Search results for: object segmentation

Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving army, moving convoys etc. The Radar operator selects one of the promising targets into Single Target Tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper we present a technique using mathematical and statistical methods like Fast Fourier Transformation (FFT) and Principal Component Analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, fft, principal component analysis, eigenvector, octave-notes, dsp

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Authors: Akiko Kondo, Akio Kondo

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We are facing regional problems to low birth rate and longevity in Japan. Under this situation, there are some local municipalities which lose their vitality. The aims of this study are to clarify the present state of local public transportation services in local municipalities and relation between local public transportation services and population quantitatively. We conducted a questionnaire survey concerning regional agenda in all local municipalities in Japan. We obtained responses concerning the present state of convenience in use of public transportation and local public transportation services. Based on the data gathered from the survey, it is apparent that we should some sort of measures concerning public transportation services. Convenience in use of public transportation becomes an object of public concern in many rural regions. It is also clarified that some local municipalities introduce a demand bus for the purpose of promotion of administrative and financial efficiency. They also introduce a demand taxi in order to secure transportation to weak people in transportation and eliminate of blank area related to public transportation services. In addition, we construct a population model which includes explanatory variables of present states of local public transportation services. From this result, we can clarify the relation between public transportation services and population quantitatively.

Keywords: public transportation, local municipality, regional analysis, regional issue

Procedia PDF Downloads 397

Authors: Manuela Nayantara Jeyaraj

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PostgreSQL is an Object-Relational Database Management System (ORDBMS) with an architecture that ensures optimal quality data management. But due to the shading growth of similar ORDBMS, PostgreSQL has not been renowned among the database user community. Despite having its features and in-built functionalities shadowed, PostgreSQL renders a vast range of utilities for data manipulation and hence calling for it to be upheld more among users. But introducing PostgreSQL in order to stimulate its advantageous features among users, mandates endorsing learn-ability as an add-on as the target groups considered consist of both amateur as well as professional PostgreSQL users. The scope of this paper deliberates providing easy contemplation of query formulations and flows through a visual editor designed according to user interface principles that standby to support every aspect of making PostgreSQL learn-able by self-operation and creation of queries within the visual editor. This paper tends to scrutinize the importance of choosing PostgreSQL as the working database environment, the visual perspectives that influence human behaviour and ultimately learning, the modes in which learn-ability can be provided via visualization and the advantages reaped by the implementation of the proposed system features.

Keywords: database, learn-ability, PostgreSQL, query, visual-editor

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Authors: Vaishali Joshi, Pallav Joshi

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The main objective of this study is to examine the fashion consumption behavior of females with respect to their age group. Differences were studied in the pre-purchase, purchase and post-purchase behavior of females belonging to three age cohorts such as Generation X, Generation Y, and Generation Z. Quantitative approach was used to conduct this research. Data was collected through structured questionnaire. The questionnaire consisted of three sections. Section one included a question of the source of information of purchasing fashion apparels which measure the pre-purchase behavior. Section two measures purchase behavior which included two questions: i. motivations for purchasing fashion apparel and ii. important attributes considered for purchasing fashion apparel. The last section included a question regarding disposal of fashion apparel which measures the post-purchase behavior. Hundred females were selected as the respondents for this study through convenience sampling in the fashion streets. They were categorized into three age groups and then the results were analyzed. Four hypotheses were developed after reviewing the existing literature. Regression analysis was conducted for testing the hypothesis. Hypothesis one was accepted which stated that ‘social influence’ as a source of information for purchasing fashion apparels decreases with age. Hypothesis two was accepted which suggested that motivation of ‘Attention seeking’ for purchasing fashion apparel decreases with age. Hypothesis three and four also accepted which suggested that the importance of ‘Quality’ and ‘Price’ increases with age but hypothesis five was rejected which suggested that the importance of ‘Fit’ increases with age and last but not the least hypothesis six was accepted which suggested that the ‘duration’ of using fashion apparel increases with age. Limitation of the study deals with the sample of only female respondents. Implication can be made from this research in the field of Fashion apparel industry with respect to consumer segmentation and better marketing approaches can be implemented by the marketers form this study. Further research can be concluded by including male respondents also.

