Search results for: statistical features

Authors: Mostafa M. Abo Elsoud, Heba I. Elkhouly, Nagwa M. Sidkey

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Production of rhamnolipids by Pseudomonas aeruginosa has attracted a growing interest during the last few decades due to its high productivity compared with other microorganisms. In the current work, rhamnolipids production by P. aeruginosa PAO1 was statistically modeled using Taguchi orthogonal array, numerically optimized and validated. Prickly Pear Peel (Opuntia ficus-indica) has been used as a carbon source for production of rhamnolipid. Finally, the optimum conditions for rhamnolipid production were applied in 5L working volume bioreactors at different aerations, agitation and controlled pH for maximum rhamnolipid production. In addition, kinetic studies of rhamnolipids production have been reported. At the end of the batch bioreactor optimization process, rhamnolipids production by P. aeruginosa PAO1 has reached the worldwide levels and can be applied for its industrial production.

Keywords: rhamnolipids, pseudomonas aeruginosa, statistical optimization, tagushi, opuntia ficus-indica

Procedia PDF Downloads 160

Authors: Wilma Coetzee

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Statistics lecturers experience that many students who are taking a service course in statistics do not like statistics. Students in these courses tend to struggle and do not perform well. This research takes a look at the student’s perspective, with the aim to determine how to change the teaching of statistics so that students will enjoy it more and perform better. Questionnaires were used to determine the perspectives of first year service statistics students at a South African university. Factors addressed included motivation to study, attitude toward statistics, statistical anxiety, mathematical abilities and tendency to procrastinate. Logistic regression was used to determine what contributes to students performing badly in statistics. The results show that the factors that contribute the most to students performing badly are: statistical anxiety, not being motivated and having had mathematical literacy instead of mathematics in secondary school. Two open ended questions were included in the questionnaire: 'I will enjoy statistics more if…' and 'I will perform better in statistics if…'. The answers to these questions were analyzed using qualitative methods. Frequent themes were identified for each of the questions. A simulation study incorporating bootstrapping was done to determine the saturation of the themes. The majority of the students indicated that they would perform better in statistics if they studied more, managed their time better, had a flare for mathematics and if the lecturer was able to explain difficult concepts better. They also want more active learning. To ensure that students enjoy statistics more, they want an active learning experience. They want fun activities, more interaction with the lecturer and with one another, more computer based problems, and more challenges. They want a better understanding of the subject, want to understand the relevance of statistics to their future career and want excellent lecturers. These findings can be used to direct the improvement of the tuition of statistics.

Keywords: active learning, performance in statistics, statistical anxiety, statistics education

Procedia PDF Downloads 135

Authors: Loubna Tani, Nabih Elouzzani

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Crosstalk among interconnects and printed-circuit board (PCB) traces is a major limiting factor of signal quality in high-speed digital and communication equipments especially when fast data buses are involved. Such a bus is considered as a planar multiconductor transmission line. This paper will demonstrate how the finite difference time domain (FDTD) method provides an exact solution of the transmission-line equations to analyze the near end and the far end crosstalk. In addition, this study makes it possible to analyze the rise time effect on the near and far end voltages of the victim conductor. The paper also discusses a statistical analysis, based upon a set of several simulations. Such analysis leads to a better understanding of the phenomenon and yields useful information.

Keywords: multiconductor transmission line, crosstalk, finite difference time domain (FDTD), printed-circuit board (PCB), rise time, statistical analysis

Procedia PDF Downloads 416

Authors: Chiu-Pin Wei

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This research aims to explore the impact of music preferences on the emotional well-being of military academy students, recognizing the potential long-term implications for their high-stress careers post-graduation. Given the significance of positive emotion regulation in military personnel, this study focuses on understanding the types of music preferred by military cadets and analyzing how these preferences correlate with their emotional states. The study employs a quantitative approach, utilizing the Music Category Scale and Mood Scale to collect data. Statistical tools, such as Statistical Product and Service Solutions (SPSS), are employed for inferential analysis, including t-tests for emotional responses to instrumental and vocal music, one-way variance analysis for different demographic factors (grades, genders, and music listening frequencies), and Pearson's correlation to examine the relationship between music preferences and moods of military students.

