Abstract: In this paper, we investigate the multiple attribute decision making (MADM) problem based on the Hamacher aggregation operators and Choquet integral with dual Pythagorean hesitant fuzzy information. Then, motivated by the ideal of Hamacher operation and Choquet integral, we have developed some Hamacher correlated operators for aggregating dual hesitant Pythagorean fuzzy information. The prominent characteristic of these proposed operators is studied. Then, we have utilized these two operators to develop some approaches to solve the dual hesitant Pythagorean fuzzy MADM problems. Finally, a practical example for supplier selection in supply chain management is given to verify the developed approach and…to demonstrate its practicality and effectiveness.
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Abstract: BACKGROUND: The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today’s mental work mastered society. OBJECTIVE: Improving the usability of a product and minimizing user’s thinking mapping and interpreting load in human-machine interactions. METHODS: An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users’ intentions and affordance of product interface states. By analyzing the users’ thinking mapping problem, an…operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets. RESULTS: Considering multiple factors to minimize users’ thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case. CONCLUSIONS: The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.
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Keywords: Usability, mental load, user model, interface design
Abstract: Ultrasound (US) has emerged as a non-invasive imaging modality that can provide anatomical structure information in real time. To enable the experimental analysis of new 2-D array ultrasound beamforming methods, a pre-beamformed parallel raw data acquisition system was developed for 3-D data capture of 2D array transducer. The transducer interconnection adopted the row-column addressing (RCA) scheme, where the columns and rows were active in sequential for transmit and receive events, respectively. The DAQ system captured the raw data in parallel and the digitized data were fed through the field programmable gate array (FPGA) to implement the pre-beamforming. Finally, 3-D images…were reconstructed through the devised platform in real-time.
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Abstract: The raw trajectories contain large amounts of redundant data that bring challenges to storage, transmission and processing. Trajectory compression algorithms can reduce the number of positioning points while minimizing the loss of information. This paper proposes a heading maintaining oriented trajectory compression algorithm, which takes into account both position information and direction information. By setting an angle threshold, the algorithm can achieve a more accurate approximation of trajectories than traditional position-preserving trajectory compression algorithms. The experimental results show that the algorithm can ensure certain effect on the direction information and is more flexible.
Abstract: In the hot strip rolling process, accurate prediction of bending force is beneficial to improve the accuracy of strip crown and flatness, and further improve the strip shape quality. Due to outliers and noise are commonly present in the data generated in the rolling process, not only the prediction accuracy should be considered, but also the uncertainty of prediction results should be described quantitatively. Therefore, for the first time, the authors establish an interval prediction model for bending force in hot strip rolling process. In this paper, we use Artificial Neural Network (ANN) and whale optimization algorithm (WOA) to produce…a prediction interval model (WOA-ANN) for bending force in hot strip rolling. Based on the point prediction by ANN, interval prediction is completed by using lower upper bound estimation (LUBE) and WOA, and three indexes are used to evaluate the performance of the model. This paper uses real world data from steel factory to determine the optimal network structure and parameters of the interval prediction model. Furthermore, the proposed WOA-ANN model is compared with other interval prediction models established by other three optimization algorithms. The experimental results show that the proposed WOA-ANN model has high reliability and narrow interval width, and can well complete the interval prediction of bending force in hot strip rolling. This study provides a more detailed and rigorous basis for setting bending force in hot strip rolling process.
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Abstract: To improve the processing efficiency on batch query for MapReduce, a multiple query optimization approach based on Hive+ is proposed to reduce the number of MapReduce tasks on multiple query, decrease the start time of MapReduce task and the overhead of fault tolerance, improve the query efficiency. TPC-H benchmark test set is selected as the use cases to experiment on Hive-0.12. The experiment shows that the processing efficiency of batch query is effectively improved.
Abstract: We report a pilot study designed to test elastic light-scattering (ELS) spectroscopy for characterizing normal, tumor, and tumor-infiltrated brain tissues. ELS spectra were measured from 393 sites on 36 ex vivo tissue specimen obtained from 29 patients. We employed and compared the performances of three methods of spectral classification for tissue characterization, including spectral slope analysis, principle component analysis (PCA), and artificial neural network (ANN) classification. The ANN classifier yielded the best correlation between spectral pattern and histopathological diagnosis, with a typical sensitivity of 80% and specificity of 93% for differentiating tumor from normal brain tissues. We also demonstrate that…all three classification methods discriminate between tumor and normal tissue and have the potential to identify and quantitatively characterize tumor-infiltrated brain tissues.