Keywords: fashion, consumption behavior, age cohorts, motivation

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Authors: Jiří Barta, Teodor Baláž, Tomáš Ludík, Jiří. F. Urbánek

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This article deals with the outputs of project acronym ADAPTIV of Czech Defence Research Project. This Project solved tends to adaptive camouflage. The camouflage is concealment by means of disguise. Perceptive interface between recipient and camouflaged object is visualized by means of textile modular screens. Screens special light semi-permeability enables front/ back projection with nearly identical light parameters. Information permeability, towards illusion creation, must be controlled by the camouflage provider by means sophisticated and mastered illusion with perfect scenarios. The project ADAPTIV was primarily funded with the maximum possible use of COTS (Commercial-Off-The-Shelf) principle asks special definition of feasibility conditions, especially recipient space position. This paper deals with uses the ADAPTIV output with name DATAsam with modification for infrared camouflage. It is focused on active camouflage in infrared spectrum of emissivity at <8;14> μm for laboratory conditions. The main chapter provides basic experiments and testing physical properties needed for camouflage in infrared environment. The evaluation experiments revealed the possibility of use case in various types of camouflage.

Keywords: camouflage, ADAPTIV, infrared camouflage, computer-aided, COTS

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Authors: Tomáš Ludík, Jiří Barta, Sabina Chytilová, Josef Navrátil

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Emergency management is a discipline of dealing with and avoiding risks. Appropriate emergency management software allows better management of these risks and has a direct influence on reducing potential negative impacts. Although there are several emergency management software products in the Czech Republic, they cover user requirements from the emergency management field only partially. Therefore, the paper focuses on the issues of requirement analysis within development of emergency management software. Analysis of the current state describes the basic features and properties of user requirements for software development as well as basic methods and approaches for gathering these requirements. Then, the paper presents more specific mechanisms for requirement analysis based on chosen software development approach: structured, object-oriented or agile. Based on these experiences it is designed new methodology for requirement analysis. Methodology describes how to map user requirements comprehensively in the field of emergency management and thus reduce misunderstanding between software analyst and emergency manager. Proposed methodology was consulted with department of fire brigade and also has been applied in the requirements analysis for their current emergency management software. The proposed methodology has general character and can be used also in other specific areas during requirement analysis.

Keywords: emergency software, methodology, requirement analysis, stakeholders, use case diagram, user stories

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Authors: Shu-Yuan Lin, Tung-Chin Chou

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As products become increasingly similar in competitive markets, achieving product segmentation and differentiation through packaging design has become the primary task when designing retail product packaging. When the main focus of brand marketing is no longer the product itself, emotional marketing, such as the use of humorous packaging designs, may be employed to successfully promote the brand. Such efforts will capture the hearts of consumers, generate discussions, and allow the brand to leave a deep impression in consumers. In this study, snack packaging was used to develop a relationship model that illustrated the effect of humorous packaging designs on brand awareness and brand attitude. The study was divided into three stages: In the first stage, in-depth interviews and focus group interviews were conducted with experts to construct 24 indicators for assessing humorous packaging designs. In the second stage, survey questionnaires were distributed to a young consumer group; the results showed that the group had a high and low product involvement with chocolate and dried shredded squid, respectively. Humorous packaging designs were subsequently created for two snack types to produce a study sample of 12 different packaging. In the third stage, packaging designs were evaluated by obtaining scores for the consumers’ brand awareness, brand attitude, and perceived effects of the packaging designs. Finally, a relationship model was developed to show the effect of humorous packaging designs on brand awareness and brand attitude, confirming that two perceived effects of humorous packaging designs (i.e., ‘pleasant and emotionally healing’ and ‘connected to people’s daily life’) exhibited a significant and positive effect on ‘perceived brand value,’ where the effect of ‘pleasant and emotionally healing’ was the most significant. In addition, ‘pleasant and emotionally healing’ exerted a significant and positive effect on ‘brand purchase intention.’ Furthermore, packaging designs with humorous elements helped foster brand awareness.

