Table Of Rules Research Articles

Frequent Pattern to Promote Sale for Selling Associated Items for Recommender in e-Commerce

Background/Objectives: Nowadays, many people enjoy the direct purchase on e-commerce which many kinds of item are increasing explosively. Existing method has a weakness of the accuracy to forecast, leaving customers unsatisfied. Methods/Statistical Analysis: We search association rule into customers’ buying behavior and discover items of group purchase which promoted cooperative buying. We can also create the table of association rules in the whole purchase history data to join customer’s record. If a customer would want additional sale for cross selling and up selling, the system could recommend the items which worked on the rate threshold of association rules to promote sale. Findings: We search association rule into customers' purchase data and discover frequent pattern to promote sale for selling associated items, to forecast frequent pattern of customer’s interest of item. We need to make clustering the category of associated items to reflect the customer’s propensity to reflect customer’s interest of associated item as well as to create the table of association rules in the whole purchased history data to join customer’s record. Our proposing system is higher 19.15% in F-measure than existing system. We execute the preprocessing for clustering the category of associated items to reflect the customer’s propensity. Our proposing system is higher 27.42% in recall, even if it is lower 12.23% in precision than previous system which conducted the analysis of segmentation to have different weights, is advanced than existing system. Our proposing system is better in F-measure than both the previous system and existing system. We could recommend associated items which worked on the rate threshold of association rules to promote sale, if a customer bought associated items. Improvements/Applications: We have the improvement that proposing system is higher 12.54% in F-measure than existing system. We make application to promote sale for selling associated items in e-commerce.

A knowledge-driven approach for specifying the requirements of multi-agent system

Agents exhibit a high degree of cooperation to achieve designated goals that may not be feasible otherwise. In such a cooperative environment, the specification of the static and dynamic requirements of agents is a prime concern. Knowledge is the acquaintance with the facts or principles for extracting the non-trivial and potentially useful information from data. This work comprehends the knowledge in terms of decision table, indiscernible relation, information system, equivalence classes and reduction of rules. To specify the static requirements, this work proposes a coding scheme that assists the developers in obtaining the equivalence classes and enhances the readability and maintainability of the requirements. To specify the dynamic requirements, this work utilises the knowledge rule-base and visual state flow SF charts. Rule-base consists of the rules to identify appropriate service provider agents whereas visual state flow SF charts are used to comprehend the inter-agent dependency requirements and also enhance the chances of detecting inconsistent requirements.

The Texas Children's Hospital immunization forecaster: conceptualization to implementation.

Immunization forecasting systems evaluate patient vaccination histories and recommend the dates and vaccines that should be administered. We described the conceptualization, development, implementation, and distribution of a novel immunization forecaster, the Texas Children's Hospital (TCH) Forecaster. In 2007, TCH convened an internal expert team that included a pediatrician, immunization nurse, software engineer, and immunization subject matter experts to develop the TCH Forecaster. Our team developed the design of the model, wrote the software, populated the Excel tables, integrated the software, and tested the Forecaster. We created a table of rules that contained each vaccine's recommendations, minimum ages and intervals, and contraindications, which served as the basis for the TCH Forecaster. We created 15 vaccine tables that incorporated 79 unique dose states and 84 vaccine types to operationalize the entire United States recommended immunization schedule. The TCH Forecaster was implemented throughout the TCH system, the Indian Health Service, and the Virginia Department of Health. The TCH Forecast Tester is currently being used nationally. Immunization forecasting systems might positively affect adherence to vaccine recommendations. Efforts to support health care provider utilization of immunization forecasting systems and to evaluate their impact on patient care are needed.

Fault Diagnosis of Communication Equipment Based on Rough Set Theory

According to the communication equipment fault characteristics of complexity, hierarchy, relevance, delay and uncertainty, the method of extracting diagnosis rules based on rough set is presented in order to obtain diagnostic decision rules with good adaptability and maximum matching capabilities. This method can process a large amount of fault data and it can reduce both attribute and attribute value.4 evaluations are selected to evaluate the effectiveness and reasonableness of decision rules, and finally core value table of decision rules which has a certain ability to adapt. Example shows that this method can remove noise in fault diagnosis sample, reduce the computation and improve the efficiency of diagnosis effectively.