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Abstract: Due to the high voltage and high current working characteristics of EV driving system, strong electromagnetic interference is formed in the working process, and the shielding effectiveness of the high-voltage connector assembly connecting each part of the driving system is directly related to the level of its electromagnetic interference emission. The high-voltage connector is usually made by triaxial method. However, due to the influence of sample coupling length and cutoff frequency, the triaxial method is prone to produce a region of more than 25 MHz, resulting in test failure. Therefore, modeling analysis of the shielding effectiveness of the connector assembly in…the early stage is crucial for the final development of a connector assembly with good shielding effectiveness. In this paper, an analytical optimization model named Z TD -Demoulin is proposed to calculate the transfer impedance value of the dynamic shielded wire (double-layer shielded) of the electric vehicle. The model takes into account the influence of the double-layer shielded cable (braided-belt and aluminum foil) on the DC resistance and small-hole inductance of the shielding layer, and analyzes the transfer impedance value which presents the shielding effectiveness of the connector assembly. Based on the double-layer shield optimization model, an optimization model of high-voltage connector assembly is established. This established model takes into account the influence of connector contact resistance and inductance value. Comparison between the triaxial coaxial method and the established model found that when K connector = 6K cable , the fitting effect of the model was the best and the transfer impedance value above 25 MHz could be predicted.
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Keywords: Power cable, transfer impedance, triaxial method, double layer shielding
Abstract: Aiming at predicting the purity of the extract and raffinate components in the simulated moving bed (SMB) chromatographic separation process, a soft-sensor modeling method was proposed by adoptig the hybrid learning algorithm based on an improved particle swarm optimization (PSO) algorithm and the least means squares (LMS) method to optimize the adaptive neural fuzzy inference system (ANFIS) parameters. The hybrid learning algorithm includes a premise parameter learning phase and a conclusion parameter learning phase. In the premise parameter learning stage, the input data space division of the SMB chromatographic separation process and the initialization of the premise parameters are realized…based on the fuzzy C-means (FCM) clustering algorithm. Then, the improved PSO algorithm is used to calculate the excitation intensity and normalized excitation intensity of all the rules for each individual in the population. In the conclusion parameter learning phase, these linear parameters are identified by the LMS method. In order to improve population diversity and convergence accuracy, the population evolution rate function was defined. According to the relationship between population diversity, population fitness function and particle position change, a new adaptive population evolution particle swarm optimization (NAPEPSO) algorithm was proposed. The inertia weight is adaptively adjusted according to the evolution of the population and the change of the particle position, thereby improving the diversity of the particle swarm and the ability of the algorithm to jump out of the local optimal solution. The simulation results show that the proposed soft-sensor model can effectively predict the key economic and technical indicators of the SMB chromatographic separation process so as to meet the real-time and efficient operation of the SMB chromatographic separation process.
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Abstract: BACKGROUND: Endovascular aortic aneurysm repair (EVAR) is currently established as the first-line treatment for anatomically suitable abdominal aortic aneurysm (AAA). OBJECTIVE: To establish a deep convolutional neural networks (DCNN) model for fully automatic segmentation intraluminal thrombosis (ILT) of abdominal aortic aneurysm (AAA) in pre-operative computed tomography angiography (CTA) images. METHODS: We retrospectively reviewed 340 patients of AAA with ILT at our single center. The software ITKSNAP was used to draw AAA and ILT region of interests (ROIs), respectively. Image preprocessing and DCNN model build using MATLAB. Randomly divided, 80% of patients was classified as training set, 20% of patients was…classified as test set. Accuracy, intersection over union (IOU), Boundary F1 (BF) Score were used to evaluate the predictive effect of the model. RESULTS: By training in 34760–35652 CTA images (n = 204) and validation in 6968–7860 CTA images (n = 68), the DCNN model achieved encouraging predictive performance in test set (n = 68, 6898 slices): Global accuracy 0.9988 ± 5.7735E-05, mean accuracy 0.9546 ± 0.0054, ILT IOU 0.8650 ± 0.0033, aortic lumen IOU 0.8595 ± 0.0085, ILT weighted IOU 0.9976 ± 0.0001, mean IOU 0.9078 ± 0.0029, mean BF Score 0.9829 ± 0.0011. Our DCNN model achieved a mean IOU of more than 90.78% for segmentation of ILT and aortic lumen. It provides a mean relative volume difference between automatic segmentation and ground truth (P > 0.05). CONCLUSION: An end-to-end DCNN model could be used as an efficient and adjunctive tool for fully automatic segmentation of abdominal aortic thrombus in pre-operative CTA image.
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