Search results for: mapping algorithm

Authors: Panagiotis Roupas, Yota Passia

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This paper aims at establishing an index of design mechanisms - immanent in spatial objects - based on the affective capacity of their material formations. While spatial objects (design objects, buildings, urban configurations, etc.) are regarded as systems composed of interacting parts, within the premises of assemblage theory, their ability to affect and to be affected has not yet been mapped or sufficiently explored. This ability lies in excess, a latent potentiality they contain, not transcendental but immanent in their pre-subjective aesthetic power. As spatial structures are theorized as assemblages - composed of heterogeneous elements that enter into relations with one another - and since all assemblages are parts of larger assemblages, their components' ability to engage is contingent. We thus seek to unfold the mechanisms inherent in spatial objects that allow to the constituent parts of design assemblages to perpetually enter into new assemblages. To map architectural assemblage's affective ability, spatial objects are analyzed in two axes. The first axis focuses on the relations that the assemblage's material and expressive components develop in order to enter the assemblages. Material components refer to those material elements that an assemblage requires in order to exist, while expressive components includes non-linguistic (sense impressions) as well as linguistic (beliefs). The second axis records the processes known as a-signifying signs or a-signs, which are the triggering mechanisms able to territorialize or deterritorialize, stabilize or destabilize the assemblage and thus allow it to assemble anew. As a-signs cannot be isolated from matter, we point to their resulting effects, which without entering the linguistic level they are expressed in terms of intensity fields: modulations, movements, speeds, rhythms, spasms, etc. They belong to a molecular level where they operate in the pre-subjective world of perceptions, effects, drives, and emotions. A-signs have been introduced as intensities that transform the object beyond meaning, beyond fixed or known cognitive procedures. To that end, from an archive of more than 100 spatial objects by contemporary architects and designers, we have created an effective mechanisms index is created, where each a-sign is now connected with the list of effects it triggers and which thoroughly defines it. And vice versa, the same effect can be triggered by different a-signs, allowing the design object to lie in a perpetual state of becoming. To define spatial objects, A-signs are categorized in terms of their aesthetic power to affect and to be affected on the basis of the general categories of form, structure and surface. Thus, different part's degree of contingency are evaluated and measured and finally, we introduce as material information that is immanent in the spatial object while at the same time they confer no meaning; they only convey some information without semantic content. Through this index, we are able to analyze and direct the final form of the spatial object while at the same time establishing the mechanism to measure its continuous transformation.

Keywords: affective mechanisms index, architectural assemblages, a-signifying signs, cartography, virtual

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Authors: G. Shiha, S. Seif, W. Samir, K. Zalata

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Prospective Validation of the FibroTest Score in assessing Liver Fibrosis in Hepatitis C Infection with Genotype 4 FibroTest (FT) is non-invasive score of liver fibrosis that combines the quantitative results of 5 serum biochemical markers (alpha-2-macroglobulin, haptoglobin, apolipoprotein A1, gamma glutamyl transpeptidase (GGT) and bilirubin) and adjusted with the patient's age and sex in a patented algorithm to generate a measure of fibrosis. FT has been validated in patients with chronic hepatitis C (CHC) (Halfon et al., Gastroenterol. Clin Biol.( 2008), 32 6suppl 1, 22-39). The validation of fibro test ( FT) in genotype IV is not well studied. Our aim was to evaluate the performance of FibroTest in an independent prospective cohort of hepatitis C patients with genotype 4. Subject was 122 patients with CHC. All liver biopsies were scored using METAVIR system. Our fibrosis score(FT) were measured, and the performance of the cut-off score were done using ROC curve. Among patients with advanced fibrosis, the FT was identically matched with the liver biopsy in 18.6%, overestimated the stage of fibrosis in 44.2% and underestimated the stage of fibrosis in 37.7% of cases. Also in patients with no/mild fibrosis, identical matching was detected in 39.2% of cases with overestimation in 48.1% and underestimation in 12.7%. So, the overall results of the test were identical matching, overestimation and underestimation in 32%, 46.7% and 21.3% respectively. Using ROC curve it was found that (FT) at the cut-off point of 0.555 could discriminate early from advanced stages of fibrosis with an area under ROC curve (AUC) of 0.72, sensitivity of 65%, specificity of 69%, PPV of 68%, NPV of 66% and accuracy of 67%. As FibroTest Score overestimates the stage of advanced fibrosis, it should not be considered as a reliable surrogate for liver biopsy in hepatitis C infection with genotype 4.

Keywords: fibrotest, chronic Hepatitis C, genotype 4, liver biopsy

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Authors: Indu Chandran, Shubham Sharma, Rohan Mehta, Vipin Kizheppatt

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Unmanned aerial vehicles are increasingly being explored as the most promising solution to disaster monitoring, assessment, and recovery. Current relief operations heavily rely on intelligent robot swarms to capture the damage caused, provide timely rescue, and create road maps for the victims. To perform these time-critical missions, efficient path planning that ensures quick coverage of the area is vital. This study aims to develop a technically balanced approach to provide maximum coverage of the affected area in a minimum time using the optimal number of UAVs. A coverage trajectory is designed through area decomposition and task assignment. To perform efficient and autonomous coverage mission, solution to a TSP-based optimization problem using meta-heuristic approaches is designed to allocate waypoints to the UAVs of different flight capacities. The study exploits multi-agent simulations like PX4-SITL and QGroundcontrol through the ROS framework and visualizes the dynamics of UAV deployment to different search paths in a 3D Gazebo environment. Through detailed theoretical analysis and simulation tests, we illustrate the optimality and efficiency of the proposed methodologies.

