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نتیجه جستجو - Optimization

تعداد مقالات یافته شده: 1045
ردیف عنوان نوع
771 Passenger distribution modelling at the subway platform based on ant colony optimization algorithm
مدل سازی توزیع مسافر در ایستگاه مترو بر اساس الگوریتم بهینه سازی کلونی مورچه-2017
In the subway platform, not all passengers distribute randomly but gather in the waiting areas, especially when a train is coming. During emergency evacuations, passengers’ initial distribution may play a significant role in affecting the escape efficiency. In this paper, a passenger distribution modelling method is proposed to predict such waiting area choice processes based on ant colony optimization (ACO) algorithm, which is really a complicated job due to many influence factors. The model considers the distance to the target waiting area, the length of queues, the physical length of waiting areas and the train schedule as four main influence factors. Specially, a modification of the passenger’s impatience factor in the famous social force model (SFM), better reflecting the change of psychological states with an arrival of a train, is presented. The field data collected at the Xuanwumen subway platform is utilized for the model calibration and validation. The ultimate simulation re sults demonstrate that passenger distributions based on ACO algorithm basically can reflect the field distribution and also the dynamic characteristics of waiting area choice processes. Impacts of passenger distributions on evacuation dynamics under fires are further studied based on the software FDS+Evac. The results indicate that passenger distribution does has little impact on evacuation efficiency when fires are not very large, while the evacuation will be affected significantly by passenger distributions once fires are large enough. This further indicates the necessity of studying the passenger distribution at the subway plat form especially under emergencies.
Keywords: Ant colony optimization | Passenger distribution | Social force model | Subway station
مقاله انگلیسی
772 Efficient Energy Management for the Internet of Things in Smart Cities
مدیریت انرژی کارآمد برای اینترنت اشیاء در شهرهای هوشمند-2017
The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Inter net of Things offers many sophisticated and ubiq uitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy man agement is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-pow er devices and its related challenges. We detail two case studies. The first one targets energy-effi cient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case stud ies demonstrate the tremendous impact of ener gy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.
مقاله انگلیسی
773 GPU-based parallel optimization of immune convolutional neural network and embedded system
بهینه سازی موازی بر اساس GPU از شبکه عصبی کانولوشن ایمنی و سیستم جاسازی شده-2017
Up to now, the image recognition system has been utilized more and more widely in the security monitoring, the industrial intelligent monitoring, the unmanned vehicle, and even the space exploration. In designing the image recognition system, the traditional convolutional neural network has some de- fects such as long training time, easy over-fitting and high misclassification rate. In order to overcome these defects, we firstly used the immune mechanism to improve the convolutional neural network and put forward a novel immune convolutional neural network algorithm, after we analyzed the network structure and parameters of the convolutional neural network. Our algorithm not only integrated the location data of the network nodes and the adjustable parameters, but also dynamically adjusted the smoothing factor of the basis function. In addition, we utilized the NVIDIA GPU (Graphics Processing Unit) to accelerate the new immune convolutional neural network (ICNN) in parallel computing and built a real-time embedded image recognition system for this ICNN. The immune convolutional neural net- work algorithm was improved with CUDA programming and was tested with the sample data in the GPU-based environment. The GPU-based implementation of the novel immune convolutional neural network algorithm was made with the cuDNN, which was designed by NVIDIA for GPU-based accel- erating of DNNs in machine learning. Experimental results show that our new immune convolutional neural network has higher recognition rate, more stable performance and faster computing speed than the traditional convolutional neural network.& 2016 Elsevier Ltd. All rights reserved.
Keywords:Immune algorithm | Convolutionalneuralnetwork | Image recognition | Parallelcomputing | Embedded system | Security monitoring
مقاله انگلیسی
774 Optimal Aircraft Trajectories to Minimize the Radiative Impact of Contrails and CO2
مسیرهای بهینه هواپیما برای به حداقل رساندن تاثیر تابشی Contrails و CO2-2017
The rapid growth of air traffic in the Asia Pacific region in the last decade has brought about the need for more sustainable modes of flight. A key initiative is the development of a Next Generation Air Traffic Management (NG-ATM) system which allows aircraft to fly optimal trajectories. Besides fuel- and time-related costs, other considerations for optimal routing include emissions, noise and contrails. Multi-Objective Trajectory Optimisation (MOTO) allows the generation of optimal trajectories with regards to these objectives, with dynamic weights depending on the phase of flight. Contrails are a major contributor to aviation’s total Radiative Forcing (RF), being more significant than that of CO2. In particular, when formed in areas of low temperature and high relative humidity, contrails are known to persist for hours, spreading and eventually transitioning into cirrus clouds. Contrails trap heat by reflecting the long-wave infra-red radiation emitted by the earth back to its surface, producing positive RF. However, the albedo of contrails also reflects the incoming shortwave radiation from the sun, resulting in a negative component of RF. The impact of contrails, quantified by its associated RF, is thus not merely a function of environmental parameters but also a function of time. In this paper, a MOTO algorithm is used to generate optimal trajectories that minimize the radiative impact of contrails and CO2, while minimizing flight time and fuel burn. A case study of a transcontinental flight from Paris to Beijing is presented to demonstrate the feasibility of such an algorithm in providing strategic and tactical trajectory optimization capabilities.
