mainland Gujarat is comprised of marine, fluvial and aeolian sediments. It is also established that in the estuarine zone, the upper few meter thick sediments package is of marine nature. However, its thickness, characters and the depositional environment including the role of climate and tectonics is still not clearly defined. To understand few aspects of the above mentioned, in the present study, a 17m subsurface sediment core has been retrieved from the estuarine zone of Mahi river basin. The Multiproxy studies which include the textural analysis (grain size), Loss on ignition (LOI), Bulk and clay mineralogy and geochemical studies have been carried out. In the entire sedimentary sequence, the grain size largely varies from coarse sand to clay; however, a solitary gravel bed is also noticed. The lower part (depth 9-17m), is mainly comprised of sub equal proportion of sand and silt. The sediments mainly have bimodal and leptokurtic distribution and deposited in alternate sand-silt package, probably indicating flood deposits. Relatively low moisture (1.8%) and organic carbon (2.4%) with increased carbonate values (12%) indicate that conditions must have to remain oxidizing. The middle part (depth 9–6m) has a 1m thick gravel bed at the bottom and overlain by coarse sand to very fine sand showing fining upward sequence. The presence of gravel bed suggests some kind of tectonic activity resulting into change in base level or enhanced precipitation in the catchment region. The upper part (depth 6–0m; top part of sequence) mainly comprised of fine sand to silt size grains (with appreciable clay content). The sediment of this part is Unimodal and very leptokurtic in nature suggesting wave and winnowing process and deposited in low energy suspension environment. This part has relatively high moisture (2.1%) and organic carbon (2.7%) with decreased carbonate content (4.2%) indicating change in the depositional environment probably under estuarine conditions. The presence of chlorite along with smectite clay mineral further supports the significant marine contribution in the formation of upper part of the sequence.

Keywords: grain size, statistical analysis, clay minerals, late quaternary, LOI

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Authors: Dewan Ali Ahsan

Abstract:

Hilsa (Tenualosa ilisha) is one of the most important anadromous fish species of the trans-boundary ecosystem of Bangladesh, India and Myanmar. Hilsa is not only an economically important species specially for Bangladesh and India, but also for the integral part of the culture of the Bangladesh and India. This flag-ship species in Bangladesh contributed alone of 10.82% of the total fish production of the country and about 75% of world’s total catch of hilsa comes from Bangladesh alone. As hilsa is an anadromous fish, it migrates from the Bay of Bengal to rivers for spawning, nursing and growing and for all of these purposes hilsa needs freshwaters. Ripe broods prefer turbid, fast flowing freshwater for spawning but young prefer clear and slow flowing freshwater. Climate change (salinity intrusion, sea level rise, temperature rise, impact of fresh water flow), unplanned developmental activities and other anthropogenic activities all together are severely damaging the hilsa stock and its habitats. So, climate change and human interferences are predicted to have a range of direct and indirect impacts on marine and freshwater hilsa fishery, with implications for fisheries-dependent economies, coastal communities and fisherfolk. The present study identified that salinity intrusion, siltation in river bed, decrease water flow from upstream, fragmentation of river in dry season, over exploitation, use of small mesh nets are the major reasons to affect the upstream migration of hilsa and its sustainable management. It has been also noticed that Bangladesh government has taken some actions for hilsa management. Government is trying to increase hilsa production not only by conserving jatka (juvenile hilsa) but also protecting the brood hilsa during the breeding seasons by imposing seasonal ban on fishing, restricted mesh size etc. Unfortunately, no such management plans are available for Indian and Myanmar territory. As hilsa is a highly migratory trans-boundary fish in the Bay of Bengal (and all of these countries share the same stock), it is essential to adopt a joint management policy (by Bangladesh-India-Myanmar) for the sustainable management for the hilsa stock.