Keywords: area coverage, coverage path planning, heuristic algorithm, mission monitoring, optimization, task assignment, unmanned aerial vehicles

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Dessy Pinem, Rahmadian Sembiring, Adanil Bushra

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Medan Belawan is one of the subdistricts of 21 districts in Medan. Medan Belawan Sub-district is directly adjacent to the Malacca Strait in the North. Due to its direct border with the Malacca Strait, the problem in this sub-district, which has continued for many years, is a flood of rob. In 2015, rob floods inundated Sicanang urban village, Belawan I urban village, Belawan Bahagia urban village and Bagan Deli village. The extent of inundation in the flood of rob that occurred in September 2015 reached 540, 938 ha. Rob flood is a phenomenon where the sea water is overflowing into the mainland. Rob floods can also be interpreted as a puddle of water on the coastal land that occurs when the tidal waters. So this phenomenon will inundate parts of the coastal plain or lower place of high tide sea level. Rob flood is a daily disaster faced by the residents in the district of Medan Belawan. Rob floods can happen every month and last for a week. The flood is not only the residents' houses, the flood also soaked the main road to Belawan Port reaching 50 cm. To deal with the problems caused by the flood and to prepare coastal communities to face the character of coastal areas, it is necessary to know the vulnerability of the people who are always the victims of the rob flood. Are the people of Medan Belawan sub-district, especially in the flood-affected villages, able to cope with the consequences of the floods? To answer this question, it is necessary to assess the vulnerability of the Belawan District community in the face of the flood disaster. This research is descriptive, qualitative and quantitative. Data were collected by observation, interview and questionnaires in 4 urban villages often affected by rob flood. The vulnerabilities measured are physical, economic, social, environmental, organizational and motivational vulnerabilities. For vulnerability in the physical field, the data collected is the distance of the building, floor area ratio, drainage, and building materials. For economic vulnerability, data collected are income, employment, building ownership, and insurance ownership. For the vulnerability in the social field, the data collected is education, number of family members, children, the elderly, gender, training for disasters, and how to dispose of waste. For the vulnerability in the field of organizational data collected is the existence of organizations that advocate for the victims, their policies and laws governing the handling of tidal flooding. The motivational vulnerability is seen from the information center or question and answer about the rob flood, and the existence of an evacuation plan or path to avoid disaster or reduce the victim. The results of this study indicate that most people in Medan Belawan sub-district have a high-level vulnerability in physical, economic, social, environmental, organizational and motivational fields. They have no access to economic empowerment, no insurance, no motivation to solve problems and only hope to the government, not to have organizations that support and defend them, and have physical buildings that are easily destroyed by rob floods.

Keywords: disaster, rob flood, Medan Belawan, vulnerability

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Authors: Ki Hwa Lee, Eunsu Kang, Jae Hong Park

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Background: Interventional neuroradiology (INR) has been a rapidly growing and evolving neurosurgical part during the past few decades. Sevoflurane and propofol are both suitable anesthetics for INR procedure. Monitoring of depth of anesthesia is being used very widely. SEDLine™ monitor, a 4-channel processed EEG monitor, uses a proprietary algorithm to analyze the raw EEG signal and displays the Patient State Index (PSI) values. There are only a fewer studies examining the PSI in the neuro-anesthesia. We aimed to investigate the difference of PSI values and hemodynamic variables between sevoflurane and propofol anesthesia during INR procedure. Methods: We reviewed the medical records of patients who scheduled to undergo embolization of non-ruptured intracranial aneurysm by a single operator from May 2013 to December 2014, retrospectively. Sixty-five patients were categorized into two groups; sevoflurane (n = 33) vs propofol (n = 32) group. The PSI values, hemodynamic variables, and the use of hemodynamic drugs were analyzed. Results: Significant differences were seen between PSI values obtained during different perioperative stages in both two groups (P < 0.0001). The PSI values of propofol group were lower than that of sevoflurane group during INR procedure (P < 0.01). The patients in propofol group had more prolonged time of extubation and more phenylephrine requirement than sevoflurane group (p < 0.05). Anti-hypertensive drug was more administered to the patients during extubation in sevoflurane group (p < 0.05). Conclusions: The PSI can detect depth of anesthesia and changes of concentration of anesthetics during INR procedure. Extubation was faster in sevoflurane group, but smooth recovery was shown in propofol group.

Keywords: interventional neuroradiology, patient state index, propofol, sevoflurane

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Authors: Songdi Li, Louise Spry, Tony Woodall