Keywords: brand awareness, brand attitude, humorous design, packaging design

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Authors: Abdeljalil Métioui

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The purpose of this paper is to present the results of research on the conceptual representations of 128 Quebec (Canada) pre-service teachers enrolled in their third year of university in a program to train elementary teachers about heat and temperature. To identify their conceptual representations about heat and temperature, we constructed a multiple-choice questionnaire consisting of five questions. For each question, they had to explain their choice of an answer. At the methodological level, this step is essential to be able to identify the student conceptual representations. It should be noted that the selected questions were based: (1) on the works have done worldwide on primary and secondary students’ misconceptions about heat and temperature; (2) on the notions prescribed in the curriculum related to the physical world and (3) on student’s everyday contexts. As illustrations, the following are the erroneous conceptual representations identified in our analysis of the data collected: (1) The change of state of the matter does not require a constant temperature, (2) The temperature is a measure in degrees to indicate the level of heat of an object or person, (3) The mercury contained in a thermometer expands when it is heated so that the particles which constitute it expand and (4) The sensation of cold (or warm) is related to the difference in temperature. In conclusion, we will see that it is possible to develop situations of conflict, dealing specifically with the limits of the analogy between heat and temperature. These situations must consider the conceptual representations of the pre-service teachers, as well as the relevant scientific understanding of the concept of heat and temperature.

Keywords: conceptual representation, heat, temperature, pre-service teachers

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Authors: Kakhaber Djakeli

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For market leadership, iconic brands already start to establish their unique digital avatars into Metaverse and offer Non Fungible Tokens to their fans. Metaverse can be defined as an evolutionary step of Internet development. So if companies and brands use the internet, logically, they can find new solutions for them and their customers in Metaverse. Marketing and Management today must learn how to combine physical world activities with those either entitled as digital, virtual, and immersive. A “Phygital” Solution uniting physical and digital competitive activities of the company covering the questions about how to use virtual worlds for Brand Development and Non Fungible Tokens for more attractiveness soon will be most relevant question for Branding. Thinking comprehensively, we can entitle this type of branding as an Immersive one. As we see, the Immersive Brands give customers more mesmerizing feelings than traditional ones. Accordingly, the Branding can be divided by the company in its own understanding into two models: traditional and immersive. Immersive Branding being more directed to Sensorial challenges of Humans will be big job for International Universities in near future because they target the Generation - Z. To try to help those International Universities opening the door to the mesmerizing, immersive branding, the Marketing Research have been undertaken. The main goal of the study was to establish the model for Immersive Branding at International Universities and answer on many questions what logically arises in university life. The type of Delphi Surveys entitled as an Expert Studies was undertaken for one great mission, to help International Universities to open the opportunities to Phygital activities with reliable knowledge with Model of Immersive Branding. The Questionnaire sent to Experts of Education were covering professional type of questions from education to segmentation of customers, branding, attitude to students, and knowledge to Immersive Marketing. The research results being very interesting and encouraging enough to make author to establish the New Model of Immersive Experiential Branding for International Universities.

Keywords: branding, immersive marketing, students, university

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Authors: Ziwei Huang

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In order to promote Inventory development in Shenzhen, the market-oriented real estate development mode has occupied a dominant position in the urban renewal activities of Shenzhen. This research is based on the theory of role relationship and urban regime, taking the Mutoulong community as the research object. Carries on the case depth analysis found that: Under the situation of absence and dislocation of the government's role, land property rights disputes and lack of communication platforms is the main reason for the problems of nail households and market failures, and the long-term delay in the progress of old city reconstruction. Through the analysis of the cause of the transformation problem and the upper planning and interest coordination mechanism, the optimization strategy of the old city transformation is finally proposed as follows: the establishment of interest coordination platform, the risk assessment of the government's intervention in the preliminary construction of the land, the adaptive construction of laws and regulations, and the re-examination of the interest relationship between the government and the market.

