Search results for: IR tracking algorithm

Authors: Yul Y. Nazaruddin, Anas Y. Widiaribowo, Satriyo Nugroho

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Boiler is one of the critical unit in a petrochemical plant. Steam produced by the boiler is used for various processes in the plant such as urea and ammonia plant. An alternative method to optimize the boiler combustion system is presented in this paper. Adaptive Neuro-Fuzzy Inference System (ANFIS) approach is applied to model the boiler using real-time operational data collected from a boiler unit of the petrochemical plant. Nonlinear equation obtained is then used to optimize the air to fuel ratio using Genetic Algorithm, resulting an optimal ratio of 15.85. This optimal ratio is then maintained constant by ratio controller designed using inverse dynamics based on ANFIS. As a result, constant value of oxygen content in the flue gas is obtained which indicates more efficient combustion process.

Keywords: ANFIS, boiler, combustion process, genetic algorithm, optimization.

Procedia PDF Downloads 253

Authors: Samia Kouki

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Solving NP-hard combinatorial optimization problems by exact search methods, such as Branch-and-Bound, may degenerate to complete enumeration. For that reason, exact approaches limit us to solve only small or moderate size problem instances, due to the exponential increase in CPU time when problem size increases. One of the most promising ways to reduce significantly the computational burden of sequential versions of Branch-and-Bound is to design parallel versions of these algorithms which employ several processors. This paper describes a parallel Branch-and-Bound algorithm called GALB for solving the classical permutation flowshop scheduling problem as well as its implementation on a Grid computing infrastructure. The experimental study of our distributed parallel algorithm gives promising results and shows clearly the benefit of the parallel paradigm to solve large-scale instances in moderate CPU time.

Keywords: grid computing, permutation flow shop problem, branch and bound, load balancing

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Authors: Kartik Thakore, Anna-Roza Tamas, Adam Cole

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The world health organization (WHO) reports that by 2030 more than 23.7 million deaths annually will be caused by Cardiovascular Diseases (CVDs); with a 2008 economic impact of $3.76 T. Metabolic syndrome is a disorder of multiple metabolic risk factors strongly indicated in the development of cardiovascular diseases. Guided lifestyle intervention driven by live coaching has been shown to have a positive impact on metabolic risk factors. Individuals’ path to improved (decreased) metabolic risk factors are driven by personal motivation and personalized messages delivered by coaches and augmented by technology. Using interactions captured between 400 individuals and 3 coaches over a program period of 500 days, a preliminary model was designed. A novel real time event tracking system was created to track and classify clients based on their genetic profile, baseline questionnaires and usage of a mobile application with live coaching sessions. Classification of clients and coaches was done using a support vector machines application build on Apache Spark, Stanford Natural Language Processing Library (SNLPL) and decision-modeling.

Keywords: guided lifestyle intervention, metabolic risk factors, personal coaching, support vector machines application, Apache Spark, natural language processing

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Authors: Zhu-Qing Jia, Tao Lin, Tong Zhou

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The recognition method of Optical Character Recognition has been expensively utilized, while it is rare to be employed specifically in recognition of electronic components. This paper suggests a high-effective algorithm on appearance identification of integrated circuit components based on the existing methods of character recognition, and analyze the pros and cons.

Keywords: optical character recognition, fuzzy page identification, mutual correlation matrix, confidence self-adaptation

Procedia PDF Downloads 540

Authors: C. Temaneh-Nyah, F. A. Phiri, D. Karegeya

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This paper presents an algorithm for obtaining the map of subscriber’s density distribution for a mobile wireless communication network based on the actual subscriber's traffic data obtained from the base station. This is useful in statistical characterization of the mobile wireless network.

