Search results for: Algorithms decision tree

Authors: Yasser F. Hassan, Marwa Abdeen, Mustafa Fahmy

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In recent years multi-agent systems have emerged as one of the interesting architectures facilitating distributed collaboration and distributed problem solving. Each node (agent) of the network might pursue its own agenda, exploit its environment, develop its own problem solving strategy and establish required communication strategies. Within each node of the network, one could encounter a diversity of problem-solving approaches. Quite commonly the agents can realize their processing at the level of information granules that is the most suitable from their local points of view. Information granules can come at various levels of granularity. Each agent could exploit a certain formalism of information granulation engaging a machinery of fuzzy sets, interval analysis, rough sets, just to name a few dominant technologies of granular computing. Having this in mind, arises a fundamental issue of forming effective interaction linkages between the agents so that they fully broadcast their findings and benefit from interacting with others.

Keywords: Granular computing, rough sets, agents, traffic system.

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Authors: J. Bouchard, B. Prosperi, G. Bavre, M. Daudé, E. Jeandupeux

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Young patients suffering from Cerebral Palsy are facing difficult choices concerning heavy surgeries. Diagnosis settled by surgeons can be complex and on the other hand decision for patient about getting or not such a surgery involves important reflection effort. Proposed software combining prediction for surgeries and post surgery kinematic values, and from 3D model representing the patient is an innovative tool helpful for both patients and medicine professionals. Beginning with analysis and classification of kinematics values from Data Base extracted from gait analysis in 3 separated clusters, it is possible to determine close similarity between patients. Prediction surgery best adapted to improve a patient gait is then determined by operating a suitable preconditioned neural network. Finally, patient 3D modeling based on kinematic values analysis, is animated thanks to post surgery kinematic vectors characterizing the closest patient selected from patients clustering.

Keywords: Cerebral Palsy, Clustering, Crouch Gait, 3-D Modeling.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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This paper introduces new algorithms (Fuzzy relative of the CLARANS algorithm FCLARANS and Fuzzy c Medoids based on randomized search FCMRANS) for fuzzy clustering of relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd) in which the within cluster dissimilarity of each cluster is minimized in each iteration by recomputing new medoids given current memberships, FCLARANS minimizes the same objective function minimized by FCMdd by changing current medoids in such away that that the sum of the within cluster dissimilarities is minimized. Computing new medoids may be effected by noise because outliers may join the computation of medoids while the choice of medoids in FCLARANS is dictated by the location of a predominant fraction of points inside a cluster and, therefore, it is less sensitive to the presence of outliers. In FCMRANS the step of computing new medoids in FCMdd is modified to be based on randomized search. Furthermore, a new initialization procedure is developed that add randomness to the initialization procedure used with FCMdd. Both FCLARANS and FCMRANS are compared with the robust and linearized version of fuzzy c-medoids (RFCMdd). Experimental results with different samples of the Reuter-21578, Newsgroups (20NG) and generated datasets with noise show that FCLARANS is more robust than both RFCMdd and FCMRANS. Finally, both FCMRANS and FCLARANS are more efficient and their outputs are almost the same as that of RFCMdd in terms of classification rate.

Keywords: Data Mining, Fuzzy Clustering, Relational Clustering, Medoid-Based Clustering, Cluster Analysis, Unsupervised Learning.

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Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove.

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Authors: Madhukar Nagare, Pankaj Dutta, Amey Kambli

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Perishable goods constitute a large portion of retailer inventory and lose value with time due to deterioration and/or obsolescence. Retailers dealing with such goods required considering the factors of short shelf life and the dependency of sales on inventory displayed in determining optimal procurement policy. Many retailers follow the practice of using two bins - primary bin sales fresh items at a list price and secondary bin sales unsold items at a discount price transferred from primary bin on attaining certain age. In this paper, mathematical models are developed for primary bin and for secondary bin that maximizes profit with decision variables of order quantities, optimal review period and optimal selling price at secondary bin. The demand rates in two bins are assumed to be deterministic and dependent on displayed inventory level, price and age but independent of each other. The validity of the model is shown by solving an example and the sensitivity analysis of the model is also reported.

Keywords: Retail Inventory, Perishable Products, Two Bin, Profitable Sales.

