Search results for: statistical optimization

Authors: Nemir Al-Azzawi, Harsa A. Mat Sakim, Wan Ahmed K. Wan Abdullah, Yasmin Mohd Yacob

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Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images, may cause misalignments. To handle with this limitation, we propose to extract the salient edges and extracted control points (CP) of medical images by using efficiency of multiresolution representation of data nonsubsampled contourlet transform (NSCT) that finds the best feature points. The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). The experimental results showed that the proposed method produces totally accurate performance for registration medical CT-MR images.

Keywords: Feature-based registration, mutual information, nonsubsampled contourlet transform, particle swarm optimization.

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Authors: Mehrnoush Shamsfard, Azadeh Nematzadeh, Sarah Motiee

Abstract:

Increasing growth of information volume in the internet causes an increasing need to develop new (semi)automatic methods for retrieval of documents and ranking them according to their relevance to the user query. In this paper, after a brief review on ranking models, a new ontology based approach for ranking HTML documents is proposed and evaluated in various circumstances. Our approach is a combination of conceptual, statistical and linguistic methods. This combination reserves the precision of ranking without loosing the speed. Our approach exploits natural language processing techniques for extracting phrases and stemming words. Then an ontology based conceptual method will be used to annotate documents and expand the query. To expand a query the spread activation algorithm is improved so that the expansion can be done in various aspects. The annotated documents and the expanded query will be processed to compute the relevance degree exploiting statistical methods. The outstanding features of our approach are (1) combining conceptual, statistical and linguistic features of documents, (2) expanding the query with its related concepts before comparing to documents, (3) extracting and using both words and phrases to compute relevance degree, (4) improving the spread activation algorithm to do the expansion based on weighted combination of different conceptual relationships and (5) allowing variable document vector dimensions. A ranking system called ORank is developed to implement and test the proposed model. The test results will be included at the end of the paper.

Keywords: Document ranking, Ontology, Spread activation algorithm, Annotation.

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Authors: Mohamad Ghozali Hassan, Abdul Aziz Othman, Mohd Azril Ismail

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Adapting quickly to environmental dynamism is essential for an organization to develop outsourcing strategic and management in order to sustain competitive advantage. This research used the Partial Least Squares Structural Equation Modeling (PLSSEM) tool to investigate the factors of environmental dynamism impact on the strategic outsourcing success among electrical and electronic manufacturing industries in outsourcing management. Statistical results confirm that the inclusion of customer demand, technological change, and competition level as a new combination concept of environmental dynamism, has positive effects on outsourcing success. Additionally, this research demonstrates the acceptability of PLS-SEM as a statistical analysis to furnish a better understanding of environmental dynamism in outsourcing management in Malaysia. A practical finding contributes to academics and practitioners in the field of outsourcing management.

Keywords: Environmental Dynamism, Customer Demand, Technological Change, Competition Level, Outsourcing Success.

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Authors: Vinod Kumar Khera, R. K. Sharma, A. K. Gupta

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Power dissipation increases exponentially during test mode as compared to normal operation of the circuit. In extreme cases, test power is more than twice the power consumed during normal operation mode. Test vector generation scheme is key component in deciding the power hungriness of a circuit during testing. Test vector count and consequent leakage current are functions of test vector generation scheme. Fault based test vector count optimization has been presented in this work. It helps in reducing test vector count and the leakage current. In the presented scheme, test vectors have been reduced by extracting essential child vectors. The scheme has been tested experimentally using stuck at fault models and results ensure the reduction in test vector count.

Keywords: Low power VLSI testing, independent fault, essential faults, test vector reduction.