Keywords: Shenzhen city, Mutoulong community, urban regeneration, urban regime theory, role relationship theory

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Ruth Shapira

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Introduction: The rapid urbanization of the last century confronts planners, regulatory bodies, developers and most of all the public with seemingly unsolved conflicts regarding values, capital, and wellbeing of the built and un-built urban space. There is an out of control change of scale of the urban form and of the rhythm of the urban life which has known no significant progress in the last 2-3 decades despite the on-growing urban population. It is the objective of this paper to analyze some of these fundamental issues through the case study of a relatively small town in the center of Israel (Kiryat-Ono, 36,000 inhabitants), unfold the deep structure of qualities versus disruptors, present some cure that we have developed to bridge over and humbly suggest a practice that may bring about a sustainable new urban environment based on timeless values of the past, an approach that can be generic for similar cases. Basic Methodologies:The object, the town of Kiryat Ono, shall be experimented upon in a series of four action processes: De-composition, Re-composition, the Centering process and, finally, Controlled Structural Disintegration. Each stage will be based on facts, analysis of previous multidisciplinary interventions on various layers – and the inevitable reaction of the OBJECT, leading to the conclusion based on innovative theoretical and practical methods that we have developed and that we believe are proper for the open ended network, setting the rules for the contemporary urban society to cluster by – thus – a new urban vocabulary based on the old structure of times passed. The Study: Kiryat Ono, was founded 70 years ago as an agricultural settlement and rapidly turned into an urban entity. In spite the massive intensification, the original DNA of the old small town was still deeply embedded, mostly in the quality of the public space and in the sense of clustered communities. In the past 20 years, the recent demand for housing has been addressed to on the national level with recent master plans and urban regeneration policies mostly encouraging individual economic initiatives. Unfortunately, due to the obsolete existing planning platform the present urban renewal is characterized by pressure of developers, a dramatic change in building scale and widespread disintegration of the existing urban and social tissue.Our office was commissioned to conceptualize two master plans for the two contradictory processes of Kiryat Ono’s future: intensification and conservation. Following a comprehensive investigation into the deep structures and qualities of the existing town, we developed a new vocabulary of conservation terms thus redefying the sense of PLACE. The main challenge was to create master plans that should offer a regulatory basis to the accelerated and sporadic development providing for the public good and preserving the characteristics of the place consisting of a tool box of design guidelines that will have the ability to reorganize space along the time axis in a sustainable way. In conclusion: The system of rules that we have developed can generate endless possible patterns making sure that at each implementation fragment an event is created, and a better place is revealed. It takes time and perseverance but it seems to be the way to provide a healthy and sustainable framework for the accelerated urbanization of our chaotic present.

Keywords: sustainable urban design, intensification, emergent urban patterns, sustainable housing, compact urban neighborhoods, sustainable regeneration, restoration, complexity, uncertainty, need for change, implications of legislation on local planning

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Authors: Siddhant Rao

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Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.

Keywords: breast cancer, mitotic count, machine learning, convolutional neural networks

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Authors: Hyun-Koo Kim, Yong-Hun Kim, Soo-Young Suk, Ju H. Park, Ho-Youl Jung

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This paper presents an effective speed hump detection process at the day-time. we focus only on round types of speed humps in the day-time dynamic road environment. The proposed speed hump detection scheme consists mainly of two process as stereo matching and speed hump detection process. Our proposed process focuses to speed hump detection process. Speed hump detection process consist of noise reduction step, data fusion step, and speed hemp detection step. The proposed system is tested on Intel Core CPU with 2.80 GHz and 4 GB RAM tested in the urban road environments. The frame rate of test videos is 30 frames per second and the size of each frame of grabbed image sequences is 1280 pixels by 670 pixels. Using object-marked sequences acquired with an on-vehicle camera, we recorded speed humps and non-speed humps samples. Result of the tests, our proposed method can be applied in real-time systems by computation time is 13 ms. For instance; our proposed method reaches 96.1 %.