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Authors: Veli Hakkoymaz, Alpaslan Altunköprü

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A multimedia presentation system refers to the integration of a multimedia database with a presentation manager which has the functionality of content selection, organization and playout of multimedia presentations. It requires high performance of involved system components. Starting from multimedia information capture until the presentation delivery, high performance tools are required for accessing, manipulating, storing and retrieving these segments, for transferring and delivering them in a presentation terminal according to a playout order. The organization of presentations is a complex task in that the display order of presentation contents (in time and space) must be specified. A multimedia presentation contains audio, video, images and text media types. The critical decisions for presentation construction include what the contents are, how the contents are organized, and once the decision is made on the organization of the contents of the presentation, it must be conveyed to the end user in the correct organizational order and in a timely fashion. This paper introduces a framework for specification of multimedia presentations and describes the design of sample presentations using this framework from a multimedia database.

Keywords: Multimedia presentation, temporal specification, SMIL, spatial specification.

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Authors: C. Villegas-Quezada, J. Climent

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Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.

Keywords: AdaBoost Classifier, Holistic Face Recognition, Minimax Multivariate Approximation, Genetic Algorithm.

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Authors: Ki Hyun Kim, Jang-Hee Yoo, Kyo Il Chung

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This paper proposes a VPN Accelerator Board (VPN-AB), a virtual private network (VPN) protocol designed for trust channel security system (TCSS). TCSS supports safety communication channel between security nodes in internet. It furnishes authentication, confidentiality, integrity, and access control to security node to transmit data packets with IPsec protocol. TCSS consists of internet key exchange block, security association block, and IPsec engine block. The internet key exchange block negotiates crypto algorithm and key used in IPsec engine block. Security Association blocks setting-up and manages security association information. IPsec engine block treats IPsec packets and consists of networking functions for communication. The IPsec engine block should be embodied by H/W and in-line mode transaction for high speed IPsec processing. Our VPN-AB is implemented with high speed security processor that supports many cryptographic algorithms and in-line mode. We evaluate a small TCSS communication environment, and measure a performance of VPN-AB in the environment. The experiment results show that VPN-AB gets a performance throughput of maximum 15.645Gbps when we set the IPsec protocol with 3DES-HMAC-MD5 tunnel mode.

Keywords: TCSS(Trust Channel Security System), VPN(VirtualPrivate Network), IPsec, SSL, Security Processor, Securitycommunication.

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Authors: Swapnil Singhal, Santosh Kumar Singh

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The emerging Cognitive Radio is combo of both the technologies i.e. Radio dynamics and software technology. It involve wireless system with efficient coding, designing, and making them artificial intelligent to take the decision according to the surrounding environment and adopt themselves accordingly, so as to deliver the best QoS. This is the breakthrough from fixed hardware and fixed utilization of the spectrum. This software-defined approach of research is centralized at user-definition and application driven model, various software method are used for the optimization of the wireless communication. This paper focused on the Spectrum allocation technique using genetic algorithm GA to evolve radio, represented by chromosomes. The chromosomes gene represents the adjustable parameters in given radio and by using GA, evolving over the generations, the optimized set of parameters are evolved, as per the requirement of user and availability of the spectrum, in our prototype the gene consist of 6 different parameters, and the best set of parameters are evolved according to the application need and availability of the spectrum holes and thus maintaining best QoS for user, simultaneously maintaining licensed user rights. The analyzing tool Matlab is used for the performance of the prototype.

Keywords: ASDR, Cognitive Radio, QoS, Spectrum.

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity.

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Authors: Maumita Bhattacharya

Abstract:

Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model presented in our earlier work [14] reduced computation time by controlled use of meta-models to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. Situations like model formation involving variable input dimensions and noisy data certainly can not be covered by this assumption. In this paper we present an enhanced version of DAFHEA that incorporates a multiple-model based learning approach for the SVM approximator. DAFHEA-II (the enhanced version of the DAFHEA framework) also overcomes the high computational expense involved with additional clustering requirements of the original DAFHEA framework. The proposed framework has been tested on several benchmark functions and the empirical results illustrate the advantages of the proposed technique.

Keywords: Evolutionary algorithm, Fitness function, Optimization, Meta-model, Stochastic method.

