Search results for: indoor distribution networks

Authors: C. Pislaru, A. Shebani

Abstract:

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

Keywords: System identification, Nonlinear system, Neural networks, RBF neural network.

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Authors: A. Aydarous, M. El Ghazaly

Abstract:

The purpose of this study was to investigate the response of the newly released Gafchromic HD-V2 film for alpha particle of 5.5 MeV. Gafchromic HD-V2 was exposed to alpha particles of energy 5 MeV from 241Am for different durations. Then the films were scanned with a flatbed scanner. The dose response curve up to 2200 Gy has been achieved. The film’s reproducibility and sensitivity were evaluated. The results obtained show that the net optical density increases almost exponentially with the increase in the exposure time, and it becomes saturated after prolonged exposure times. The red channel shows the highest sensitivity, with a value of 4 x 10-3 Gy-1 at netOD of 0.4. The inter-film reproducibility was measured and the relative uncertainty found was 1.7 %, 2.1 % and 2.3 % for grey, red and green channels, respectively.

Keywords: Alpha dosimetry, 241Am, Gafchromic film.

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Authors: R. Prabha, N. Ramaraj

Abstract:

Routing in adhoc networks is a challenge as nodes are mobile, and links are constantly created and broken. Present ondemand adhoc routing algorithms initiate route discovery after a path breaks, incurring significant cost to detect disconnection and establish a new route. Specifically, when a path is about to be broken, the source is warned of the likelihood of a disconnection. The source then initiates path discovery early, avoiding disconnection totally. A path is considered about to break when link availability decreases. This study modifies Adhoc On-demand Multipath Distance Vector routing (AOMDV) so that route handoff occurs through link availability estimation.

Keywords: Mobile Adhoc Network (MANET), Routing, Adhoc On-demand Multipath Distance Vector routing (AOMDV), Link Availability.

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Authors: Marque Adrien, Delahaye Daniel, Marechal Pierre, Berry Isabelle

Abstract:

Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spatial grids can be defined as images with symmetric positive definite matrix elements. A data pre-processing step, a specific convolution, a specific max-pooling, and specific flatten layers are implemented to process such images. Then, the neural network is applied to spatial grids, whose elements are wind covariance matrices, to solve classification problems related to the feasibility of unmanned aerial vehicles based on wind direction and wind uncertainty.

Keywords: Wind direction, uncertainty level, Unmanned Aerial Vehicle, convolution neural network, SPD matrices.

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Authors: Soran Tarkhani

Abstract:

The phenomenon of significant Arab support for Russia's invasion of Ukraine, despite widespread condemnation from Arab leaders, poses a puzzling scenario. This paper delves into the paradox by employing multiple regression analysis on the online reactions of Arab audiences to the conflict as reported by seven major news networks: CNN Arabic, BBC Arabic, Sky News Arabic, France24 Arabic, DW, Aljazeera, and Al-Arabiya. It hypothesizes that this support stems from prevalent anti-Western sentiment within the Arab world. The empirical findings corroborate the hypothesis, providing insight into the underlying motivations for Arab backing of Russia against Ukraine, despite their historical familiarity with the harsh realities of war.

Keywords: Anti-Western Sentiment, Arab World, Russia-Ukraine Conflict, social media analysis, political sentiment, international relations, regional influence.

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Authors: Y. Heyrani Birak, R. Hizaji, J. Shahkarami

Abstract:

Reinforced Concrete (RC) deep beams are special structural elements because of their geometry and behavior under loads. For example, assumption of strain- stress distribution is not linear in the cross section. These types of beams may have simple supports or fixed supports. A lot of research works have been conducted on simply supported deep beams, but little study has been done in the fixed-end RC deep beams behavior. Recently, using of fixed-ended deep beams has been widely increased in structures. In this study, the behavior of fixed-ended deep beams is investigated, and the important parameters in capacity of this type of beams are mentioned.

Keywords: Deep beam, capacity, reinforced concrete, fixed-ended.