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Nowadays, Corporate Social responsibility (CSR) is becoming a buzz word, and more and more academics are putting efforts on CSR studies. It is believed that CSR could influence Corporate Reputation (CR), and they hold a favourable view that CSR leads to a positive CR. To be specific, the CSR related activities in the reputational context have been regarded as ways that associate to excellent financial performance, value creation, etc. Also, it is argued that CSR and CR are two sides of one coin; hence, to some extent, doing CSR is equal to establishing a good reputation. Still, there is no consensus of the CSR-CR relationship in the literature; thus, a systematic literature review is highly in need. This research conducts a systematic literature review with both bibliometric and content analysis. Data are selected from English language sources, and academic journal articles only, then, keyword combinations are applied to identify relevant sources. Data from Scopus and WoS are gathered for bibliometric analysis. Scopus search results were saved in RIS and CSV formats, and Web of Science (WoS) data were saved in TXT format and CSV formats in order to process data in the Bibexcel software for further analysis which later will be visualised by the software VOSviewer. Also, content analysis was applied to analyse the data clusters and the key articles. In terms of the topic of CSR-CR, this literature review with bibliometric analysis has made four achievements. First, this paper has developed a systematic study which quantitatively depicts the knowledge structure of CSR and CR by identifying terms closely related to CSR-CR (such as ‘corporate governance’) and clustering subtopics emerged in co-citation analysis. Second, content analysis is performed to acquire insight on the findings of bibliometric analysis in the discussion section. And it highlights some insightful implications for the future research agenda, for example, a psychological link between CSR-CR is identified from the result; also, emerging economies and qualitative research methods are new elements emerged in the CSR-CR big picture. Third, a multidisciplinary perspective presents through the whole bibliometric analysis mapping and co-word and co-citation analysis; hence, this work builds a structure of interdisciplinary perspective which potentially leads to an integrated conceptual framework in the future. Finally, Scopus and WoS are compared and contrasted in this paper; as a result, Scopus which has more depth and comprehensive data is suggested as a tool for future bibliometric analysis studies. Overall, this paper has fulfilled its initial purposes and contributed to the literature. To the author’s best knowledge, this paper conducted the first literature review of CSR-CR researches that applied both bibliometric analysis and content analysis; therefore, this paper achieves its methodological originality. And this dual approach brings advantages of carrying out a comprehensive and semantic exploration in the area of CSR-CR in a scientific and realistic method. Admittedly, its work might exist subjective bias in terms of search terms selection and paper selection; hence triangulation could reduce the subjective bias to some degree.

Keywords: corporate social responsibility, corporate reputation, bibliometric analysis, software program

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Gabriela Kloudova, Miloslav Stehlik, Peter Sos

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The quality of sleep can affect combat performance where the vigilance, accuracy and reaction time are a decisive factor. In the present study, airborne and special units are measured on duty using actigraphy fingerprint scoring algorithm and QEEG (quantitative EEG). Actigraphic variables of interest will be: mean nightly sleep duration, mean napping duration, mean 24-h sleep duration, mean sleep latency, mean sleep maintenance efficiency, mean sleep fragmentation index, mean sleep onset time, mean sleep offset time and mean midpoint time. In an attempt to determine the individual somnotype of each subject, the data like sleep pattern, chronotype (morning and evening lateness), biological need for sleep (daytime and anytime sleepability) and trototype (daytime and anytime wakeability) will be extracted. Subsequently, a series of recommendations will be included in the training plan based on daily routine, timing of the day and night activities, duration of sleep and the number of sleeping blocks in a defined time. The aim of these modifications in the training plan is to reduce day-time sleepiness, improve vigilance, attention, accuracy, speed of the conducted tasks and to optimize energy supplies. Regular improvement of the training supposed to have long-term neurobiological consequences including neuronal activity changes measured by QEEG. Subsequently, that should enhance cognitive functioning in subjects assessed by the digital cognitive test batteries and improve their overall performance.

Keywords: sleep quality, combat performance, actigraph, somnotype

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Shailendra M. Pradhan, Upendra M. Pradhan

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Disasters are perennial features of human civilization. The recurring earthquakes, floods, cyclones, among others, that result in massive loss of lives and devastation, is a grim reminder of the fact that, despite all our success stories of development, and progress in science and technology, human society is perennially at risk to disasters. The apparent threat of climate change and global warming only severe our disaster risks. Darjeeling hills, situated along Eastern Himalayan region of India, and famous for its three Ts – tea, tourism and toy-train – is also equally notorious for its disasters. The recurring landslides and earthquakes, the cyclone Aila, and the Ambootia landslides, considered as the largest landslide in Asia, are strong evidence of the vulnerability of Darjeeling hills to natural disasters. Given its geographical location along the Hindu-Kush Himalayas, the region is marked by rugged topography, geo-physically unstable structure, high-seismicity, and fragile landscape, making it prone to disasters of different kinds and magnitudes. Most of the studies on disasters in Darjeeling hills are, however, scientific and geographical in orientation that focuses on the underlying geological and physical processes to the neglect of social and political conditions. This has created a tendency among the researchers and policy-makers to endorse and promote a particular type of discourse that does not consider the social and political aspects of disasters in Darjeeling hills. Disaster, this paper argues, is a complex phenomenon, and a result of diverse factors, both physical and human. The hazards caused by the physical and geological agents, and the vulnerabilities produced and rooted in political, economic, social and cultural structures of a society, together result in disasters. In this sense, disasters are as much a result of political and economic conditions as it is of physical environment. The human aspect of disasters, therefore, compels us to address intricating social and political challenges that ultimately determine our resilience and vulnerability to disasters. Set within the above milieu, the aims of the paper are twofold: a) to provide a political and sociological account of disasters in Darjeeling hills; and, b) to identify and address the root causes of its vulnerabilities to disasters. In situating disasters in Darjeeling Hills, the paper adopts the Pressure and Release Model (PAR) that provides a theoretical insight into the study of social and political aspects of disasters, and to examine myriads of other related issues therein. The PAR model conceptualises risk as a complex combination of vulnerabilities, on the one hand, and hazards, on the other. Disasters, within the PAR framework, occur when hazards interact with vulnerabilities. The root causes of vulnerability, in turn, could be traced to social and political structures such as legal definitions of rights, gender relations, and other ideological structures and processes. In this way, the PAR model helps the present study to identify and unpack the root causes of vulnerabilities and disasters in Darjeeling hills that have largely remained neglected in dominant discourses, thereby providing a more nuanced and sociologically sensitive understanding of disasters.

