Shandong Institute of Business and Technology

About: Shandong Institute of Business and Technology is a education organization based out in Yantai, China. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 1487 authors who have published 1433 publications receiving 8915 citations.

An energy-efficent routing protocol for WSN-based intelligent mining system

Abstract: In recent years, Wireless sensor network (WSNs) has become the main technology to construct underground network for intelligent mining system. Due to the complex roadway topology, there needs an optimized energy conservation routing protocol. We proposed EERP, an energy-efficient routing protocol for for WSN-based intelligent mining system, which can prolonged lifetime of a network and reduce energy consumption through construct a dominator chain by region partition. Simulation results show that EERP has 8.2 times stable working time than LEACH. When region length is 100m, EERP still can maintain 99% energy after 3000rd transmission.

Green supplier selection based on probabilistic dual hesitant fuzzy sets: A process integrating best worst method and superiority and inferiority ranking

Abstract: China’s equipment manufacturing industry is increasingly important due to the development of economic globalization. Selecting the proper suppliers, taking into consideration the economic and environmental benefits, is strategic due to its impacts on the operation and competitiveness of an enterprise. Uncertainty in the selection of suppliers creates challenges for managers. The probabilistic dual hesitant fuzzy sets (PDHFSs) are powerful and effective tools to handle uncertain information, which integrate the strengths of both the dual hesitant fuzzy sets and probabilistic hesitant fuzzy sets. Considering that the best worst method (BWM) is an efficient weight-determination method, which can simplify the calculation process and improve the consistency degree of the results. The superiority and inferiority ranking (SIR) integrates the strengths of most multi-criteria decision making methods in handling unquantifiable, cardinal and ordinal data. In this paper, we developed an integrated group BWM and SIR to help managers select the optimum suppliers in which the evaluation is expressed in PDHFSs. In this multi-criteria group decision making (MCGDM) problem, the BWM with PDHFSs is investigated to obtain the weights of experts and criteria. A consistency reaching method based on the input-based consistency ratio is proposed to overcome the barrier of the low consistencyrelied on the pairwise comparison and reduce the computation complexity. Furthermore, with the weights of criteria and experts acquired by the PDHFS-BWM, the SIR is extended to the probabilistic dual hesitant fuzzy information environment. A numerical example is given to verify the validity and feasibility of the proposed method, and comparison are conducted to show its advantage.

A Novel Strategy for Wind Speed Prediction in Wind Farm

Abstract: The empirical mode decomposition (EMD) is well known for predicting wind speed.However, but the joint application of relevance vector machine (RVM) and empirical mode decomposition in wind speed forecasting is seldom found in the field. This paper proposes a relevance vector machine model based on empirical mode decomposition to predict the wind speed.Before the wind speed forecasting with RVM，EMD algorithm is used to decompose wind speed signal in order to weaken the disadvantageous influences of nonlinearity and uncertainty in wind spped. By the decomposition process, a series of intrinsic mode functions (IMFs) are generated. To each IMF, RVM algorithm is used to construct the model and carry on the forecast espectively. The final predicted result is obtained by the superposition of all prediction results. By the simulation experiment, the comparison of several algorithms is shown. The results showed that EMD-RVM model is effective, and has better forecasting precision DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.2770 Full Text: PDF

Weighted Brain Network Metrics for Decoding Action Intention Understanding Based on EEG.

Abstract: Background: Understanding the action intentions of others is important for social and human-robot interactions Recently, many state-of-the-art approaches have been proposed for decoding action intention understanding Although these methods have some advantages, it is still necessary to design other tools that can more efficiently classify the action intention understanding signals New Method: Based on EEG, we first applied phase lag index (PLI) and weighted phase lag index (WPLI) to construct functional connectivity matrices in five frequency bands and 63 micro-time windows, then calculated nine graph metrics from these matrices and subsequently used the network metrics as features to classify different brain signals related to action intention understanding Results: Compared with the single methods (PLI or WPLI), the combination method (PLI+WPLI) demonstrates some overwhelming victories Most of the average classification accuracies exceed 70%, and some of them approach 80% In statistical tests of brain network, many significantly different edges appear in the frontal, occipital, parietal, and temporal regions Conclusions: Weighted brain networks can effectively retain data information The integrated method proposed in this study is extremely effective for investigating action intention understanding Both the mirror neuron and mentalizing systems participate as collaborators in the process of action intention understanding