The patient privacy is danger while medical records and data are transmitted or share beyond secure big data. This is because violations push them to the margins and they begin to avoid fully revealing their stages. T...
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The patient privacy is danger while medical records and data are transmitted or share beyond secure big data. This is because violations push them to the margins and they begin to avoid fully revealing their stages. This kind of stages contains negative impact in scientific investigate. To overcome this issue, Secure Block Chain System for Managing and Sharing Electronic Medical Records in Big Data Field is proposed. In this manuscript, a Cryptographic Hash Generator (CHG) technique based Secured and Trusted Data storage and transmission using Block Chain (BC) in Hadoop Distributed File System (HDFS). Initially, the Big data collected from the health care center is partitioned into sensitive and insensitive data. Block chain system utilizes an asymmetric cryptography for validating transactions authentication. Here, the user key is created through secured bitwise cryptographic hash generator (CHG) while there is required to fetch the newly record for usage. In block chain system, when a user seeking data from a healthcare application have forward a request to CHG. The message is send back to the user with a secret key for confirmation. The key can be decrypted or even denied access if only a valid user allows the user to link to this cluster. Only sensitive data were selected to the process of encryption for the process of encryption, this CHG technique employs the Discrete Shearlet Transform (DST) for encrypting the data, and the data's are warehoused in the block chain to upgrade the level of security. And the insensitive data are put directly on the Hadoop Distributed File System. During the verification process, CHG is utilized for creating the request forward through the user. The operator creates the purpose of remote key to create the block (request) and signing the request using transaction private key, then forward to request queuing. To validate a request, the request from the queue is supplied first and an improved grey wolf optimization algorithm (IGWO)
Currently the total energy demand quantity is already larger than the total supply quantity, and the structural contradiction is serious, how to improve energy utilization efficiency of petrochemical enterprises is a ...
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Currently the total energy demand quantity is already larger than the total supply quantity, and the structural contradiction is serious, how to improve energy utilization efficiency of petrochemical enterprises is a problem that should be solved quickly. In order to correctly evaluate the energy utilization efficiency of refinery and petrochemical enterprises, the construction method of constructing energy utilization efficiency evaluation model of refining unit in petrochemical enterprises is established based on proposed Contourlet neural network optimized by improvedgreyalgorithm. The energy utilization efficiency evaluation index system is confirmed considering its impacts. The Contourlet neural network is constructed through using Contourlet as excitation function to improve the evaluation precision. The nonlinear change strategy of controlling parameter aof traditional greywolfoptimizationalgorithm is proposed, the Cubic chaotic value is used as the perturbation operator of location of greywolf to generate the new solution, and the individual memory function of Cuckoo algorithm is introduced to improve the location updating algorithm, and then the improved grey wolf optimization algorithm has better global optimization capability that is used to optimize the parameters of Contourlet neural network. The evaluation analysis of energy utilization efficiency for 250 devices for removing sulphur alcohol of liquefied gas is carried out. Results show that the proposed evaluation model has best evaluation efficiency and precision. In addition, the proposed evaluation model has highest evaluation precision of energy utilization efficiency of refinery and petrochemical enterprises. The evaluation results can effectively evaluate the energy utilization level of petrochemical enterprises, which can offer favorable theoretical basis of establishing the energy saving measurements for petrochemical enterprises. (C) 2019 Elsevier Ltd. All rights reserved.
The vibration signal of the axle box bearing of the train is affected by the track excitation and the random noise of the environment. The vibration signal is nonlinear and non-stationary, and the signal characteristi...
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The vibration signal of the axle box bearing of the train is affected by the track excitation and the random noise of the environment. The vibration signal is nonlinear and non-stationary, and the signal characteristics of the early fault are weak and easy to be submerged, which leads to the low accuracy of the weak fault diagnosis of the bearing. To solve this problem, a weak fault diagnosis method for train axle box bearing based on parameter optimization Variational Mode Decomposition (VMD) and improved Deep Belief Network (DBN) is proposed. Firstly, the nonlinear convergence factor, Levy flight theory and greedy algorithmoptimization theory are introduced into the greywolfoptimizationalgorithm (GWO), and an improved GWO algorithm based on hybrid strategy is proposed to improve the performance of the algorithm and solve the local optimal problem of the algorithm. Secondly, the improved GWO is applied to optimize the VMD parameters, which is used for signal decomposition. And the fault feature information of modal components with maximum correlation coefficient is extracted by multi-scale scatter entropy. Finally, the improved GWO algorithm is applied to optimize the parameters of the DBN to solve the parameter setting problem, and the optimized DBN is used as a pattern recognition algorithm for weak fault diagnosis of bearings. Through experimental comparison and analysis, the proposed method can effectively solve the problem of weak fault diagnosis of axle box bearings, and has high diagnostic accuracy.
