Search results for: Markovian Decision Process based Adaptive Scheduling

Authors: F. Saccucci

Abstract:

Gamification has now started to take root in the post-secondary classroom. Educators have learned much about gamification to date but there is still a great deal to learn. One definition of gamification is the ability to engage post-secondary students with games that are fun and correlate to class room curriculum. There is no shortage of literature illustrating the advantages of gamification in the class room. This study is an extension of similar thought as well as an extension of a previous study where in class testing proved with the used of paired T-test that gamification did significantly improve the students’ understanding of subject material. Gamification itself in the class room can range from high end computer simulated software to paper based games of which both have advantages and disadvantages. This analysis used a paper based game to highlight certain qualitative advantages of gamification. The paper based game in this analysis was inexpensive, required low preparation time for the faculty member and consumed approximately 20 minutes of class room time. Data for the study was collected through in class student feedback surveys and narrative from the faculty member moderating the game. Students were randomly selected into groups of four. Qualitative advantages identified in this analysis included: 1. Students had a chance to meet, connect and know other students. 2. Students enjoyed the gamification process given there was a sense of fun and competition. 3. The post assessment that followed the simulation game was not part of their grade calculation therefore it was an opportunity to participate in a low risk activity whereby students could subsequently self-assess their understanding of the subject material. 4. In the view of the student, content knowledge did increase after the gamification process. These qualitative advantages identified in this analysis contribute to the argument that there should be an attempt to use gamification in today’s post-secondary class room. The analysis also highlighted that eighty (80) percent of the respondents believe twenty minutes devoted to the gamification process was appropriate, however twenty (20) percentage of respondents believed that rather than scheduling a gamification process and its post quiz in the last week, a review for the final exam may have been more useful. An additional study to this hopes to determine if the scheduling of the gamification had any correlation to a percentage of the students not wanting to be engaged in the process. As well, the additional study hopes to determine at what incremental level of time invested in class room gamification produce no material incremental benefits to the student as well as determine if any correlation exist between respondents preferring not to have it at the end of the semester to students not believing the gamification process added to the increase of their curricular knowledge.

Keywords: Gamification, inexpensive, qualitative advantages, post-secondary.

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Authors: Zilong Feng, Hong Li, Yang Liu, Siriguleng He

Abstract:

In this article, an adaptive least-squares mixed finite element method is studied for pseudo-parabolic integro-differential equations. The solutions of least-squares mixed weak formulation and mixed finite element are proved. A posteriori error estimator is constructed based on the least-squares functional and the posteriori errors are obtained.

Keywords: Pseudo-parabolic integro-differential equation, least squares mixed finite element method, adaptive method, a posteriori error estimates.

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Authors: Danping Lin, C.K.M. Lee

Abstract:

Re-entrant scheduling is an important search problem with many constraints in the flow shop. In the literature, a number of approaches have been investigated from exact methods to meta-heuristics. This paper presents a genetic algorithm that encodes the problem as multi-level chromosomes to reflect the dependent relationship of the re-entrant possibility and resource consumption. The novel encoding way conserves the intact information of the data and fastens the convergence to the near optimal solutions. To test the effectiveness of the method, it has been applied to the resource-constrained re-entrant flow shop scheduling problem. Computational results show that the proposed GA performs better than the simulated annealing algorithm in the measure of the makespan

Keywords: Resource-constrained, re-entrant, genetic algorithm (GA), multi-level encoding

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

Abstract:

Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: Energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning.

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Authors: L. Tao, T. J. Sun, Z. H. Li

Abstract:

The knowledge base of welding defect recognition is essentially incomplete. This characteristic determines that the recognition results do not reflect the actual situation. It also has a further influence on the classification of welding quality. This paper is concerned with the study of a rough set based method to reduce the influence and improve the classification accuracy. At first, a rough set model of welding quality intelligent classification has been built. Both condition and decision attributes have been specified. Later on, groups of the representative multiple compound defects have been chosen from the defect library and then classified correctly to form the decision table. Finally, the redundant information of the decision table has been reducted and the optimal decision rules have been reached. By this method, we are able to reclassify the misclassified defects to the right quality level. Compared with the ordinary ones, this method has higher accuracy and better robustness.