Keywords: electromagnetic compatibility, statistical analysis, simulation of communication network, subscriber density

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Authors: Youngji Lee, Yonghyeon Jeon, Sunyoung Bu, Philsu Kim

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The aim of this paper is to construct an algorithm of time step control for the error correction method most recently developed by one of the authors for solving stiff initial value problems. It is achieved with the generalized Chebyshev polynomial and the corresponding error correction method. The main idea of the proposed scheme is in the usage of the duplicated node points in the generalized Chebyshev polynomials of two different degrees by adding necessary sample points instead of re-sampling all points. At each integration step, the proposed method is comprised of two equations for the solution and the error, respectively. The constructed algorithm controls both the error and the time step size simultaneously and possesses a good performance in the computational cost compared to the original method. Two stiff problems are numerically solved to assess the effectiveness of the proposed scheme.

Keywords: stiff initial value problem, error correction method, generalized Chebyshev polynomial, node points

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Authors: Guang-Ho Cha

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

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

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Authors: Prince Suka Neekia Momta, Rita Iheoma Achonyeulo

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Ant Tracking seismic attribute applied over 4-seconds seismic volume revealed structural features triggered by clay diapirism, growth fault development, rapid deltaic sedimentation and intense drilling. The attribute was extracted on vertical seismic sections and time slices. Mega tectonic structures such as growth faults and clay diapirs are visible on vertical sections with obscured minor lineaments or fractures. Fractures are distinctively visible on time slices yielding recognizable patterns corroborating established geologic models. This model seismic attribute enabled the understanding of fluid flow characteristics and production responses. Three structural patterns recognized in the field include: major growth faults, minor faults or lineaments and network of fractures. Three growth faults mapped on seismic section form major deformation bands delimiting the area into three blocks or depocenters. The growth faults trend E-W, dip down-to-south in the basin direction, and cut across the study area. The faults initiating from about 2000ms extended up to 500ms, and tend to progress parallel and opposite to the growth direction of an upsurging diapiric structure. The diapiric structures form the major deformational bands originating from great depths (below 2000ms) and rising to about 1200ms where series of sedimentary layers onlapped and pinchout stratigraphically against the diapir. Several other secondary faults or lineaments that form parallel streaks to one another also accompanied the growth faults. The fracture networks have no particular trend but form a network surrounding the well area. Faults identified in the study area have potentials for structural hydrocarbon traps whereas the presence of fractures created a fractured-reservoir condition that enhanced rapid fluid flow especially water. High aquifer flow potential aided by possible fracture permeability resulted in rapid decline in oil rate. Through the application of Ant Tracking attribute, it is possible to obtain detailed interpretation of structures that can have direct influence on oil and gas production.

Keywords: seismic, attributes, production, structural

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Authors: Helene Martin, Solmaz Boroomandi Barati, Jean-Charles Pinoli, Stephane Valette, Yann Gavet

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In the present work we developed an image processing algorithm to measure water droplets characteristics during dropwise condensation on pillared surfaces. The main problem in this process is the similarity between shape and size of water droplets and the pillars. The developed method divides droplets into four main groups based on their size and applies the corresponding algorithm to segment each group. These algorithms generate binary images of droplets based on both their geometrical and intensity properties. The information related to droplets evolution during time including mean radius and drops number per unit area are then extracted from the binary images. The developed image processing algorithm is verified using manual detection and applied to two different sets of images corresponding to two kinds of pillared surfaces.

Keywords: dropwise condensation, textured surface, image processing, watershed

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Authors: Wang Lin, Li Zhiqiang

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The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.

Keywords: behavior pattern, cooperative learning, data analyze, k-means clustering algorithm

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Authors: Danilo López, Edwin Rivas, Leyla López

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This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications. This will allow efficiently managing the information in the BS, since it precedes the arrival of the SUs in the stage of selection of the best channel in CRN. As a result, the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made.