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Authors: Y. Madani, H. Achache, B. Boutabout

Abstract:

The purpose of this paper is to analyze numerically by the three-dimensional finite element method, using ABAQUS calculation code, the mechanical behavior of a unidirectional and multidirectional delaminated stratified composite under mechanical loading in Mode II. This study consists of the determination of the energy release rate G in mode II as well as the distribution of equivalent von Mises stresses along the damaged zone by varying several parameters such as the applied load and the delamination length. It allowed us to deduce that the high energy release rate favors delamination at the free edges of a stratified plate subjected to bending.

Keywords: Delamination, energy release rate, finite element method, stratified composite.

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Authors: F. M. Ali, R. Nazar, N. M. Arifin, I. Pop

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In this paper, the problem of unsteady stagnation-point flow and heat transfer induced by a shrinking sheet in the presence of radiation effect is studied. The transformed boundary layer equations are solved numerically by the shooting method. The influence of radiation, unsteadiness and shrinking parameters, and the Prandtl number on the reduced skin friction coefficient and the heat transfer coefficient, as well as the velocity and temperature profiles are presented and discussed in detail. It is found that dual solutions exist and the temperature distribution becomes less significant with radiation parameter.

Keywords: Heat transfer, Radiation effect, Shrinking sheet Unsteady flow.

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Authors: Yuan He, Yupin Luo, Dongcheng Hu

Abstract:

In this paper, we propose a novel variational method for unsupervised texture segmentation. We use a Gabor filter bank to extract texture features. Some of the filtered channels form a multidimensional Gaborian feature space. To avoid deforming contours directly in a vector-valued space we use a Gaussian mixture model to describe the statistical distribution of this space and get the boundary and region probabilities. Then a framework of geodesic active regions is applied based on them. In the end, experimental results are presented, and show that this method can obtain satisfied boundaries between different texture regions.

Keywords: Texture segmentation, Gabor filter, snakes, Geodesicactive regions

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Authors: Gábor Szládek, Balázs Héder, János Bitó

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In a BFWA (Broadband Fixed Wireless Access Network) the evolved SINR (Signal to Interference plus Noise Ratio) is relevant influenced by the applied duplex method. The TDD (Time Division Duplex), especially adaptive TDD method has some advantage contrary to FDD (Frequency Division Duplex), for example the spectrum efficiency and flexibility. However these methods are suffering several new interference situations that can-t occur in a FDD system. This leads to reduced SINR in the covered area what could cause some connection outages. Therefore, countermeasure techniques against interference are necessary to apply in TDD systems. Synchronization is one way to handling the interference. In this paper the TDD systems – applying different system synchronization degree - will be compared by the evolved SINR at different locations of the BFWA service area and the percentage of the covered area by the system.

Keywords: Adaptive TDD, BFWA networks, duplex methods, intra system interferences.

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Authors: Radoslav Delina, Michal Tkáč

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Globalization, supported by information and communication technologies, changes the rules of competitiveness and increases the significance of information, knowledge and network cooperation. In line with this trend, the need for efficient trust-building tools has emerged. The absence of trust building mechanisms and strategies was identified within several studies. Through trust development, participation on e-business network and usage of network services will increase and provide to SMEs new economic benefits. This work is focused on effective trust building strategies development for electronic business network platforms. Based on trust building mechanism identification, the questionnairebased analysis of its significance and minimum level of requirements was conducted. In the paper, we are confirming the trust dependency on e-Skills which play crucial role in higher level of trust into the more sophisticated and complex trust building ICT solutions.

Keywords: Correlation analysis, decision trees, e-marketplace, trust building

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Authors: A. Hellany, M.Nagrial, M. Nassereddine, J. Rizk

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As new challenges emerge in power electrical workplace safety, it is the responsibility of the systems designer to seek out new approaches and solutions that address them. Design decisions made today will impact cost, safety and serviceability of the installed systems for 40 or 50 years during the useful life for the owner. Studies have shown that this cost is an order of magnitude of 7 to 10 times the installed cost of the power distribution equipment. This paper reviews some aspects of earthing system design in power substation surrounded by residential houses. The electrical potential rise and split factors are discussed and a few recommendations are provided to achieve a safety voltage in the area beyond the boundary of the substation.