Keywords: data fusion, round types speed hump, speed hump detection, surface filter

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Authors: Marc Angelo C. Balon, Maxine Joy A. Yongoyong

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Female masturbation has been an age-old controversy. In fact, it is not widely talked about specifically in the Philippines since the Filipino culture still preserves the space for conservativeness. Although, considering the numerous and emerging studies on female masturbation, this study will focus on the perspectives of secular and non-secular male groups with regard to female masturbation. The objectives of this study is to identify the perceptions of these male groups and their taking on considering women who masturbate as their sexual partner, as a sexual object, and as a life partner and lastly, to have a comparative analysis of the perceptions of these male groups drawing out their sense of meaning on the masturbation of women. The researchers made use of purposive sampling technique and interview guide questionnaire. The secular male group were psychology students while the non-secular male group was drawn from a Catholic Church seminary in Tagaytay City, Cavite. Results showed that the secular male group had scientific perspectives such as exploring the genitals, contradicting moral perspectives on masturbation as a regular practice, while the non-secular male groups had theological perspectives in accordance with the fundamental moral theology, moral perspectives and perspectives on masturbation as a regular practice. Moreover, men who came from the non-secular group highly believe that masturbation is immoral. Otherwise, men who came from the secular group noted that masturbation is primary physiological need.

Keywords: secular, non-secular, masturbation, comparative analysis

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Rafael Marinho de Andrade, Elcio Hideti Shiguemori, Rafael Duarte Coelho dos Santos

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Relying on computer vision, many clever things are possible in order to make the world safer and optimized on resource management, especially considering time and attention as manageable resources, once the modern world is very abundant in cameras from inside our pockets to above our heads while crossing the streets. Thus, automated solutions based on computer vision techniques to detect, react, or even prevent relevant events such as robbery, car crashes and traffic jams can be accomplished and implemented for the sake of both logistical and surveillance improvements. In this paper, we present an approach for vehicles’ abnormal behaviors detection from highway footages, in which the vectorial data of the vehicles’ displacement are extracted directly from surveillance cameras footage through object detection and tracking with a deep convolutional neural network and inserted into a long-short term memory neural network for behavior classification. The results show that the classifications of behaviors are consistent and the same principles may be applied to other trackable objects and scenarios as well.

Keywords: artificial intelligence, behavior detection, computer vision, convolutional neural networks, LSTM, highway footage

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Authors: Yunwei Tang, Linhai Jing, Hui Li, Qingjie Liu, Xiuxia Li, Qi Yan, Haifeng Ding

