دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش بیست و یکم
منطق فازی (Fuzzy Logic) اولین بار در پی تنظیم نظریه مجموعه‌های فازی به وسیله پروفسور لطفی زاده (۱۹۶۵ میلادی) در صحنه محاسبات نو ظاهر شد. در واقع منطق فازی از منطق ارزش‌های «صفر و یک» نرم‌افزارهای کلاسیک فراتر رفته و درگاهی جدید برای دنیای علوم نرم‌افزاری و رایانه‌ها می‌گشاید، زیرا فضای شناور و نامحدود بین اعداد صفر و یک را نیز در منطق و استدلال‌های خود به کار می‌گیرد. در ادامه مقالات علمی انتشارات بین المللی اشپرینگر (Springer) در زمینه منطق فازی (Fuzzy Logic) برای دانلود آمده است. می توانید برای دانلود هر یک از مقالات از سرور دانلود متلب سایت، بر روی لینک دانلود هر یک از آن ها، کلیک کنید.
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دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — فهرست اصلی
| عنوان اصلی مقاله | Direct load control by distributed imperialist competitive algorithm |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Fengji LUO, Junhua ZHAO, Haiming WANG, Xiaojiao TONG, Yingying CHEN, Zhao Yang DONG |
| چکیده / توضیح | Demand side management techniques have drawn significant attentions along with the development of smart grid. This paper proposes a new direct load control (DLC) model for scheduling interruptible air conditioner loads. The model is coordinated with the unit commitment and economic dispatch to minimize the total operation cost over the whole dispatch horizon. The network constraints are also considered in the model. To ensure the thermal comfort of the occupants, we are among the first to incorporate the advanced two-parameter thermal inertia dynamical model of customer houses into the DLC model to calculate the indoor temperature variation. This paper also proposes a distributed imperialist competitive algorithm to effectively solve the model. The simulation studies prove the efficiency of the proposed methodology. |
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| عنوان اصلی مقاله | Frequency regulation by fuzzy and binary control in a hybrid islanded microgrid |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Pamela MANJARRES, Om MALIK |
| چکیده / توضیح | Islanded microgrids must be self-sufficient in terms of frequency and voltage control due to their islanded operation. A control strategy for frequency regulation by combining the operation of a wind generator, a diesel generator, a battery energy storage system and a dump load in a microgrid is proposed in this paper. In the proposed strategy, the control task is partitioned into two subtasks: 1) choosing the appropriate element to be used for regulation, and 2) providing frequency regulation. A global controller chooses the element to operate. Then, the frequency regulation is provided by separate individual controllers. The proposed control strategy is tested on a microgrid with mixed types of generation and modeled on Simulink. By monitoring the power of individual elements and system frequency, it is shown that the proposed control strategy operates efficiently. The proposed strategy facilitates the integration of renewable energy sources and enhances frequency regulation. |
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| عنوان اصلی مقاله | A novel statistically tracked particle swarm optimization method for automatic generation control |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Cheshta JAIN, H. K. VERMA, L. D. ARYA |
| چکیده / توضیح | Particle swarm optimization (PSO) is one of the popular stochastic optimization based on swarm intelligence algorithm. This simple and promising algorithm has applications in many research fields. In PSO, each particle can adjust its ‘flying’ according to its own flying experience and its companions’ flying experience. This paper proposes a new PSO variant, called the statistically tracked PSO, which uses group statistical characteristics to update the velocity of the particle after certain iterations, thus avoiding local minima and helping particles to explore global optimum with an improved convergence. The performance of the proposed algorithm is tested on a deregulated automatic generation control problem in power systems and encouraging results are obtained. |
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| عنوان اصلی مقاله | Backstepping sliding mode control of a variable speed wind turbine for power optimization |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Saravanakumar RAJENDRAN, Debashisha JENA |
| چکیده / توضیح | To optimize the energy capture from the wind, wind turbine (WT) should operate at variable speed. Based on the wind speed, the operating regions of the WT are divided into two parts: below and above the rated wind speed. The main aim at below rated wind speed is to maximize the energy capture from the wind with reduced oscillation on the drive train. At above rated wind speed, the aim is to maintain the rated power by using pitch control. This paper presents the control of WT at below rated wind speed by using backstepping sliding mode control (BSMC). In BSMC, generator torque is considered as the control input that depends on the optimal rotor speed. Usually, this optimal rotor speed is derived from effective wind speed. In this paper, effective wind speed is estimated from aerodynamic torque and rotor speed by using the modified Newton Rapshon (MNR) algorithm. Initially, a conventional sliding mode controller (SMC) is applied to the WT, but the performance of the controller was found to be less robust with respect to disturbances. Generally, WT external disturbance is not predictable. To overcome the above drawback, BSMC is proposed and both the controllers are tested with mathematical model and finally validated with the fatigue, aerodynamics, structures, and turbulence (FAST) WT simulator in the presence of disturbances. From the results, it is concluded that the proposed BSMC is more robust than conventional SMC in the presence of disturbances. |
