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1 Data analysis in process of energetics resource optimization
تجزیه و تحلیل داده ها در فرایند بهینه سازی منابع انرژی-2018
The distribution and management of energetic resources are the source of big amount of data in structured or unstructured form. These data are collected in a targeted or a random fashion manner for the purpose of efficient distribution and management in the required timeframe. Urban as well as industrial complexes are typical environments where are generated these data. In this process, there are data that are deliberately collected in order to generate the required reporting and outputs for management decision. Input data are the basis of reporting which can have simple or detailed format. In this work we are collecting random input data with intricate structure that cannot simply interpreted and explain in relation with others. The topic of our paper is focused on the interpretation of collected data that is oriented to energetic resources in regional city. We are describing the process of data collection, data cleaning, analysis, identification of relations, informational links, visualization, and interpretation. By analysis of these data we propose information value, which can be exploited in management decision to reduce the cost of electricity and the gas there.
Keywords: Big data, data analysis, energetic resources, optimization
مقاله انگلیسی
2 مدیریت انرژی مبتنی بر مصرف کننده آینده نگر و اشتراک گذاری انرژی در شبکه هوشمند
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 39
انتظار بزرگی که از شبکه برق هوشمند می رود، این است که با استفاده از جریان دو طرفه ی داده ها و انرژی برقی که با اطلاعات پیشرفته فعال شده است و همچنین با استفاده از ارتباطات و زیرساخت های کنترلی، سرویس های انرژی پایدار را ارائه دهد. یک عنصر مهم از چنین شبکه ی هوشمندی مصرف کننده های آینده نگر هستند، یعنی مصرف کنندگانی که از طریق شبکه، انرژی اضافی را تولید و با سایر کاربران به اشتراک می گذارند. مصرف کننده های آینده نگر نه تنها مهمترین ذینفع در شبکه های هوشمندِ متشکل از مصرف کنندگانِ آینده نگر هستند، بلکه نقشی حیاتی در مدیریتِ حداکثر تقاضا ایفا می کنند. بنابراين، لازم است که به تحقیق و بررسی پیرامونِ مدیریت انرژی مبتنی بر مصرف کننده ی آینده نگر و اشتراک گذاریِ انرژی ( PEMS) و چالش های مرتبط با آن بپردازیم. این بررسی برای درک و تحلیل تاثیر مصرف کننده ها ی آینده نگر در شبکه های هوشمند آینده به ما کمک خواهد کرد. به منظور دستیابی به این اهداف، این مقاله به بررسی جامعِ PEMS (یا مدیریت انرژی مبتنی بر مصرف کننده ی آینده نگر و اشتراک گذاریِ انرژی) در محیط شبکه هوشمند و بررسیِ تأثیرات مرتبط با قابلیت اطمینان سیستم و پایداری انرژی می پردازد. فرایند به اشتراک گذاریِ انرژی میان مصرف کننده های آینده نگر ، شامل دو عنصر کلیدی می باشد: فناوری اطلاعات و ارتباطات و تکنیک های بهینه سازی. این دو عنصر به طور کامل مورد بحث قرار می گیرند تا نیازمندی های اجراییِ PEMS را تحت پوشش قرار دهند. آن دسته از فناوری های مرتط با ارتباطات که در این مقاله ارائه شده اند، عبارتند از: فناوری های سیمی، فناوری های بی سیم، گزینه های کوتاه مدت و بلند مدت در تکنیک های بهینه سازی خطی و غیرخطی، که در زمینه ی PEMS، شرح داده شده اند. در این مقاله، فناوری های مختلف، روش ها و مکانیزم های پذیرفته شده برای PEMS به صورت جامع مورد بحث قرار می گیرند تا به افزایش شهود خوانندگان کمک شود. چالش ها و مسائلی که در جوامع مصرف کننده ی آینده نگر با آنها مواجه هستند، و همچنین مسأله ی اشتراک گذاریِ انرژی، به طور دقیق مورد بررسی قرار گرفته است.