Keywords: music preference, emotional regulation, military academic students, SPASS

Procedia PDF Downloads 48

Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

Procedia PDF Downloads 307

Authors: Daniel Pastor, Hyo-Sang Shin

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This paper presents a novel method to use optical flow navigation for long-term navigation. Unlike standard SLAM approaches for augmented reality, OFLAAM is designed for Micro Air Vehicles (MAV). It uses an optical flow camera pointing downwards, an IMU and a monocular camera pointing frontwards. That configuration avoids the expensive mapping and tracking of the 3D features. It only maps these features in a vocabulary list by a localization module to tackle the loss of the navigation estimation. That module, based on the well-established algorithm DBoW2, will be also used to close the loop and allow long-term navigation in confined areas. That combination of high-speed optical flow navigation with a low rate localization algorithm allows fully autonomous navigation for MAV, at the same time it reduces the overall computational load. This framework is implemented in ROS (Robot Operating System) and tested attached to a laptop. A representative scenarios is used to analyse the performance of the system.

Keywords: vision, UAV, navigation, SLAM

Procedia PDF Downloads 588

Authors: Youssef R. Hassan, Mohamed S. Hasanin, Reda M. Abdelhameed

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Recently, innovations in packaging security features become more important to see the originality of packaging in industrial application. Luminescent 3d printing materials have been a promising property which can provides a unique opportunity for the design and application of 3D printing. Lack emission of terbium ions, as a source of green emission, in salt form prevent its uses in industrial applications, so searching about stable and highly emitter material become essential. Nowadays, metal organic frameworks (MOFs) play an important role in designing light emitter material. In this work, fluorescent high density polyethylene (FHDPE) composite filament with Tb-benzene 1,3,5-tricarboxylate (Tb-BTC) MOFs for 3D printing have been successfully developed.HDPE pellets were mixed with Tb-BTC and melting extrustion with single screw extruders. It was found that Tb-BTCuniformly dispersed in the HDPE matrix and significantly increased the crystallinity of PE, which not only maintained the good thermal property but also improved the mechanical properties of Tb-BTC@HDPE composites. Notably, the composite filaments emitted ultra-bright green light under UV lamp, and the fluorescence intensity increased as the content of Tb-BTC increased. Finally, several brightly luminescent exquisite articles could be manufactured by fused deposition modeling (FDM) 3D printer with these new fluorescent filaments. In this context, the development of novel fluorescent Tb-BTC@HDPE composites was combined with 3D printing technology to amplified the packaging Security Features.

Keywords: 3D printing, fluorescent, packaging, security

Procedia PDF Downloads 82

Authors: Jerald Ruta, Ricardo Villar, Jojemar Bantugan, Nycel Barbadillo, Jigg Pelayo

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Streams are important in providing water supply for industrial, agricultural and human consumption, In short when there are streams there are lives. Identifying streams are essential since many developed cities are situated in the vicinity of these bodies of water and in flood management, it serves as basin for surface runoff within the area. This study aims to process and generate features from high-resolution digital terrain model (DTM) with 1-meter resolution using Hydrology Tools of ArcGIS. The raster was then filled, processed flow direction and accumulation, then raster calculate and provide stream order, converted to vector, and clearing undesirable features using the ancillary or google earth. In field validation streams were classified whether perennial, intermittent or ephemeral. Results show more than 90% of the extracted feature were accurate in assessment through field validation.

Keywords: digital terrain models, hydrology tools, strahler method, stream classification

Procedia PDF Downloads 254

Authors: Mustapha Benssalah, Mustapha Djeddou, Karim Drouiche

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The random number generation (RNG) presents a significant importance for the security and the privacy of numerous applications, such as RFID technology and smart cards. Since, the quality of the generated bit sequences is paramount that a weak internal generator for example, can directly cause the entire application to be insecure, and thus it makes no sense to employ strong algorithms for the application. In this paper, we propose a new pseudo random number generator (PRNG), suitable for cryptosystems ECC-based, constructed by randomly selecting points from several elliptic curves randomly selected. The main contribution of this work is the increasing of the generator internal states by extending the set of its output realizations to several curves auto-selected. The quality and the statistical characteristics of the proposed PRNG are validated using the Chi-square goodness of fit test and the empirical Special Publication 800-22 statistical test suite issued by NIST.