Bulanık Mantık ile Kefir Üretiminin Modellenmesi

The fermentation is ended with pH 4.6 values in industrial production of kefir. In this study, the incubation temperature, the incubation time and inoculums of culture were chose as variable parameters of kefir. In conventional control systems, the value of pH can be found by trial method. In these systems, if the number of input parameters is greater, the method of trial and error creates a system dependent on the person as well as troublesome. Fuzzy logic can be used in such cases. Modeling studies with this fuzzy logic control are examined in two portions. The first part consists of fuzzy rules and membership functions, while the second part consists of clarify. Kefir incubation temperature between 20 and 25°C, the incubation period between 18 to 22 hours and the inoculum ratio of culture between 1-5% are selected for optimum production conditions. Three separate fuzzy sets (triangular membership function) are used to blur the incubation temperature, the incubation time and the inoculum ratio of culture. Because the membership function numbers belonging to the the input parameters are 3 units, 3x3x3=27 line rule is obtained by multiplying these numbers. The table of fuzzy rules was obtained using the method of Mamdani. The membership function values were determined by the method of average weight using three trapezoidal area of membership functions created for clarification. The success of the system will be found, comparing the numerical values obtained with pH values that should be. Eventually, to achieve the desired pH value of 4.6 in the production of kefir, with the using of fuzzy logic, the workload of people will be decreased and the productivity of business can be increased. In this case, it can be provided savings in both cost and time.

Fuzzy logic control to improve the performance of the direct power control based DFIG

Purpose – A direct power control (DPC) of the doubly-fed induction generator (DFIG) is presented. A new method, which is based on the rotation of the space sector, clockwise or vice versa, is proposed to improve the performance of the switching table. Then, it is combined with a fuzzy system to have advantages of both rotation sector and fuzzy controller. The paper aims to discuss these issues. Design/methodology/approach – In this paper, a new DPC of the DFIG is presented. To improve the performance of the switching table, a new method is proposed. The method is based on the rotation of the space sector, clockwise or vice versa. The excellence of the proposed method is proven. Then, it is shown that the performance of the system can be enhanced by using a fuzzy logic controller. The rotation method is combined with a fuzzy system. Findings – Simulation shows that although sector rotation and fuzzy controller can improve the performance of the DFIG, a combination of both demonstrates a smoother response in order that reactive and active power ripples and THD of the injected current decrease in different speeds. Also, it is demonstrated that the proposed method is robust against parameters variations. However, a hardware experiment should be performed to be practically verified. Originality/value – A sector rotation is proposed and its effect on the performance of the DFIG is considered. A simple method to write rules table is presented and the performance of sector rotation and fuzzy controller on the DFIG is analysed.

Fuzzy Force Compliance Control for Cleaning Processing

A special designed dual-spindle tool was assembled on the end of a Cartesian robot for purpose of cleaning up mounting holes of carbon anodes used in aluminum electrolytic production. Spiral grooves distributed around inner wall of the mounting holes lead to force compliance issues during cleaning process. Decomposing the task space into force and position control directions, the compliance movement of the scraping tools was accomplished based on hybrid force-position method. Fuzzy strategy combining with conventional PI controller was utilized to perform torque control of scraping blades of the cleaning tool while the environment stiffness is uncertain. The control algorithm was realized in a programmable logic controller based on look-up table of fuzzy rules. Cleaning tests in actual production line were also performed that indicated the validity of the force compliance controller.

The Attribute Reduction of Regional Logistics Demand Based on Rough Set Theory

This paper proposes a novel approach for logistics data analysis. A large number of four regions historical data was collected by building a complete demand influencing factors of regional logistics system. Through using the minimum entropy discrete the condition attributes and using genetic algorithm to construct decision table, we develop a mechanism to generate Attribute Reduction. The validation of the proposed approach is illustrated on various data sets and experiment showed that it can efficiently avoid the data choice impacted by the subjective factor of qualitative analysis and the resulting decision rules table can make a certain degree prediction of regional logistics.

The Large Numbers in a Quantized Universe

The article relates to decades-old problem of the mysterious coincidence of various Large numbers of magnitude ranging from 1040 to 10120 which sometimes appears in cosmology and quantum physics. Using well-known classical relations as well as the ideal Schwarzschild solution the exact relations of various large numbers, fine structure constant α and were found. The new Largest number law is claimed. The hypothetical approximations of the Hubble parameter—68.7457(82) km/s/Mpc, Hubble radius—14.2330(17) Gly, and some others were proposed. The exact formulae supporting P. Diracs Large number hypothesis and H. Weyls proposition were found. It is shown that all major physical constants with length dimension (from Compton wave length of universe through Planck and atomic scale up to Hubble sphere radius) could be derived from each other, and the table of the specific conversion rules has been developed. The model shows that Eddington-Weinberg relation can be transformed to precise identity. It is shown that both Bekenstein universal entropy bound and Hooft-Susskind holographic entropy bound are equal to the Largest number doubled.