Keywords: cognitive radio, base station, best effort, MLPNN, prediction, real time

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Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Abbas Moradi, Mohsen Safajoy, Reza Yazdanparast

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Analysis of blade-disc dovetail joints is one of the biggest challenges facing designers of aero-engines. To avoid comparatively expensive experimental full-scale tests, numerical methods can be used to simulate loaded disc-blades assembly. Mortar method provides a powerful and flexible tool for solving frictional contact problems. In this study, 2D frictional contact in dovetail has been analysed based on the mortar algorithm. In order to model the friction, the classical law of coulomb and moving friction cone algorithm is applied. The solution is then obtained by solving the resulting set of non-linear equations using an efficient numerical algorithm based on Newton–Raphson Method. The numerical results show that this approach has better convergence rate and accuracy than other proposed numerical methods.

Keywords: computational contact mechanics, dovetail joints, nonlinear FEM, mortar approach

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Authors: Tahseen Saad, Halim Ceylan, Jonathan Weaver, Osman Nuri Çelik, Onur Gungor Sahin

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A new concept of uniform delay offset dependent mathematical optimization problem is derived as the main objective for this study using a differential evolution algorithm. To control the coordination problem, which depends on offset selection and to estimate uniform delay based on the offset choice in a traffic signal network. The assumption is the periodic sinusoidal function for arrival and departure patterns. The cycle time is optimized at the entry links and the optimized value is used in the non-entry links as a common cycle time. The offset optimization algorithm is used to calculate the uniform delay at each link. The results are illustrated by using a case study and are compared with the canonical uniform delay model derived by Webster and the highway capacity manual’s model. The findings show new model minimizes the total uniform delay to almost half compared to conventional models. The mathematical objective function is robust. The algorithm convergence time is fast.

Keywords: area traffic control, traffic flow, differential evolution, sinusoidal periodic function, uniform delay, offset variable

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Authors: V. Sacca, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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In the last few years, resting-state functional MRI (rs-fMRI) was widely used to investigate the architecture of brain networks by investigating the Blood Oxygenation Level Dependent response. This technique represented an interesting, robust and reliable approach to compare pathologic and healthy subjects in order to investigate neurodegenerative diseases evolution. On the other hand, the elaboration of rs-fMRI data resulted to be very prone to noise due to confounding factors especially the head motion. Head motion has long been known to be a source of artefacts in task-based functional MRI studies, but it has become a particularly challenging problem in recent studies using rs-fMRI. The aim of this work was to evaluate in MS patients a well-known motion correction algorithm from the FMRIB's Software Library - MCFLIRT - that could be applied to minimize the head motion distortions, allowing to correctly interpret rs-fMRI results.

Keywords: head motion correction, MCFLIRT algorithm, multiple sclerosis, resting state fMRI

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Authors: Maryam Moosavi, Hadi Khatibzadeh

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The main purpose of this paper is to study the proximal point algorithm for quasi-nonexpansive mappings in Hadamard spaces. △-convergence and strong convergence of cyclic resolvents for a finite family of quasi-nonexpansive mappings one to a fixed point of the mappings are established

Keywords: Fixed point, Hadamard space, Proximal point algorithm, Quasi-nonexpansive sequence of mappings, Resolvent

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Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool

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Authors: Yao-Hong Tsai

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This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: image fusion, iris recognition, local binary pattern, wavelet

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Authors: Dimitris Varsamis, Christos Talagkozis, Alkiviadis Tsimpiris, Paris Mastorocostas

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The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the execution time. Specifically, a new function in MATLAB for serial k-means algorithm is developed, which meets all the requirements for the conversion to a function in MATLAB with parallel computations. Additionally, two different variants for the definition of initial values are presented. In the sequel, the parallel approach is presented. Finally, the performance tests for the computation times respect to the numbers of features and classes are illustrated.