Keywords: PRNG, security, cryptosystem, ECC

Procedia PDF Downloads 429

Authors: H. Nouri, S. Chavoshi Borujeni, S. Anderson, S. Beecham, P. Sutton

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The potential risk of salt leaching through wastewater irrigation is of concern for most local governments and city councils. Despite the necessity of salinity monitoring and management in urban greenery, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using near sensing and remote sensing approaches using EM38 surveys, and high-resolution multispectral image of WorldView3. Veale Gardens within the Adelaide Parklands was selected as the experimental site. The results of the near sensing investigation were validated by testing soil salinity samples in the laboratory. Over 30 band combinations forming salinity indices were tested using image processing techniques. The outcomes of the remote sensing and near sensing approaches were compared to examine whether remotely sensed salinity indicators could map and predict the spatial variation of soil salinity through a potential statistical model. Statistical analysis was undertaken using the Stata 13 statistical package on over 52,000 points. Several regression models were fitted to the data, and the mixed effect modelling was selected the most appropriate one as it takes to account the systematic observation-specific unobserved heterogeneity. Results showed that SAVI (Soil Adjusted Vegetation Index) was the only salinity index that could be considered as a predictor for soil salinity but further investigation is needed. However, near sensing was found as a rapid, practical and realistically accurate approach for salinity mapping of heterogeneous urban vegetation.

Keywords: WorldView3, remote sensing, EM38, near sensing, urban green spaces, green smart cities

Procedia PDF Downloads 143

Authors: Cheng Sheng, Yuemin Zhao, Chenlong Duan

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Air dense medium fluidized bed is a typical application of fluidization techniques for coal particle separation in arid areas, where it is costly to implement wet coal preparation technologies. In the last three decades, air dense medium fluidized bed, as an efficient dry coal separation technique, has been studied in many aspects, including energy and mass transfer, hydrodynamics, bubbling behaviors, etc. Despite numerous researches have been published, the fluidization features, especially dual dense medium fluidization features have been rarely reported. In dual dense medium fluidized beds, different combinations of different dense mediums play a significant role in fluidization quality variation, thus influencing coal separation efficiency. Moreover, to what extent different dense mediums mix and to what extent the two-component particulate mixture affects the fluidization performance and quality have been in suspense. The proposed work attempts to reveal underlying mechanisms of generation and evolution of two-component particulate mixture in the fluidization process. Based on computational fluid dynamics methods and discrete particle modelling, movement and evolution of dual dense mediums in air dense medium fluidized bed have been simulated. Dual dense medium fluidization experiments have been conducted. Electrical capacitance tomography was employed to investigate the distribution of two-component mixture in experiments. Underlying mechanisms involving two-component particulate fluidization are projected to be demonstrated with the analysis and comparison of simulation and experimental results.

Keywords: air dense medium fluidized bed, particle separation, computational fluid dynamics, discrete particle modelling

Procedia PDF Downloads 363

Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

Procedia PDF Downloads 300

Authors: Swetha Priya Darshini Thammadi, Sateesh Kumar Pisini, Sanjay Kumar Shukla

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Source apportionment using Dispersion model depends primarily on the quality of Emission Inventory. In the present study, a CMB receptor model has been used to identify the sources of PM_2.5, while the AERMOD dispersion model has been used to account for missing sources of PM_2.5 in the Emission Inventory. A statistical approach has been developed to quantify the missing sources not considered in the Emission Inventory. The inventory of each grid was improved by adjusting emissions based on road lengths and deficit in measured and modelled concentrations. The results showed that in CMB analyses, fugitive sources - soil and road dust - contribute significantly to ambient PM_2.5 pollution. As a result, AERMOD significantly underestimated the ambient air concentration at most locations. The revised Emission Inventory showed a significant improvement in AERMOD performance which is evident through statistical tests.