Fuzzy based Optimization for Power Management in Wireless Sensor Networks

Wireless Sensor Networks (WSN), sensor node deployment is essential for maximizing the coverage and detection probabilities. But the existing optimization solution suffers from limited energy storage, node death, increased network traffic etc. To solve these issues, we propose a fuzzy based optimization model for power management in wireless sensor networks. The objectives considered in the paper include maximizing network coverage, connectivity, network lifetime and minimizing traffic load. A fuzzy rules table is constructed with the input parameters such as node degree, link quality, residual energy and traffic rate. Depending upon the outcome of the fuzzy logic, the nodes are categorized into good, normal and bad. After the initial deployment of good nodes, the SLEEP and WAKEUP Procedure is applied to maximize the lifetime of wireless sensor networks. By putting nodes to sleep when there are no events, the energy consumption of the sensor nodes can be significantly reduced. In SLEEP and WAKEUP Procedure, asynchronous type is used; it allows each node in the wireless sensor network to set its own SLEEP and WAKEUP schedule independently in order to save energy. Simulation result shows that the proposed Procedure provides maximization of the lifetime of Wireless Sensor Network (WSN).

Research and Application of Fuzzy Control in Boiler Combustion System Based on ACA

In the complex control processes of the industrial boiler combustion system with non-linear, time-varying and multivariable, aim at the original control parameters fixed, slow response, delay regulation and other problems, this paper introduces a control method that combines ant colony algorithm with fuzzy PID control, improves the design of fuzzy control rules table and optimizes the PID control parameters. Simulation results show that this control scheme is superior to conventional PID control and fuzzy PID control.

The Reform of the EC GSP Rules of Origin: Per aspera ad astra?

On 1 January 2011, a new regulation on EC Generalized System of Preferences (GSP) rules of origin came into force, ending a reform process that was initiated by the Commission Green Paper of 2003 on the future of rules of origin in preferential trade arrangements. The new EC GSP rules of origin herald a new era and set a benchmark for other preference-giving countries: they are far more liberal than the previous ones with the notable exception of fishery products and processed agricultural food. The Regulation also introduces as of 2017 a new administration of rules of origin whereby origin declarations are made upon registered exporters designation of the certifying authorities. The aim of this article is twofold. The first is to analyse the EC reform to assess the major changes from previous rules of origin; the second aim is to briefly analyse the foundation, evidence, and studies that have underpinned such reform. It discusses the methodology used by the EC Commission and developed by the author in previous UNCTAD papers in setting a given rule of origin. The article concludes with a comparative table of old and new product-specific rules of origin outlining the major changes in key industrial sectors such as agri-food products, clothing, machineries, and electronics.

Table of Arbitration Rules

Table of Arbitration Rules

Spatial Object Segmentation Using Stereo Images

The framework of our proposed for segmenting objects using spatial location information from stereo images. An efficient graph-based image segmentation algorithm within this framework for combining changes in optical features and physical location to segment reality scenes into perceptually and semantically uniform regions. Optical and physical location are extracted usingk-means clustering, and we propose a rules table for combining optical and spatial features together. The performance of our proposed framework is demonstrated in a series of reality-scene images using experimental data from the Middlebury stereo image data(http://vision.middlebury.edu/stereo/data/).

A Rule-Based Prefetching Approach for Object-Based Storage Device

Capturing the advantages of NAS and SAN, Object-based Storage System (OBSS) is becoming an active research area. OBSS is composed of clients, metadata server, and Object-based Storage Devices (OSDs). Similar to the prefetching of file and block, object prefetching is also an effective method to reduce the object access latency in OSD. However, the existing methods focus on intra-object prefetching [ 1 ] and they limit the prefetching capability among objects to a certain extent. In this paper, we present a novel inter-object prefetching approach which considers the characteristics of the object access in OSD. This approach adopts a kind of data mining technique called sequential pattern mining to find the object access sequential patterns [ 2 ], then produces the object sequential rules according to the patterns, and finally prefetches objects according to the rules and the object access context. The whole process includes the following three modules: Object Request Service Module. It maintains a current object access context, which is a l-length recently accessed object sequence. Furthermore, it appends new object information to the historic object access stream when an object is accessed. Sequential Rules Mining Module. It is composed of the object sequences preprocessing, the sequential pattern mining and the sequential rules producing modules. The preprocessing module divides the object access stream into many subsequences. Base on the discovered object sequential patterns gained by the sequential pattern mining module, object access sequential rules table is constructed. Prefetching Engine Module. When the current requested object is not in the object cache, the prefetching engine selects a matched rule with the highest confidence from the rules table according to the context, then chooses the corresponding objects and preloads them into the object cache. Simulated experimental results show that both prediction accuracy and object hit rate can be improved comparing with the Group-based prefetching method [ 3 ] which can also be used for object prefetching.

Table of Arbitration Rules

Table of Arbitration Rules

Table of Arbitration Rules

Table of Arbitration Rules

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.

Table of Arbitration Rules

Table of Arbitration Rules

Table of Arbitration Rules

Table of Arbitration Rules