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The giant panda (Ailuropoda melanoleuca) is an endangered species, mainly live in central China, where bamboos act as the main food source of wild giant pandas. Knowledge of spatial distribution of bamboos therefore becomes important for identifying the habitat of giant pandas. There have been ongoing studies for mapping bamboos and other tree species using remote sensing. WorldView-2 (WV-2) is the first high resolution commercial satellite with eight Multi-Spectral (MS) bands. Recent studies demonstrated that WV-2 imagery has a high potential in classification of tree species. The advanced classification techniques are important for utilising high spatial resolution imagery. It is generally agreed that object-based image analysis is a more desirable method than pixel-based analysis in processing high spatial resolution remotely sensed data. Classifiers that use spatial information combined with spectral information are known as contextual classifiers. It is suggested that contextual classifiers can achieve greater accuracy than non-contextual classifiers. Thus, spatial correlation can be incorporated into classifiers to improve classification results. The study area is located at Wuyipeng area in Wolong, Sichuan Province. The complex environment makes it difficult for information extraction since bamboos are sparsely distributed, mixed with brushes, and covered by other trees. Extensive fieldworks in Wuyingpeng were carried out twice. The first one was on 11th June, 2014, aiming at sampling feature locations for geometric correction and collecting training samples for classification. The second fieldwork was on 11th September, 2014, for the purposes of testing the classification results. In this study, spectral separability analysis was first performed to select appropriate MS bands for classification. Also, the reflectance analysis provided information for expanding sample points under the circumstance of knowing only a few. Then, a spatially weighted object-based k-nearest neighbour (k-NN) classifier was applied to the selected MS bands to identify seven land cover types (bamboo, conifer, broadleaf, mixed forest, brush, bare land, and shadow), accounting for spatial correlation within classes using geostatistical modelling. The spatially weighted k-NN method was compared with three alternatives: the traditional k-NN classifier, the Support Vector Machine (SVM) method and the Classification and Regression Tree (CART). Through field validation, it was proved that the classification result obtained using the spatially weighted k-NN method has the highest overall classification accuracy (77.61%) and Kappa coefficient (0.729); the producer’s accuracy and user’s accuracy achieve 81.25% and 95.12% for the bamboo class, respectively, also higher than the other methods. Photos of tree crowns were taken at sample locations using a fisheye camera, so the canopy density could be estimated. It is found that it is difficult to identify bamboo in the areas with a large canopy density (over 0.70); it is possible to extract bamboos in the areas with a median canopy density (from 0.2 to 0.7) and in a sparse forest (canopy density is less than 0.2). In summary, this study explores the ability of WV-2 imagery for bamboo extraction in a mountainous region in Sichuan. The study successfully identified the bamboo distribution, providing supporting knowledge for assessing the habitats of giant pandas.

Keywords: bamboo mapping, classification, geostatistics, k-NN, worldview-2

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Authors: Ekaterina Lukianchenko

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The present article addresses the nuances of teaching English vocabulary to Russian-speaking students. The experiment involving 39 participants aged 17 to 21 proves that the key to understanding spoken coinages is not only the knowledge of their constituents, but rather the understanding of the context and co-text. The volunteers who took part knew the constituents, but did not know the meaning of the words. The assumption of the authors consists in the fact that the structure of the concept has a direct relation with the form of the particular vocabulary unit, but its form is secondary to its meaning, if the word is a spoken coinage, which is partly proved by the fact that in modern slang words have multiple meanings, as well as one notion can have various embodiments that have virtually nothing in common. The choice of vocabulary items that youngsters use is not exactly arbitrary, but, even if complex nominals are taken into consideration, whose meaning seems clear, as it looks like a sum of their constituents’ meanings, they are still impossible to understand without any context or co-text, as a lot of them are idiomatic, non-transparent. It is further explained what methods might be effective in teaching students how to deal with new words they encounter in real-life situations and how student’s knowledge of vocabulary might be enhanced.

Keywords: spoken language, cognitive linguistics, complex nominals, nominals with the incorporated object, concept, EFL, communicative language teaching

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Authors: Elisa Bertagna

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A frame analysis of the work done by various energy multinational companies concerning diversity issues and gender equality is presented. Documents of four multinational companies - two from Finland and two from Italy - have been studied. The array of companies’ documents includes data from their websites, policies and so on. The Finnish and Italian contexts have been chosen as a sample of North and South Europe, of 'advanced' and 'less advanced'. The aim of the analysis is to understand if and how human resource and diversity management in Finnish and Italian multinational energy companies communicate their activity towards the employees. Attention is given on how employees are reacting in their role and on the consequences of its social positioning. The findings of this essay are crucially important. They show how the companies in object tend to focus on the HR and DM positive actions towards female employees’ struggles since the industry is characterized by multinationals with male-dominated employees. In this way, other categories, which are also depicted as sensitive such as young and elderly people or foreigners, do not receive the same amount of attention. Consequently, power hierarchies can be found: 'women' as a social category are given more importance and space in the companies’ data than others. Consequently, the present work analysis reflects on possible struggles that such companies might be facing concerning gender biases and further diverse issues.

Keywords: energy, diversity, gender, multinationals, power hierarchies

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Authors: Vinay P., Srinivas K. S.

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Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown.