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Authors: Olimpia Matarazzo, Lucia Abbamonte

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In two studies we challenged the well consolidated position in regret literature according to which the necessary condition for the emergence of regret is a bad outcome ensuing from free decisions. Without free choice, and, consequently, personal responsibility, other emotions, such as disappointment, but not regret, are supposed to be elicited. In our opinion, a main source of regret is being obliged by circumstance out of our control to chose an undesired option. We tested the hypothesis that regret resulting from a forced choice is more intense than regret derived from a free choice and that the outcome affects the latter, not the former. Besides, we investigated whether two other variables – the perception of the level of freedom of the choice and the choice justifiability – mediated the relationships between choice and regret, as well as the other four emotions we examined: satisfaction, anger toward oneself, disappointment, anger towards circumstances. The two studies were based on the scenario methodology and implied a 2 x 2 (choice x outcome) between design. In the first study the foreseen short-term effects of the choice were assessed; in the second study the experienced long-term effects of the choice were assessed. In each study 160 students of the Second University of Naples participated. Results largely corroborated our hypotheses. They were discussed in the light of the main theories on regret and decision making.

Keywords: Choice, outcome, regret.

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Authors: Amira Kheriji Abbes, Faouzi Bouani, Mekki Ksouri

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Model Predictive Control (MPC) is an established control technique in a wide range of process industries. The reason for this success is its ability to handle multivariable systems and systems having input, output or state constraints. Neverthless comparing to PID controller, the implementation of the MPC in miniaturized devices like Field Programmable Gate Arrays (FPGA) and microcontrollers has historically been very small scale due to its complexity in implementation and its computation time requirement. At the same time, such embedded technologies have become an enabler for future manufacturing enterprisers as well as a transformer of organizations and markets. In this work, we take advantage of these recent advances in this area in the deployment of one of the most studied and applied control technique in the industrial engineering. In this paper, we propose an efficient firmware for the implementation of constrained MPC in the performed STM32 microcontroller using interior point method. Indeed, performances study shows good execution speed and low computational burden. These results encourage to develop predictive control algorithms to be programmed in industrial standard processes. The PID anti windup controller was also implemented in the STM32 in order to make a performance comparison with the MPC. The main features of the proposed constrained MPC framework are illustrated through two examples.

Keywords: Embedded software, microcontroller, constrainedModel Predictive Control, interior point method, PID antiwindup, Keil tool, C/Cµ language.

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Authors: Abdulbaset Saad, Adel Younis, Zuomin Dong

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For design optimization with high-dimensional expensive problems, an effective and efficient optimization methodology is desired. This work proposes a series of modification to the Differential Evolution (DE) algorithm for solving computation Intensive Black-Box Problems. The proposed methodology is called Radial Basis Meta-Model Algorithm Assisted Differential Evolutionary (RBF-DE), which is a global optimization algorithm based on the meta-modeling techniques. A meta-modeling assisted DE is proposed to solve computationally expensive optimization problems. The Radial Basis Function (RBF) model is used as a surrogate model to approximate the expensive objective function, while DE employs a mechanism to dynamically select the best performing combination of parameters such as differential rate, cross over probability, and population size. The proposed algorithm is tested on benchmark functions and real life practical applications and problems. The test results demonstrate that the proposed algorithm is promising and performs well compared to other optimization algorithms. The proposed algorithm is capable of converging to acceptable and good solutions in terms of accuracy, number of evaluations, and time needed to converge.

Keywords: Differential evolution, engineering design, expensive computations, meta-modeling, radial basis function, optimization.

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Authors: O. Z. Jasim

Abstract:

An accuracy digital map must satisfy the users for two main requirements, first, map must be visually readable and second, all the map elements must be in a good representation. These two requirements hold especially true for map generalization which aims at simplifying the representation of cartographic data. Different scales of maps are very important for any decision in any maps with different scales such as master plan and all the infrastructures maps in civil engineering. Cartographer cannot project the data onto a piece of paper, but he has to worry about its readability. The map layout of any geodatabase is very important, this layout is help to read, analyze or extract information from the map. There are many principles and guidelines of generalization that can be find in the cartographic literature. A manual reduction method for generalization depends on experience of map maker and therefore produces incompatible results. Digital generalization, rooted from conventional cartography, has become an increasing concern in both Geographic Information System (GIS) and mapping fields. This project is intended to review the state of the art of the new technology and help to understand the needs and plans for the implementation of digital generalization capability as well as increase the knowledge of production topographic maps.