Keywords: intelligent decision, rough set, welding defects, welding quality level

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Authors: M.M. Namazi, S.M. Saghaiannejad, A. Rashidi

Abstract:

In this paper by using the port-controlled Hamiltonian (PCH) systems theory, a full-order nonlinear controlled model is first developed. Then a nonlinear passivity-based robust adaptive control (PBRAC) of switched reluctance motor in the presence of external disturbances for the purpose of torque ripple reduction and characteristic improvement is presented. The proposed controller design is separated into the inner loop and the outer loop controller. In the inner loop, passivity-based control is employed by using energy shaping techniques to produce the proper switching function. The outer loop control is employed by robust adaptive controller to determine the appropriate Torque command. It can also overcome the inherent nonlinear characteristics of the system and make the whole system robust to uncertainties and bounded disturbances. A 4KW 8/6 SRM with experimental characteristics that takes magnetic saturation into account is modeled, simulation results show that the proposed scheme has good performance and practical application prospects.

Keywords: Switched Reluctance Motor, Port HamiltonianSystem, Passivity-Based Control, Torque Ripple Minimization

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Authors: Jadwiga R. Ziolkowska

Abstract:

In this paper, a fuzzy algorithm and a fuzzy multicriteria decision framework are developed and used for a practical question of optimizing biofuels policy making. The methodological framework shows how to incorporate fuzzy set theory in a decision process of finding a sustainable biofuels policy among several policy options. Fuzzy set theory is used here as a tool to deal with uncertainties of decision environment, vagueness and ambiguities of policy objectives, subjectivities of human assessments and imprecise and incomplete information about the evaluated policy instruments.

Keywords: Fuzzy set theory, multi-criteria decision-makingsupport, uncertainties, policy making, biofuels

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Authors: Dharmendra Chourishi “Maitraya”, Sridevi Seshadri

Abstract:

Bluetooth is a personal wireless communication technology and is being applied in many scenarios. It is an emerging standard for short range, low cost, low power wireless access technology. Current existing MAC (Medium Access Control) scheduling schemes only provide best-effort service for all masterslave connections. It is very challenging to provide QoS (Quality of Service) support for different connections due to the feature of Master Driven TDD (Time Division Duplex). However, there is no solution available to support both delay and bandwidth guarantees required by real time applications. This paper addresses the issue of how to enhance QoS support in a Bluetooth piconet. The Bluetooth specification proposes a Round Robin scheduler as possible solution for scheduling the transmissions in a Bluetooth Piconet. We propose an algorithm which will reduce the bandwidth waste and enhance the efficiency of network. We define token counters to estimate traffic of real-time slaves. To increase bandwidth utilization, a back-off mechanism is then presented for best-effort slaves to decrease the frequency of polling idle slaves. Simulation results demonstrate that our scheme achieves better performance over the Round Robin scheduling.

Keywords: Piconet, Medium Access Control, Polling algorithm, Scheduling, QoS, Time Division Duplex (TDD).

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Authors: Wanatchapong Kongkaew

Abstract:

This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

 

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness.

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Authors: Hyeong-Geon Lee, Jae-Young Park, Suk-ki Lee, Jong-Tae Kim

Abstract:

The LMS adaptive filter has several parameters which can affect their performance. From among these parameters, most papers handle the step size parameter for controlling the performance. In this paper, we approach three parameters: step-size, filter tap-size and filter form. The regression analysis is used for defining the relation between parameters and performance of LMS adaptive filter with using the system level simulation results. The results present that all parameters have performance trends in each own particular form, which can be estimated from equations drawn by regression analysis.

Keywords: System level model, adaptive LMS FIR filter, regression analysis, systemC.