Keywords: CMB, GIS, AERMOD, PM₂.₅, fugitive, emission inventory

Procedia PDF Downloads 316

Authors: Sadia Afroza, Rakiba Rouf

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Road safety and associated behaviors have received significant attention in recent years, reflecting general public concern. This paper portrays a statistical scenario of the young drivers in UAE with emphasis on various concern points of young driver’s behavior and license issuance. Although there are many factors contributing to road accidents, statistically it is evident that age plays a major role in road accidents. Despite ensuring strict road safety laws enforced by the UAE government, there is a staggering correlation among road accidents and young driver’s at UAE. However, private organizations like BMW and RoadSafetyUAE have extended its support on conducting surveys on driver’s behavior with an aim to ensure road safety. Various strategies such as road safety law enforcement, license issuance, adapting new technologies like safety cameras and raising awareness can be implemented to improve the road safety concerns among young drivers.

Keywords: driving behavior, Graduated Driver Licensing System (GLDS), road safety, UAE drivers, young drivers

Procedia PDF Downloads 240

Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu

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Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.

Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature

Procedia PDF Downloads 195

Authors: Manana Lobzhanidze

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Introduction: The paper will argue that labor migration is the most important problem Georgia faces today. The structure of labor migration by age and gender of Georgia is analyzed. The main driving factors of circular labor migration during the last ten years are identified. While studying migration, it is necessary to discuss the interconnection of economic, social, and demographic features, also taking into consideration the policy of state regulations in terms of education and professional training. Methodology: Different research methods are applied in the presented paper: statistical, such as selection, grouping, observation, trend, and qualitative research methods, namely; analysis, synthesis, induction, deduction, comparison ones. Main Findings: Labour migrants are filling the labor market as a low salary worker. The main positive feedback of migration from developing countries is poverty eradication, but this process is accompanied by problems, such as 'Brain Drain'. The country loses an important part of its intellectual potential, and it is invested by households or state itself. Conclusions: Labor migration is characterized to be temporary, but socio-economic problems of the country often push the labor migration in the direction of longterm and illegal migration. Countries with developed economies try to stricter migration policy and fight illegal migration with different methods; circular migration helps solve this problem. Conclusions and recommendations are included about circular labor migration consequences in Georgia and its influence on the reduction of unemployment level.

Keywords: migration, circular labor migration, labor migration employment, unemployment

Procedia PDF Downloads 152

Authors: Ashish Bohre, Blaž Likozar, Saikat Dutta, Dionisios G. Vlachos, Basudeb Saha

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Graphene oxide, decorated with surface oxygen functionalities, has emerged as a sustainable alternative to precious metal catalysts for many reactions. Herein, we report for the first time that graphene oxide becomes super active for C-C coupling upon incorporation of multilayer crystalline features, highly oxidized surface, Brønsted acidic functionalities and defect sites on the surface and edges via modified oxidation. The resulting improved graphene oxide (IGO) demonstrates superior activity to commonly used framework zeolites for upgrading of low carbon biomass furanics to long carbon chain aviation fuel precursors. A maximum 95% yield of C15 fuel precursor with high selectivity is obtained at low temperature (60 C) and neat conditions via hydroxyalkylation/alkylation (HAA) of 2-methylfuran (2-MF) and furfural. The coupling of 2-MF with carbonyl molecules ranging from C3 to C6 produced the precursors of carbon numbers 12 to 21. The catalyst becomes inactive in the 4th cycle due to the loss of oxygen functionalities, defect sites and multilayer features; however, regains comparable activity upon regeneration. Extensive microscopic and spectroscopic characterization of the fresh and reused IGO is presented to elucidate high activity of IGO and to establish a correlation between activity and surface and structural properties. Kinetic Monte Carlo (KMC) and density functional theory (DFT) calculations are presented to further illustrate the surface features and the reaction mechanism.