کلمه های کلیدی: شبکه هوشمند | مصرف کننده های آینده نگر | مدیریت انرژی | اشتراک انرژی | بهينه سازي.
مقاله ترجمه شده
3 Towards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds
به سوی تخصیص منابع منصفانه حداقل-حداکثر برای تحلیل داده های بزرگ جریانی در ابرهای مشترک-2018
Distributed stream big data analytics platforms have emerged to tackle the continuously generated data streams. In stream big data analytics, the data processing workflow is abstracted as a directed graph referred to as a topology. Data are read from the storage and processed tuple by tuple, and these processing results are updated dynamically. The performance of a topology is evaluated by its throughput. This paper proposes an efficient resource allocation scheme for a heterogeneous stream big data analytics cluster shared by multiple topologies, in order to achieve max-min fairness in the utilities of the throughput for all the topologies. We first formulate a novel resource allocation problem, which is a mixed 0-1 integer program. The NP-hardness of the problem is rigorously proven. To tackle this problem, we transform the non-convex constraint to several linear constraints using linearization and reformulation techniques. Based on the analysis of the problem-specific structure and characteristics, we propose an approach that iteratively solves the continuous problem with a fixed set of discrete variables optimally, and updates the discrete variables heuristically. Simulations show that our proposed resource allocation scheme remarkably improves the max-min fairness in utilities of the topology throughput, and is low in computational complexity
Index Terms: Stream big data analytics, optimization
مقاله انگلیسی
4 A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security
یک بردار حمایتی بیش از حد حقیقی بی هدف ماشین آلات برای داده های بزرگ امنیت سایبری -2018
Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to define the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. In this paper, we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. The high-level strategy uses the search performance to control the selection of which low-level heuristic should be used to generate a new SVM configuration. The low-level heuristics each use different rules to effectively explore the SVM configuration search space. To address bi-objective optimization, the proposed framework adaptively integrates the strengths of decomposition- and Paretobased approaches to approximate the Pareto set of SVM configurations. The effectiveness of the proposed framework has been evaluated on two cyber security problems: Microsoft malware big data classification and anomaly intrusion detection. The obtained results demonstrate that the proposed framework is very effective, if not superior, compared with its counterparts and other algorithms.
INDEX TERMS: Hyper-heuristics, big data, cyber security, optimisation
مقاله انگلیسی
5 Closed loop supply chain networks: Designs for energy and time value efficiency
شبکه های زنجیره تامین حلقه بسته: طرح های بازده انرژی و زمان-2017
Product recovery has become a viable option for many industries to realize economic gains while pro tecting the environment. However, insufficient investment and inefficient supply chains have hampered the viability of reuse and/or recycling because of the extended time intervals between the recycling process of recovery and reuse. Manufacturers and distributors face the challenge and necessity to reduce these process delays in order to recover the maximum value of the returned products through an effective, responsive closed loop supply chain (CLSC). This paper quantitatively measures the effective responsiveness of the CLSC model in terms of time and energy efficiency. The proposed multi-objective mixed integer linear programming (MOMILP) model evaluates delay parameters with decision variables that maximize profit, optimize customer surplus and minimize energy use. The model suggests decision makers may achieve an optimal tradeoff among differing objectives in a multiple-objective CLSC sce nario. We employed a multi-objective particle swarm optimization (MOPSO) approach to solve the proposed MOMILP model and compared our approach with the Non-Dominated Sorted Genetic Algo rithm (NSGA-II) for optimal solution. Results of the comparative evolutionary approaches shows that MOPSO outperforms NSGA-II in almost all cases in achieving the best trade-off solutions. Sensitivity analysis carried out to test the robustness of the model confirms that substantially less cost is feasible through the reduction of return process delays. This paper aims to formulate a multi-objective CLSC problem based on a network-flow model measuring the time value to recover maximum assets lost due to delay at different stages of the recycle process. We also developed a particle swarm approach for a multi-objective CLSC. Our study also offers valuable insights for designers wishing to create a product flow network with an optimal capacity level in case of prioritized objectives scenarios.