Keywords: Darjeeling, disasters, PAR, vulnerabilities

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Authors: Jingjie Li, Wenjie Hu

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Phraseological units in academic English texts have been a central focus in recent corpus linguistic research. A wide variety of phraseological units have been explored, including collocations, chunks, lexical bundles, patterns, semantic sequences, etc. This paper describes a special category of clause-level phraseological units, namely, Characteristic Sentence Stems (CSSs), with a view to describing their defining criteria and extraction method. CSSs are contiguous lexico-grammatical sequences which contain a subject-predicate structure and which are frame expressions characteristic of academic writing. The extraction of CSSs consists of six steps: Part-of-speech tagging, n-gram segmentation, structure identification, significance of occurrence calculation, text range calculation, and overlapping sequence reduction. Significance of occurrence calculation is the crux of this study. It includes the computing of both the internal association and the boundary independence of a CSS and tests the occurring significance of the CSS from both inside and outside perspectives. A new normalization algorithm is also introduced into the calculation of LocalMaxs for reducing overlapping sequences. It is argued that many sentence stems are so recurrent in academic texts that the most typical of them have become the habitual ways of making meaning in academic writing. Therefore, studies of CSSs could have potential implications and reference value for academic discourse analysis, English for Academic Purposes (EAP) teaching and writing.

Keywords: characteristic sentence stem, extraction method, phraseological unit, the statistical measure

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Authors: Ahmed Fradi

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Nowadays, recent technological advances in the acquisition, modeling, and processing of three-dimensional (3D) objects data lead to the creation of models stored in huge databases, which are used in various domains such as computer vision, augmented reality, game industry, medicine, CAD (Computer-aided design), 3D printing etc. On the other hand, the industry is currently benefiting from powerful modeling tools enabling designers to easily and quickly produce 3D models. The great ease of acquisition and modeling of 3D objects make possible to create large 3D models databases, then, it becomes difficult to navigate them. Therefore, the indexing of 3D objects appears as a necessary and promising solution to manage this type of data, to extract model information, retrieve an existing model or calculate similarity between 3D objects. The objective of the proposed research is to develop a framework allowing easy and fast access to 3D objects in a CAD models database with specific indexing algorithm to find objects similar to a reference model. Our main objectives are to study existing methods of similarity calculation of 3D objects (essentially shape-based methods) by specifying the characteristics of each method as well as the difference between them, and then we will propose a new approach for indexing and comparing 3D models, which is suitable for our case study and which is based on some previously studied methods. Our proposed approach is finally illustrated by an implementation, and evaluated in a professional context.

Keywords: CAD, 3D object retrieval, shape based retrieval, similarity calculation

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Authors: Masoud Rabbani, Majede Smizadeh, Hamed Farrokhi-Asl

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We study jointly determining prices and production in a multiple period horizon under a general non-stationary stochastic demand with continuous prices. In some periods we need to increase capacity of production to satisfy demand. This paper presents a model to aid multi-period production capacity planning by quantifying the trade-off between product quality and production cost. The product quality is estimated as the statistical variation from the target performances obtained from the output tolerances of the production machines that manufacture the components. We consider different tolerance for different machines that use to increase capacity. The production cost is estimated as the total cost of owning and operating a production facility during the planning horizon.so capacity planning has cost that impact on price. Pricing products often turns out to be difficult to measure them because customers have a reservation price to pay that impact on price and demand. We decide to determine prices and production for periods after enhance capacity and consider reservation price to determine price. First we use an algorithm base on fuzzy set of the optimal objective function values to determine capacity planning by determine maximize interval from upper bound in minimum objectives and define weight for objectives. Then we try to determine inventory and pricing policies. We can use a lemma to solve a problem in MATLAB and find exact answer.

Keywords: price policy, inventory policy, capacity planning, product quality, epsilon -constraint

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Authors: A. A. Balkhi, G. M. Mir, Javid A. Sheikh

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To cater the demands of increasing traffic with new applications the cellular mobile networks face new changes in deployment in infrastructure for making cellular networks heterogeneous. To reduce overhead processing the densely deployed cells require smart behavior with self-organizing capabilities with high adaptation to the neighborhood. We propose self-organization of unused resources usually excessive unused channels of neighbouring cells with densely populated cells to reduce handover failure rates. The neighboring cells share unused channels after fulfilling some conditional candidature criterion using threshold values so that they are not suffered themselves for starvation of channels in case of any abrupt change in traffic pattern. The cells are classified as ‘red’, ‘yellow’, or ‘green’, as per the available channels in cell which is governed by traffic pattern and thresholds. To combat the deficiency of channels in red cell, migration of unused channels from under-loaded cells, hierarchically from the qualified candidate neighboring cells is explored. The resources are returned back when the congested cell is capable of self-contained traffic management. In either of the cases conditional sharing of resources is executed for enhanced traffic management so that User Equipment (UE) is provided uninterrupted services with high Quality of Service (QoS). The fuzzy logic-based simulation results show that the proposed algorithm is efficiently in coincidence with improved successful handoffs.