Keywords: methacrylic acid, itaconic acid, biomass, monomer, solid base catalyst

Procedia PDF Downloads 153

Authors: M. Volpini, D. Alves, A. Horta, M. Borges, P. Reis

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The gait pattern in people that present motor limitations foment the demand for auxiliary locomotion devices. These artifacts for movement assistance vary according to its shape, size and functional features, following the clinical applications desired. Among the ortheses of lower limbs, the ankle-foot orthesis aims to improve the ability to walk in people with different neuromuscular limitations, although they do not always answer patients' expectations for their aesthetic and functional characteristics. The purpose of this study is to explore the possibility of using new design in additive manufacturer to reproduce the shape and functional features of a ankle-foot orthesis in an efficient and modern way. Therefore, this work presents a study about the performance of the mechanical forces through the analysis of finite elements in an ankle-foot orthesis. It will be demonstrated a study of distribution of the stress on the orthopedic device in orthostatism and during the movement in the course of patient's walk.

Keywords: additive manufacture, new designs, orthoses, finite elements

Procedia PDF Downloads 193

Authors: Angelina Anani, Ignacio Ortiz Flores, Haitao Li

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The use of underground mining methods have increased significantly over the past decades. This increase has also been spared on by several mines transitioning from surface to underground mining. However, determining the transition depth can be a challenging task, especially when coupled with production schedule optimization. Several researchers have simplified the problem by excluding operational features relevant to production schedule optimization. Our research objective is to investigate the extent to which operational features of transition mines accounted for affect the optimal production schedule. We also provide a framework for factors to consider in production schedule optimization for transition mines. An integrated mixed-integer linear programming (MILP) model is developed that maximizes the NPV as a function of production schedule and transition depth. A case study is performed to validate the model, with a comparative sensitivity analysis to obtain operational insights.

Keywords: underground mining, transition mines, mixed-integer linear programming, production schedule

Procedia PDF Downloads 153

Authors: Dalwinder Singh, Amandeep Verma, Manpreet Kaur, Birmohan Singh

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The detection of pre-cancerous changes using a Pap smear test of cervical cell is the important step for the early diagnosis of cervical cancer. The Pap smear test consists of a sample of human cells taken from the cervix which are analysed to detect cancerous and pre-cancerous stage of the given subject. The manual analysis of these cells is labor intensive and time consuming process which relies on expert cytotechnologist. In this paper, a computer assisted system for the automated analysis of the cervical cells has been proposed. We propose a morphology based approach to the nucleus detection and segmentation of the cytoplasmic region of the given single or multiple overlapped cell. Further, various texture and region based features are calculated from these cells to classify these into normal and abnormal cell. Experimental results on public available dataset show that our system has achieved satisfactory success rate.

Keywords: cervical cancer, cervical tissue, mathematical morphology, texture features

Procedia PDF Downloads 505

Authors: B. X. Tchomeni, A. A. Alugongo, T. B. Tengen

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In this paper, de Laval rotor system has been characterized by a hinge model and its transient response numerically treated for a dynamic solution. The effect of the ensuing non-linear disturbances namely rub and breathing crack is numerically simulated. Subsequently, three analysis methods: Orbit Analysis, Fast Fourier Transform (FFT) and Wavelet Transform (WT) are employed to extract features of the vibration signal of the faulty system. An analysis of the system response orbits clearly indicates the perturbations due to the rotor-to-stator contact. The sensitivities of WT to the variation in system speed have been investigated by Continuous Wavelet Transform (CWT). The analysis reveals that features of crack, rubs and unbalance in vibration response can be useful for condition monitoring. WT reveals its ability to detect non-linear signal, and obtained results provide a useful tool method for detecting machinery faults.

