During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia frui...
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During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia fruit tree trunk wouldn't loosen or damage, which may affect its later growth, during the picking process. In this regard, this paper derived the state space model of the valve-controlled clamping hydraulic motor system of the push-shaking camellia fruit picking manipulator, and the fuzzy wavelet neural network (FWNN) was designed on the basis of the traditional incremental PID control principle and the parameters of the neural network were optimized by the improvedgreywolf optimizer (GWO). And then, the control system was simulated with the MATLAB/Simulink software without and with external interference, and compared and analyzed it with traditional PID controller and fuzzy PID (FPID) controller. The results show that the traditional PID controller and the FPID control have slow response and poor robustness, while the improved fuzzy wavelet neural network PID (IFWNN PID) controller possesses the characteristics of fast response and strong robustness, which can well meet the requirement of the constant clamping force of hydraulic motors. Finally, the field clamping test was carried out on the picking manipulator. The results show that the manipulator controlled by the IFWNN PID controller shortens the clamping time by 20.0% and reduces the clamping damage by 13.6% compared with the PID controller, which is verified that the designed controller can meet the clamping operation requirements of the camellia fruit picking machine.
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.
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.
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)
Current research on the disassembly line balancing problem ignores the influence of non-disassemblability of components. And this problem can lead to failure of the disassembly task, which can seriously affect the dis...
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Current research on the disassembly line balancing problem ignores the influence of non-disassemblability of components. And this problem can lead to failure of the disassembly task, which can seriously affect the disassembly efficiency. This study integrates destructive operation into the human-robot disassembly line while considering noise. First, a mixed integer programming model is established for human-robot hybrid partial destructive disassembly line balancing problem to accurately obtain the number of stations, smoothness index, costs and negative impact of noise pollution on workers. Then, an improved grey wolf optimization algorithm is proposed for the NP-hard characteristic of problem. A three-layer encoding and two-stage decoding strategy is designed to constrain the uniqueness of the solution, considering the noise constraints, and the different disassembly times of the human-robot. A disturbance factor is also designed to prevent local optimality, which enhances the performance of the proposed algorithm. Different cases are also used to verify the correctness and superiority of the proposed method. Finally, an engine case is used to validate the practicality of the proposed method. The results of the comparison of the different disassembly schemes show that: (1) The proposed algorithm outperforms the three classical Swarm Intelligence methods and other eleven algorithms in the disassembly line balancing problem. (2) The human-robot hybrid partial destructive disassembly line can effectively avoid the problem of task failure, and the smoothing index is reduced by 12.27 % compared with the original scheme. Disassembly costs increased by 1.28 %, but this was minimal compared to line-wide smooth running and worker health. (3) The human-robot hybrid disassembly line is more appropriate to solve the actual production process compared to worker disassembly and robot disassembly, and has a greater advantage in solving the actual disassembly line balance problem
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.
A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the s...
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A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the scheduling optimization problem for ship plane block flow line involves dual resource constraints, including work teams and spare parts supply limitations. This can be seen as a Dual Resource Constrained Blocked Flow Shop Scheduling Problem (DRCBFSP). This paper presents a scheduling optimization method for this kind of problem to minimize the maximum completion time. To address the dual constraints, chromosomes are encoded as a two-dimensional array composed of positive integers representing the assembly order of blocks and the allocation of work teams. An improved grey wolf optimization algorithm (IGWO) is proposed to solve the problem, and the Rank Order Value (ROV) rule is used to transform the discrete scheduling solution with the continuous individual position vector. The IGWO algorithm also incorporates nonlinear search factors, dynamic inertia weight factors, and Gaussian mutation perturbation strategies to enhance its development and exploration capabilities. The experimental results suggest that the mathematical model and the IGWO algorithm established in this paper can effectively solve the DRCBFSP encountered in ship block building.
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