Keywords: EPR, Split Factor, Earthing Design

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Authors: Mario Mastriani, Marcelo Naiouf

Abstract:

This paper introduces a Quantum Correlation Matrix Memory (QCMM) and Enhanced QCMM (EQCMM), which are useful to work with quantum memories. A version of classical Gram-Schmidt orthogonalisation process in Dirac notation (called Quantum Orthogonalisation Process: QOP) is presented to convert a non-orthonormal quantum basis, i.e., a set of non-orthonormal quantum vectors (called qudits) to an orthonormal quantum basis, i.e., a set of orthonormal quantum qudits. This work shows that it is possible to improve the performance of QCMM thanks QOP algorithm. Besides, the EQCMM algorithm has a lot of additional fields of applications, e.g.: Steganography, as a replacement Hopfield Networks, Bilevel image processing, etc. Finally, it is important to mention that the EQCMM is an extremely easy to implement in any firmware.

Keywords: Quantum Algebra, correlation matrix memory, Dirac notation, orthogonalisation.

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Authors: Musbah M. Aqel, P. K. Mahanti, Soumya Banerjee

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This paper describes a simulation model for analyzing artificial emotion injected to design the game characters. Most of the game storyboard is interactive in nature and the virtual characters of the game are equipped with an individual personality and dynamic emotion value which is similar to real life emotion and behavior. The uncertainty in real expression, mood and behavior is also exhibited in game paradigm and this is focused in the present paper through a fuzzy logic based agent and storyboard. Subsequently, a pheromone distribution or labeling is presented mimicking the behavior of social insects.

Keywords: Artificial Emotion, Fuzzy logic, Game character, Pheromone label

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Authors: Ghassan A. Abed, Akbal O. Salman, Bayan M. Sabbar

Abstract:

Transmission Control Protocol (TCP) among the wired and wireless networks, it still has a practical problem; where the congestion control mechanism does not permit the data stream to get complete bandwidth over the existing network links. To solve this problem, many TCP protocols have been introduced with high speed performance. Therefore, an enhanced congestion window (cwnd) for the congestion control mechanism is proposed in this article to improve the performance of TCP by increasing the number of cycles of the new window to improve the transmitted packet number. The proposed algorithm used a new mechanism based on the available bandwidth of the connection to detect the capacity of network path in order to improve the regular clocking of congestion avoidance mechanism. The work in this paper based on using Network Simulator 2 (NS-2) to simulate the proposed algorithm.

Keywords: TCP, cwnd, Congestion Control, NS-2.

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Authors: Juha Kettunen

Abstract:

This study analyzes the quality and the size of the strategic network of higher education institutions. The study analyses the concept of fitness for purpose in quality assurance. It also analyses the transaction costs of networking that have consequences on the number of members in the network. Empirical evidence is presented of the Consortium on Applied Research and Professional Education, which is a European strategic network of six higher education institutions. The results of the study support the argument that the number of members in the strategic network should be relatively small to provide high quality results. The practical importance is that networking has been able to promote international research and development projects. The results of this study are important for those who want to design and improve international networks in higher education.

Keywords: Higher education, network, research and development, strategic management.

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Authors: Jaedo Kim, Youngjoon Han, Hernsoo Hahn

Abstract:

The purpose of this paper is to detect human in images. This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with various sub-region size. The result shows that the accuracy level of proposed method similar to Histogram of Oriented Gradients(HOG) feature descriptor and feature extraction process is simple and faster than existing methods.

Keywords: Human detection, Projected edge descriptor, Linear SVM, Local appearance feature

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

Abstract:

The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: Deregulated energy market, forecasting, machine learning, system marginal price, energy efficiency and quality.