Keywords: hilsa, climate change, south-east Asia, fishery management

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Authors: Ihn Sook Jeong, Eun Joo Lee, Jae Hyung Kim, Gun Ho Kim, Young Jun Hwang

Abstract:

This study investigated intravenous (IV) infiltration using bioelectrical impedance for 27 hospitalized patients in a long-term care hospital. Impedance parameters showed significant differences before and after infiltration as follows. First, the resistance (R) after infiltration significantly decreased compared to the initial resistance. This indicates that the IV solution flowing from the vein due to infiltration accumulates in the extracellular fluid (ECF). Second, the relative resistance at 50 kHz was 0.94 ± 0.07 in 9 subjects without infiltration and was 0.75 ± 0.12 in 18 subjects with infiltration. Third, the magnitude of the reactance (Xc) decreased after infiltration. This is because IV solution and blood components released from the vein tend to aggregate in the cell membrane (and acts analogously to the linear/parallel circuit), thereby increasing the capacitance (Cm) of the cell membrane and reducing the magnitude of reactance. Finally, the data points plotted in the R-Xc graph were distributed on the upper right before infiltration but on the lower left after infiltration. This indicates that the infiltration caused accumulation of fluid or blood components in the epidermal and subcutaneous tissues, resulting in reduced resistance and reactance, thereby lowering integrity of the cell membrane. Our findings suggest that bioelectrical impedance is an effective method for detection of infiltration in a noninvasive and quantitative manner.

Keywords: intravenous infiltration, impedance, parameters, resistance, reactance

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Authors: Nour Charara, Hussein Charara, Omar Abou Khaled, Hani Abdallah, Elena Mugellini

Abstract:

In this paper, we present a human behavior modeling approach in videos scenes. This approach is used to model the normal behaviors in the conference halls. We exploited the Probabilistic Latent Semantic Analysis technique (PLSA), using the 'Bag-of-Terms' paradigm, as a tool for exploring video data to learn the model by grouping similar activities. Our term vocabulary consists of 3D spatio-temporal patch groups assigned by the direction of motion. Our video representation ensures the spatial information, the object trajectory, and the motion. The main importance of this approach is that it can be adapted to detect abnormal behaviors in order to ensure and enhance human security.

Keywords: activity modeling, clustering, PLSA, video representation

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Authors: Wafa’ Alsharafat, Khalid Batiha, Alaa Kassab

Abstract:

Wireless sensor network (WSN) is comprised of a huge number of small and cheap devices known as sensor nodes. Usually, these sensor nodes are massively and deployed randomly as in Ad-hoc over hostile and harsh environment to sense, collect and transmit data to the needed locations (i.e., base station). One of the main advantages of WSN is that the ability to work in unattended and scattered environments regardless the presence of humans such as remote active volcanoes environments or earthquakes. In WSN expanding network, lifetime is a major concern. Clustering technique is more important to maximize network lifetime. Nature-inspired algorithms are developed and optimized to find optimized solutions for various optimization problems. We proposed Energy Efficient Firefly Algorithm to improve network lifetime as long as possible.

Keywords: wireless network, SN, Firefly, energy efficiency

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Authors: Jaroslav Kraus

Abstract:

Spatial information about demographic processes are a standard part of outputs in the Czech Republic. That was also the case of Population and Housing Census which was held on 2011. This is a starting point for a follow up study devoted to two basic types of households: single person households and households of one completed family. Single person households and one family households create more than 80 percent of all households, but the share and spatial structure is in long-term changing. The increase of single households is results of long-term fertility decrease and divorce increase, but also possibility of separate living. There are regions in the Czech Republic with traditional demographic behavior, and regions like capital Prague and some others with changing pattern. Population census is based - according to international standards - on the concept of currently living population. Three types of geospatial approaches will be used for analysis: (i) firstly measures of geographic distribution, (ii) secondly mapping clusters to identify the locations of statistically significant hot spots, cold spots, spatial outliers, and similar features and (iii) finally analyzing pattern approach as a starting point for more in-depth analyses (geospatial regression) in the future will be also applied. For analysis of this type of data, number of households by types should be distinct objects. All events in a meaningful delimited study region (e.g. municipalities) will be included in an analysis. Commonly produced measures of central tendency and spread will include: identification of the location of the center of the point set (by NUTS3 level); identification of the median center and standard distance, weighted standard distance and standard deviational ellipses will be also used. Identifying that clustering exists in census households datasets does not provide a detailed picture of the nature and pattern of clustering but will be helpful to apply simple hot-spot (and cold spot) identification techniques to such datasets. Once the spatial structure of households will be determined, any particular measure of autocorrelation can be constructed by defining a way of measuring the difference between location attribute values. The most widely used measure is Moran’s I that will be applied to municipal units where numerical ratio is calculated. Local statistics arise naturally out of any of the methods for measuring spatial autocorrelation and will be applied to development of localized variants of almost any standard summary statistic. Local Moran’s I will give an indication of household data homogeneity and diversity on a municipal level.

