دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش چهل و پنجم

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 Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia
نوع مقاله مقاله ژورنال
نویسندگان Matebie Meten, Netra PrakashBhandary, Ryuichi Yatabe
چکیده / توضیح Database construction for landslide factors (slope, aspect, profile curvature, plan curvature, lithology, land use, distance from lineament & distance from river) and landslide inventory map is an important step in landslide susceptibility modelling. Using the frequency ratio model, the weights for each factor classes were calculated and assigned in GIS so as to add these factors and produce landslide susceptibility index maps based on mathematical combination theory. However, before combining them, their independence among each other should be ascertained. For this, the correlation matrix of logistic regression was applied and this showed that most of the correlations between factors were either absent or very insignificant suggesting that all landslide factors are independent. From a set of eight landslide factors, a total of 247 landslide susceptibility map combinations can be generated. However, for simplification, only 28 landslide susceptibility maps were chosen. Then the best landslide susceptibility map was selected based on high prediction accuracy. But, when there is similarity in the prediction accuracies of different combinations, the landslide susceptibility index difference values can be used as another selection criterion. Hence, the susceptibility map from a combination of all landslide factors except distance from river was found to be the best one. Among the 28 representative combinations, landslide susceptibility maps with the same prediction accuracy of 87.7% have been found in spite of their dissimilarity in their difference values. The combination, with a limited number of landslide factors and the highest prediction accuracy of 87.7%, was found from a combination of slope, lithology, land use and distance from lineament. In order to validate the prediction model, landslides were overlaid over the landslide susceptibility map and the number of landslides that fall into each susceptibility class was calculated. From this analysis 0.39%, 1.84%, 9.1%, 32.04% and 56.63% of the landslides fall in the very low, low, medium, high and very high landslide susceptibility classes respectively. Since 88.67% of the landslides fall in the high and very high susceptibility classes, the landslide susceptibility map can be considered reliable to predict future landslides.
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عنوان اصلی مقاله Study of the RESS Process for Producing Beclomethasone-17,21-Dipropionate Particles Suitable for Pulmonary Delivery
نوع مقاله مقاله ژورنال
نویسندگان Paul A. Charpentier, Ming Jia, Rahima A. Lucky
چکیده / توضیح The purpose of this research was to micronize beclomethasone-17,21-dipropionate (BDP), an anti-inflammatory inhaled corticosteroid commonly used to treat asthma, using the rapid expansion of supercritical solution (RESS) technique. The RESS technique was chosen for its ability to produce both micron particles of high purity for inhalation, and submicron/nano particles as a powder handling aid for use in next generation dry powder inhalers (DPIs). Particle formation experiments were carried out with a capillary RESS system to determine the effect of experimental conditions on the particle size distribution (PSD). The results indicated that the RESS process conditions strongly influenced the particle size and morphology; with the BDP mean particle size decreasing to sub-micron and nanometer dimensions. An increase in the following parameters, i.e. nozzle diameter, BDP mol fraction, system pressure, and system temperature; led to larger particle sizes. Aerodynamic diameters were estimated from the SEM data using three separate relations, which showed that the RESS technique is promising to produce particles suitable for pulmonary delivery.
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عنوان اصلی مقاله Quality by Design Approach: Application of Artificial Intelligence Techniques of Tablets Manufactured by Direct Compression
نوع مقاله مقاله ژورنال
نویسندگان Buket Aksu, Anant Paradkar, Marcel de Matas, Özgen Özer, Tamer Güneri, Peter York
چکیده / توضیح The publication of the International Conference of Harmonization (ICH) Q8, Q9, and Q10 guidelines paved the way for the standardization of quality after the Food and Drug Administration issued current Good Manufacturing Practices guidelines in 2003. “Quality by Design”, mentioned in the ICH Q8 guideline, offers a better scientific understanding of critical process and product qualities using knowledge obtained during the life cycle of a product. In this scope, the “knowledge space” is a summary of all process knowledge obtained during product development, and the “design space” is the area in which a product can be manufactured within acceptable limits. To create the spaces, artificial neural networks (ANNs) can be used to emphasize the multidimensional interactions of input variables and to closely bind these variables to a design space. This helps guide the experimental design process to include interactions among the input variables, along with modeling and optimization of pharmaceutical formulations. The objective of this study was to develop an integrated multivariate approach to obtain a quality product based on an understanding of the cause–effect relationships between formulation ingredients and product properties with ANNs and genetic programming on the ramipril tablets prepared by the direct compression method. In this study, the data are generated through the systematic application of the design of experiments (DoE) principles and optimization studies using artificial neural networks and neurofuzzy logic programs.