Keywords: Cartography, digital generalization, mapping, GIS.

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Authors: Karunakar A K, Manohara Pai M M

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In 3D-wavelet video coding framework temporal filtering is done along the trajectory of motion using Motion Compensated Temporal Filtering (MCTF). Hence computationally efficient motion estimation technique is the need of MCTF. In this paper a predictive technique is proposed in order to reduce the computational complexity of the MCTF framework, by exploiting the high correlation among the frames in a Group Of Picture (GOP). The proposed technique applies coarse and fine searches of any fast block based motion estimation, only to the first pair of frames in a GOP. The generated motion vectors are supplied to the next consecutive frames, even to subsequent temporal levels and only fine search is carried out around those predicted motion vectors. Hence coarse search is skipped for all the motion estimation in a GOP except for the first pair of frames. The technique has been tested for different fast block based motion estimation algorithms over different standard test sequences using MC-EZBC, a state-of-the-art scalable video coder. The simulation result reveals substantial reduction (i.e. 20.75% to 38.24%) in the number of search points during motion estimation, without compromising the quality of the reconstructed video compared to non-predictive techniques. Since the motion vectors of all the pair of frames in a GOP except the first pair will have value ±1 around the motion vectors of the previous pair of frames, the number of bits required for motion vectors is also reduced by 50%.

Keywords: Motion Compensated Temporal Filtering, predictivemotion estimation, lifted wavelet transform, motion vector

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: Iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, scale invariant feature transform.

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Authors: A. Alrasheed, I. Connerton

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Daily provision of high quality food and hygiene to patients is a challenging goal of the healthcare. In Saudi Arabia, matters related to food safety and hygiene are regulated by the Ministry of Health (MOH) and the Saudi Food and Drugs Authority (SFDA). The purpose of this research is to discuss the food safety management inconsistencies and flaws, in particular the ones related to Hazard Analysis and Critical Control Points (HACCP) in Riyadh’s MOH hospitals. As required by law, written HACCP regulations must be implemented, and food handlers need to receive the training accordingly. However, in Saudi hospitals, this is not a requirement, and the food handlers do not need to hold training certificates in food safety or HACCP. Nowadays, the matter of food safety and hygiene have become increasingly important since the decision makers want to align these regulations with the majority of the world and to implement HACCP fully and for this purpose, the SFDA was established. 

Keywords: Food safety, patients, hospitals, HACCP, Saudi Arabia.

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Authors: Z. Bottyán, F. Wantuch, A. Z. Gyöngyösi, Z. Tuba, K. Hadobács, P. Kardos, R. Kurunczi

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The sensitivity of UAVs to the atmospheric effects are apparent. All the same the meteorological support for the UAVs missions is often non-adequate or partly missing. In our paper we show a new complex meteorological support system for different types of UAVs pilots, specialists and decision makers, too. The mentioned system has two important parts with different forecasts approach such as the statistical and dynamical ones. The statistical prediction approach is based on a large climatological data base and the special analog method which is able to select similar weather situations from the mentioned data base to apply them during the forecasting procedure. The applied dynamic approach uses the specific WRF model runs twice a day and produces 96 hours, high resolution weather forecast for the UAV users over the Hungary. An easy to use web-based system can give important weather information over the Carpathian basin in Central-Europe. The mentioned products can be reached via internet connection.

Keywords: Aviation meteorology, statistical weather prediction, unmanned aerial systems, WRF.

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Authors: J. Prakash, K. Rajesh

Abstract:

In this paper we present a new method for coin identification. The proposed method adopts a hybrid scheme using Eigenvalues of covariance matrix, Circular Hough Transform (CHT) and Bresenham-s circle algorithm. The statistical and geometrical properties of the small and large Eigenvalues of the covariance matrix of a set of edge pixels over a connected region of support are explored for the purpose of circular object detection. Sparse matrix technique is used to perform CHT. Since sparse matrices squeeze zero elements and contain only a small number of non-zero elements, they provide an advantage of matrix storage space and computational time. Neighborhood suppression scheme is used to find the valid Hough peaks. The accurate position of the circumference pixels is identified using Raster scan algorithm which uses geometrical symmetry property. After finding circular objects, the proposed method uses the texture on the surface of the coins called texton, which are unique properties of coins, refers to the fundamental micro structure in generic natural images. This method has been tested on several real world images including coin and non-coin images. The performance is also evaluated based on the noise withstanding capability.