Keywords: K-means algorithm, clustering, parallel computations, Matlab

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Authors: Juan Heredia, Naci Dilekli

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The Ecuadorian Rainforest has been polluted for almost 60 years with little to no regard to oversight, law, or regulations. The consequences have been vast environmental damage such as pollution and deforestation, as well as sickness and the death of many people and animals. The aim of this paper is to quantify and localize the polluted zones, which something that has not been conducted and is the first step for remediation. To approach this problem, multi-spectral Remote Sensing imagery was utilized using a novel algorithm developed for this study, based on four normalized indices available in the literature. The algorithm classifies the pixels in polluted or healthy ones. The results of this study include a new algorithm for pixel classification and quantification of the polluted area in the selected image. Those results were finally validated by ground control points found in the literature. The main conclusion of this work is that using hyperspectral images, it is possible to identify polluted vegetation. The future work is environmental remediation, in-situ tests, and more extensive results that would inform new policymaking.

Keywords: remote sensing, oil pollution quatification, amazon forest, hyperspectral remote sensing

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Authors: Trapti Sharma, Devesh Kumar Srivastava

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This paper is basically a analysis study of above MapReduce implementation and also to verify and validate the MapReduce solution model for Parallel K-Mediod algorithm on Hadoop Cluster. MapReduce is a programming model which authorize the managing of huge amounts of data in parallel, on a large number of devices. It is specially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce has slowly become the framework of choice for “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e. makespan) of a set of MapReduce duty. In this paper, we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Mediod clustering algorithm. We have found that as the amount of nodes increases the completion time decreases.

Keywords: hadoop, mapreduce, k-mediod, validation, verification

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Authors: D. M. S. Bandara, Yunqi Lei, Ye Luo

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Fingerprints are suitable as long-term markers of human identity since they provide detailed and unique individual features which are difficult to alter and durable over life time. In this paper, we propose an algorithm to encrypt and decrypt fingerprint images by using a specially designed Elliptic Curve Cryptography (ECC) procedure based on block ciphers. In addition, to increase the confusing effect of fingerprint encryption, we also utilize a chaotic-behaved method called Arnold Cat Map (ACM) for a 2D scrambling of pixel locations in our method. Experimental results are carried out with various types of efficiency and security analyses. As a result, we demonstrate that the proposed fingerprint encryption/decryption algorithm is advantageous in several different aspects including efficiency, security and flexibility. In particular, using this algorithm, we achieve a margin of about 0.1% in the test of Number of Pixel Changing Rate (NPCR) values comparing to the-state-of-the-art performances.

Keywords: arnold cat map, biometric encryption, block cipher, elliptic curve cryptography, fingerprint encryption, Koblitz’s encoding

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Authors: Krzysztof Malczewski

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Magnetic Resonance Imaging (MRI) reconstruction algorithm using compressed sensing is presented in this paper. It is exhibited that the offered approach improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging method struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the objective is to combine super-resolution image enhancement algorithm with CS framework benefits to achieve high resolution MR output image. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements.

Keywords: super-resolution, MRI, compressed sensing, sparse-sense, image enhancement

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Authors: Yeonjune Kang

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A triangulation of a planar region is a partition of that region into triangles. In the finite element method, triangulations are often used as the grid underlying a computation. In order to be suitable as a finite element mesh, a triangulation must have well-shaped triangles, according to criteria that depend on the details of the particular problem. For instance, most methods require that all triangles be small and as close to the equilateral shape as possible. Stated differently, one wants to avoid having either thin or flat triangles in the triangulation. There are many triangulation procedures, a particular one being the one known as the longest edge bisection algorithm described below. Starting with a given triangle, locate the midpoint of the longest edge and join it to the opposite vertex of the triangle. Two smaller triangles are formed; apply the same bisection procedure to each of these triangles. Continuing in this manner after n steps one obtains a triangulation of the initial triangle into 2n smaller triangles. The longest edge algorithm was first considered in the late 70’s. It was shown by various authors that this triangulation has the desirable properties for the finite element method: independently of the number of iterations the angles of these triangles cannot get too small; moreover, the size of the triangles decays exponentially. In the present paper we consider a related triangulation algorithm we refer to as the largest angle bisection procedure. As the name suggests, rather than bisecting the longest edge, at each step we bisect the largest angle. We study the properties of the resulting triangulation and prove that, while the general behavior resembles the one in the longest edge bisection algorithm, there are several notable differences as well.