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Authors: Andrey A. Chernousov, Ben Y. B. Chan

Abstract:

The thermal behavior of a large-scale, phase change material (PCM) enhanced building envelope system was studied in regard to the need for pre-fabricated construction in subtropical regions. The proposed large-scale envelope consists of a reinforced aluminum skin, insulation core, phase change material and reinforced gypsum board. The PCM impact on an energy efficiency of an enveloped room was resolved by validation of the EnergyPlus numerical scheme and optimization of a smart material location in the core. The PCM location was optimized by a minimization method of a cooling energy demand. It has been shown that there is good agreement between the test and simulation results. The optimal location of the PCM layer in Hong Kong summer conditions has been then recomputed for core thicknesses of 40, 60 and 80 mm. A non-dimensional value of the optimal PCM location was obtained to be same for all the studied cases and the considered external and internal conditions.

Keywords: Thermal performance, phase change material, energy efficiency, PCM optimization.

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Authors: Muammer Ozkan, Orkun Ozener, Irfan Yavasliol

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The fuel cost of the motor vehicle operating on its common route is an important part of the operating cost. Therefore, the importance of the fuel saving is increasing day by day. One of the parameters which improve fuel saving is the regulation of driving characteristics. The number and duration of stop is increased by the heavy traffic load. It is possible to improve the fuel saving with regulation of traffic flow and driving characteristics. The researches show that the regulation of the traffic flow decreases fuel consumption, but it is not enough to improve fuel saving without the regulation of driving characteristics. This study analyses the fuel consumption of two trips of city bus operating on its common route and determines the effect of traffic density and driving characteristics on fuel consumption. Finally it offers some suggestions about regulation of driving characteristics to improve the fuel saving. Fuel saving is determined according to the results obtained from simulation program. When experimental and simulation results are compared, it has been found that the fuel saving was reached up the to 40 percent ratios.

Keywords: Fuel Consumption, Fuel Economy, Driving Characteristics, Optimization

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Authors: Dong-Chul Lee, Byung-Kwan Oh, Se-Woon Choi, Hyo-Sun Park

Abstract:

The unanticipated brittle fracture of connection of the steel moment resisting frame (SMRF) occurred in 1994 the Northridge earthquake. Since then, the researches for the vulnerability of connection of the existing SMRF and for rehabilitation of those buildings were conducted. This paper suggests performance-based optimal seismic retrofit technique using connection upgrade. For optimal design, a multi-objective genetic algorithm(NSGA-II) is used. One of the two objective functions is to minimize initial cost and another objective function is to minimize lifetime seismic damages cost. The optimal algorithm proposed in this paper is performed satisfying specified performance objective based on FEMA 356. The nonlinear static analysis is performed for structural seismic performance evaluation. A numerical example of SAC benchmark SMRF is provided using the performance-based optimal seismic retrofit technique proposed in this paper

Keywords: connection upgrade, performace-based seismicdesign, seismic retrofit, multi-objective optimization

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Authors: Jain-Shing Liu

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In this paper, we present a matrix game-theoretic cross-layer optimization formulation to maximize the network lifetime in wireless ad hoc networks with network coding. To this end, we introduce a cross-layer formulation of general NUM (network utility maximization) that accommodates routing, scheduling, and stream control from different layers in the coded networks. Specifically, for the scheduling problem and then the objective function involved, we develop a matrix game with the strategy sets of the players corresponding to hyperlinks and transmission modes, and design the payoffs specific to the lifetime. In particular, with the inherit merit that matrix game can be solved with linear programming, our cross-layer programming formulation can benefit from both game-based and NUM-based approaches at the same time by cooperating the programming model for the matrix game with that for the other layers in a consistent framework. Finally, our numerical example demonstrates its performance results on a well-known wireless butterfly network to verify the cross-layer optimization scheme.

Keywords: Cross-layer design, Lifetime maximization, Matrix game, Network coding

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Authors: Abdulqader M. Mohsen, Ahamad Tajudin Khader, Dhanesh Ramachandram, Abdullatif Ghallab

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The physical methods for RNA secondary structure prediction are time consuming and expensive, thus methods for computational prediction will be a proper alternative. Various algorithms have been used for RNA structure prediction including dynamic programming and metaheuristic algorithms. Musician's behaviorinspired harmony search is a recently developed metaheuristic algorithm which has been successful in a wide variety of complex optimization problems. This paper proposes a harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similar to the native structure. HSRNAFold is compared with dynamic programming benchmark mfold and metaheuristic algorithms (RnaPredict, SetPSO and HelixPSO). The results showed that HSRNAFold is comparable to mfold and better than metaheuristics in finding the minimum free energies and the number of correct base pairs.

