To a good number of medium and small enterprises, it is a serious problem how to decline production cost and improve the response to the market by normative management and sharing the information.
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As an advanced management method and an IT tool, ERP can solve all these problems well. And how to combine ERP with other advanced management methods such as SCM and make ERP fit to the mid-small enterprises becomes a hot question recently. Purchase stands on the upstream of SC and takes a strategic effect on cost and quality control.
Advanced and efficient purchase management not only can supply continuous material to keep enterprise run perfectly but also can make enterprise more competitive in controlling the whole SC. MRP algorithm designed in this paper can be a good application of procurement management in the ERP system, and achieved good economic. This security system consists of a variety of different sensor functions. The data collected will be analyzed and judged through the Arduino microprocessor and the information is sent to the control end by the ZigBee wireless transmission module.
The control end will activate all buzzers and the searchlight if an invasion has been confirmed. Also, the installation of the CCD camera will activate when suspicious activity occurs and will thus take clear and definite photos as evidence and send an email to notify the user. The rising and developing of information technologies has made information overflow. Even in the same topics, a lot of different aspects of knowledge were setup. So, the knowledge integration is one of the important research topics. In this paper, the diabetes drug prescriptionsare usedas examples to do the knowledge integration which according to American Diabetes Association ADA , American Journal of Clinical Endocrinology Society AACE , the Republic of China Diabetes Association and the British National Health Service Bureau NHS.
The system will integrate the four medication diabetes associations to establish knowledge ontologies. The system includes three parts. First, the ontologies pre-processing will calculate the similarity between the ontologies and then find out the correlation between ontologies, Next, the system transfers the ontologies format into Joseki, and finally the user through a graphical user interface to obtain information. In recent years, with rapid development of social networking websites, more and more travelers make their travel and accommodation decisions by referring to online comments electronic word of mouth.
Social media marketing could one of cheap and powerful internet advertising channels. However, most of bed and breakfast enterprises lack sufficient human resource and time to interact to the online users of social networking websites. A survey of social media marketing in Facebook will be provided to demonstrate the effectiveness of our utilized methods. Diverse sustainable supplier selection SSS methodologies have been suggested by the practitioners in earlier, to find a solution to the SSS problem.
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A SSS problem fundamentally is a multi-criteria practice. It is a judgment of tactical significance to enterprises. The nature of this decision usually is difficult and unstructured. Optimization practices might be useful tools for these types of decision-making difficulties. During last few years, Differential Evolution has arisen as a dominating tool used for solving a variety of problems arising in numerous fields.
In the current study, we present an approach to find a solution to the SSS problem using Differential Evolution in pulp and paper industry. Hence this paper presents a novel approach is to practice Differential Evolution to select the efficient sustainable suppliers providing the maximum fulfillment for the sustainable criteria determined. Finally, an illustrative example on pulp and paper industry validates the application of the present approach. Clustering is a hot topic of data mining.
After studying the existing classical algorithm of clustering, this paper proposes a new clustering algorithm based on probability, and makes a new definition for clustering and outlier. According to the distribution characteristics of sample data, this algorithm determines the initial clustering center automatically. It also implements eliminating outliers in the process of clustering. The experiment results on IRIS show that this algorithm can clustering effectively. Foreign exchange rate forecasting catches many researchers interests in recent years.
Problems of the foreign exchange rate forecasting model selection and the improvement on forecasting accuracy are not easy to be solved. In this paper, the forecasting results obtained by conventional time-series models and by the Inter-active Artificial Bee Colony IABC , which is a young artificial intelligent meth-od, are compared with each other with 4 years historical data.
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The sliding win-dow strategy is used in the experiment for both the training and the testing phases. In our experiments, we use continuous previous three days data as the training set, and use the training result to forecast the foreign exchange rate on the fourth day. In addition, we evaluate the forecasting accuracy with three criteria, namely, Mean Square Error MSE , Mean Absolute Error MAE , and Root Mean Square Error RMSE.
The experimental results indicate that feeding macroeco-nomic factors to IABC as the input data is capable to produce higher accurate data in the foreign exchange rate than the conventional time-series models such as EGARCH. Visual secret sharing VSS has attracted considerable attention to scientists and engineers as another branch alongside conventional cryptography to protect the sensitive visual information from several rapacious behaviors. In the literature, there are a number of several techniques used to protect the visual information, among which traditional VSS and random grid RG -based VSS are the primary branches.
