Concept extraction and e-commerce applications

Concept extraction is the technique of mining the most important topic of a document. In the e-commerce context, concept extraction can be used to identify what a shopping related Web page is talking about. This is practically useful in applications like search relevance and product matching. In this paper, we investigate two concept extraction methods: Automatic Concept Extractor (ACE) and Automatic Keyphrase Extraction (KEA). ACE is an unsupervised method that looks at both text and HTML tags. We upgrade ACE into Improved Concept Extractor (ICE) with significant improvements. KEA is a supervised learning system. We evaluate the methods by comparing automatically generated concepts to a gold standard. The experimental results demonstrate that ICE significantly outperforms ACE and also outperforms KEA in concept extraction. To demonstrate the practical use of concept extraction in the e-commerce context, we use ICE and KEA to showcase two e-commerce applications, i.e. product matching and topic-based opinion mining.     

Loop-closing: A typicality approach

Loop-closing has long been identified as a critical issue when building maps from local observations. Topological mapping methods abstract the problem of how loops are closed from the problem of how to determine the metrical layout of places in the map and how to deal with noisy sensors.The typicality problem refers to the identification of new classes in a general classification context. This typicality concept is used in this paper to help a robot acquire a topological representation of the environment during its exploration phase. The problem is addressed using the INCA statistic which follows a distance-based approach.In this paper we describe a place recognition approach based on match testing by means of the INCA test. We describe the theoretical basis of the approach and present extensive experimental results performed in both a simulated and a real robot-environment system; Behaviour Based philosophy is used to construct the whole control architecture. Obtained results show the validity of the approach.     

Modeling energy consumption in automated vacuum waste collection systems

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. A smart city is a city in which information and communications technology are merged with traditional infrastructures, coordinated and integrated using new digital technologies. The increasing amount of waste generated, and the collection and treatment of waste poses a major challenge to modern urban planning in general, and to smart cities in particular. To cope with this problem, automated vacuum waste collection (AVWC) uses air suction on a closed network of underground pipes to transport waste from the drop off points scattered throughout the city to a central collection point, reducing greenhouse gas emissions and the inconveniences of conventional methods (odours, noise, etc.). Since a significant part of the cost of operating AVWC systems is energy consumption, we have developed a model with the aim of applying constraint programming technology to schedule the daily emptying sequences of the drop off points in such a way that energy consumption is minimized. In this paper we describe how the problem of deciding the drop off points that should be emptied at a given time can be modeled as a constraint integer programming (CIP) problem. Moreover, we report on experiments using real data from AVWC systems installed in different cities that provide empirical evidence that CIP offers a suitable technology for reducing energy consumption in AVWC.     

通过遗传算法进行系统级软硬件划分

介绍采用遗传算法解决软硬件划分问题，具体讨论在遗传算法实现过程中的编码和解码，适应值函数的选取，选择，交叉，变异算子的实现、收敛准则的决定等问题的处理，与已发表文献的处理方法进行比较，最后通过随机实验取得好的结果。     

Constructing Virtual Cities by Using Panoramic Images

Simultaneously acquired omni-directional images contain rays of 360 degree viewing directions. To take advantage of this unique characteristic, we have been developing several methods for constructing virtual cities. In this paper, we first describe a system to generate the appearance of a virtual city; the system, which is based on image-based rendering (IBR) techniques, utilizes the characteristics of omni-directional images to reduce the number of samplings required to construct such IBR images. We then describe a method to add geometric information to the IBR images; this method is based on the analysis of a sequence of omni-directional images. Then, we describe a method to seamlessly superimpose a new building model onto a previously created virtual city image; the method enables us to estimate illumination distributions by using an omni-directional camera. Finally, to demonstrate the methods' effectiveness, we describe how we implemented and applied them to urban scenes.     

A GEP-based spatial decision support system for multisite land use allocation

Multisite Land Use Allocation Problem (MLUA) refers to the problem of allocating more than one land use type in an area. MLUA problem is one of the truly NP Complete (combinatorial optimization) problems. To cope with this type of problems, intelligent techniques such as genetic algorithms, and simulated annealing, have been used. Research in the area of Spatial Decision Support Systems (SDSS) for resource allocation issues, a new scientific area of information system applications developed to support semi-structured or unstructured spatial decisions, has recently generated attention for integrating Artificial Intelligence (AI) techniques with Geographic Information Systems (GIS). In this paper we demonstrate how GIS can be integrated with Gene Expression Programming (GEP), a recently developed AI approach, for solving MLUA problems. The feasibility of the proposed approach in solving MLUA problems was checked using a fictive case study. The results indicated that the proposed approach gives good and satisfactory results.     