Keywords: Metaheuristic algorithms, dynamic programming algorithms, harmony search optimization, RNA folding, Minimum free energy.

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Authors: Kolar Jan, Dvorak Vaclav

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In this study, an optimization of supersonic air-to-air ejector is carried out by a recently developed single-objective genetic algorithm based on adaption of sequence of individuals. Adaptation of sequence is based on Shape-based distance of individuals and embedded micro-genetic algorithm. The optimal sequence found defines the succession of CFD-aimed objective calculation within each generation of regular micro-genetic algorithm. A spring-based deformation mutates the computational grid starting the initial individualvia adapted population in the optimized sequence. Selection of a generation initial individual is knowledge-based. A direct comparison of the newly defined and standard micro-genetic algorithm is carried out for supersonic air-to-air ejector. The only objective is to minimize the loose of total stagnation pressure in the ejector. The result is that sequence-adopted micro-genetic algorithm can provide comparative results to standard algorithm but in significantly lower number of overall CFD iteration steps.

Keywords: Grid deformation, Micro-genetic algorithm, shapebased sequence, supersonic ejector.

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Authors: A. D. Parekh, P. R. Tailor, H.R Jivanramajiwala

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The present work deals with optimization of cascade refrigeration system using eco friendly refrigerants pair R507A and R23. R507A is azeotropic mixture composed of HFC refrigerants R125/R143a (50%/50% by wt.). R23 is a single component HFC refrigerant used as replacement to CFC refrigerant R13 in low temperature applications. These refrigerants have zero ozone depletion potential and are non-flammable. Optimization of R507AR23 cascade refrigeration system performance parameters such as minimum work required, refrigeration effect, coefficient of performance and exergetic efficiency was carried out in terms of eight operating parameters- combinations using Genetic Algorithm tool. The eight operating parameters include (1) low side evaporator temperature (2) high side condenser temperature (3) temperature difference in the cascade heat exchanger (4) low side condenser temperature (5) low side degree of subcooling (6) high side degree of subcooling (7) low side degree of superheating (8) high side degree of superheating. Results show that for minimum work system should operate at high temperature in low side evaporator, low temperature in high side condenser, low temperature difference in cascade condenser, high temperature in low side condenser and low degree of subcooling and superheating in both side. For maximum refrigeration effect system should operate at high temperature in low side evaporator, high temperature in high side condenser, high temperature difference in cascade condenser, low temperature in low side condenser and higher degree of subcooling in LT and HT side. For maximum coefficient of performance and exergetic efficiency, system should operate at high temperature in low side evaporator, low temperature in high side condenser, low temperature difference in cascade condenser, high temperature in low side condenser and higher degree of subcooling and superheating in low side of the system.

Keywords: Cascade refrigeration system, Genetic Algorithm, R507A, R23,

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Authors: H. Shayeghi, M. Mahdavi, A. Kazemi

Abstract:

Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, transmission expansion planning considering network adequacy restriction has not been investigated. Thus, in this paper, STNEP problem is being studied considering network adequacy restriction using discrete particle swarm optimization (DPSO) algorithm. The goal of this paper is obtaining a configuration for network expansion with lowest expansion cost and a specific adequacy. The proposed idea has been tested on the Garvers network and compared with the decimal codification genetic algorithm (DCGA). The results show that the network will possess maximum efficiency economically. Also, it is shown that precision and convergence speed of the proposed DPSO based method for the solution of the STNEP problem is more than DCGA approach.

Keywords: DPSO algorithm, Adequacy restriction, STNEP.