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Authors: S. Ghorbani, N. I. Polushin

Abstract:

In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: Cutting condition, surface roughness, decision tree, CART algorithm.

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Authors: Ibrahim F. Jasim

Abstract:

This paper presents a new stable robust adaptive controller and observer design for a class of nonlinear systems that contain i. Coupling of unmeasured states and unknown parameters ii. Unknown dead zone at the system actuator. The system is firstly cast into a modified form in which the observer and parameter estimation become feasible. Then a stable robust adaptive controller, state observer, parameter update laws are derived that would provide global adaptive system stability and desirable performance. To validate the approach, simulation was performed to a single-link mechanical system with a dynamic friction model and unknown dead zone exists at the system actuation. Then a comparison is presented with the results when there is no dead zone at the system actuation.

Keywords: Dead Zone, Nonlinear Systems, Observer, Robust Adaptive Control.

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Authors: Tomáš Ludík, Josef Navrátil

Abstract:

Managing the emergency situations at the Emergency Staff requires a high co-operation between its members and their fast decision making. For these purpose it is necessary to prepare Emergency Staff members adequately. The aim of this paper is to describe the development of information support that focuses to emergency staff processes and effective decisions. The information support is based on the principles of process management, and Process Framework for Emergency Management was used during the development. The output is the information system that allows users to simulate an emergency situation, including effective decision making. The system also evaluates the progress of the emergency processes solving by quantitative and qualitative indicators. By using the simulator, a higher quality education of specialists can be achieved. Therefore, negative impacts resulting from arising emergency situations can be directly reduced.

Keywords: Information Support for Emergency Staff, Effective Decisions, Process Framework, Simulation of Emergency Processes, System Development.

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Authors: Omar Al Denali, Abdelaziz Badi

Abstract:

The ASTM A516 Grade 70 steel is a suitable material used for the fabrication of boiler pressure vessels working in moderate and lower temperature services, and it has good weldability and excellent notch toughness. The post-weld heat treatment (PWHT) or stress-relieving heat treatment has significant effects on avoiding the martensite transformation and resulting in high hardness, which can lead to cracking in the heat-affected zone (HAZ). An adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the material tensile strength of PWHT experiments. The ANFIS models presented excellent predictions, and the comparison was carried out based on the mean absolute percentage error between the predicted values and the experimental values. The ANFIS model gave a Mean Absolute Percentage Error of 0.556%, which confirms the high accuracy of the model.

Keywords: Prediction, post-weld heat treatment, adaptive neuro-fuzzy inference system, ANFIS, mean absolute percentage error.

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Authors: Ming Wen, Nasim Nezamoddini

Abstract:

Sophisticated numerical simulations like finite element analysis (FEA) involve a complicated process from model setup to post-processing tasks that require replication of time-consuming steps. Utilizing FEA automation program simplifies the complexity of the involved steps while minimizing human errors in analysis set up, calculations, and results processing. One of the main challenges in designing FEA automation programs is to identify user requirements and link them to possible design alternatives. This paper presents a decision-making framework to design a Python based FEA automation program for modal analysis, frequency response analysis, and random vibration fatigue (RVF) analysis procedures. Analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are applied to evaluate design alternatives considering the feedback received from experts and program users.

Keywords: FEA, random vibration fatigue, process automation, AHP, TOPSIS, multiple-criteria decision-making, MCDM.

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Authors: M. Heydari, S. A. Torabi

Abstract:

The problem of lot sizing, sequencing and scheduling multiple products in flow line production systems has been studied by several authors. Almost all of the researches in this area assumed that setup times and costs are sequence –independent even though sequence dependent setups are common in practice. In this paper we present a new mixed integer non linear program (MINLP) and a heuristic method to solve the problem in sequence dependent case. Furthermore, a genetic algorithm has been developed which applies this constructive heuristic to generate initial population. These two proposed solution methods are compared on randomly generated problems. Computational results show a clear superiority of our proposed GA for majority of the test problems.