Keywords: Contrail modelling | trajectory optimisation | sustainable aviation
مقاله انگلیسی
775 New framework that uses patterns and relations to understand terrorist behaviors
چارچوب جدیدی که از الگوها و روابط برای درک رفتارهای تروریستی استفاده می کند-2017
Article history:Received 6 September 2016Revised 19 January 2017Accepted 16 February 2017Available online 17 February 2017Keywords:Link formation Feature selection Adaptive optimization NetworksDecision making Homeland securityTerrorism is a complex phenomenon with high uncertainties in user strategy. The uncertain nature of terrorism is a main challenge in the design of counter-terrorism policy. Government agencies (e.g., CIA, FBI, NSA, etc.) cannot always use social media and telecommunications to capture the intentions of ter- rorists because terrorists are very careful in the use of these environments to plan and prepare attacks. To address this issue, this research aims to propose a new framework by defining the useful patterns of suicide attacks to analyze the terrorist activity patterns and relations, to understand behaviors and their future moves, and finally to prevent potential terrorist attacks. In the framework, a new network model is formed, and the structure of the relations is analyzed to infer knowledge about terrorist at- tacks. More specifically, an Evolutionary Simulating Annealing Lasso Logistic Regression (ESALLOR) model is proposed to select key features for similarity function. Subsequently, a new weighted heterogeneous similarity function is proposed to estimate the relationships among attacks. Moreover, a graph-based out- break detection is proposed to define hazardous places for the outbreak of violence. Experimental results demonstrate the effectiveness of our framework with high accuracy (more than 90% accuracy) for finding patterns when compared with that of actual terrorism events in 2014 and 2015. In conclusion, by using this intelligent framework, governments can understand automatically how terrorism will impact future events, and governments can control terrorists’ behaviors and tactics to reduce the risk of future events.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Link formation | Feature selection | Adaptive optimization | Networks Decision making | Homeland security
مقاله انگلیسی
776 An effective and efficient approximate two-dimensional dynamic programming algorithm for supporting advanced computer vision applications
یک الگوریتم برنامه ریزی پویا دو بعدی مؤثر و کارآمد برای پشتیبانی از برنامه های کاربردی پیشرفته کامپیوتری کامپیوتری-2017
Article history:Received 7 January 2017Accepted 11 July 2017Available online 2 August 2017Keywords:Two-dimensional dynamic programming CUDA platformComputer vision Intelligent systemsDynamic programming is a popular optimization technique, developed in the 60’s and still widely used today in several fields for its ability to find global optimum. Dynamic Programming Algorithms (DPAs) can be developed in many dimension. However, it is known that if the DPA dimension is greater or equal to two, the algorithm is an NP complete problem. In this paper we present an approximation of the fully two-dimensional DPA (2D-DPA) with polynomial complexity. Then, we describe an implementation of the algorithm on a recent parallel device based on CUDA architecture. We show that our parallel implemen- tation presents a speed-up of about 25 with respect to a sequential implementation on an Intel I7 CPU. In particular, our system allows a speed of about ten 2D-DPA executions per second for 85 × 85 pixels images. Experiments and case studies support our thesis.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Two-dimensional dynamic programming | CUDA platform | Computer vision | Intelligent systems
مقاله انگلیسی
777 Dominant-set clustering: A review
خوشه بندی سری غالب: یک مرور-2017
Clustering refers to the process of extracting maximally coherent groups from a set of objects using pair wise, or high-order, similarities. Traditional approaches to this problem are based on the idea of partition ing the input data into a predetermined number of classes, thereby obtaining the clusters as a by-product of the partitioning process. A radically different perspective of the problem consists in providing a for malization of the very notion of a cluster and considering the clustering process as a sequential search of structures in the data adhering to this cluster notion. In this manuscript we review one of the pio neering approaches falling in the latter class of algorithms, which has been proposed in the early 2000s and has been found since then a number of applications in different domains. It is known as dominant set clustering and provides a notion of a cluster (a.k.a. dominant set) that has intriguing links to game theory, graph-theory and optimization theory. From the game-theoretic perspective, clusters are regarded as equilibria of non-cooperative “clustering” games; in the graph-theoretic context, it can be shown that they generalize the notion of maximal clique to edge-weighted graphs; finally, from an optimization point of view, they can be characterized in terms of solutions to a simplex-constrained, quadratic optimization problem, as well as in terms of an exquisitely combinatorial entity. Besides introducing dominant sets from a theoretical perspective, we will also focus on the related algorithmic issues by reviewing two state-of-the-art methods that are used in the literature to find dominant sets clusters, namely the Repli cator Dynamics and the Infection and Immunization Dynamics. Finally, we conclude with an overview of different extensions of the dominant set framework and of applications where dominant sets have been successfully employed.
