A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

Computational Visual Media - The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning. While voxel-based deep learning frameworks have been proposed for this...     

Energy Efficient Cluster Based Routing Protocol for WSN Using Firefly Algorithm and Ant Colony Optimization

Maximizing network lifetime is the main goal of designing a wireless sensor network. Clustering and routing can effectively balance network energy consumption and prolong network lifetime. This paper presents a novel cluster-based routing protocol called EECRAIFA. In order to select the optimal cluster heads, Self-Organizing Map neural network is used to perform preliminary clustering on the network nodes, and then the relative reasonable level of the cluster, the cluster head energy, the average distance within the cluster and other factors are introduced into the firefly algorithm (FA) to optimize the network clustering. In addition, the concept of decision domain is introduced into the FA to further disperse cluster heads and form reasonable clusters. In the inter-cluster routing stage, the inter-cluster routing is established by an improved ant colony optimization (ACO). Considering factors such as the angle, distance and energy of the node, the heuristic function is improved to make the selection of the next hop more targeted. In addition, the coefficient of variation in statistics is introduced into the process of updating pheromones, and the path is optimized by combining energy and distance. In order to further improve the network throughput, a polling control mechanism based on busy/idle nodes is introduced during the intra-cluster communication phase. The simulation experiment results prove that under different application scenarios, EECRAIFA can effectively balance the network energy consumption, extend the network lifetime, and improve network throughput.

     

Unraveling the Charge Storage and Activity-Enhancing Mechanisms of Zn-Doping Perovskite Fluorides and Engineering the Electrodes and Electrolytes for Wide-Temperature Aqueous Supercabatteries

Herein, a trimetallic Ni–Co–Zn perovskite fluoride (ABF₃) (denoted as KNCZF) electrode material is explored for advanced aqueous supercabatteries (ASCBs), with KNCZF and activated carbon–FeBiCu@reduced graphene oxides (AC–FeBiCu@rGO) as cathode and anode, respectively, which outperform aqueous supercapacitors (ASCs) and batteries (ABs) with AC and FeBiCu@rGO anodes because of the synergistic effect of pseudocapacitive (KNCZF), capacitive (AC), and faradaic (FeBiCu@rGO) responses. One of the important findings is that the KNCZF shows a typical bulk phase conversion mechanism for charge storage in the alkaline media with the transition of ABF₃ perovskite nanocrystals into amorphous metal oxides/(oxy)hydroxides nanosheets, showing the redox-active and redox-inert roles for the Ni/Co and Zn species, respectively, which can be deduced by various ex-situ techniques. Another interesting finding is that the redox-inert Zn species largely enhance the activity of Ni/Co redox-active species in the ABF₃ materials, mainly owing to the promotion of surface electroactive sites, adsorption of OH^?, and charge transfer of surface Ni/Co atoms by Zn-doping, which can be proved by ex-situ characterizations and theoretical calculations. Overall, this study reveals the structure–activity relationship and charge storage mechanisms of Zn-doping ABF₃ materials for advanced ASCBs, showing a great impact on developing advanced electrochemical energy storage.     

Chatter detection for milling using novel p-leader multifractal features

Journal of Intelligent Manufacturing - Chatter in machining results in poor workpiece surface quality and short tool life. An accurate and reliable chatter detection method is needed before its...     

Neural network-based robust finite-time attitude stabilization for rigid spacecraft under angular velocity constraint

Neural Computing and Applications - In this paper, the problem of attitude stabilization control of spacecraft under angular velocity constraint is investigated. A state-constrained finite-time...     

Measuring Mental Workload Using ERPs Based on FIR,ICA, and MARA

Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman’s correlation during a simulated computer task. We found that participants responded faster and performed better in the easy task condition, followed by the medium and high-difficulty conditions, which verifies the validity of the ERP filtering. Moreover, larger P2 and P3 waveforms were evoked as the task difficulty increased, and a higher task difficulty elicited a more enhanced N300. Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings, and a positive relationship between the P3 amplitude and accuracy. The results presented in this paper demonstrate that a combination of FIR, ICA, and MARA methods can filter ERPs in the non-invasive real-time measurement of workload. Additionally, frontocentral P2, N3, and parietal P3 components showed differences between genders. The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human–computer interaction in the future.     

Construction and implementation of a college talent cultivation system under deep learning and data mining algorithms

This paper aims to improve the quality and efficiency of talent cultivation in colleges and universities and effectively analyze and count the number of talents. The cultivation of computer professionals in higher vocational education is taken as the research object. First, a talent cultivation information system for colleges and universities is developed, using deep learning neural networks and data mining algorithms and introducing recurrent neural networks (RNN) and a fusion attention model. With a view to the needs of employment forms and employment data, a diversified talent demand cultivation program is formulated. Based on this, an efficient talent cultivation quality indicator system is established through analyzing talent cultivation data in higher vocational education. Finally, the effectiveness of the system is verified by the performance analysis. The results show an average prediction accuracy for the proposed Attention RNN Word2 algorithm of 80.61%, which is significantly higher than achieved by the latest research algorithm model, and the talent training has a classification accuracy of 82%. This model is not only feasible for categorizing computer professional personnel cultivation but can also accurately reflect problems arising in personnel cultivation, and the results are made obvious by visualization. To sum up, the system reported here can give an intuitive demonstration of the quality of computer professional personnel cultivation in higher vocational education. This research provides a theoretical basis for related research into the cultivation of computer professionals in higher vocational education for application in industry, universities, and research.

     

Multi-scale Interest Dynamic Hierarchical Transformer for sequential recommendation

Neural Computing and Applications - Existing sequential recommendation methods focus on modeling the temporal relationships of users’ historical behaviors and excel in exploiting users’...     

葡萄酒中总酸测定的不确定度评定

测量不确定度是对测量结果的质量的定量评定,意味着对测量结果可信性、有效性的怀疑程度或不肯定程度。本文依据JJF1059-1999《测量不确定度的评定和表示》,计算实验过程中各因素引入的不确定度,建立葡萄酒中总酸的测定不确定度评定方法。     

Three-dimensional elasticity solution of layered plates with viscoelastic interlayers

An analytical solution for simply supported layered plates with viscoelastic interlayers under a transverse load is proposed. The deformation of each plate layer is described by the exact three-dimensional elasticity equations. The viscoelastic property of interlayer is simulated by the generalized Maxwell model. The constitutive relation of the interlayer is simplified by the quasi-elastic approximation, which significantly simplifies the analytical process. The solution of stress and displacement fields with undetermined coefficients is derived by solving a group of ordinary differential equations. The undetermined coefficients can be efficiently deduced by using the recursive matrix technique for the plate with any number of layers. The practical convergence is observed during numerical tests. The comparison analysis indicates that the present solution has a close agreement with the finite element solution. However, the solution based on the Mindlin–Reissner hypothesis is significantly different from the present solution for thick plates. Finally, the effect of interlayer thickness on stress and displacement distributions of a five-layer plate is discussed in detail.