Keywords: Closed loop supply chain | Product recovery | Time-sensitive product returns | Multi-objective particle swarm | optimization
مقاله انگلیسی
6 Optimization and coordination of supply chain with revenue sharing contracts and service requirement under supply and demand uncertainty
بهینه سازی و هماهنگی زنجیره تامین با قراردادهای اشتراک درآمد و نیاز به خدمات تحت عدم اطمینان عرضه و تقاضا-2017
In a one-supplier-one-buyer supply chain, uncertainty occurs not only at the demand side, but also at the supply side, which is commonly observed in business. Optimization and coordination of such a supply chain is rarely investigated in literature, especially with service requirement. In this paper we model the supply chain with revenue sharing contract and service requirement under supply and demand uncertainty. Firstly, we derive the buyers and the suppliers optimal policies, and find the conditions to coordinate the supply chain. Secondly, we prove that the buyers and the suppliers optimal quantities are both non-decreasing of the service requirement. We also find there exists an optimal supply quantity for the supplier, if the buyers ordering quantity based on her service requirement exceeds the suppliers optimal supply quantity, then the suppliers profit is a non increasing function of the buyers order quantity; otherwise, the suppliers profit is a non-decreasing function of the buyers order quantity. Thirdly, by comparing with the benchmark supply chain model where only demand uncertainty is considered, we find that in the coordinated supply chain under supply and demand uncertainty, the revenue sharing ratio for the supplier will be higher if the wholesale price remains the same, or the wholesale price will be higher if the revenue sharing ratio for the supplier keeps the same.
Keywords: Supply and demand uncertainty | Revenue sharing contracts |Service requirement |Supply chain coordination |Optimization
مقاله انگلیسی
7 ترکیب دانش تخصصی با فراگیری ماشین براساس آموزش فازی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 18
در این مقاله یک رویکرد آموزش فازی مبتنی بر تنظیم غیر خطی که تلاش آن به منظور ممانعت در طول آموزش است، معرفی می کند. ایده اصلی به منظور محدود کردن آموزش بدین منظور است که دانش تخصصی مبنا برای ساخت مدلی که هنوز هم قابل مشاهده است، بکار برده شود. اجرای این ایده با یک روش جدید تنظیم غیر خطی که برای هر نوع مجموعه داده¬ی آموزشی قابل اجراست، صورت گرفت. این روش با استفاده از مجموعه داده¬ی عملکرد محصول بزرگ (> 4500 محصول زراعی) برای چغندرقند که در مزارع کشاورزی در طی یک دوره 14 ساله (1976-1989) در شرق آلمان جمع آوری شده، اثبات است. این نرم افزار در SAMT2، نرم افزار رایگان و منبع گسترده، با استفاده از زبان برنامه نویسی پایتون اجرا گردید.
کلید واژه ها: مدل سازی فازی | دانش تخصصی | فراگیری ماشین | تنظیم غیر خطی | بهينه سازي | مدل سازی عملکرد
مقاله ترجمه شده
8 Reviewing the use of the theory of inventive problem solving (TRIZ) in green supply chain problems
بررسی استفاده از تئوری حل مسئله اختراع (TRIZ) در مشکلات زنجیره تامین سبز-2017
The purpose of the paper is to review the practice of the theory of inventive problem solving (TRIZ) in Green Supply Chain (GSC) problems and to identify new research challenges focusing on the question: “To what extent is it necessary to evolve TRIZ tools, methods and theoretical grounding for addressing GSC inventive problems?” First, a review of the past contributions of TRIZ based methods to GSC problem resolution is presented. As the result of the papers review did not provide a comprehensive under standing of the limitations and areas of potential application of TRIZ in GSC, three experiments were conducted to complete the literature review, in order to provide a more comprehensive answer to the posed question and identify research challenges. The experiments addressing GSC problems were also conducted to explore to what extent the more mature meta-methods of classical TRIZ, namely ARIZ 85 A, C and the related sub-methods, can be used as in GSM problems. The examples were chosen to explore types of GSC problems that were not yet addressed with TRIZ. The experiment results highlight limi tations on the use of the TRIZ in GSC inventive problems, which were not mentioned in the GSC liter ature. Moreover it highlights the limitation of using the more mature meta-methods of TRIZ (ARIZ 85A and ARIZ 85C) when the conflict to overcome contains more than two evaluation parameters and one action parameter. Finally, research challenges to overcome the limitations and to improve the use of TRIZ in GSC inventive problems are stated. Among them, methods for quickly establishing the existence of classical TRIZ contradictions or for informing the problem solver when no TRIZ contradictions are present in a given inventive problem in GSC should be proposed. Such methods would permit determining whether ARIZ 85C could be used and avoid a long and fruitless search for a system of contradictions. Find alternatives to the algorithms proposed in the past to be able to establish the generalized contradictions of inventive problems. Make evolve meta-methods ARIZ 85C or substitute it with methods which can address the inventive problems that cannot be treated by ARIZ 85C.