Keywords: Artificial intelligence | Clustering | Dominant sets | ESS equilibria | Maximal cliques
مقاله انگلیسی
778 An optimization model for green supply chain management by using a big data analytic approach
یک مدل بهینه سازی برای مدیریت زنجیره تامین سبز با استفاده از رویکرد تحلیلی داده های بزرگ-2017
This paper presents a multi-objective optimization model for a green supply chain management scheme that minimizes the inherent risk occurred by hazardous materials, associated carbon emission and economic cost. The model related parameters are capitalized on a big data analysis. Three scenarios are proposed to improve green supply chain management. The first scenario divides optimization into three options: the first involves minimizing risk and then dealing with carbon emissions (and thus economic cost); the second minimizes both risk and carbon emissions first, with the ultimate goal of minimizing overall cost; and the third option attempts to minimize risk, carbon emissions, and economic cost simultaneously. This paper provides a case study to verify the optimization model. Finally, the limitations of this research and approach are discussed to lay a foundation for further improvement.
Keywords: Hazardous materials | Inherent risk | Carbon emissions | Multi-objective optimization | Green supply chain management | Big data analysis
مقاله انگلیسی
779 PA-Star: A disk-assisted parallel A-Star strategy with locality-sensitive hash for multiple sequence alignment
PA-Star: یک راهبرد موازی دیسکی PA-Star با اختلاط حساس به مکان برای همترازی ترتیبی چندگانه-2017
Multiple Sequence Alignment (MSA) is a basic operation in Bioinformatics, and is used to highlight the similarities among a set of sequences. The MSA problem was proven NP-Hard, thus requiring a high amount of memory and computing power. This problem can be modeled as a search for the path with minimum cost in a graph, and the A-Star algorithm has been adapted to solve it sequentially and in parallel. The design of a parallel version for MSA with A-Star is subject to challenges such as irregular dependency pattern and substantial memory requirements. In this paper, we propose PA-Star, a locality sensitive multithreaded strategy based on A-Star, which computes optimal MSAs using both RAM and disk to store nodes. The experimental results obtained in 3 different machines show that the optimizations used in PA-Star can achieve an acceleration of 1.88× in the serial execution, and the parallel execution can attain an acceleration of 5.52× with 8 cores. We also show that PA-Star outperforms a state-of-the-art MSA tool based on A-Star, executing up to 4.77× faster. Finally, we show that our disk-assisted strategy is able to retrieve the optimal alignment when other tools fail.
Keywords: Multiple sequence alignment | Locality-sensitive hash | A-Star | Parallel algorithms
مقاله انگلیسی
780 Liquefied natural gas importing security strategy considering multi-factor: A multi-objective programming approach
استراتژی امنیتی وارداتی گاز مایع با توجه به چند عامل: یک رویکرد برنامه ریزی چند هدفه-2017
Article history:Received 28 March 2016Revised 28 May 2017Accepted 29 May 2017Available online 7 June 2017Keywords:Liquefied natural gas (LNG ) Multi-objective Programming Extreme eventsImproved Simulated Annealing Algorithm Software implementationLNG importing strategies, in the literature, are primarily studied under a common single-factor frame- work. However, LNG importing strategies are affected by a variety of factors. To address this existing gap, this paper proposes a Multi-Objective Programming model, which takes into account the cost, the coun- try risk, the shipping risk, and the impact of extreme events. A pure structural change model is used to determine the risk impact coefficient for extreme events. An enhanced Simulated Annealing Algorithm is then used to solve the LNG-importing optimization problem. An experimental study is further con- ducted to verify the practicability of the proposed approach in the case of China’s LNG-importing data. The software implementation of the proposed model is developed in Python. The proposed model pro- vides a decision support tool for LNG importing companies to find an efficient portfolio strategy for LNG importing. The optimization model can be used for analyzing similar scenarios involving such dimensions as economy, energy security, and especially energy diversification.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Liquefied natural gas (LNG ) | Multi-objective Programming | Extreme events | Improved Simulated Annealing Algorithm | Software implementation
مقاله انگلیسی
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