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Authors: T. Ganesan, M. S. Aris, I. Elamvazuthi, Momen Kamal Tageldeen

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Terms set in power purchase agreements (PPA) challenge power utility companies in balancing between the returns (from maximizing power production) and securing long term supply contracts at capped production. The production limitation set in the PPA has driven efforts to maximize profits through efficient and economic power production. In this paper, a combined industrial-scale gas turbine (GT) - absorption chiller (AC) system is considered to cool the GT air intake for reducing the plant’s heat rate (HR). This GT-AC system is optimized while considering power output limitations imposed by the PPA. In addition, the proposed formulation accounts for uncertainties in the ambient temperature using Type-2 fuzzy programming. Using the enhanced chaotic differential evolution (CEDE), the Pareto frontier was constructed and the optimization results are analyzed in detail.

Keywords: Absorption chillers, turbine inlet air cooling, power purchase agreement, multiobjective optimization, type-2 fuzzy programming, chaotic differential evolution.

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

Abstract:

The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: Data mining, Korean linguistic feature, literary fiction, relationship extraction.

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Authors: Swetha Priya Darshini Thammadi, Sateesh Kumar Pisini, Sanjay Kumar Shukla

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Source apportionment using Dispersion model depends primarily on the quality of Emission Inventory. In the present study, a CMB receptor model has been used to identify the sources of PM_2.5, while the AERMOD dispersion model has been used to account for missing sources of PM_2.5 in the Emission Inventory. A statistical approach has been developed to quantify the missing sources not considered in the Emission Inventory. The inventory of each grid was improved by adjusting emissions based on road lengths and deficit in measured and modelled concentrations. The results showed that in CMB analyses, fugitive sources - soil and road dust - contribute significantly to ambient PM_2.5 pollution. As a result, AERMOD significantly underestimated the ambient air concentration at most locations. The revised Emission Inventory showed a significant improvement in AERMOD performance which is evident through statistical tests.

Keywords: CMB, GIS, AERMOD, PM2.5, fugitive, emission inventory.

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Authors: Barath Kumar, Oliver Niggemann, Juergen Jasperneite

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Model-based approaches have been applied successfully to a wide range of tasks such as specification, simulation, testing, and diagnosis. But one bottleneck often prevents the introduction of these ideas: Manual modeling is a non-trivial, time-consuming task. Automatically deriving models by observing and analyzing running systems is one possible way to amend this bottleneck. To derive a model automatically, some a-priori knowledge about the model structure–i.e. about the system–must exist. Such a model formalism would be used as follows: (i) By observing the network traffic, a model of the long-term system behavior could be generated automatically, (ii) Test vectors can be generated from the model, (iii) While the system is running, the model could be used to diagnose non-normal system behavior. The main contribution of this paper is the introduction of a model formalism called 'probabilistic regression automaton' suitable for the tasks mentioned above.

Keywords: Model-based approach, Probabilistic regression automata, Statistical models and Timed automata.

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Authors: Rakesh Prajapati, Purvik Patel, Hardik Patel

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Productivity and quality are two important aspects that have become great concerns in today’s competitive global market. Every production/manufacturing unit mainly focuses on these areas in relation to the process, as well as the product developed. The electrical discharge machining (EDM) process, even now it is an experience process, wherein the selected parameters are still often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. Material Removal Rate (MRR) during the process has been considered as a productivity estimate with the aim to maximize it, with an intention of minimizing surface roughness taken as most important output parameter. These two opposites in nature requirements have been simultaneously satisfied by selecting an optimal process environment (optimal parameter setting). Objective function is obtained by Regression Analysis and Analysis of Variance. Then objective function is optimized using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness improved using optimized machining parameters.

Keywords: Material removal rate, TWR, OC, DOE, ANOVA, MINITAB.