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Authors: Yasser F. Hassan

Abstract:

Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: Cellular automata, neural cellular automata, deep learning, classification.

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Authors: G. Candel, D. Naccache

Abstract:

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 embedding. 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: Tin Hninn Hninn Maung

Abstract:

This paper introduces a hand gesture recognition system to recognize real time gesture in unstrained environments. Efforts should be made to adapt computers to our natural means of communication: Speech and body language. A simple and fast algorithm using orientation histograms will be developed. It will recognize a subset of MAL static hand gestures. A pattern recognition system will be using a transforrn that converts an image into a feature vector, which will be compared with the feature vectors of a training set of gestures. The final system will be Perceptron implementation in MATLAB. This paper includes experiments of 33 hand postures and discusses the results. Experiments shows that the system can achieve a 90% recognition average rate and is suitable for real time applications.

Keywords: Hand gesture recognition, Orientation Histogram, Myanmar Alphabet Language, Perceptronnetwork, MATLAB.

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Authors: Che Z. Zulkifli, Nursyahida M. Noor, Siti N. Semunab, Shafawati A. Malek

Abstract:

Wireless sensors, also known as wireless sensor nodes, have been making a significant impact on human daily life. The Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two complementary technologies; hence, an integrated implementation of these technologies expands the overall functionality in obtaining long-range and real-time information on the location and properties of objects and people. An approach for integrating ZigBee and RFID networks is proposed in this paper, to create an energy-efficient network improved by the benefits of combining ZigBee and RFID architecture. Furthermore, the compatibility and requirements of the ZigBee device and communication links in the typical RFID system which is presented with the real world experiment on the capabilities of the proposed RFID system.

Keywords: Mesh network, RFID, wireless sensor network, zigbee.

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Authors: Chan Weng Howe, Mohd Saberi Mohamad

Abstract:

Protein residue contact map is a compact representation of secondary structure of protein. Due to the information hold in the contact map, attentions from researchers in related field were drawn and plenty of works have been done throughout the past decade. Artificial intelligence approaches have been widely adapted in related works such as neural networks, genetic programming, and Hidden Markov model as well as support vector machine. However, the performance of the prediction was not generalized which probably depends on the data used to train and generate the prediction model. This situation shown the importance of the features or information used in affecting the prediction performance. In this research, support vector machine was used to predict protein residue contact map on different combination of features in order to show and analyze the effectiveness of the features.

Keywords: contact map, protein residue contact, support vector machine, protein structure prediction

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Authors: S.B. Tavakoly Sany, A. Salleh, A.H. Sulaiman, G.H. Monazami

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The coastal sediments of West Port of Malaysia was monitored from Nov. 2009 to Oct. 2010 to assess spatial distribution of heavy metals As, Cu, Cd, Cr, Hg, Ni, Zn and Pb. Sediment samples were collected from 10 stations in dry and rainy season in West Port. The range concentrations measured (Mg/g dry weight ) were from 23.4 to 98.3 for Zn, 22.3 to 80 for Pb, 7.4 to 27.6 Cu, 0.244 to 3.53 for Cd, 7.2 to 22.2 for Ni, 20.2 to 162 for As, 0.11 to 0.409 for Hg and 11.5 to 61.5 for Cr. The geochemical indexes used in this study were Geoaccumulation (Igeo), Contamination Factor (CF) and Pollution Load Index (PLI); these indexes were used to evaluate the levels of sediment contaminations. The results of these indexes show that, the status of West Port sediment quality are moderately polluted by heavy metals except in arsenic which shows the high level of pollution.

Keywords: Heavy metals, Sediment Quality, West Port.