Keywords: angle bisectors, geometry, triangulation, applied mathematics

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Authors: Maryam Kalkatechi

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Nowadays, parametric design, scientific analysis, and digital fabrication are dominant. Many architectural practices are increasingly seeking to incorporate advanced digital software and fabrication in their projects. Proposing an erudite design process that combines digital and practical aspects in a strong frame within the method was resulted from the dissertation research. The digital aspects are the progressive advancements in algorithm design and simulation software. These aspects have assisted the firms to develop more holistic concepts at the early stage and maintain collaboration among disciplines during the design process. The erudite design process enhances the current design processes by encouraging the designer to implement the construction and architecture knowledge within the algorithm to make successful design processes. The erudite design process also involves the ongoing improvements of applying the new method of 3D printing in construction. This is achieved through the ‘data-sketches’. The term ‘data-sketch’ was developed by the author in the dissertation that was recently completed. It accommodates the decisions of the architect on the algorithm. This paper introduces the erudite design process and its components. It will summarize the application of this process in development of the ‘3D printed construction unit’. This paper contributes to overlaying the academic and practice with advanced technology by presenting a design process that transfers the dominance of tool to the learned architect and encourages innovation in design processes.

Keywords: erudite, data-sketch, algorithm design in architecture, design process

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Authors: Devika Tanna

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'Biometrics' means 'life measurement' but the term is usually associated with the use of unique physiological characteristics to identify an individual. It is important to secure the privacy of digital face image that is stored in central database. To impart privacy to such biometric face images, first, the digital face image is split into two host face images such that, each of it gives no idea of existence of the original face image and, then each cover image is stored in two different databases geographically apart. When both the cover images are simultaneously available then only we can access that original image. This can be achieved by using the XM2VTS and IMM face database, an adaptive algorithm for spatial greyscale. The algorithm helps to select the appropriate host images which are most likely to be compatible with the secret image stored in the central database based on its geometry and appearance. The encryption is done using GEVCS which results in a reconstructed image identical to the original private image.

Keywords: adaptive algorithm, database, host images, privacy, visual cryptography

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Authors: Analise Borg, Paul Micallef

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Over the past few years, the online multimedia collection has grown at a fast pace. Several companies showed interest to study the different ways to organize the amount of audio information without the need of human intervention to generate metadata. In the past few years, many applications have emerged on the market which are capable of identifying a piece of music in a short time. Different audio effects and degradation make it much harder to identify the unknown piece. In this paper, an audio fingerprinting system which makes use of a non-parametric based algorithm is presented. Parametric analysis is also performed using Gaussian Mixture Models (GMMs). The feature extraction methods employed are the Mel Spectrum Coefficients and the MPEG-7 basic descriptors. Bin numbers replaced the extracted feature coefficients during the non-parametric modelling. The results show that non-parametric analysis offer potential results as the ones mentioned in the literature.

Keywords: audio fingerprinting, mapping algorithm, Gaussian Mixture Models, MFCC, MPEG-7

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Authors: A. Degale Desta, Cheng Jian

Abstract:

Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

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Authors: Nattachote Rugthaicharoencheep

Abstract:

This paper presents a feeder reconfiguration problem in distribution systems. The objective is to minimize the system power loss and to improve bus voltage profile. The optimization problem is subjected to system constraints consisting of load-point voltage limits, radial configuration format, no load-point interruption, and feeder capability limits. A method based on genetic algorithm, a search algorithm based on the mechanics of natural selection and natural genetics, is proposed to determine the optimal pattern of configuration. The developed methodology is demonstrated by a 33-bus radial distribution system with distributed generations and feeder capacitors. The study results show that the optimal on/off patterns of the switches can be identified to give the minimum power loss while respecting all the constraints.

Keywords: network reconfiguration, distributed generation capacitor placement, loss reduction, genetic algorithm

Procedia PDF Downloads 177