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Authors: K. Prajindra Sankar, S.K. Tiong, S.P. Johnny Koh

Abstract:

This paper presents a tested research concept that implements a complex evolutionary algorithm, genetic algorithm (GA), in a multi-microcontroller environment. Parallel Distributed Genetic Algorithm (PDGA) is employed in adaptive beam forming technique to reduce power usage of adaptive antenna at WCDMA base station. Adaptive antenna has dynamic beam that requires more advanced beam forming algorithm such as genetic algorithm which requires heavy computation and memory space. Microcontrollers are low resource platforms that are normally not associated with GAs, which are typically resource intensive. The aim of this project was to design a cooperative multiprocessor system by expanding the role of small scale PIC microcontrollers to optimize WCDMA base station transmitter power. Implementation results have shown that PDGA multi-microcontroller system returned optimal transmitted power compared to conventional GA.

Keywords: Microcontroller, Genetic Algorithm, Adaptiveantenna, Power optimization.

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Authors: J. Barati, A. Saeedian, S. S. Mortazavi

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The main objective of this paper is a comparative investigate in enhancement of damping power system oscillation via coordinated design of the power system stabilizer (PSS) and static synchronous series compensator (SSSC) and static synchronous compensator (STATCOM). The design problem of FACTS-based stabilizers is formulated as a GA based optimization problem. In this paper eigenvalue analysis method is used on small signal stability of single machine infinite bus (SMIB) system installed with SSSC and STATCOM. The generator is equipped with a PSS. The proposed stabilizers are tested on a weakly connected power system with different disturbances and loading conditions. This aim is to enhance both rotor angle and power system stability. The eigenvalue analysis and non-linear simulation results are presented to show the effects of these FACTS-based stabilizers and reveal that SSSC exhibits the best effectiveness on damping power system oscillation.

Keywords: Power system stability, PSS, SSSC, STATCOM, Coordination, Optimization, Damping Oscillations.

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Authors: D.Venkatesan, K.Kannan, S. Raja Balachandar

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In this paper a new Genetic Algorithm based on a heuristic operator and Centre of Mass selection operator (CMGA) is designed for the unbounded knapsack problem(UKP), which is NP-Hard combinatorial optimization problem. The proposed genetic algorithm is based on a heuristic operator, which utilizes problem specific knowledge. This center of mass operator when combined with other Genetic Operators forms a competitive algorithm to the existing ones. Computational results show that the proposed algorithm is capable of obtaining high quality solutions for problems of standard randomly generated knapsack instances. Comparative study of CMGA with simple GA in terms of results for unbounded knapsack instances of size up to 200 show the superiority of CMGA. Thus CMGA is an efficient tool of solving UKP and this algorithm is competitive with other Genetic Algorithms also.

Keywords: Genetic Algorithm, Unbounded Knapsack Problem, Combinatorial Optimization, Meta-Heuristic, Center of Mass

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Authors: Khaled M. EL-Naggar

Abstract:

Synchronizing and damping torque coefficients of a synchronous machine can give a quite clear picture for machine behavior during transients. These coefficients are used as a power system transient stability measurement. In this paper, a crow search optimization algorithm is presented and implemented to study the power system stability during transients. The algorithm makes use of the machine responses to perform the stability study in time domain. The problem is formulated as a dynamic estimation problem. An objective function that minimizes the error square in the estimated coefficients is designed. The method is tested using practical system with different study cases. Results are reported and a thorough discussion is presented. The study illustrates that the proposed method can estimate the stability coefficients for the critical stable cases where other methods may fail. The tests proved that the proposed tool is an accurate and reliable tool for estimating the machine coefficients for assessment of power system stability.

Keywords: Optimization, estimation, synchronous, machine, crow search.

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Authors: Thi T. Nguyen, Lee N. Gordon-Brown

Abstract:

A new deployment of the multiple criteria decision making (MCDM) techniques: the Simple Additive Weighting (SAW), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for portfolio allocation, is demonstrated in this paper. Rather than exclusive reference to mean and variance as in the traditional mean-variance method, the criteria used in this demonstration are the first four moments of the portfolio distribution. Each asset is evaluated based on its marginal impacts to portfolio higher moments that are characterized by trapezoidal fuzzy numbers. Then centroid-based defuzzification is applied to convert fuzzy numbers to the crisp numbers by which SAW and TOPSIS can be deployed. Experimental results suggest the similar efficiency of these MCDM approaches to selecting dominant assets for an optimal portfolio under higher moments. The proposed approaches allow investors flexibly adjust their risk preferences regarding higher moments via different schemes adapting to various (from conservative to risky) kinds of investors. The other significant advantage is that, compared to the mean-variance analysis, the portfolio weights obtained by SAW and TOPSIS are consistently well-diversified.