Keywords: census, geo-demography, households, the Czech Republic

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Authors: V. Nagammai

Abstract:

Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. Development of technology increases the complexity in IC manufacturing which may vary the power consumption, increase the size and latency period. Topology defines a number of connections between network. In this project, NoC topology is generated using atlas tool which will increase performance in turn determination of constraints are effective. The routing is performed by XY routing algorithm and wormhole flow control. In NoC topology generation, the value of power, area and latency are predetermined. In previous work, placement, routing and shortest path evaluation is performed using an algorithm called floor planning with cluster reconstruction and path allocation algorithm (FCRPA) with the account of 4 3x3 switch, 6 4x4 switch, and 2 5x5 switches. The usage of the 4x4 and 5x5 switch will increase the power consumption and area of the block. In order to avoid the problem, this paper has used one 8x8 switch and 4 3x3 switches. This paper uses IPRCA which of 3 steps they are placement, clustering, and shortest path evaluation. The placement is performed using min – cut placement and clustering are performed using an algorithm called cluster generation. The shortest path is evaluated using an algorithm called Dijkstra's algorithm. The power consumption of each block is determined. The experimental result shows that the area, power, and wire length improved simultaneously.

Keywords: application specific noc, b* tree representation, floor planning, t tree representation

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Authors: Manfred F. Maute, Olga Naumenko, Raymond T. Kong

Abstract:

Using data from a nationally representative financial panel of Canadian households, this study develops a psychographic segmentation of the customers of a value-expressive credit card service and tests for effects on relational response differences. The variety of segments elicited by agglomerative and k means clustering and the familiar profiles of individual clusters suggest that the face validity of the psychographic segmentation was quite high. Segmentation had a significant effect on customer satisfaction and relationship depth. However, when socio-demographic characteristics like household size and income were accounted for in the psychographic segmentation, the effect on relational response differences was magnified threefold. Implications for the segmentation of financial services markets are considered.

Keywords: customer satisfaction, financial services, psychographics, response differences, segmentation

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Authors: Arash Jafari, Mehdi Taghaddosi, Azin Parvin

Abstract:

In this study, a vibrational differential equation governing on swinging single-degree-of-freedom pendulum in a viscous fluid has been investigated. The damping process is characterized according to two different regimes: at first, damping in stationary viscous fluid, in the second, damping in flowing viscous fluid with constant velocity. Our purpose is to enhance the ability of solving the mentioned nonlinear differential equation with a simple and innovative approach. Comparisons are made between new method and Numerical Method (rkf45). The results show that this method is very effective and simple and can be applied for other nonlinear problems.

Keywords: oscillating systems, angular frequency and damping ratio, pendulum at fluid, locus of maximum

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Authors: R. S. Hassani, S. Chikh, L. Tadrist, S. Radev

Abstract:

A bubbly flow in a vertical miniature tube is analyzed theoretically. The liquid and gas phase are co-current flowing upward. The gas phase is injected via a nozzle whose inner diameter is 0.11mm and it is placed on the axis of the tube. A force balance is applied on the bubble at its detachment. The set of governing equations are solved by use of Mathematica software. The bubble diameter and the bubble generation frequency are determined for various inlet phase velocities represented by the inlet mass quality. The results show different behavior of bubble growth and detachment depending on the tube size.

Keywords: two phase flow, bubble growth, mini-channel, generation frequency

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