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Authors: Yi-Ting Mai, Chun-Chuan Yang, Cheng-Jung Wen

Abstract:

IEEE 802.16 is a new wireless technology standard, it has some advantages, including wider coverage, higher bandwidth, and QoS support. As the new wireless technology for last mile solution, there are designed two models in IEEE 802.16 standard. One is PMP (point to multipoint) and the other is Mesh. In this paper we only focus on IEEE 802.16 Mesh model. According to the IEEE 802.16 standard description, Mesh model has two scheduling modes, centralized and distributed. Considering the pros and cons of the two scheduling, we present the combined scheduling QoS framework that the BS (Base Station) controls time frame scheduling and selects the shortest path from source to destination directly. On the other hand, we propose the Expedited Queue mechanism to cut down the transmission time. The EQ mechanism can reduce a lot of end-to-end delay in our QoS framework. Simulation study has shown that the average delay is smaller than contrasts. Furthermore, our proposed scheme can also achieve higher performance.

Keywords: IEEE 802.16 Mesh, Scheduling, Expedited Queue, QoS.

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Authors: Mohammad Rakibul Islam

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Link reliability and transmitted power are two important design constraints in wireless network design. Error control coding (ECC) is a classic approach used to increase link reliability and to lower the required transmitted power. It provides coding gain, resulting in transmitter energy savings at the cost of added decoder power consumption. But the choice of ECC is very critical in the case of wireless sensor network (WSN). Since the WSNs are energy constraint in nature, both the BER and power consumption has to be taken into count. This paper develops a step by step approach in finding suitable error control codes for WSNs. Several simulations are taken considering different error control codes and the result shows that the RS(31,21) fits both in BER and power consumption criteria.

Keywords: Error correcting code, RS, BCH, wireless sensor networks.

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Authors: S. Sarumathi, N. Shanthi, S. Vidhya

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In the present world, predicting rainfall is considered to be an essential and also a challenging task. Normally, the climate and rainfall are presumed to have non-linear as well as intricate phenomena. For predicting accurate rainfall, we necessitate advanced computer modeling and simulation. When there is an enhanced understanding of the spatial and temporal distribution of precipitation then it becomes enrichment to applications such as hydrologic, climatic and ecological. Conversely, there may be some kind of challenges occur in the community due to some application which results in the absence of consistent precipitation observation in remote and also emerging region. This survey paper provides a multifarious collection of methodologies which are epitomized by various researchers for predicting the rainfall. It also gives information about some technique to forecast rainfall, which is appropriate to all methods like numerical, traditional and statistical.

Keywords: Satellite Image, Segmentation, Feature Extraction, Classification, Clustering, Precipitation Estimation.

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Authors: Jan Sindelar

Abstract:

The paper presents an applied study of a multivariate AR(p) process fitted to daily data from U.S. commodity futures markets with the use of Bayesian statistics. In the first part a detailed description of the methods used is given. In the second part two BVAR models are chosen one with assumption of lognormal, the second with normal distribution of prices conditioned on the parameters. For a comparison two simple benchmark models are chosen that are commonly used in todays Financial Mathematics. The article compares the quality of predictions of all the models, tries to find an adequate rate of forgetting of information and questions the validity of Efficient Market Hypothesis in the semi-strong form.

Keywords: Vector auto-regression, forecasting, financial, Bayesian, efficient markets.

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Authors: Jaeyoung Lee

Abstract:

Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: Edge network, embedded network, MMA, matrix multiplication accelerator and semantic segmentation network.

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Authors: Todor Penchev, Dimitar Karastojanov, Ivan Altaparmakov

Abstract:

For briquetting of metal chips are used hydraulic and mechanical presses. The density of the briquettes in this case is about 60% - 70 % on the density of solid metal. In this work are presented the results of experimental studies for briquetting of metal chips, by using a new technology for impact briquetting. The used chips are by Armco iron, steel, cast iron, copper, aluminum and brass. It has been found that: (i) in a controlled impact the density of the briquettes can be increases up to 30%; (ii) at the same specific impact energy Es (J/sm3) the density of the briquettes increases with increasing of the impact velocity; (iii), realization of the repeated impact leads to decrease of chips density, which can be explained by distribution of elastic waves in the briquette.

Keywords: Briquetting, chips briquetting, impact briquetting.

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