Keywords: Circular Hough Transform, Coin detection, Covariance matrix, Eigenvalues, Raster scan Algorithm, Texton.

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Authors: Pirom Thangchitpianpol, Suttipong Jumroonrut

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This research investigates the distribution of food demand for animal food and the optimum amount of that food production at minimum cost. The data consist of customer purchase orders for the food of laying hens, price of food for laying hens, cost per unit for the food inventory, cost related to food of laying hens in which the food is out of stock, such as fine, overtime, urgent purchase for material. They were collected from January, 1990 to December, 2013 from a factory in Nakhonratchasima province. The collected data are analyzed in order to explore the distribution of the monthly food demand for the laying hens and to see the rate of inventory per unit. The results are used in a stochastic linear programming model for aggregate planning in which the optimum production or minimum cost could be obtained. Programming algorithms in MATLAB and tools in Linprog software are used to get the solution. The distribution of the food demand for laying hens and the random numbers are used in the model. The study shows that the distribution of monthly food demand for laying has a normal distribution, the monthly average amount (unit: 30 kg) of production from January to December. The minimum total cost average for 12 months is Baht 62,329,181.77. Therefore, the production planning can reduce the cost by 14.64% from real cost.

Keywords: Animal food, Stochastic linear programming, Production planning, Demand Uncertainty.

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Authors: Zeitoun D. G., Thierry Dana-Picard

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The study of a real function of two real variables can be supported by visualization using a Computer Algebra System (CAS). One type of constraints of the system is due to the algorithms implemented, yielding continuous approximations of the given function by interpolation. This often masks discontinuities of the function and can provide strange plots, not compatible with the mathematics. In recent years, point based geometry has gained increasing attention as an alternative surface representation, both for efficient rendering and for flexible geometry processing of complex surfaces. In this paper we present different artifacts created by mesh surfaces near discontinuities and propose a point based method that controls and reduces these artifacts. A least squares penalty method for an automatic generation of the mesh that controls the behavior of the chosen function is presented. The special feature of this method is the ability to improve the accuracy of the surface visualization near a set of interior points where the function may be discontinuous. The present method is formulated as a minimax problem and the non uniform mesh is generated using an iterative algorithm. Results show that for large poorly conditioned matrices, the new algorithm gives more accurate results than the classical preconditioned conjugate algorithm.

Keywords: Function singularities, mesh generation, point allocation, visualization, collocation least squares method, Augmented Lagrangian method, Uzawa's Algorithm, Preconditioned Conjugate Gradien

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Authors: Rahmad Wisnu Wardana, Eakachai Warinsiriruk, Sutep Joy-A-Ka

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Selecting the most suitable welding process usually depends on experiences or common application in similar companies. However, this approach generally ignores many criteria that can be affecting the suitable welding process selection. Therefore, knowledge automation through knowledge-based systems will significantly improve the decision-making process. The aims of this research propose integrated data envelopment analysis (DEA) and fuzzy credibility constrained programming approach for identifying the best welding process for stainless steel storage tank in the food and beverage industry. The proposed approach uses fuzzy concept and credibility measure to deal with uncertain data from experts' judgment. Furthermore, 12 parameters are used to determine the most appropriate welding processes among six competitive welding processes.

Keywords: Welding process selection, data envelopment analysis, fuzzy credibility constrained programming, storage tank.

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Authors: C. Pacheco, A. Cipriano

Abstract:

A new concept of response system is proposed for filling the gap that exists in reducing vulnerability during immediate response to natural disasters. Real Time Early Response Systems (RTERSs) incorporate real time information as feedback data for closing control loop and for generating real time situation assessment. A review of the state of the art on works that fit the concept of RTERS is presented, and it is found that they are mainly focused on manmade disasters. At the same time, in response phase of natural disaster management many works are involved in creating early warning systems, but just few efforts have been put on deciding what to do once an alarm is activated. In this context a RTERS arises as a useful tool for supporting people in their decision making process during natural disasters after an event is detected, and also as an innovative context for applying well-known automation technologies and automatic control concepts and tools.