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عنوان اصلی مقاله Techniques of EMG signal analysis: detection, processing, classification and applications
نوع مقاله مقاله ژورنال
نویسندگان M. B. I. Reaz, M. S. Hussain, F. Mohd-Yasin
چکیده / توضیح Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.
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عنوان اصلی مقاله Proportional-integral-derivative control of nonlinear half-car electro-hydraulic suspension systems
نوع مقاله مقاله ژورنال
نویسندگان John E. D. Ekoru, Jimoh O. Pedro
چکیده / توضیح This paper presents the development of a proportional-integral-derivative (PID)-based control method for application to active vehicle suspension systems (AVSS). This method uses an inner PID hydraulic actuator force control loop, in combination with an outer PID suspension travel control loop, to control a nonlinear half-car AVSS. Robustness to model uncertainty in the form of variation in suspension damping is tested, comparing performance of the AVSS with a passive vehicle suspension system (PVSS), with similar model parameters. Spectral analysis of suspension system model output data, obtained by performing a road input disturbance frequency sweep, provides frequency response plots for both nonlinear vehicle suspension systems and time domain vehicle responses to a sinusoidal road input disturbance on a smooth road. The results show the greater robustness of the AVSS over the PVSS to parametric uncertainty in the frequency and time domains.
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عنوان اصلی مقاله Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods
نوع مقاله مقاله ژورنال
نویسندگان Danial Behnia, Kaveh Ahangari, Ali Noorzad, Sayed Rahim Moeinossadat
چکیده / توضیح This paper deals with the estimation of crest settlement in a concrete face rockfill dam (CFRD), utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonification and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods. In addition, dam height (H), shape factor (S f), and time (t, time after first operation) are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values ofR 2 for both equations of GEP (with and without constant) were 0.9603 and 0.9734, and for the three approaches of ANFIS (grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering (FCM)) were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs.
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عنوان اصلی مقاله Material selection combined with optimal structural design for mechanical parts
نوع مقاله مقاله ژورنال
نویسندگان Le-miao Qiu, Liang-feng Sun, Xiao-jian Liu, Shu-you Zhang
چکیده / توضیح To reduce the environmental impact of mechanical parts, an approach integrating structural design and material selection was studied. Adding the discrete variable of material, a hybrid optimization model was built with the aim of minimizing environmental impact and based on an ordinary structure optimization model. An optional material set was built by combining measures of qualitative and quantitative screening, while the lifecycle environmental impact of the materials was quantified using the method of Eco-indicator 99. Two groups of structurally optimal solutions were calculated with ideal and negative-ideal materials selected respectively, and then the hybrid model was simplified by comparing the solutions. A material environmental performance index was calculated using an analytic method. By comparing this index for every material in the optional material set, the optimal material can be found and the structural solutions calculated. This method was applied to a dowel bar design process as a case study. The results show that the environmental impact of each material has a significant effect on the optimal structural solution, and it is necessary to study the integration of structural design and material selection.