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| عنوان اصلی مقاله | A survey on software fault detection based on different prediction approaches |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Golnoush Abaei, Ali Selamat |
| چکیده / توضیح | One of the software engineering interests is quality assurance activities such as testing, verification and validation, fault tolerance and fault prediction. When any company does not have sufficient budget and time for testing the entire application, a project manager can use some fault prediction algorithms to identify the parts of the system that are more defect prone. There are so many prediction approaches in the field of software engineering such as test effort, security and cost prediction. Since most of them do not have a stable model, software fault prediction has been studied in this paper based on different machine learning techniques such as decision trees, decision tables, random forest, neural network, Naïve Bayes and distinctive classifiers of artificial immune systems (AISs) such as artificial immune recognition system, CLONALG and Immunos. We use four public NASA datasets to perform our experiment. These datasets are different in size and number of defective data. Distinct parameters such as method-level metrics and two feature selection approaches which are principal component analysis and correlation based feature selection are used to evaluate the finest performance among the others. According to this study, random forest provides the best prediction performance for large data sets and Naïve Bayes is a trustable algorithm for small data sets even when one of the feature selection techniques is applied. Immunos99 performs well among AIS classifiers when feature selection technique is applied, and AIRSParallel performs better without any feature selection techniques. The performance evaluation has been done based on three different metrics such as area under receiver operating characteristic curve, probability of detection and probability of false alarm. These three evaluation metrics could give the reliable prediction criteria together. |
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| عنوان اصلی مقاله | Hybrid intelligent diagnosis approach based on soft computing from signal and image knowledge representations for a biomedical application |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Amine Chohra, Nadia Kanaoui, Véronique Amarger, Kurosh Madani |
| چکیده / توضیح | Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods as neural networks and fuzzy logic, have shown great potential in the development of decision support systems. Dealing with expert (human) knowledge consideration, Computer-Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine classification and decision-making. In this paper, a brief survey on fault diagnosis systems is given first. Then, from a fault diagnosis system analysis of the classification and decision-making problem, a global diagnosis synopsis is deduced. Afterwards, a hybrid intelligent diagnosis approach, based on soft computing implying modular neural networks for classification and fuzzy logic for decision-making, is suggested from signal and image representations. The suggested approach is developed in biomedicine for a CAD, from Auditory Brainstem Response test, and the prototype design and experimental results are presented. In fact, a double classification is exploited in a primary fuzzy diagnosis, to ensure a satisfactory reliability. Then, this reliability is reinforced using a confidence parameter with the primary diagnosis result, exploited in a final fuzzy diagnosis giving the appropriate diagnosis with a confidence index. Indeed, experimental results demonstrate the efficiency and reliability of CAD for three classes: two auditory pathologies Retro-cochlear Class (RC) and Endo-cochlear Class (EC), and Normal auditory Class (NC). The generalization rate of NC is clearly higher for primary fuzzy diagnosis and final fuzzy diagnosis than that of the two classifications. The obtained rates for RC and EC are higher than obtained by image classification but quite similar than those obtained by signal classification. An important contribution of the final fuzzy diagnosis is the fact that a confidence index is associated with each fault diagnosis. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach. |
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| عنوان اصلی مقاله | Cognitive information processing |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Chi Tran |
| چکیده / توضیح | Today, under the “true or false” philosophy, we are confined within the ‘syntactic’ restrictions, in connection with the adoption of the counter-concepts/binary concepts presented by common words and Boolean numbers concerned with logical reasoning based on binary logic. In contrast, truth, according to Brouwer and Heyting (intuitive logic), comes from the future. It, according to Łukasiewicz (multi-valued logic), is neither true nor false. It should be a third option that transcends both, i.e., transcends the ‘syntactic’ restrictions to reach the ‘semantic’ and ‘pragmatic’ descriptions of available data. Truth, in this case, will be formulated in the new form of the meta-concept: true-false which is neither true nor false, likewise the ‘affirmation and denial’ idea of the Indian logic. It becomes a cognitive measure used to describe the human cognitive processing. It turned out that we need not only a new term that transcends limits of words and Boolean numbers to describe meta-concepts, but also a new approach based on new philosophy, logic and mathematics to process cognitive information using both physical and mental data. It refers, in this article, as denotation computing for determining the relative density of sand using CPT data. Instead of the counter-concept, “loose or consolidation or high consolidation” we use the meta-concept, “loose-consolidation-high consolidation” to represent the objective–subjective data. Their processing is developed for certain types of data structures designed for representing information content including cognitive measures depending on perception of engineers to exceed the syntax limits of binary concepts towards their semantic and pragmatic aspects. It is based on the integration of modern philosophy, epistemology, logic, and denotational mathematics. |
