Search results for: Scale-Invariant Feature Transformation (SIFT)

Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

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Authors: Yang Zhong, Mei-Jie Xu

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To study the dynamic mechanics response of asphalt pavement under the temperature load and vehicle loading, asphalt pavement was regarded as multilayered elastic half-space system, and theory analysis was conducted by regarding dynamic modulus of asphalt mixture as the parameter. Firstly, based on the dynamic modulus test of asphalt mixture, function relationship between the dynamic modulus of representative asphalt mixture and temperature was obtained. In addition, the analytical solution for thermal stress in the single layer was derived by using Laplace integral transformation and Hankel integral transformation respectively by using thermal equations of equilibrium. The analytical solution of calculation model of thermal stress in asphalt pavement was derived by transfer matrix of thermal stress in multilayer elastic system. Finally, the variation of thermal stress in pavement structure was analyzed. The result shows that there is an obvious difference between the thermal stress based on dynamic modulus and the solution based on static modulus. Therefore, the dynamic change of parameter in asphalt mixture should be taken into consideration when the theoretical analysis is taken out.

Keywords: asphalt pavement, dynamic modulus, integral transformation, transfer matrix, thermal stress

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Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: Hemadri Raut

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The city is a blend of the possible interactions of the built form, open spaces and their spatial organization layout in a geographical area to obtain an integrated pattern and environment with building facades being a dominant figure in the body of a city. Façades of each city have their own inherent properties responsive to the human behaviour, weather conditions, safety factors, material availability and composition along with the necessary aesthetics in coordination with adjacent building facades. Cities experience a huge transformation in the culture, lifestyle; socioeconomic conditions and technology nowadays because of the increasing population, urban sprawl, industrialization, contemporary architectural style, post-disaster consequences, war reconstructions, etc. This leads to the loss of the actual identity and architectural character of the city which in turn induces chaos and turbulence in the city. This paper attempts to identify and learn from the traditional elements that would make us more aware of the unique identity of the local communities in a city. It further studies the architectural style, color, shape, and design techniques through the case studies of contextual cities. The work focuses on the observation and transformation of the image of the city through these considerations in the designing of the facades to achieve the reconciliation of the people with urban spaces.

Keywords: building facades, city, community, heritage, identity, transformation, urban

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

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Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

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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

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Authors: Rungpansa Noichan, Bart Julian Dewancker

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The conservation and regeneration of historical industrial building is one of the most important issues to be solved in today’s urban development in the world. There are growing numbers of industrial building in which promoting heritage conservation maybe a helpful tool for a sustainable city in social, urban restructuring, environmental and economic component. This paper identifies the key attributes of conservation and regeneration industrial building from the literature, were discussed by reviewing its development at home and abroad. The authors have investigated 93 industrial buildings, which were used as industrial building before and reused into buildings with another function afterward. The data to be discussed below were mainly collected from various publications but also from available internet sources. This study focuses on green transformation, historical culture heritage, transformation techniques, and urban regeneration based on the empirical researches on the historical industrial building and site. Moreover, we focus on social, urban environment and sustainable development. The implications of the study provide suggestions for future improvements in the conservation and regeneration of historical industrial building, and inspire new ways of use, so the building becomes flexible and can consequently be adaptable to changes in order to survive time. Therefore, the building does not take into account only its future impact in the environment and society. Instead, it focuses on its entire life cycle.

Keywords: industrial building, heritage conservation, green transformation, regeneration, sustainable development

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Authors: Yanisa Chaiya, Jintana Sanwong

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Let V be a vector space over a field and W a subspace of V. Let Fix(V,W) denote the set of all linear transformations on V with fix all elements in W. In this paper, we show that Fix(V,W) is a semigroup under the composition of maps and describe Green’s relations on this semigroup in terms of images, kernels and the dimensions of subspaces of the quotient space V/W where V/W = {v+W : v is an element in V} with v+W = {v+w : w is an element in W}. Let dim(U) denote the dimension of a vector space U and Vα = {vα : v is an element in V} where vα is an image of v under a linear transformation α. For any cardinal number a let a'= min{b : b > a}. We also show that the ideals of Fix(V,W) are precisely the sets. Fix(r) ={α ∊ Fix(V,W) : dim(Vα/W) < r} where 1 ≤ r ≤ a' and a = dim(V/W). Moreover, we prove that if V is a finite-dimensional vector space, then every ideal of Fix(V,W) is principle.