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عنوان اصلی مقاله Energy management strategy for a parallel hybrid electric vehicle equipped with a battery/ultra-capacitor hybrid energy storage system
نوع مقاله مقاله ژورنال
نویسندگان Jun-yi Liang, Jian-long Zhang, Xi Zhang, Shi-fei Yuan, Cheng-liang Yin
چکیده / توضیح To solve the low power density issue of hybrid electric vehicular batteries, a combination of batteries and ultra-capacitors (UCs) could be a solution. The high power density feature of UCs can improve the performance of battery/UC hybrid energy storage systems (HESSs). This paper presents a parallel hybrid electric vehicle (HEV) equipped with an internal combustion engine and an HESS. An advanced energy management strategy (EMS), mainly based on fuzzy logic, is proposed to improve the fuel economy of the HEV and the endurance of the HESS. The EMS is capable of determining the ideal distribution of output power among the internal combustion engine, battery, and UC according to the propelling power or regenerative braking power of the vehicle. To validate the effectiveness of the EMS, numerical simulation and experimental validations are carried out. The results indicate that EMS can effectively control the power sources to work within their respective efficient areas. The battery load can be mitigated and prolonged battery life can be expected. The electrical energy consumption in the HESS is reduced by 3.91% compared with that in the battery only system. Fuel consumption of the HEV is reduced by 24.3% compared with that of the same class conventional vehicles under Economic Commission of Europe driving cycle.
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عنوان اصلی مقاله Fuzzy-based risk prioritization for a hydrogen refueling facility in Malaysia
نوع مقاله مقاله ژورنال
نویسندگان Hue-yee Chong, Mahidzal Dahari, Hwa-jen Yap, Ying-tai Loong
چکیده / توضیح Hydrogen is starting to be mentioned as an alternative fuel to replace the fossil fuel in future transportation applications due to its characteristics of zero greenhouse gas emission and high energy efficiency. Before hydrogen fuel and its facilities can be introduced to the public, relevant safety issues and its hazards must be assessed in order to avoid any chance of injury or loss. While a traditional risk assessment has difficulty in prioritizing the risk of failure modes, this paper proposes a new fuzzy-based risk evaluation technique which uses fuzzy value to prioritize the risk of various scenarios. In this study, the final risk of each failure modes was prioritized by using the MATLAB fuzzy logic tool box with a combination of two assessments. The first assessment was concerned with the criteria which affected the actual probability of occurrence. This assessment considered the availability of the standard that was applied to prevent the likelihood of the scenario occurring. On the other hand, the second assessment was focused on evaluating the consequence of the failure by taking into account the availability of detection and the complexity of the failure rather than only the severity of the scenarios. A total of 87 failure scenarios were identified using failure modes and effect analysis (FMEA) procedures on hydrogen refueling station models. Fuzzy-based assessments were performed through risk prioritizing various failure scenarios with a fuzzy value (0 to 1) and risk level (low, medium, and high) while a traditional risk assessment approach presented the risks only in forms of level (low, medium, and/or high). Availability of the fuzzy value enabled further prioritizing on the risk results that fell in the same level of risk. This study concluded that fuzzy-based risk evaluation is able to further prioritize the decisions when compared with a traditional risk assessment method.
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عنوان اصلی مقاله A novel method for fast identification of a machine tool selected point temperature rise based on an adaptive unscented Kalman filter
نوع مقاله مقاله ژورنال
نویسندگان Chen-hui Xia, Jian-zhong Fu, Yue-tong Xu, Zi-chen Chen
چکیده / توضیح A novel method is presented for fast identification of a machine tool selected point temperature rise, based on an adaptive unscented Kalman filter. The major advantage of the method is its ability to predict the selected point temperature rise in a short period of measuring time, like 30 min, instead of 3 to 6 h in conventional temperature rise tests. A fast identification algorithm is proposed to predict the selected point temperature rise and the steady-state temperature. An adaptive law is applied to adjust parameters dynamically by the actual measured temperature, which can effectively avoid the failure of prediction. A vertical machining center was used to validate the effectiveness of the presented method. Taking any selected point, we could identify the temperature rise at that point in 28 min. However, if the method was not used, it took 394 min to obtain the temperature rise curve from the start-up of the machine tool to the time when it reached a steady-state temperature. The root mean square error (RMSE) between the estimated and measured temperatures in the period of 394 min was 0.1291 °C, and the error between the estimated and measured steady-state temperatures was 0.097 °C. Therefore, this method can effectively and quickly identify a machine tool selected point temperature rise.
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