A bi-level school bus routing problem with bus stops selection and possibility of demand outsourcing

This paper develops a bi-level mathematical model for the school bus routing problem aiming at designing an efficient transportation system considering the possibility of predicting the students’ response. In the real world, the demand for using private cars depends on how well public transportation systems are operating especially in metropolitan cities. An inefficient public transportation will lead to an increase in the demand for using private cars. This issue will result in problems such as increased traffics and urban pollutions. To address this issue, an efficient public transportation system is designed by developing a new bi-level mathematical model. In the proposed model, the designer of the public transportation system, as the upper-level decision-maker, will locate appropriate bus stops and identify bus navigation routes. Subsequently, the decision regarding the allocation of students to transportation systems or outsourcing them will be made at the lower level which is considered as an operational-level decision-making. To solve this problem, two hybrid metaheuristic approaches named GA-EX-TS and SA-EX-TS have been proposed based on location-allocation-routing (LAR) strategy. The performance of these proposed methods is compared with exact solutions achieved from an explicit enumeration approach followed in the small-scale instances. Finally, the proposed approaches are used to solve 50 random instance problems. Comparing the results of the two tuned hybrid algorithms and conducting the sensitivity analysis of the model provide evidence for the good performance of the proposed approach.     

Nonlinear system identification using memetic differential evolution trained neural networks

Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis.     

A self-organised travelling salesman

Using the principles of self-organisation and Darwin's theory of evolution, an algorithm has been developed to solve the geometric travelling salesman problem (TSP). In this approach, we have virtual and real nodes (cities) which can have equal or different masses (weights). The virtual nodes and their neighours are attracted toward the fixed cities by a Newtonian force. The birth and death of the virtual nodes creates a world in which only the fittest survive. This approach has been successfully tested on many problems of different sizes, with a constant error of about 4.6% across the whole range. The computing time follows a power series (square law) versus the number of cities. Comparison of our results with those obtained by a simulated annealing method showed the solutions that obtained by this self-organisation method are of a better quality, especially for large size problems.     

一种求解矩形块装填问题的拟人算法

在货物装载、木材下料、超大规模集成电路（VLSI）设计等工作中提出了矩形块装填与切割问题，对这一问题，国内外学者提出了诸如模拟退火算法、遗传算法及其它一些启发式算法等求解算法。本文利用人类的智慧和他们上万年以来形成的经验，提出了一种求解矩形块装填问题的拟人算法。谊算法使用了两个主要的思想策略，即矩形块选择策略和矩形块放置策略。用本文提出的算法，对21个测试算例进行了实算测试，测试结果表明：算法所得装填结果的优度高，计算时间短。对这21个测试算例。用本文算法计算，得到了其中16个算例的最优解，而计算时间都在2秒以内。进一步的测试表明，本文提出的算法对求解矩形块装填问题十分有效。     

Detection of a brain aneurysm and a way to search for a brain aneurysm aimed at the development of a system to simulate an operation

In this article, a system is proposed for a simulated operation which would help a trainee surgeon to perform a medical operation to ensure that a cerebral aneurysm does not burst. The physician will have acquired empirical medical technologies from operations in traditional clinical teaching. However, there is a problem with safety and the burden to the patient. Therefore, recently a new training approach for a simulated medical operation using virtual reality has been explored. With the aim of developing a simulated system for a medical operation for a cerebral aneurysm, we considered the necessary functions such as detecting the brain aneurysm that is the target of the operation, and searching for a suitable blood vessel to make a plan for the operation.     

A query-based approach for test selection in diagnosis

Test selection in diagnosis is a procedure suggesting tests to be executed when trying to answer the query “What is the diagnosis for this problem?”. However, other queries, such as “Is h the diagnosis for this problem?”, are relevant as they can involve faster test selection algorithms and they can result in a lower test execution cost. Usually, a one step lookahead entropy minimization strategy is adopted to implement the test selection procedure. However, we show that this strategy can be arbitrarily bad and therefore, it is important to consider several strategies to solve a query. Each strategy taking a different position in the tradeoff computation time vs test execution cost. In this paper, we consider a query-based approach where test selection is justified and driven by a user’s specific query. We also study different strategies, optimal and approximate, for test selection. Finally, we illustrate how the operating system discovery (OSD) problem can be solved using a diagnosis framework and how it benefits from a query-based approach.     

Information technology capital budgeting using a knapsack problem

In this paper, we describe an information technology capital budgeting (ITCB) problem, show that the ITCB problem can be modeled as a 0–1 knapsack optimization problem, and propose two different simulated annealing (SA) heuristic solution procedures to solve the ITCB problem. Using several simulations, we empirically compare the performance of two SA heuristic procedures with the performance of two well‐known ranking methods for capital budgeting. Our results indicate that the information technology (IT) investments selected using the SA heuristics have higher after‐tax profits than the IT investments selected using the two ranking methods.     