Keywords: Green’s relations, ideals, linear transformation semi-groups, principle ideals

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Authors: Dennis Kreutzer, Sarah Müller-Abdelrazeq, Ingrid Isenhardt

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Resource scarcity, energy transition and the planned climate neutrality pose enormous challenges for manufacturing companies. In order to achieve these goals and a holistic sustainable development, the European Union has listed the circular economy as part of the Circular Economy Action Plan. In addition to a reduction in resource consumption, reduced emissions of greenhouse gases and a reduced volume of waste, the principles of the circular economy also offer enormous economic potential for companies, such as the generation of new circular business models. However, many manufacturing companies, especially small and medium-sized enterprises, do not have the necessary capacity to plan their transformation. They need support and strategies on the path to circular transformation, because this change affects not only production but also the entire company. Maturity models offer an approach, as they enable companies to determine the current status of their transformation processes. In addition, companies can use the models to identify transformation strategies and thus promote the transformation process. While maturity models are established in other areas, e.g. IT or project management, only a few circular economy maturity models can be found in the scientific literature. The aim of this paper is to analyse the identified maturity models of the circular economy through a systematic literature review (SLR) and, besides other aspects, to check their completeness as well as their quality. Since the terms "maturity model" and "readiness model" are often used to assess the transformation process, this paper considers both types of models to provide a more comprehensive result. For this purpose, circular economy maturity models at the company (micro) level were identified from the literature, compared, and analysed with regard to their theoretical and methodological structure. A specific focus was placed, on the one hand, on the analysis of the business units considered in the respective models and, on the other hand, on the underlying metrics and indicators in order to determine the individual maturity level of the entire company. The results of the literature review show, for instance, a significant difference in the holism of their assessment framework. Only a few models include the entire company with supporting areas outside the value-creating core process, e.g. strategy and vision. Additionally, there are large differences in the number and type of indicators as well as their metrics. For example, most models often use subjective indicators and very few objective indicators in their surveys. It was also found that there are rarely well-founded thresholds between the levels. Based on the generated results, concrete ideas and proposals for a research agenda in the field of circular economy maturity models are made.

Keywords: maturity model, circular economy, transformation, metric, assessment

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Authors: Khushalini N. Ulman, Samane Maroufi, Veena H. Sahajwalla

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Globally, electronic waste (e-waste) continues to grow at an alarming rate. Several technologies have been developed to recover valuable materials from e-waste, however, their efficiency can be increased with a better knowledge of the e-waste components. Random access memory cards (RAMs) are considered as high value scrap for the e-waste recyclers. Despite their high precious metal content, RAMs are still recycled in a conventional manner resulting in huge loss of resources. Our research work highlights the precious metal rich components of a RAM. Inductively coupled plasma (ICP) analysis of RAMs of six different generations have been carried out and the trends in their metal content have been investigated. Over the past decade, the copper content of RAMs has halved and their tin content has increased by 70 %. The stricter environmental laws have facilitated ~96 % drop in the lead content of RAMs. To comprehend the fundamentals of thermal transformation of RAMs, our research provides their detailed kinetic study. This can assist the e-waste recyclers in optimising their metal recovery processes. Thus, understanding the chemical and thermal behaviour of RAMs can open new avenues for efficient e-waste recycling.

Keywords: electronic waste, kinetic study, recycling, thermal transformation

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Authors: Serge Provost, Yishan Zang

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Following a review of various approaches that are utilized for classifying the tail behavior of a distribution, an easily implementable methodology that relies on an arctangent transformation is presented. The classification criterion is actually based on the difference between two specific quantiles of the transformed distribution. The resulting categories enable one to classify distributional tails as distinctly short, short, nearly medium, medium, extended medium and somewhat long, providing that at least two moments exist. Distributions possessing a single moment are said to be long tailed while those failing to have any finite moments are classified as having an extremely long tail. Several illustrative examples will be presented.