Keywords: Economic lot scheduling problem, finite horizon, genetic algorithm, mixed zero-one nonlinear programming, sequence-dependent.

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Authors: Ahmed Abbas, M.R. M Rizk, Mohamed El-Sayed

Abstract:

This paper presents a new adaptive DMC controller that improves the controller performance in case of plant-model mismatch. The new controller monitors the plant measured output, compares it with the model output and calculates weights applied to the controller move. Simulations show that the new controller can help improve control performance and avoid instability in case of severe model mismatches.

Keywords: Adaptive control, dynamic matrix control, DMC, model predictive control

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Authors: P. Visutsak

Abstract:

The study of the interaction between humans and computers has been emerging during the last few years. This interaction will be more powerful if computers are able to perceive and respond to human nonverbal communication such as emotions. In this study, we present the image-based approach to emotion classification through lower facial expression. We employ a set of feature points in the lower face image according to the particular face model used and consider their motion across each emotive expression of images. The vector of displacements of all feature points input to the Adaptive Support Vector Machines (A-SVMs) classifier that classify it into seven basic emotions scheme, namely neutral, angry, disgust, fear, happy, sad and surprise. The system was tested on the Japanese Female Facial Expression (JAFFE) dataset of frontal view facial expressions [7]. Our experiments on emotion classification through lower facial expressions demonstrate the robustness of Adaptive SVM classifier and verify the high efficiency of our approach.

Keywords: emotion classification, facial expression, adaptive support vector machines, facial expression classifier.

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Authors: S. da Costa, D. Páez, E. Martínez, A. Torres, M. Beramendi, D. Hermosilla, M. Muratori

Abstract:

This study examined the association between creative performance, organizational climate and leadership, affectivity, shared flow, and group decision making. The sample consisted of 315 cadets of a military academic unit of South America. Satisfaction with the decision-making process during a creative task was associated with the usefulness and effectiveness of the ideas generated by the teams with a weighted average correlation of r = .18. Organizational emotional climate, positive and innovation leadership were associated with this group decision-making process r = .25, with shared flow, r = .29 and with positive affect felt during the performance of the creative task, r = .12. In a sequential mediational analysis positive organizational leadership styles were significantly associated with decision-making process and trough cohesion with utility and efficacy of the solution of a creative task. Satisfactory decision-making was related to shared flow during the creative task at collective or group level, and positive affect with flow at individual level.This study examined the association between creative performance, organizational climate and leadership, affectivity, shared flow, and group decision making. The sample consisted of 315 cadets of a military academic unit of South America. Satisfaction with the decision-making process during a creative task was associated with the usefulness and effectiveness of the ideas generated by the teams with a weighted average correlation of r = .18. Organizational emotional climate, positive and innovation leadership were associated with this group decision-making process r = .25, with shared flow, r = .29 and with positive affect felt during the performance of the creative task, r = .12. In a sequential mediational analysis positive organizational leadership styles were significantly associated with decision-making process and trough cohesion with utility and efficacy of the solution of a creative task. Satisfactory decision-making was related to shared flow during the creative task at collective or group level, and positive affect with flow at individual level.

Keywords: Creativity, innovation, military, organization, teams.

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Authors: Govind Sharan Dangayach, Himanshu Bhatt

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The paper attempts to overcome the fluctuations occurring in demand of the components in an automotive sector company. Resource and time being the strict constraints, the production is not able to match the pace of the fluctuating demand. So, we introduce some production schedules that help in meeting out the required demand. The merits and demerits of the approaches are also highlighted.

Keywords: Production scheduling, Demand rise, Capacity constrained resource (CCR), Overtime.