Keywords: low light image enhancement, real-time video, computer vision, machine learning

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Authors: Laura Fernanda Pedreros Araque, Sebastian Rivera Pardo

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The massive removal phenomena have been one of the most frequent natural disasters in the world, causing thousands of deaths, victims, damage to homes and diseases. In Pitalito, and Campoalegre department of Huila municipalities - Colombia, disasters have occurred due to various events such as high rainfall, earthquakes; it has caused landslides, floods, among others, affected the economy, the community, and transportation. For this reason, a study was carried out on the area’s most prone to suffer these phenomena to take preventive measures in favor of the protection of the population, the resources of management, and the planning of civil works. For the proposed object, the Mora-Varshon method was used, which allows classifying the degree of susceptibility to landslides in which the areas are found. Also, various factors or parameters were evaluated such as the soil moisture, lithology, slope, seismicity, and rain, each of these indicators were obtained using information from IDEAM, Servicio Geologico Colombiano (SGC) and using geographic information for geoprocessing in the Arcgis software to realize a mapping to indicate the susceptibility to landslides, classifying the areas of the municipalities such as very low, low, medium, moderate, high or very high.

Keywords: geographic information system, landslide, mass removal phenomena, Mora-Varshon method

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Authors: Dianxun Zheng, Wuxing Jing, Lin Hetong

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Optical remote sensing satellite, always running on the Sun-synchronous orbit, equipped laser warning equipment to alert CCD camera from laser attack. There have three ways to protect the CCD camera, closing the camera cover satellite attitude maneuver and satellite orbit avoidance. In order to enhance the safety of optical remote sensing satellite in orbit, this paper explores the strategy of satellite avoidance. The avoidance strategy is expressed as the evasion of pre-determined target points in the orbital coordinates of virtual satellite. The so-called virtual satellite is a passive vehicle which superposes a satellite at the initial stage of avoidance. The target points share the consistent cycle time and the same semi-major axis with the virtual satellite, which ensures the properties of the Sun-synchronous orbit remain unchanged. Moreover, to further strengthen the avoidance capability of satellite, it can perform multi-object avoid maneuvers. On occasions of fulfilling the orbit tasks of the satellite, the orbit can be restored back to virtual satellite through orbit maneuvers. There into, the avoid maneuvers adopts pulse guidance. and the fuel consumption is also optimized. The avoidance strategy discussed in this article is applicable to avoidance for optical remote sensing satellite when encounter the laser hostile attacks.

Keywords: optical remote sensing satellite, always running on the sun-synchronous

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Authors: Jamil Ahmed

Abstract:

Left to Right-Right Most (LR) parsing algorithm is a widely used algorithm of syntax analysis. It is contingent on a parsing table, whereas the parsing tables are extracted from the grammar. The parsing table specifies the actions to be taken during parsing. It requires that the parsing table should have no action conflicts for the same input symbol. This requirement imposes a condition on the class of grammars over which the LR algorithms work. However, there are grammars for which the parsing tables hold action conflicts. In such cases, the algorithm needs a capability of scanning (looking-ahead) next input symbols ahead of the current input symbol. In this paper, a ‘Left to Right’-‘Right Most’ parsing algorithm with lookahead capability is introduced. The 'look-ahead' capability in the LR parsing algorithm is the major contribution of this paper. The practicality of the proposed algorithm is substantiated by the parser implementation of the Context Free Grammar (CFG) of an already proposed programming language 'State Controlled Object Oriented Programming' (SCOOP). SCOOP’s Context Free Grammar has 125 productions and 192 item sets. This algorithm parses SCOOP while the grammar requires to ‘look ahead’ the input symbols due to action conflicts in its parsing table. Proposed LR parsing algorithm with lookahead capability can be viewed as an optimization of ‘Simple Left to Right’-‘Right Most’ (SLR) parsing algorithm.