Keywords: arctangent transformation, tail classification, heavy-tailed distributions, distributional moments

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: rough set theory, attribute reduction, fuzzy logic, memetic algorithms, record to record algorithm, great deluge algorithm

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Authors: Shumaila Ehtisham

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In Statistical Distribution Theory, considering an additional parameter to classical distributions is a usual practice. In this study, a new distribution referred to as α-Power Pareto distribution is introduced by including an extra parameter. Several properties of the proposed distribution including explicit expressions for the moment generating function, mode, quantiles, entropies and order statistics are obtained. Unknown parameters have been estimated by using maximum likelihood estimation technique. Two real datasets have been considered to examine the usefulness of the proposed distribution. It has been observed that α-Power Pareto distribution outperforms while compared to different variants of Pareto distribution on the basis of model selection criteria.

Keywords: α-power transformation, maximum likelihood estimation, moment generating function, Pareto distribution

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Authors: Fatma Abdedayem

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The most effective and efficient methods in image categorization are almost based on bag-of-words (BOW) which presents image by a histogram of occurrence of visual words. In this paper, we propose a novel extension to this method. Firstly, we extract features in multi-scales by applying a color local descriptor named opponent-SIFT. Secondly, in order to represent image we use Spatial Pyramid Representation (SPR) and an extension to the BOW method which based on weighting visual words. Typically, the visual words are weighted during histogram assignment by computing the ratio of their occurrences in the image to the occurrences in the background. Finally, according to classical BOW retrieval framework, only a few words of the vocabulary is useful for image representation. Therefore, we select the useful weighted visual words that respect the threshold value. Experimentally, the algorithm is tested by using different image classes of PASCAL VOC 2007 and is compared against the classical bag-of-visual-words algorithm.

Keywords: BOW, useful visual words, weighted visual words, bag of visual words

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Authors: Hakan Akin

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The aim of this study was to examine the policy making process in Turkish Health System. This policy making process will be examined through public policy change theories. Since political actors played in the formulation of public policies also explains the type of policy change, this actors will be inspected in the supranational and national basis. Also the transformation of public policy in the Turkish health care system will be analysed under the concepts of New right ideology, neo-liberalism, neo-conservatism and governance. And after this analyse, the outputs and outcomes of this transformation will be discussed in the context of developing countries.

Keywords: policy transfer, policy diffusion, policy convergence, new right, governance

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Authors: Evangelos Baltas, Elissavet Feloni, Dimitrios Karpouzos

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A crucial issue in hydrologic design is the sizing of structures and flood-control works in areas with limited data. This research work highlights the significant variation in probable maximum flood (PMF) for a design hyetograph, using different theoretical functions of rainfall-runoff transformation. The analysis focuses on seven subbasins with different characteristics in the municipality of Florina, northern Greece. This area is a semi-agricultural one which hosts important activities, such as the operation of one of the greatest fields of lignite for power generation in Greece. Results illustrate the notable variation in estimations among the methodologies used for the examined subbasins.

Keywords: rainfall, runoff, hydrologic design, PMF

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Adel Gurel, Ozge Ceylin Yildirim

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Nowadays, design and architecture are being affected and underwent change with the rapid advancements in technology, economics, politics, society and culture. Architecture has been transforming with the latest developments after the inclusion of computers into design. Integration of design into the computational environment has revolutionized the architecture and new perspectives in architecture have been gained. The history of architecture shows the various technological developments and changes in which the architecture has transformed with time. Therefore, the analysis of integration between technology and the history of the architectural process makes it possible to build a consensus on the idea of how architecture is to proceed. In this study, each period that occurs with the integration of technology into architecture is addressed within historical process. At the same time, changes in architecture via technology are identified as important milestones and predictions with regards to the future of architecture have been determined. Developments and changes in technology and the use of technology in architecture within years are analyzed in charts and graphs comparatively. The historical process of architecture and its transformation via technology are supported with detailed literature review and they are consolidated with the examination of focal points of 20th-century architecture under the titles; parametric design, genetic architecture, simulation, and biomimicry. It is concluded that with the historical research between past and present; the developments in architecture cannot keep up with the advancements in technology and recent developments in technology overshadow the architecture, even the technology decides the direction of architecture. As a result, a scenario is presented with regards to the reach of technology in the future of architecture and the role of the architect.