Keywords:Green supply chain (GSC)|Theory of inventive problem solving (TRIZ)|Green innovation|Optimization
مقاله انگلیسی
9 Optimization for Speculative Execution in Big Data Processing Clusters
بهینه سازی برای اجرای احتمالی در خوشه های پردازش داده های بزرگ -2017
A big parallel processing job can be delayed substantially as long as one of its many tasks is being assigned to an unreliable or congested machine. To tackle this so-called straggler problem, most parallel processing frameworks such as MapReduce have adopted various strategies under which the system may speculatively launch additional copies of the same task if its progress is abnormally slow when extra idling resource is available. In this paper, we focus on the design of speculative execution schemes for parallel processing clusters from an optimization perspective under different loading conditions. For the lightly loaded case, we analyze and propose one cloning scheme, namely, the Smart Cloning Algorithm (SCA) which is based on maximizing the overall system utility. We also derive the workload threshold under which SCA should be used for speculative execution. For the heavily loaded case, we propose the Enhanced Speculative Execution (ESE) algorithm which is an extension of the Microsoft Mantri scheme to mitigate stragglers. Our simulation results show SCA reduces the total job flowtime, i.e., the job delay/ response time by nearly 6 percent comparing to the speculative execution strategy of Microsoft Mantri. In addition, we show that the ESE Algorithm outperforms the Mantri baseline scheme by 71 percent in terms of the job flowtime while consuming the same amount of computation resource
Index Terms: Job scheduling | speculative execution | cloning, straggler detection | optimization
مقاله انگلیسی
10 Towards Max-Min Fair Resource Allocation for Stream Big Data Analytics in Shared Clouds
به سمت تخصیص منابع MAX-MIN برای تحلیل جریان داده های بزرگ در ابرهای به اشتراک گذاشته شده-2017
Distributed stream big data analytics platforms have emerged to tackle the continuously generated data streams. In stream big data analytics, the data processing workflow is abstracted as a directed graph referred to as a topology. Data are read from the storage and processed tuple by tuple, and these processing results are updated dynamically. The performance of a topology is evaluated by its throughput. This paper proposes an efficient resource allocation scheme for a heterogeneous shared stream big data analytics cluster shared by multiple topologies, in order to achieve max-min fairness in the utilities of the throughput for all the topologies. We first formulate a novel model resource allocation problem, which is a mixed 0-1 integer program. The NP-hardness of the problem is rigorously proven. To tackle this problem, we transform the non-convex constraint to several linear constraints using linearization and reformulation techniques. Based on the analysis of the problem-specific structure and characteristics, we propose an approach that iteratively solves the continuous problem with a fixed set of discrete variables optimally, and updates the discrete variables heuristically. Simulations show that our proposed resource allocation scheme remarkably improves the max-min fairness in utilities of the topology throughput, and is low in computational complexity.
Index Terms: Stream big data analytics | optimization
مقاله انگلیسی
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