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Authors: Saeed Mian Qaisar, Laurent Fesquet, Marc Renaudin

Abstract:

Mostly the systems are dealing with time varying signals. The Power efficiency can be achieved by adapting the system activity according to the input signal variations. In this context an adaptive rate filtering technique, based on the level crossing sampling is devised. It adapts the sampling frequency and the filter order by following the input signal local variations. Thus, it correlates the processing activity with the signal variations. Interpolation is required in the proposed technique. A drastic reduction in the interpolation error is achieved by employing the symmetry during the interpolation process. Processing error of the proposed technique is calculated. The computational complexity of the proposed filtering technique is deduced and compared to the classical one. Results promise a significant gain of the computational efficiency and hence of the power consumption.

Keywords: Level Crossing Sampling, Activity Selection, Rate Filtering, Computational Complexity, Interpolation Error.

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Authors: Mohsen Pirdashti, Arezou Ghadi, Mehrdad Mohammadi, Gholamreza Shojatalab

Abstract:

Chemical industry project management involves complex decision making situations that require discerning abilities and methods to make sound decisions. Project managers are faced with decision environments and problems in projects that are complex. In this work, case study is Research and Development (R&D) project selection. R&D is an ongoing process for forward thinking technology-based chemical industries. R&D project selection is an important task for organizations with R&D project management. It is a multi-criteria problem which includes both tangible and intangible factors. The ability to make sound decisions is very important to success of R&D projects. Multiple-criteria decision making (MCDM) approaches are major parts of decision theory and analysis. This paper presents all of MCDM approaches for use in R&D project selection. It is hoped that this work will provide a ready reference on MCDM and this will encourage the application of the MCDM by chemical engineering management.

Keywords: Chemical Engineering, R&D Project, MCDM, Selection.

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Authors: Fatemeh Azimi, Razeeh Sadat Aboutalebi, Amir Abbas Najafi

Abstract:

In this paper the multi-mode resource-constrained project scheduling problem with discounted cash flows is considered. Minimizing the makespan and maximization the net present value (NPV) are the two common objectives that have been investigated in the literature. We apply one evolutionary algorithm named multiobjective particle swarm optimization (MOPSO) to find Pareto front solutions. We used standard sets of instances from the project scheduling problem library (PSPLIB). The results are computationally compared respect to different metrics taken from the literature on evolutionary multi-objective optimization.

Keywords: Evolutionary multi-objective optimization makespan, multi-mode, resource constraint, net present value.

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Authors: N. Mahendran, R. Madhuranthi

Abstract:

Wireless sensor networks (WSNs), are constantly in demand to process information more rapidly with less energy and area cost. Presently, processor based solutions have difficult to achieve high processing speed with low-power consumption. This paper presents a simple and accurate data processing scheme for low power wireless sensor node, based on reduced number of processing element (PE). The presented model provides a simple recursive structure (SRS) to process the sampled data in the wireless sensor environment and to reduce the power consumption in wireless sensor node. Based on this model, to process the incoming samples and produce a smaller amount of data sufficient to reconstruct the original signal. The ModelSim simulator used to simulate SRS structure. Functional simulation is carried out for the validation of the presented architecture. Xilinx Power Estimator (XPE) tool is used to measure the power consumption. The experimental results show the average power consumption of 91 mW; this is 42% improvement compared to the folded tree architecture.

Keywords: Power consumption, energy efficiency, low power WSN node, recursive structure, sleep/wake scheduling.

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Authors: C. Ardil

Abstract:

This paper introduces a new method for multiplecriteria decision making (MCDM) that avoids order reversal and ensures consistency in decision-making. The proposed method involves range targeting of benefit and cost criteria vectors for range normalization of the initial decision matrix. The Reference Linear Combination (RLC) is used to avoid the rank reversal problem. The preference order generated from the target score matrix does not require relative comparisons between alternatives but relies on a chosen reference solution point after transforming the original decision matrix into an MCDM problem by specifying the minimum and maximum bounds of each criterion. The efficiency and applicability of the proposed RLC method were demonstrated in the selection of commercial passenger aircraft. 