To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed alg...
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To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improvedgreywolfoptimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.
To improve the accuracy and the recognition efficiency of a bearing fault diagnosis, a fault diagnosis method based upon the improvedgreywolfoptimization (IGWO) algorithm and support vector machine (SVM) is propose...
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To improve the accuracy and the recognition efficiency of a bearing fault diagnosis, a fault diagnosis method based upon the improvedgreywolfoptimization (IGWO) algorithm and support vector machine (SVM) is proposed in the following manners. First, the data are pre-processed by using the set ensemble empirical mode decomposition (EEMD), Shannon wavelet packet entropy (SWPE), and principal component analysis (PCA). Next, the idea of updating the host bird nest by the cuckoo search (CS) optimizationalgorithm is introduced into the greywolfoptimization (GWO) algorithm to obtain the IGWO algorithm. Then, the SVM is optimized by the IGWO algorithm to obtain optimal parameters for a new diagnostic model. This model improves the problem where the algorithm easily to falls into a local optimum. The learning ability and the generalization ability of the SVM are also enhanced. Finally, the effectiveness of the optimization model is tested by two different bearing data sets. The results show that compared to the genetic algorithm (GA), particle swarm optimization (PSO) and GWO algorithmoptimization, the IGWO algorithm can be more accurate and efficient when diagnosing bearings.
Construction simulation is an effective tool to provide schedule plans. Vehicle speed is one of the most significant factors in earthwork construction simulation. However, neglecting the strong correlation with contex...
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Construction simulation is an effective tool to provide schedule plans. Vehicle speed is one of the most significant factors in earthwork construction simulation. However, neglecting the strong correlation with contextual factors, random distribution methods will lead to inaccurate prediction of vehicle speed. To address such issues, an improved extreme gradient boosting (XGBoost) approach to vehicle speed prediction is proposed for earthwork construction simulation. Firstly, to improve the global searching ability, an improved grey wolf optimization algorithm (IGWO) is put forward. Secondly, XGBoost is optimized by IGWO to construct an IGWO-XGBoost model. Then, the prediction model is embedded in the earthwork construction simulation model. The case study proves that the simulation results of the proposed method are more consistent with an actual construction schedule. It is expected that the vehicle speed prediction embedded into a simulation program facilitated an accurate development of schedule plan, thereby improving the efficiency of construction management.
Since its introduction, kernel extreme learning machine (KELM) has been widely used in a number of areas. The parameters in the model have an important influence on the performance of KELM. Therefore, model parameters...
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Since its introduction, kernel extreme learning machine (KELM) has been widely used in a number of areas. The parameters in the model have an important influence on the performance of KELM. Therefore, model parameters must be properly adjusted before they can be put into practical use. This study proposes a new parameter learning strategy based on an improvedgreywolfoptimization (IGWO) strategy, in which a new hierarchical mechanism was established to improve the stochastic behavior, and exploration capability of grey wolves. In the proposed mechanism, random local search around the optimal greywolf was introduced in Beta grey wolves, and random global search was introduced in Omega grey wolves. The effectiveness of IGWO strategy is first validated on 10 commonly used benchmark functions. Results have shown that the proposed IGWO can find good balance between exploration and exploitation. In addition, when IGWO is applied to solve the parameter adjustment problem of KELM model, it also provides better performance than other seven meta-heuristic algorithms in three practical applications, including students' second major selection, thyroid cancer diagnosis and financial stress prediction. Therefore, the method proposed in this paper can serve as a good candidate tool for tuning the parameters of KELM, thus enabling the KELM model to achieve more promising results in practical applications. (C) 2019 Elsevier Ltd. All rights reserved.
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