Keywords: Continuous wavelet, crack, discrete wavelet, high acceleration, low acceleration, nonlinear, rotor-stator, rub

Procedia PDF Downloads 335

Authors: Sepideh Jabbari Behnam, Shadabeh Gashtasbi Iraei, Elnaz Mohsenin, MohammadAli Aghajani

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Urban neighborhoods, as important urban forming cells, play a key role in creating urban texture and integrated form. Nowadays, most of neighborhood divisions are based on urban management systems but without considering social issues and the other aspects of urban life. This can cause problems such as providing inappropriate services for city dwellers, the loss of local identity and etc. In this regard for regenerating of such neighborhoods, it is essential to locate neighborhood centers with appropriate access and services for all residents. The main objective of this article is reaching to the location of neighborhood centers in a way that, most of issues relating to the physical features (such as the form of access network and texture permeability and etc.) and other qualities such as land uses, densities and social and economic features can be done simultaneously. This paper attempts to use methods of spatial analysis in order to surveying spatial structure and space syntax of urban textures and Urban Network Analysis Systems. This can be done by one of GIS toolbars which is named UNA (Urban Network Analysis) with the use of its five functions (include: Reach, Betweenness, Gravity, Closeness, Straightness).These functions were written according to space syntax theory and offer its relating output. This paper tries to locate and evaluate the optimal location of neighborhood centers in order to create local centers. This is done through weighing of each of these functions and taking into account of spatial features.

Keywords: evaluate optimal location, Local centers, location of neighborhood centers, Spatial analysis, Urban network

Procedia PDF Downloads 445

Authors: Asaad Y. Shamseldin

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Analytical probabilistic approach is an innovative approach for urban stormwater modeling. It can provide information about the long-term performance of a stormwater management facility without being computationally very demanding. This paper explores the application of the analytical probabilistic approach in New Zealand. The paper presents the results of a case study aimed at development of an objective way of identifying what constitutes a rainfall storm event and the estimation of the corresponding statistical properties of storms using two selected automatic rainfall stations located in the Auckland region in New Zealand. The storm identification and the estimation of the storm statistical properties are regarded as the first step in the development of the analytical probabilistic models. The paper provides a recommendation about the definition of the storm inter-event time to be used in conjunction with the analytical probabilistic approach.

Keywords: hydrology, rainfall storm, storm inter-event time, New Zealand, stormwater management

Procedia PDF Downloads 325

Authors: Sanjaya Humagain, Rajendra Koju

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Background: Rheumatic heart disease (RHD) is the most common cardiovascular disease in children and young adults. Though declined and almost non-existent in developed nations, RHD is still one of the leading cause for premature death and disability in developing countries. Prevalence of RHD is high in both rural as well as urban area of Nepal. Present study is designed to look at the pattern of valvular involvement and demographic features in RHD. Methods: 326 patients indicated for inj. Benzathine penicillin were selected and echocardiograph performed to see the pattern of vavular involvement. Data analysis was done using SPSS 17. Result: The most common type of lesion was mixed type with mitral valve involvement. MR was the most common isolated lesion. MS was more commonly seen in females whereas AS was more common in males. Secondary prophylaxis was more common than primary prophylaxis. Conclusion: RHD still being a major problem and a preventable disease so extensive screening program is required to identify them early and prevent the complication.

Keywords: acute rheumatic fever, RHD, MS, MR, AS, AR, Inj benzathine penicillin

Procedia PDF Downloads 303

Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

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Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

Procedia PDF Downloads 344

Authors: Eui-Jin Kim, Hasik Lee, Su-Jin Park, Dong-Kyu Kim

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Smart-card data are expected to provide information on activity pattern as an alternative to conventional person trip surveys. The focus of this study is to propose a method for training the person trip surveys to supplement the smart-card data that does not contain the purpose of each trip. We selected only available features from smart card data such as spatiotemporal information on the trip and geographic information system (GIS) data near the stations to train the survey data. XGboost, which is state-of-the-art tree-based ensemble classifier, was used to train data from multiple sources. This classifier uses a more regularized model formalization to control the over-fitting and show very fast execution time with well-performance. The validation results showed that proposed method efficiently estimated the trip purpose. GIS data of station and duration of stay at the destination were significant features in modeling trip purpose.

Keywords: activity pattern, data fusion, smart-card, XGboost

Procedia PDF Downloads 225

Authors: Guang-Ho Cha

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This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.