Denoising methods for thermomechanical decomposition for quasi-equilibrium molecular dynamics simulations

In this work, we applied robust denoising methods well established in the signal processing field for the thermomechanical decomposition of velocity data obtained from molecular dynamics (MD) simulations. In the decomposition, the atomic velocity was assumed to be the sum of the mechanical velocity and the thermal velocity, which can be linked to the stress and temperature field at the continuum scale, respectively. For the quasi-equilibrium process, with the thermal velocity treated as the Gaussian distributed stationary noise with zero mean and a positive variance that is linearly proportional to temperature and mechanical velocity as the clean signal, the velocity decomposition can be recasted into a denoising problem, for which powerful denoising methods have been developed to estimate a clean signal from noisy data. We investigated the widely-used linear parametric real-domain, linear nonparametric Fourier-based, and nonlinear nonparametric wavelet-based denoising methods, first on their theoretical properties and then made comparsion among them for denosing some synthetic noisy 1-dimensional (1D) data generated from MD simulations. The nonlinear wavelet-based thresholding estimator possessed better optimality properties than the other estimators, and also outperformed the other estimators in the synthetic data test. A further test comparing the various denoising methods for an adiabatic shear crack nucleation and propagation process simulated using MD simulations showed better performance by the wavelet-based denoisng method. Results from this work reveal good potential of applying wavelet-based denoising method to the study of thermomechanical processes simulated using MD simulations.     

Computational Ability of Cells based on Cell Dynamics and Adaptability

Learning how biological systems solve problems could help to design new methods of computation. Information processing in simple cellular organisms is interesting, as they have survived for almost 1 billion years using a simple system of information processing. Here we discuss a well-studied model system: the large amoeboid Physarum plasmodium. This amoeba can find approximate solutions for combinatorial optimization problems, such as solving a maze or a shortest network problem. In this report, we describe problem solving by the amoeba, and the computational methods that can be extracted from biological behaviors. The algorithm designed based on Physarum is both simple and useful. Tutorial series of three invited papers

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FUNCTIONAL REPRESENTATION AND REASONING FOR REFLECTIVE SYSTEMS

Functional models have been extensively investigated in the context of several problemsolving tasks such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, and use structure-behavior-function models to represent how they work. The model representing the functioning of a problem solver explicitly specifies how the knowledge and reasoning of the problem solver result in the achievement of its goals. Then, we employ these models for performance-driven reflective learning. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver to improve its performance. We use the model of the problem solver to monitor its reasoning. Assign blame when it fails, and appropriately redesign its knowledge and reasoning. This paper focuses on the model-based redesign of a path planner's task structure. It illustrates the modelbased reflection using examples from an operational system called the Autognostic system.     

User-Centric Learning and Evaluation of Interactive Segmentation Systems

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user??a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.     

Channel order and RMS delay spread estimation with application to AC power line communications

AC power lines have been considered as a convenient and low-cost medium for intra-building automation systems. In this paper, we investigate the problem of estimating the channel order and root mean squared (RMS) delay spread associated with the power lines, which are channel parameters that provide important information for determining the data transmission rate and designing appropriate equalization techniques for power lines communications (PLC). We start by showing that the key to the RMS delay spread estimation problem is the determination of the channel order, i.e., the effective duration of the channel impulse response. We next discuss various ways to estimate the impulse response length from a noise-corrupted channel estimate. In particular, four different methods, namely a signal energy estimation (SEE) technique, a generalized Akaike information criterion (GAIC) based test, a generalized likelihood ratio test (GLRT), and a modified GLRT, are derived for determining the effective length of a signal contaminated by noise. These methods are compared with one another using both simulated and experimentally measured power line data. The experimental data was collected for power line characterization in frequencies between 1 and 60 MHz.     

Using the theory of constraints thinking processes to complement system dynamics' causal loop diagrams in developing fundamental solutions

Standard OR/MS methods mainly focus on ‘hard’ aspects of problems represented by quantitative or mathematical formulations. Organisational settings typically pose challenges in the use of such methods, in that they may be inadequate to capture the ‘softer’ issues surrounding human behaviours, organisational practices and policies. Over the last 20 years, a number of ‘soft OR’ tools, methods and methodologies have emerged as a means of addressing such challenges, and in this paper, we selectively examine how such methodologies can complement hard and other soft methods. In particular, we examine the potential contribution of the theory of constraints (TOC) and system dynamics (SD) to multi‐methodological intervention. We begin by discussing their philosophical underpinnings in relation to other OR/MS methods, and by exploring how such an understanding can provide a theoretical basis for mixing methodologies and for their complementary use. Then, using a case suitable for classroom discussion, the paper provides insights into how the systemic qualities of selected TOC methods and tools may be harnessed in multi‐methodological intervention by identifying the communality and complementarity of TOC and other hard and soft OR/MS approaches to problem solving, in particular, the causal loop diagramming method of SD.