Keywords: candidate cell, channel sharing, fuzzy logic, handover, small cells

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Authors: Naydelis Brito Suárez, Deni Librado Torres Román, Fernando Hermosillo Reynoso

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Automatic detection and generation of a dynamic ROI (Region of Interest) in vehicle traffic surveillance videos based on a static camera in Intelligent Transportation Systems is challenging for computer vision-based systems. The dynamic ROI, being a changing ROI, should capture any other moving object located outside of a static ROI. In this work, the video is represented by a Tensor model composed of a Background and a Foreground Tensor, which contains all moving vehicles or objects. The values of each pixel over a time interval are represented by time series, and some pixel rows were selected. This paper proposes a pixel entropy-based algorithm for automatic detection and generation of a dynamic ROI in traffic videos under the assumption of two types of theoretical pixel entropy behaviors: (1) a pixel located at the road shows a high entropy value due to disturbances in this zone by vehicle traffic, (2) a pixel located outside the road shows a relatively low entropy value. To study the statistical behavior of the selected pixels, detecting the entropy changes and consequently moving objects, Shannon, Tsallis, and Approximate entropies were employed. Although Tsallis entropy achieved very high results in real-time, Approximate entropy showed results slightly better but in greater time.

Keywords: convex hull, dynamic ROI detection, pixel entropy, time series, moving objects

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Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

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Authors: Teresa Azevedo PerdicoúLis, Paulo Lopes Dos Santos

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The conceptualisation of the problem of network optimisation as a noncooperative game sets up a holistic interactive approach that brings together different network features (e.g., com-pressor stations, sources, and pipelines, in the gas context) where the optimisation objectives are different, and a single optimisation procedure becomes possible without having to feed results from diverse software packages into each other. A mathematical model of this type, where independent entities take action, offers the ideal modularity and subsequent problem decomposition in view to design a decentralised algorithm to optimise the operation and management of the network. In a game framework, compressor stations and sources are under-stood as players which communicate through network connectivity constraints–the pipeline model. That is, in a scheme similar to tatonnementˆ, the players appoint their best settings and then interact to check for network feasibility. The devolved degree of network unfeasibility informs the players about the ’quality’ of their settings, and this two-phase iterative scheme is repeated until a global optimum is obtained. Due to network transients, its optimisation needs to be assessed at different points of the control interval. For this reason, the proposed approach to optimisation has two stages: (i) the first stage computes along the period of optimisation in order to fulfil the requirement just mentioned; (ii) the second stage is initialised with the solution found by the problem computed at the first stage, and computes in the end of the period of optimisation to rectify the solution found at the first stage. The liability of the proposed scheme is proven correct on an abstract prototype and three example networks.

Keywords: connectivity matrix, gas network optimisation, large-scale, noncooperative game, system decomposition

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Authors: Maryam Ghoreishi, Christian Larsen

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In this paper, we investigate the effect of stochastic inputs on problem of joint optimal pricing and lot-sizing decisions where the inventory cycle is divided into advance and spot sales periods. During the advance sales period, customer can make reservations while customer with reservations can cancel their order. However, during the spot sales period customers receive the order as soon as the order is placed, but they cannot make any reservation or cancellation during that period. We assume that the inter arrival times during the advance sales and spot sales period are exponentially distributed where the arrival rate is decreasing function of price. Moreover, we assume that the number of cancelled reservations is binomially distributed. In addition, we assume that deterioration process follows an exponential distribution. We investigate two cases. First, we consider two-state case where we find the optimal price during the spot sales period and the optimal price during the advance sales period. Next, we develop a generalized case where we extend two-state case also to allow dynamic prices during the spot sales period. We apply the Markov decision theory in order to find the optimal solutions. In addition, for the generalized case, we apply the policy iteration algorithm in order to find the optimal prices, the optimal lot-size and maximum advance sales amount.

Keywords: inventory control, pricing, Markov decision theory, advance sales system

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Authors: Mayank Mundhra, Chester Rebeiro

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Cryptocurrencies are rapidly finding wide application in areas such as Real Time Gross Settlements and Payments Systems. Ripple is a cryptocurrency that has gained prominence with banks and payment providers. It solves the Byzantine General’s Problem with its Ripple Protocol Consensus Algorithm (RPCA), where each server maintains a list of servers, called Unique Node List (UNL) that represents the network for the server, and will not collectively defraud it. The server believes that the network has come to a consensus when members of the UNL come to a consensus on a transaction. In this paper we improve Ripple to achieve better speed, security, last mile connectivity and ease of use. We implement guidelines and automated systems for building and maintaining UNLs for resilience, robustness, improved security, and efficient information propagation. We enhance the system so as to ensure that each server receives information from across the whole network rather than just from the UNL members. We also introduce the paradigm of UNL overlap as a function of information propagation and the trust a server assigns to its own UNL. Our design not only reduces vulnerabilities such as eclipse attacks, but also makes it easier to identify malicious behaviour and entities attempting to fraudulently Double Spend or stall the system. We provide experimental evidence of the benefits of our approach over the current Ripple scheme. We observe ≥ 4.97x and 98.22x in speedup and success rate for information propagation respectively, and ≥ 3.16x and 51.70x in speedup and success rate in consensus.