Keywords: computer technologies, future architecture, scientific developments, transformation

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Authors: Osman Adiguzel

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Shape memory effect is a peculiar property exhibited by certain alloy systems and based on martensitic transformation, and shape memory properties are closely related to the microstructures of the material. Shape memory effect is linked with martensitic transformation, which is a solid state phase transformation and occurs with the cooperative movement of atoms by means of lattice invariant shears on cooling from high-temperature parent phase. Lattice twinning and detwinning can be considered as elementary processes activated during the transformation. Thermally induced martensite occurs as martensite variants, in self-accommodating manner and consists of lattice twins. Also, this martensite is called the twinned martensite or multivariant martensite. Deformation of shape memory alloys in martensitic state proceeds through a martensite variant reorientation. The martensite variants turn into the reoriented single variants with deformation, and the reorientation process has great importance for the shape memory behavior. Copper based alloys exhibit this property in metastable β- phase region, which has DO3 –type ordered lattice in ternary case at high temperature, and these structures martensiticaly turn into the layered complex structures with lattice twinning mechanism, on cooling from high temperature parent phase region. The twinning occurs as martensite variants with lattice invariant shears in two opposite directions, <110 > -type directions on the {110}- type plane of austenite matrix. Lattice invariant shear is not uniform in copper based ternary alloys and gives rise to the formation of unusual layered structures, like 3R, 9R, or 18R depending on the stacking sequences on the close-packed planes of the ordered lattice. The unit cell and periodicity are completed through 18 atomic layers in case of 18R-structure. On the other hand, the deformed material recovers the original shape on heating above the austenite finish temperature. Meanwhile, the material returns to the twinned martensite structures (thermally induced martensite structure) in one way (irreversible) shape memory effect on cooling below the martensite finish temperature, whereas the material returns to the detwinned martensite structure (deformed martensite) in two-way (reversible) shape memory effect. Shortly one can say that the microstructural mechanisms, responsible for the shape memory effect are the twinning and detwinning processes as well as martensitic transformation. In the present contribution, x-ray diffraction, transmission electron microscopy (TEM) and differential scanning calorimetry (DSC) studies were carried out on two copper-based ternary alloys, CuZnAl, and CuAlMn.

Keywords: shape memory effect, martensitic transformation, twinning and detwinning, layered structures

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Bing Gu

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Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system.

Keywords: DBN, face recognition, light field, Lytro

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Authors: Jinjun Jiang, Lianzhong Chen, Rui Xu

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The accurate of model attitude angel plays an important role on the aerodynamic test results in the wind tunnel test. The original method applies the spherical coordinate system transformation to obtain attitude angel calculation.The model attitude angel is obtained by coordinate transformation and spherical surface mapping applying the nominal attitude angel (the balance attitude angel in the wind tunnel coordinate system) indicated by the mechanism. First, the coordinate transformation of this method is not only complex but also difficult to establish the transformed relationship between the space coordinate systems especially after many steps of coordinate transformation, moreover it cannot realize the iterative calculation of the interference relationship between attitude angels; Second, during the calculate process to solve the problem the arc is approximately used to replace the straight line, the angel for the tangent value, and the inverse trigonometric function is applied. Therefore, in the calculation of attitude angel, the process is complex and inaccurate, which can be solved approximately when calculating small attack angel. However, with the advancing development of modern aerodynamic unsteady research, the aircraft tends to develop high or super large attack angel and unsteadyresearch field.According to engineering practice and vector theory, the concept of vector angel coordinate systemis proposed for the first time, and the vector angel coordinate system of attitude angel is established.With the iterative correction calculation and avoiding the problem of approximate and inverse trigonometric function solution, the model attitude calculation process is carried out in detail, which validates that the calculation accuracy and accuracy of model attitude angels are improved.Based on engineering and theoretical methods, a vector angel coordinate systemis established for the first time, which gives the transformation and angel definition relations between different flight attitude coordinate systems, that can accurately calculate the attitude angel of the corresponding coordinate systemand determine its direction, especially in the channel coupling calculation, the calculation of the attitude angel between the coordinate systems is only related to the angel, and has nothing to do with the change order s of the coordinate system, whichsimplifies the calculation process.