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| عنوان اصلی مقاله | An agile operations management system for green factory |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Hyunjei Jo, Sang Do Noh, Yongju Cho |
| چکیده / توضیح | The globalization of suppliers and personalization of customers require new manufacturing strategy for environmental issues, region’s special regulation and energy besides manufacturing factors from manufacturers. In this study, green factory is defined as a manufacturing floor that reduces waste elements such as extra work, energy, time and cost through quickly responding to external uncontrollable changes like a regulation, due date and supply. Therefore, the green factory should respond by establishing and implementing strategies for controlling their production volumes, changing their dispatch rules, adjusting their work schedules, increasing and decreasing the number of their machines and workers based on their own internal data and capacities, and other measures. To achieve such objective, manufacturing companies should realize a manufacturing intelligence that can visualize their shop-floor data, and detect and solve problems at the business planning level. In this study, an agile operations management (AOM) system that can quickly respond to field problems was developed for manufacturing intelligence. The AOM system refers to a system that collects internal data, monitors such data in real time, immediately detects problems, if any, and provides solutions to the detected issues within the shortest time. |
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| عنوان اصلی مقاله | Mathematical modeling of hybrid renewable energy system: A review on small hydro-solar-wind power generation |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Binayak Bhandari, Shiva Raj Poudel, Kyung-Tae Lee, Sung-Hoon Ahn |
| چکیده / توضیح | Harnessing energy from alternative energy source has been recorded since early history. Renewable energy is abundantly found anywhere, free of cost and has non-polluting characteristics. However, these energy sources are based on the weather condition and possess inherited intermittent nature, which hinders stable power supply. Combining multiple renewable energy resources can be a possible solution to overcome defects, which not only provides reliable power but also leads to reduction in required storage capacity. Although an oversized hybrid system satisfies the load demand, it can be unnecessarily expensive. An undersized hybrid system is economical, but may not be able to meet the load demand. The optimal sizing of the renewable energy power system depends on the mathematical model of system components. This paper summarizes the mathematical modeling of various renewable energy system particularly PV, wind, hydro and storage devices. Because of the nonlinear power characteristics, wind and PV system require special techniques to extract maximum power. Hybrid system has complex control system due to integration of two (or more) different power sources. The complexity of system increases with maximum power point tracking (MPPT) techniques employed in their subsystems. This paper also summarizes mathematical modeling of various MPPT techniques for hybrid renewable energy systems. |
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| عنوان اصلی مقاله | System dynamics approach for the impacts of FINEX technology and carbon taxes on steel demand: Case study of the POSCO |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Suk-Jae Jeong |
| چکیده / توضیح | The steel industry accounts for the largest share, approximately 27 percent, of global carbon dioxide emissions from the manufacturing sector. Changes in material and energy use, and emissions profiles of the iron and steel manufacturing are the result of complex interrelationships between technological and economic drivers. A better understanding of these changes require special attention paid to the time dependent consequences that technology and economic influences contribute to the choice of inputs and their associated outputs. We assess the impact that climate-change policies may have on energy use and carbon emissions in the Korean iron and steel industry. We then present models of the iron and steel industry’s energy and product flow of POSCO corporation, with respect to environmental regulations by using the system dynamics (SD). Time series data and engineering information are combined to specify changes in technologies, fuel mix, and production processes. Various scenarios are used to analyze the consequences of climate change on the iron and steel industry. The results of these studies show strategies to prevent environmental changes are devised from various perspectives to minimize the risks associated with future uncertainties, all in an effort to be conducive to the sustainable development of the energy-intensive industry. |
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