Keywords: Ripple, Kelips, unique node list, consensus, information propagation

Procedia PDF Downloads 145

Authors: Niamh Herlihy, Helen Fischer, Rainer Sauerborn, Anneliese Depoux, Avner Bar-Hen, Antoine Flauhault, Stefanie Schütte

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In the recent decades, there has been an increase in the number of publications both in the scientific and grey literature on the potential health risks associated with climate change. Though interest in climate change and health is growing, there are still many gaps to adequately assess our future health needs in a warmer world. Generating a greater understanding of the health impacts of climate change could be a key step in inciting the changes necessary to decelerate global warming and to target new strategies to mitigate the consequences on health systems. A long term and broad overview of existing scientific literature in the field of climate change and health is currently missing in order to ensure that all priority areas are being adequately addressed. We conducted a scoping review of published peer-reviewed literature on climate change and health from two large databases, PubMed and Web of Science, between 1990 and 2015. A scoping review allowed for a broad analysis of this complex topic on a meta-level as opposed to a thematically refined literature review. A detailed search strategy including specific climate and health terminology was used to search the two databases. Inclusion and exclusion criteria were applied in order to capture the most relevant literature on the human health impact of climate change within the chosen timeframe. Two reviewers screened the papers independently and any differences arising were resolved by a third party. Data was extracted, categorized and coded both manually and using R software. Analytics and infographics were developed from results. There were 7269 articles identified between the two databases following the removal of duplicates. After screening of the articles by both reviewers 3751 were included. As expected, preliminary results indicate that the number of publications on the topic has increased over time. Geographically, the majority of publications address the impact of climate change and health in Europe and North America, This is particularly alarming given that countries in the Global South will bear the greatest health burden. Concerning health outcomes, infectious diseases, particularly dengue fever and other mosquito transmitted infections are the most frequently cited. We highlight research gaps in certain areas e.g climate migration and mental health issues. We are developing a database of the identified climate change and health publications and are compiling a report for publication and dissemination of the findings. As health is a major co-beneficiary to climate change mitigation strategies, our results may serve as a useful source of information for research funders and investors when considering future research needs as well as the cost-effectiveness of climate change strategies. This study is part of an interdisciplinary project called 4CHealth that confronts results of the research done on scientific, political and press literature to better understand how the knowledge on climate change and health circulates within those different fields and whether and how it is translated to real world change.

Keywords: climate change, health, review, mapping

Procedia PDF Downloads 317

Authors: Riccardo Fratini, Riccardo Santini, Jacopo Serafini, Massimo Gennaretti, Stefano Panzieri

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This paper presents a nonlinear differential model, for a three-bladed horizontal axis wind turbine (HAWT) suited for control applications. It is based on a 8-dofs, lumped parameters structural dynamics coupled with a quasi-steady sectional aerodynamics. In particular, using the Euler-Lagrange Equation (Energetic Variation approach), the authors derive, and successively validate, such model. For the derivation of the aerodynamic model, the Greenbergs theory, an extension of the theory proposed by Theodorsen to the case of thin airfoils undergoing pulsating flows, is used. Specifically, in this work, the authors restricted that theory under the hypothesis of low perturbation reduced frequency k, which causes the lift deficiency function C(k) to be real and equal to 1. Furthermore, the expressions of the aerodynamic loads are obtained using the quasi-steady strip theory (Hodges and Ormiston), as a function of the chordwise and normal components of relative velocity between flow and airfoil Ut, Up, their derivatives, and section angular velocity ε˙. For the validation of the proposed model, the authors carried out open and closed-loop simulations of a 5 MW HAWT, characterized by radius R =61.5 m and by mean chord c = 3 m, with a nominal angular velocity Ωn = 1.266rad/sec. The first analysis performed is the steady state solution, where a uniform wind Vw = 11.4 m/s is considered and a collective pitch angle θ = 0.88◦ is imposed. During this step, the authors noticed that the proposed model is intrinsically periodic due to the effect of the wind and of the gravitational force. In order to reject this periodic trend in the model dynamics, the authors propose a collective repetitive control algorithm coupled with a PD controller. In particular, when the reference command to be tracked and/or the disturbance to be rejected are periodic signals with a fixed period, the repetitive control strategies can be applied due to their high precision, simple implementation and little performance dependency on system parameters. The functional scheme of a repetitive controller is quite simple and, given a periodic reference command, is composed of a control block Crc(s) usually added to an existing feedback control system. The control block contains and a free time-delay system eτs in a positive feedback loop, and a low-pass filter q(s). It should be noticed that, while the time delay term reduces the stability margin, on the other hand the low pass filter is added to ensure stability. It is worth noting that, in this work, the authors propose a phase shifting for the controller and the delay system has been modified as e^(−(T−γk)), where T is the period of the signal and γk is a phase shifting of k samples of the same periodic signal. It should be noticed that, the phase shifting technique is particularly useful in non-minimum phase systems, such as flexible structures. In fact, using the phase shifting, the iterative algorithm could reach the convergence also at high frequencies. Notice that, in our case study, the shifting of k samples depends both on the rotor angular velocity Ω and on the rotor azimuth angle Ψ: we refer to this controller as a spatial repetitive controller. The collective repetitive controller has also been coupled with a C(s) = PD(s), in order to dampen oscillations of the blades. The performance of the spatial repetitive controller is compared with an industrial PI controller. In particular, starting from wind speed velocity Vw = 11.4 m/s the controller is asked to maintain the nominal angular velocity Ωn = 1.266rad/s after an instantaneous increase of wind speed (Vw = 15 m/s). Then, a purely periodic external disturbance is introduced in order to stress the capabilities of the repetitive controller. The results of the simulations show that, contrary to a simple PI controller, the spatial repetitive-PD controller has the capability to reject both external disturbances and periodic trend in the model dynamics. Finally, the nominal value of the angular velocity is reached, in accordance with results obtained with commercial software for a turbine of the same type.