Keywords: Aircraft selection, reference linear combination (RLC), multiple criteria decision-making, MCDM

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Authors: Gaurav Kumar, Dharmbir Prasad, Rudra Pratap Singh

Abstract:

In the current work, adaptive extended Kalman filter (AEKF) is presented for solution of ground radar based ballistic missile (BM) tracking problem in re-entry phase with unknown ballistic coefficient. The estimation of trajectory of any BM in re-entry phase is extremely difficult, because of highly non-linear motion of BM. The estimation accuracy of AEKF has been tested for a typical test target tracking problem adopted from literature. Further, the approach of AEKF is compared with extended Kalman filter (EKF). The simulation result indicates the superiority of the AEKF in solving joint parameter and state estimation problems.

Keywords: Adaptive, AEKF, ballistic missile, EKF, re-entry phase, target tracking.

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Authors: Yaojun Wang, Yaoqing Wang

Abstract:

Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: Case-based reasoning, decision tree, stock selection, machine learning.

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Authors: Axel Panning, Robert Niese, Ayoub Al-Hamadi, Bernd Michaelis

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The segmentation of mouth and lips is a fundamental problem in facial image analyisis. In this paper we propose a method for lip segmentation based on rg-color histogram. Statistical analysis shows, using the rg-color-space is optimal for this purpose of a pure color based segmentation. Initially a rough adaptive threshold selects a histogram region, that assures that all pixels in that region are skin pixels. Based on that pixels we build a gaussian model which represents the skin pixels distribution and is utilized to obtain a refined, optimal threshold. We are not incorporating shape or edge information. In experiments we show the performance of our lip pixel segmentation method compared to the ground truth of our dataset and a conventional watershed algorithm.

Keywords: Feature extraction, Segmentation, Image processing, Application

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Authors: Remica Aggarwal, Sanjeet Singh

Abstract:

The decision to recruit manpower in an organization requires clear identification of the criteria (attributes) that distinguish successful from unsuccessful performance. The choice of appropriate attributes or criteria in different levels of hierarchy in an organization is a multi-criteria decision problem and therefore multi-criteria decision making (MCDM) techniques can be used for prioritization of such attributes. Analytic Hierarchy Process (AHP) is one such technique that is widely used for deciding among the complex criteria structure in different levels. In real applications, conventional AHP still cannot reflect the human thinking style as precise data concerning human attributes are quite hard to be extracted. Fuzzy logic offers a systematic base in dealing with situations, which are ambiguous or not well defined. This study aims at defining a methodology to improve the quality of prioritization of an employee-s performance measurement attributes under fuzziness. To do so, a methodology based on the Extent Fuzzy Analytic Hierarchy Process is proposed. Within the model, four main attributes such as Subject knowledge and achievements, Research aptitude, Personal qualities and strengths and Management skills with their subattributes are defined. The two approaches conventional AHP approach and the Extent Fuzzy Analytic Hierarchy Process approach have been compared on the same hierarchy structure and criteria set.

Keywords: AHP, Extent analysis method, Fuzzy AHP, Prioritization.

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Authors: Orhan Feyzioğlu, Gülçin Büyüközkan

Abstract:

As a vital activity for companies, new product development (NPD) is also a very risky process due to the high uncertainty degree encountered at every development stage and the inevitable dependence on how previous steps are successfully accomplished. Hence, there is an apparent need to evaluate new product initiatives systematically and make accurate decisions under uncertainty. Another major concern is the time pressure to launch a significant number of new products to preserve and increase the competitive power of the company. In this work, we propose an integrated decision-making framework based on neural networks and fuzzy logic to make appropriate decisions and accelerate the evaluation process. We are especially interested in the two initial stages where new product ideas are selected (go/no go decision) and the implementation order of the corresponding projects are determined. We show that this two-staged intelligent approach allows practitioners to roughly and quickly separate good and bad product ideas by making use of previous experiences, and then, analyze a more shortened list rigorously.

Keywords: Decision Making, Neural Networks, Fuzzy Theory and Systems, Choquet Integral, New Product Development.

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