Keywords: left to right-right most parsing, syntax analysis, bottom-up parsing algorithm

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Authors: Mikheil Kalmakhelidze

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Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

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Authors: Shi-Yu Xu

Abstract:

The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

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Authors: Tao Chen, ShiHua Liu, JiWei Zhang

Abstract:

Assembly of the proton exchange membrane fuel cells (PEMFC) has a very important influence on its performance and efficiency. The various components of PEMFC stack are usually locked and fixed by bolts. Locking bolt will cause the deformation of the bipolar plate and the other components, which will affect directly the deformation degree of the integral parts of the PEMFC as well as the performance of PEMFC. This paper focuses on the object of three-cell stack of PEMFC. Finite element simulation is used to investigate the deformation of bipolar plate caused by quantity and layout of bolts, bolt locking pressure, and bolt locking sequence, etc. Finally, we made a conclusion that the optimal combination packaging scheme was adopted to assemble the fuel cell stack. The scheme was in use of 3.8 MPa locking pressure imposed on the fuel cell stack, type Ⅱ of four locking bolts and longitudinal locking method. The scheme was obtained by comparatively analyzing the overall displacement contour of PEMFC stack, absolute displacement curve of bipolar plate along the given three paths in the Z direction and the polarization curve of fuel cell. The research results are helpful for the fuel cell stack assembly.

Keywords: bipolar plate, deformation, finite element simulation, fuel cell, locking bolt

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Authors: Richard O’Riordan, Saritha Unnikrishnan

Abstract:

Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Nawt Almutairi, Ramzi El-Haddadeh

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With the extensive increase in the number of online shops, loyalty becomes the most purpose for e-retailers by which they can maintain their exit customers and regular income instead of spending large deal of money to target new segmentation. To obtain customers’ loyalty e-marketers should firstly satisfy customers by providing a high quality of services that could fulfil their demand. They have to satisfy them to trust the web-site then increase their intention to re-visit it. This study intends to investigate to what extend the elements of e-service quality presented in the literature affect customers’ satisfaction and how these influences contribute to customers’ trust and loyalty. Three dimensions of service quality are estimated. The first element is web-site interactivity, which is perceived the quality of interactive support and the accessible communications-tool. The second aspect is security/privacy, which is perceived the quality of controlling security and privacy while transaction over the web-site. The third element is web-design that perceived a pleasant user interface with visual appealing. These elements present positive effects on shoppers’ satisfaction. Thus, To examine the proposed constructs of this research, some measurements scale-items adapted from similar prior studies. Survey data collected online from Saudi customers (n=106) were utilized to test the research hypotheses. After that, the hypotheses were analyzed by using a variety of regression tools. The analytical results of this study propose that perceived quality of interactivity and security/privacy affects customers’ satisfaction. As well as trust seems to be a substantial construct that highly affects loyalty in online shopping. This study provides a developed model to obtain a simple understanding of the series of customers’ loyalty in online shopping. One construct presenting in the research model is web-design appears to be not important antecedent of satisfaction (the path to loyalty) in online shopping.

Keywords: e-service, satisfaction, trust, loyalty

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Authors: Shreya Borthakur, Aastha Vartak

Abstract:

This Electroencephalogram (EEG) project on Rapid Visual Serial Processing (RSVP) paradigm explores the temporal dynamics of visual attention processing in response to rapidly presented visual stimuli. The study builds upon previous research that used real-world images in RSVP tasks to understand the emergence of object representations in the human brain. The objectives of the research include investigating the differences in accuracy and reaction times between 5 Hz and 20 Hz presentation rates, as well as examining the prominent brain waves, particularly alpha and beta waves, associated with the attention task. The pre-processing and data analysis involves filtering EEG data, creating epochs for target stimuli, and conducting statistical tests using MATLAB, EEGLAB, Chronux toolboxes, and R. The results support the hypotheses, revealing higher accuracy at a slower presentation rate, faster reaction times for less complex targets, and the involvement of alpha and beta waves in attention and cognitive processing. This research sheds light on how short-term memory and cognitive control affect visual processing and could have practical implications in fields like education.

Keywords: RSVP, attention, visual processing, attentional blink, EEG

Procedia PDF Downloads 65