Keywords: wind turbines, aeroelasticity, repetitive control, periodic systems

Procedia PDF Downloads 249

Authors: Szu-Kai Hsu, Po-Ching Lin

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As more and more multi-core processors emerge, traditional sequential programming paradigm no longer suffice. Yet only few modern dynamic programming languages can leverage such advantage. Ruby, for example, despite its wide adoption, only includes threads as a simple parallel primitive. The global virtual machine lock of official Ruby runtime makes it impossible to exploit full parallelism. Though various alternative Ruby implementations do eliminate the global virtual machine lock, they only provide developers dated locking mechanism for data synchronization. However, traditional locking mechanism error-prone by nature. Software Transactional Memory is one of the promising alternatives among others. This paper introduces a new virtual machine: GobiesVM to provide a native software transactional memory based solution for dynamic programming languages to exploit parallelism. We also proposed a simplified variation of Transactional Locking II algorithm. The empirical results of our experiments show that support of STM at virtual machine level enables developers to write straightforward code without compromising parallelism or sacrificing thread safety. Existing source code only requires minimal or even none modi cation, which allows developers to easily switch their legacy codebase to a parallel environment. The performance evaluations of GobiesVM also indicate the difference between sequential and parallel execution is significant.

Keywords: global interpreter lock, ruby, software transactional memory, virtual machine

Procedia PDF Downloads 285

Authors: Gleyce Assis Da Silva Barbosa

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In recent years, we have witnessed a vertiginous growth of large education companies. Daughters of national and world capital, these companies expand both through consolidated physical networks in the form of branches spread across the territory and through institutional networks such as business networks through mergers, acquisitions, creation of new companies and influence. They do this by incorporating small, medium and large schools and universities, teaching systems and other products and services. They are also able to weave their webs directly or indirectly in philanthropic circles, limited partnerships, family businesses and even in public education through various mechanisms of outsourcing, privatization and commercialization of products for the sector. Although the growth of these groups in basic education seems to us a recent phenomenon in peripheral countries such as Brazil, its diffusion is closely linked to higher education conglomerates and other sectors of the economy forming oligopolies, which began to expand in the 1990s with strong state support and through political reforms that redefined its role, transforming it into a fundamental agent in the formation of guidelines to boost the incorporation of neoliberal logic. This expansion occurred through the objectification of education, commodifying it and transforming students into consumer clients. Financial power combined with the neo-liberalization of state public policies allowed the profusion of social exclusion, the increase of individuals without access to basic services, deindustrialization, automation, capital volatility and the indetermination of the economy; in addition, this process causes capital to be valued and devalued at rates never seen before, which together generates various impacts such as the precariousness of work. Understanding the connection between these processes, which engender the economy, allows us to see their consequences in labor relations and in the territory. In this sense, it is necessary to analyze the geographic-economic context and the role of the facilitating agents of this process, which can give us clues about the ongoing transformations and the directions of education in the national and even international scenario since this process is linked to the multiple scales of financial globalization. Therefore, the present research has the general objective of analyzing the socio-spatial impacts of financialization and the formation of oligopolies in Brazilian basic education. For this, the survey of laws, data, and public policies on the subject in question was used as a methodology. As a methodology, the work was based on some data from these companies available on websites for investors. Survey of information from global and national companies that operate in Brazilian basic education. In addition to mapping the expansion of educational oligopolies using public data on the location of schools. With this, the research intends to provide information about the ongoing commodification process in the country. Discuss the consequences of the oligopolization of education, considering the impacts that financialization can bring to teaching work.

Keywords: financialization, oligopolies, education, Brazil

Procedia PDF Downloads 64

Authors: Khandoker Akib Mohammad

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While analyzing time-to-event data, it is possible that a certain fraction of subjects will never experience the event of interest, and they are said to be cured. When this feature of survival models is taken into account, the models are commonly referred to as cure models. In the presence of covariates, the conditional survival function of the population can be modelled by using the cure model, which depends on the probability of being uncured (incidence) and the conditional survival function of the uncured subjects (latency), and a combination of logistic regression and Cox proportional hazards (PH) regression is used to model the incidence and latency respectively. In this paper, we have shown the asymptotic normality of the profile likelihood estimator via asymptotic expansion of the profile likelihood and obtain the explicit form of the variance estimator with an implicit function in the profile likelihood. We have also shown the efficient score function based on projection theory and the profile likelihood score function are equal. Our contribution in this paper is that we have expressed the efficient information matrix as the variance of the profile likelihood score function. A simulation study suggests that the estimated standard errors from bootstrap samples (SMCURE package) and the profile likelihood score function (our approach) are providing similar and comparable results. The numerical result of our proposed method is also shown by using the melanoma data from SMCURE R-package, and we compare the results with the output obtained from the SMCURE package.

Keywords: Cox PH model, cure model, efficient score function, EM algorithm, implicit function, profile likelihood

Procedia PDF Downloads 144

Authors: Zhipeng Chen, Peng Zhang, Qingyun Liu, Li Guo

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With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.