Keywords: context-sensitive search, image search, similarity ranking, similarity search

Procedia PDF Downloads 346

Authors: Seulki Lee, Seoung Bum Kim

Abstract:

Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

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Authors: Hamilton V. Niculescu

Abstract:

Following a three year study conducted on eighteen Irish people that are involved in growing vegetables in various community gardens around Dublin, Republic of Ireland, it was revealed that addition of some automated features aimed at improving agricultural practices represented a process which was regarded as potentially beneficial, and as a great tool to closely monitor climate conditions inside the greenhouses. The participants were provided with a free custom-built mobile app through which they could remotely monitor and control features such as irrigation, air ventilation, and windows to ensure optimal growing conditions for vegetables growing inside purpose-built greenhouses. While the initial interest was generally high, within weeks, the participants' level of interaction with the enclosures slowly declined. By employing a constructivist grounded theory methodology, following focus group discussions, in-depth semi-structured interviews, and observations, it was revealed that participants' trust in newer technologies, and renewables, in particular, was low. There are various reasons for this, but because the participants in this study consist of mainly working-class people, it can be argued that lack of education and knowledge are the main barriers acting against the adoption of innovations. Consequently, it was revealed that most participants eventually decided to "set and forget" the systems in automatic working mode, indicating that the immediate effect of introducing people to assisting technologies also introduced some unintended consequences into their lifestyle. It is argued that this occurrence also indicates the fact that people initially "read" newer technologies and only adopt those features that they find useful and less intrusive in regards to their current lifestyle.

Keywords: automation, communication, greenhouse, sustainable

Procedia PDF Downloads 105

Authors: Mladen Milicevic

Abstract:

This presentation will address how the statistical analysis of digitized popular music influences the music creation and emotionally manipulates consumers.Furthermore, it will deal with semiological aspect of uniformization of musical taste in order to predict the potential revenues generated by popular music sales. In the USA, we live in an age where most of the popular music (i.e. music that generates substantial revenue) has been digitized. It is safe to say that almost everything that was produced in last 10 years is already digitized (either available on iTunes, Spotify, YouTube, or some other platform). Depending on marketing viability and its potential to generate additional revenue most of the “older” music is still being digitized. Once the music gets turned into a digital audio file,it can be computer-analyzed in all kinds of respects, and the similar goes for the lyrics because they also exist as a digital text file, to which any kin of N Capture-kind of analysis may be applied. So, by employing statistical examination of different popular music metrics such as tempo, form, pronouns, introduction length, song length, archetypes, subject matter,and repetition of title, the commercial result may be predicted. Polyphonic HMI (Human Media Interface) introduced the concept of the hit song science computer program in 2003.The company asserted that machine learning could create a music profile to predict hit songs from its audio features Thus,it has been established that a successful pop song must include: 100 bpm or more;an 8 second intro;use the pronoun 'you' within 20 seconds of the start of the song; hit the bridge middle 8 between 2 minutes and 2 minutes 30 seconds; average 7 repetitions of the title; create some expectations and fill that expectation in the title. For the country song: 100 bpm or less for a male artist; 14-second intro; uses the pronoun 'you' within the first 20 seconds of the intro; has a bridge middle 8 between 2 minutes and 2 minutes 30 seconds; has 7 repetitions of title; creates an expectation,fulfills it in 60 seconds.This approach to commercial popular music minimizes the human influence when it comes to which “artist” a record label is going to sign and market. Twenty years ago,music experts in the A&R (Artists and Repertoire) departments of the record labels were making personal aesthetic judgments based on their extensive experience in the music industry. Now, the computer music analyzing programs, are replacing them in an attempt to minimize investment risk of the panicking record labels, in an environment where nobody can predict the future of the recording industry.The impact on the consumers taste through the narrow bottleneck of the above mentioned music selection by the record labels,created some very peculiar effects not only on the taste of popular music consumers, but also the creative chops of the music artists as well. What is the meaning of this semiological shift is the main focus of this research and paper presentation.

Keywords: music, semiology, commercial, taste

Procedia PDF Downloads 374