Keywords: Disaster management, emergency response system, natural disasters, real time.

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Authors: D. K. Lee, S. M. Shin, J. H. Lee

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Topology optimization technique utilizes constant element densities as design parameters. Finally, optimal distribution contours of the material densities between voids (0) and solids (1) in design domain represent the determination of topology. It means that regions with element density values become occupied by solids in design domain, while there are only void phases in regions where no density values exist. Therefore the void regions of topology optimization results provide design information to decide appropriate depositions of web-opening in structure. Contrary to the basic objective of the topology optimization technique which is to obtain optimal topology of structures, this present study proposes a new idea that topology optimization results can be also utilized for decision of proper web-opening’s position. Numerical examples of linear elastostatic structures demonstrate efficiency of methodological design processes using topology optimization in order to determinate the proper deposition of web-openings.

Keywords: Topology optimization, web-opening, structure, element density, material.

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Authors: K. Murali Krishna, Abhijit Mitra, C. Ardil

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This paper presents a single correlator RAKE receiver for direct sequence code division multiple access (DS-CDMA) systems. In conventional RAKE receivers, multiple correlators are used to despread the multipath signals and then to align and combine those signals in a later stage before making a bit decision. The simplified receiver structure presented here uses a single correlator and single code sequence generator to recover the multipaths. Modified Walsh- Hadamard codes are used here for data spreading that provides better uncorrelation properties for the multipath signals. The main advantage of this receiver structure is that it requires only a single correlator and a code generator in contrary to the conventional RAKE receiver concept with multiple correlators. It is shown in results that the proposed receiver achieves better bit error rates in comparison with the conventional one for more than one multipaths.

Keywords: RAKE receiver, Code division multiple access, ModifiedWalsh-Hadamard codes, Single correlator.

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Authors: F. Gökgöz, M. E. Atmaca

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Electricity plays an indispensable role in human life and the economy. It is a unique product or service that must be balanced instantaneously, as electricity is not stored, generation and consumption should be proportional. Effective and efficient use of electricity is very important not only for society, but also for the environment. A competitive electricity market is one of the best ways to provide a suitable platform for effective and efficient use of electricity. On the other hand, it carries some risks that should be carefully managed by the market players. Risk management is an essential part in market players’ decision making. In this paper, risk management through diversification is applied with the help of Markowitz’s Mean-variance, Down-side and Semi-variance methods for a case study. Performance of optimal electricity sale solutions are measured and evaluated via Sharpe-Ratio, and the optimal portfolio solutions are improved. Two years of historical weekdays’ price data of the Turkish Day Ahead Market are used to demonstrate the approach.

Keywords: Electricity market, portfolio optimization, risk management in electricity market, Sharpe ratio.

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

Abstract:

Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfil requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: Single classifier, machine learning, ensemble learning, multi-sensor target tracking.

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Authors: Arun Prasad Jaganathan

Abstract:

In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper presents an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: Artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization.

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Authors: Anusua Ghosh, Andrew Nafalski, Jeffery Tweedale, Maureen Dollard

Abstract:

This paper presents anapproach of hybridizing two or more artificial intelligence (AI) techniques which arebeing used to fuzzify the workstress level ranking and categorize the rating accordingly. The use of two or more techniques (hybrid approach) has been considered in this case, as combining different techniques may lead to neutralizing each other-s weaknesses generating a superior hybrid solution. Recent researches have shown that there is a need for a more valid and reliable tools, for assessing work stress. Thus artificial intelligence techniques have been applied in this instance to provide a solution to a psychological application. An overview about the novel and autonomous interactive model for analysing work-stress that has been developedusing multi-agent systems is also presented in this paper. The establishment of the intelligent multi-agent decision analyser (IMADA) using hybridized technique of neural networks and fuzzy logic within the multi-agent based framework is also described.

Keywords: Fuzzy logic, intelligent agent, multi-agent systems, neural network, workplace stress.

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