Keywords: Fuzzy numbers, SAW, TOPSIS, portfolio optimization, higher moments, risk management.

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Authors: Ilze Stokmane

Abstract:

Economic processes underway in the country directly and indirectly affect the welfare of the people and the social environment, starting with job security and having a direct impact on the qualitative and safe living environment.

The paper describes existing situation and gives analysis of the regional development policy determination and implementation in the all three Baltic countries. According statistical indicators there are differences between implementation of the regional development activities between all Baltic countries and in regions of inside each country.

It is analyzed more detail differences between regions in Latvia, Lithuania and Estonia according possibility to evaluate success of development processes in regions of the Baltic countries. The descriptive analyze of documents, statistical indicators at national level and regional level were used in the research.

Keywords: Baltic countries, city environment, regional development, urban areas.

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Authors: A. Beheshti, S. Salkhordeh, H. Amini

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Based on statistics released by Islamic Republic of Iran Police (IRIP), from among the total 9555 motorcycle accidents that happened in 2007, 857 riders died and 11219 one got injured. If we also consider the death toll and injuries of other vehicles' accidents resulted from traffic violation by motorcycle riders, then paying attention to the motorcycle accidents seems to be very necessary. Therefore, in this study we tried to investigate the traits and issues related to production, application, and training, along with causes of motorcycle accidents from 4 perspectives of road, human, environment and vehicle and also based on statistical and geographical analysis of accident-sheets prepared by Iran Road Patrol Department (IRPD). Unfamiliarity of riders with regulations and techniques of motorcycling, disuse of safety equipments, inefficiency of roads and design of junctions for safe trafficking of motorcycles and finally the lack of sufficient control of responsible organizations are among the major causes which lead to these accidents.

Keywords: Motorcycle, Motorcycle riders, Road accidents, Statistical analysis of accidents.

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Authors: H. Ashrafi, S. Ebrahimi, H. Kamalzadeh

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With the development of business competition, it is important for healthcare providers to improve their service qualities. In order to improve service quality of a clinic, four important dimensions are defined: tangibles, responsiveness, empathy, and reliability. Moreover, there are several service stages in hospitals such as financial screening and examination. One of the most challenging limitations for improving service quality is budget which impressively affects the service quality. In this paper, we present an approach to address budget uncertainty and provide guidelines for service resource allocation. In this paper, a service quality improvement approach is proposed which can be adopted to multistage service processes to improve service quality, while controlling the costs. A multi-objective function based on the importance of each area and dimension is defined to link operational variables to service quality dimensions. The results demonstrate that our approach is not ultra-conservative and it shows the actual condition very well. Moreover, it is shown that different strategies can affect the number of employees in different stages.

Keywords: Service quality assessment, healthcare resource allocation, robust optimization, budget uncertainty.

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Authors: L. H. Chong, Y. Y. Chen

Abstract:

Information is increasing in volumes; companies are overloaded with information that they may lose track in getting the intended information. It is a time consuming task to scan through each of the lengthy document. A shorter version of the document which contains only the gist information is more favourable for most information seekers. Therefore, in this paper, we implement a text summarization system to produce a summary that contains gist information of oil and gas news articles. The summarization is intended to provide important information for oil and gas companies to monitor their competitor-s behaviour in enhancing them in formulating business strategies. The system integrated statistical approach with three underlying concepts: keyword occurrences, title of the news article and location of the sentence. The generated summaries were compared with human generated summaries from an oil and gas company. Precision and recall ratio are used to evaluate the accuracy of the generated summary. Based on the experimental results, the system is able to produce an effective summary with the average recall value of 83% at the compression rate of 25%.