Keywords: attitude angel, angel vector coordinate system, iterative calculation, spherical coordinate system, wind tunnel test

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Authors: Ju-Young Kim, Tae-Heon Moon, Jung-Hun Cho

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Urban regeneration projects have been actively promoted in Korea. In particular, Jeonju Hanok Village is evaluated as one of representative cases in terms of utilizing local cultural heritage sits in the urban regeneration project. However, recently, there has been a growing concern in this area, due to the ‘gentrification’, caused by the excessive commercialization and surging tourists. This trend was changing land and building use and resulted in the loss of identity of the region. In this regard, this study analyzed the land use transformation between 2010 and 2016 to identify the commercialization trend in Jeonju Hanok Village. In addition, it conducted SNS big data analysis on Jeonju Hanok Village from February 14th, 2016 to March 31st, 2016 to identify visitors’ awareness of the village. The study results demonstrate that rapid commercialization was underway, unlikely the initial intention, so that planners and officials in city government should reconsider the project direction and rebuild deliberate management strategies. This study is meaningful in that it analyzed the land use transformation and SNS big data to identify the current situation in urban regeneration area. Furthermore, it is expected that the study results will contribute to the vitalization of regeneration area.

Keywords: land use, SNS, text mining, urban regeneration

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Authors: Kemi Olalekan Oduntan

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Transformation is a strong word that portends a radical, structural and basic reappraisal of the basic assumptions that underline our economic reform and developmental efforts. The challenges before government are how to move the nation away from an oil-dominated economy, institute the basics for a private sector-driven economy, build the local economy on international best practices, transform a passive oil industry to a more pro-active one and reposition the country along the lines of a more decentralized federalism. But beyond this, Nigeria is faced with management and leadership challenges to contend with building an efficient and effective polity, inspiring a shared vision, remodeling a corrupt polity, redefining the essentials of transformational leadership and creating Nigerian dream that will inspire patriotism and commitment in the citizenry.

Keywords: economic, economic growth, patriotism, polity, transformational

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Authors: Mengjie Zhou

Abstract:

Under the rapid promotion of digital logistics, the old consumption space in cities is undergoing profound transformation and reconstruction. This article systematically analyzes the impact of digital logistics on existing consumer spaces in cities and how these spaces can adapt to and lead this change through distinct ‘spatial production’ models. The digital transformation of the logistics industry has significantly improved logistics efficiency and service quality while also putting forward new requirements for the form and function of consumer space. In this process, the old consumption space in cities not only faces the trend of material consumption transforming into spiritual consumption but also needs to face profound changes in consumer behavior patterns. Taking Fengying Xili in Changsha as an empirical case, this article explores in detail how it successfully transformed from a traditional consumption space to a modern cultural consumption space by introducing new business formats, optimizing spatial layout, and improving service quality while preserving its historical heritage. This case not only provides valuable practical experience for the transformation of old urban consumption spaces but also demonstrates the feasibility and potential of the new model of ‘spatial production’.

Keywords: digital logistics, urban consumption space, space production, urban renewal

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Authors: Geetha Baskaran, Andrzej Barjiela, Rong Qu

Abstract:

Motivation: Nurse scheduling is a complex combinatorial optimization problem. It is also known as NP-hard. It needs an efficient re-scheduling to minimize some trade-off of the measures of violation by reducing selected constraints to soft constraints with measurements of their violations. Problem Statement: In this paper, we extend our novel approach to solve the nurse scheduling problem by transforming it through Information Granulation. Approach: This approach satisfies the rules of a typical hospital environment based on a standard benchmark problem. Generating good work schedules has a great influence on nurses' working conditions which are strongly related to the level of a quality health care. Domain transformation that combines the strengths of operation research and artificial intelligence was proposed for the solution of the problem. Compared to conventional methods, our approach involves judicious grouping (information granulation) of shifts types’ that transforms the original problem into a smaller solution domain. Later these schedules from the smaller problem domain are converted back into the original problem domain by taking into account the constraints that could not be represented in the smaller domain. An Integer Programming (IP) package is used to solve the transformed scheduling problem by expending the branch and bound algorithm. We have used the GNU Octave for Windows to solve this problem. Results: The scheduling problem has been solved in the proposed formalism resulting in a high quality schedule. Conclusion: Domain transformation represents departure from a conventional one-shift-at-a-time scheduling approach. It offers an advantage of efficient and easily understandable solutions as well as offering deterministic reproducibility of the results. We note, however, that it does not guarantee the global optimum.

Keywords: domain transformation, nurse scheduling, information granulation, artificial intelligence, simulation

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Authors: Julio Albuja, David Zaldumbide

Abstract:

Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

Procedia PDF Downloads 362