Keywords: bipartite graph, one-mode projection, clustering, web proxy detection

Procedia PDF Downloads 245

Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

Procedia PDF Downloads 229

Authors: Satish Kumar Peddapelli

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This paper presents advances in pulse width modulation techniques which refers to a method of carrying information on train of pulses and the information be encoded in the width of pulses. Pulse Width Modulation is used to control the inverter output voltage. This is done by exercising the control within the inverter itself by adjusting the ON and OFF periods of inverter. By fixing the DC input voltage we get AC output voltage. In variable speed AC motors the AC output voltage from a constant DC voltage is obtained by using inverter. Recent developments in power electronics and semiconductor technology have lead improvements in power electronic systems. Hence, different circuit configurations namely multilevel inverters have become popular and considerable interest by researcher are given on them. A fast Space-Vector Pulse Width Modulation (SVPWM) method for five-level inverter is also discussed. In this method, the space vector diagram of the five-level inverter is decomposed into six space vector diagrams of three-level inverters. In turn, each of these six space vector diagrams of three-level inverter is decomposed into six space vector diagrams of two-level inverters. After decomposition, all the remaining necessary procedures for the three-level SVPWM are done like conventional two-level inverter. The proposed method reduces the algorithm complexity and the execution time. It can be applied to the multilevel inverters above the five-level also. The experimental setup for three-level diode-clamped inverter is developed using TMS320LF2407 DSP controller and the experimental results are analysed.

Keywords: five-level inverter, space vector pulse wide modulation, diode clamped inverter, electrical engineering

Procedia PDF Downloads 388

Authors: Minjung Maing, Sibei Liu

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This research discusses the results of a study of window opening behavior of large housing developments in the high-density megacity of Hong Kong. The methods used for the study involved field observations using photo documentation of the four cardinal elevations (north, south-east, and west) of two large housing developments in a very dense urban area of approx. 46,000 persons per square meter within the city of Hong Kong. The targeted housing developments (A and B) are large public housing with a population of about 13,000 in each development of lower income. However, the mean income level in development A is about 40% higher than development B and home ownership is 60% in development A and 0% in development B. Mapping of the surrounding amenities and layout of the developments were also studied to understand the available activities to the residents. The photo documentation of the elevations was taken from November 2016 to February 2018 to gather a full spectrum of different seasons and both in the morning and afternoon (am/pm) times. From the photograph, the window opening behavior was measured by counting the amount of windows opened as a percentage of all the windows on that façade. For each date of survey data collected, weather data was recorded from weather stations located in the same region to collect temperature, humidity and wind speed. To further understand the behavior, simulation studies of microclimate conditions of the housing development was conducted using the software ENVI-met, a widely used simulation tool by researchers studying urban climate. Four major conclusions can be drawn from the data analysis and simulation results. Firstly, there is little change in the amount of window opening during the different seasons within a temperature range of 10 to 35 degrees Celsius. This means that people who tend to open their windows have consistent window opening behavior throughout the year and high tolerance of indoor thermal conditions. Secondly, for all four elevations the lower-income development B opened more windows (almost two times more units) than higher-income development A meaning window opening behavior had strong correlations with income level. Thirdly, there is a lack of correlation between outdoor horizontal wind speed and window opening behavior, as the changes of wind speed do not seem to affect the action of opening windows in most conditions. Similar to the low correlation between horizontal wind speed and window opening percentage, it is found that vertical wind speed also cannot explain the window opening behavior of occupants. Fourthly, there is a slightly higher average of window opening on the south elevation than the north elevation, which may be due to the south elevation being well shaded from high angle sun during the summer and allowing heat into units from lower angle sun during the winter season. These findings are important to providing insight into how to better design urban environments and indoor thermal environments for a liveable high density city.

Keywords: high-density housing, subtropical climate, urban behavior, window opening

Procedia PDF Downloads 125

Authors: Ayad A. Abdulkafi, Shahir F. Nawaf, Mohammed K. Hussein, Ibrahim K. Sileh, Fouad A. Abdulkafi

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Visible light communication (VLC) is a new approach of optical wireless communication proposed to support the congested radio frequency (RF) spectrum. VLC systems are combined with orthogonal frequency division multiplexing (OFDM) to achieve high rate transmission and high spectral efficiency. In this paper, we investigate the Pilot-Assisted Channel Estimation for DC biased Optical OFDM (PACE-DCO-OFDM) systems to reduce the effects of the distortion on the transmitted signal. Least-square (LS) and linear minimum mean-squared error (LMMSE) estimators are implemented in MATLAB/Simulink to enhance the bit-error-rate (BER) of PACE-DCO-OFDM. Results show that DCO-OFDM system based on PACE scheme has achieved better BER performance compared to conventional system without pilot assisted channel estimation. Simulation results show that the proposed PACE-DCO-OFDM based on LMMSE algorithm can more accurately estimate the channel and achieves better BER performance when compared to the LS based PACE-DCO-OFDM and the traditional system without PACE. For the same signal to noise ratio (SNR) of 25 dB, the achieved BER is about 5×10^-4 for LMMSE-PACE and 4.2×10^-3 with LS-PACE while it is about 2×10^-1 for system without PACE scheme.

Keywords: channel estimation, OFDM, pilot-assist, VLC

Procedia PDF Downloads 180