Keywords: Information retrieval, text summarization, statistical approach.

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Authors: Hui-Min Ting, Tsair-Fwu Lee, Ming-Yuan Cho, Pei-Ju Chao, Chun-Ming Chang, Long-Chang Chen, Fu-Min Fang

Abstract:

To evaluate the ability to predict xerostomia after radiotherapy, we constructed and compared neural network and logistic regression models. In this study, 61 patients who completed a questionnaire about their quality of life (QoL) before and after a full course of radiation therapy were included. Based on this questionnaire, some statistical data about the condition of the patients’ salivary glands were obtained, and these subjects were included as the inputs of the neural network and logistic regression models in order to predict the probability of xerostomia. Seven variables were then selected from the statistical data according to Cramer’s V and point-biserial correlation values and were trained by each model to obtain the respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65 for SSE, and 13.7% and 19.0% for MAPE, respectively. These parameters demonstrate that both neural network and logistic regression methods are effective for predicting conditions of parotid glands.

Keywords: NPC, ANN, logistic regression, xerostomia.

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Authors: Mohammed I. Abouheaf, Sofie Haesaert, Wei-Jen Lee, Frank L. Lewis

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Economic Dispatch is one of the most important power system management tools. It is used to allocate an amount of power generation to the generating units to meet the load demand. The Economic Dispatch problem is a large scale nonlinear constrained optimization problem. In general, heuristic optimization techniques are used to solve non-convex Economic Dispatch problem. In this paper, ideas from Reinforcement Learning are proposed to solve the non-convex Economic Dispatch problem. Q-Learning is a reinforcement learning techniques where each generating unit learn the optimal schedule of the generated power that minimizes the generation cost function. The eligibility traces are used to speed up the Q-Learning process. Q-Learning with eligibility traces is used to solve Economic Dispatch problems with valve point loading effect, multiple fuel options, and power transmission losses.

Keywords: Economic Dispatch, Non-Convex Cost Functions, Valve Point Loading Effect, Q-Learning, Eligibility Traces.

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Authors: Vivian Guo, Yan Qu

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Online marketplaces are not only digital places where consumers buy and sell merchandise, and they are also destinations for brands to connect with real consumers at the moment when customers are in the shopping mindset. For many marketplaces, brands have been important partners through advertising. There can be, however, a risk of advertising impacting a consumer’s shopping journey if it hurts the use experience or takes the user away from the site. Both could lead to the loss of transaction revenue for the marketplace. In this paper, we present user-based methods for cannibalization control by selectively turning off ads to users who are likely to be cannibalized by ads subject to business objectives. We present ways of measuring cannibalization of advertising in the context of an online marketplace and propose novel ways of measuring cannibalization through purchase propensity and uplift modeling. A/B testing has shown that our methods can significantly improve user purchase and engagement metrics while operating within business objectives. To our knowledge, this is the first paper that addresses cannibalization mitigation at the user-level in the context of advertising.

Keywords: Cannibalization, machine learning, online marketplace, revenue optimization, yield optimization.

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Authors: Seong-Bae Park, Hee-Geun Yoon

Abstract:

It is an important task in Korean-English machine translation to classify the gender of names correctly. When a sentence is composed of two or more clauses and only one subject is given as a proper noun, it is important to find the gender of the proper noun for correct translation of the sentence. This is because a singular pronoun has a gender in English while it does not in Korean. Thus, in Korean-English machine translation, the gender of a proper noun should be determined. More generally, this task can be expanded into the classification of the general Korean names. This paper proposes a statistical method for this problem. By considering a name as just a sequence of syllables, it is possible to get a statistics for each name from a collection of names. An evaluation of the proposed method yields the improvement in accuracy over the simple looking-up of the collection. While the accuracy of the looking-up method is 64.11%, that of the proposed method is 81.49%. This implies that the proposed method is more plausible for the gender classification of the Korean names.

Keywords: machine translation, natural language processing, gender of proper nouns, statistical method

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