In this letter, we show, by examples, the two means are equal. In addition, the color representation of traditional VSS and RG-based VSS found it different from digital applications like images. Based on the given examples, it is demonstrated that the color representation of the two means can be the same and confirm with digital processing applications. Enhancing the quality of hearing perception in noisy environments plays a significant role to improve life quality of elderly persons and hearing impaired people. Accordingly, this study presents a human-like auditory processing HAP algorithm to enhance the speech signal in low signal-to-noise ratio SNR and non-stationary noise environments.
The proposed algorithm comprises two modules, namely Cochlear Wavelet Transform CWT and AM-FM Demodulation AFD ; mimicking the human peripheral auditory processing system and the human cortical auditory processing system, respectively. The performance of the proposed HAP algorithm is evaluated in accordance with the ITU Perceptual Evaluation of Speech Quality PESQ standard. The results show that the proposed algorithm improves the speech quality by In the other words, the algorithm has significant potential for assistive listening in noisy environments. Harmony search HS can be applied to various optimization problems and easy to implement.
In this paper, we try to improve HS by change the reference probability distribution of harmony memory.
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Zipf distribution is used to balance the intensification and diversification. In addition, we propose the adaptive mechanism to avoid setting the new parameter. Experimental results show that the improvement is effective on the high dimensional numerical function optimization problem. The integration of LED lighting with microprocessor MCU or PSoC at system levels proliferate many new applica-tions toward a digital life style.
Meanwhile, due to its long life span, compact-ness and efficient energy consumption LED lightings are environmental friend-ly. Universal computing is a global goal worthy of the most serious consideration of engineering, e-commerce, and educational leaders.
For the benefits of the information age to effect general humanity, it is essential that computing become more widely embraced across the diverse groups of society, to confront the increasing problem of the digital divide. Currently, disenfranchisement, disinterest, and overt contempt for technology still abounds Macionis, The markets for e-commerce, let alone the benefits of telecommunication for humanity, require continuing energy to educate the majority of society about tools and visions for change.
Pregnancy and fetus development is an extremely complex biological process that, while generally successful and without complications, can go wrong. One of the methods to determine if the fetus is developing according to expectations is cardiotocography. Outputs of a cardiotocogram are usually interpreted as belonging to one of three states: physiological, suspicious and pathological. Automatic classification of these states based on cardiotocographic data is the goal of this paper.
In this research, the Random Forest method is show to perform very well, capable of classifying the data with A comparison with the Classification and Regression Tree and Self-organizing Map methods is also provided. Recognition of an EEG signal is a very complex but very important problem. In this paper we focus on a simplified classification problem which consists of detection finger movement based on an analysis of seven EEG sensors. The signals gathered by each sensor are subsequently classified by the respective classification algorithm, which is based on data compression and so called LZ-Complexity.
To improve overall accuracy of the system, the Evolutionary Weighted Ensemble EWE system is proposed.
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The parameters of the EWE are set in a learning procedure which uses an evolutionary algorithm tailored for that purpose. To take full advantage of information returned by sensor classifiers, setting negative weights are permitted, which significantly raises overall accuracy. Evaluation of EWE and its comparison against selected traditional ensemble algorithm is carried out using empirical data consisting of almost 5 hundred samples. The results show that the EWE algorithm exploits the knowledge represented by the sensor classifiers very effectively, and greatly improves classification accuracy.
In this paper, we propose a novel hierarchical ensemble classifier for texture classification by combining global Fourier features and local Gabor features. Specifically, in our method, global features are extracted from images firstly by 2D Discrete Fourier Transform. Then, real and imaginary components of low frequency band are concatenated to form a single feature set for further processing.
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Gabor wavelet transform is exploited for local feature extraction. Firstly, Gabor wavelets are used to extract local features from the whole image. Then, these features are spatially partitioned into a number of feature sets, each corresponding to a local patch of the image. After the above processes, an image can be represented by one Global Fourier Feature Set GFFS and multiple Local Gabor Feature Sets LGFSes.
These feature sets contain different discriminative information: GGS contains global discriminative information and each LGFS contains different local discriminative information. In order to make full use of all these diverse discriminative information, we propose multiple component classifiers by applying Fisher Discriminant Analysis FDA on GFFS and each LGFS, respectively.
At last, we combine them into one ensemble by weighted sum rule.