基于GLR算法的维吾尔语句法分析研究

采用GLR算法对维吾尔语句子进行句法分析,并且与平行LR算法进行比较,比较它们的分析过程,针对于维吾尔语在单词集上进行句法分析。分析结果采用最优规则,选取最优的句法分析树,对下一步的句法分析研究提供很大的帮助。     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

Preparation and biological evaluation of <Superscript>99<Emphasis Type="Italic">m</Emphasis></Superscript>Tc-timonacic acid as a new complex for hepatobiliary imaging

Timonacic acid (TCA) was successfully labeled with ^99m Tc. The influence exerted on the reaction by the substrate and reducing agent concentrations, pH of the reaction mixture, and reaction time was examined, and in vitro stability of ^99m Tc-TCA was evaluated. The maximum labeling yield was 98.5 ± 0.6%. The complex was stable throughout the working period (6 h). A study of in-vivo biodistribution in mice showed that the maximum uptake of ^99m Tc-TCA in the liver was 22.3 ± 0.3% of the injected activity per gram of the tissue or organ (% ID/g) at 30 min post injection. The clearance from the mice appeared to proceed via the circulation mainly through the kidneys and urine (approximately 56% of the injected dose at 1 h after injection). The liver uptake of ^99m Tc-TCA is higher than that of ^99m Tc-UDCA (ursodeoxycholic acid); therefore, ^99m Tc-TCA shows more promise for liver SPECT.     

Generalized thermoelastic diffusion in a nanoscale beam using eigenvalue approach

In this paper, the effect of mass diffusion in a thermoelastic nanoscale beam in context Lord and Shulman theory is studied. The analytical solution in the Laplace domain is obtained for lateral deflection, temperature, displacement, concentration, stress and chemical potential. The both ends of the nanoscale beam are simply supported. The basic equations have been written in the form of a vector-matrix differential equation in the Laplace transform domain, which is then solved by an eigenvalue approach. The results obtained are presented graphically for the effect of time and mass diffusion to display the phenomena physical meaning.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Variational sensitivity analysis and design optimization

Variational method (VM) is employed to derive the co-state equations, boundary (transversality) conditions, and functional sensitivity derivatives. The converged solutions of the state equations together with the steady state solution of the co-state equations are integrated along the domain boundary to uniquely determine the functional sensitivity derivatives with respect to the design function. The application of the variational method to aerodynamic shape optimization problems is demonstrated on internal flow problems at supersonic Mach number range of 1.5. Optimization results for flows with and without shock phenomena are presented. The study shows that while maintaining the accuracy of aerodynamical objective function and constraint within the reasonable range for engineering prediction purposes, variational method provides a substantial gain in computational efficiency, i.e., computer time and memory, when compared with the finite difference computations.     

Rotary flexible joint control by fractional order controllers

In this paper, a fractional order control law is proposed and implemented for the evaluation of trajectory tracking performance of a rotary flexible-joint system. A state feedback based fractional integral control scheme is used in this proposed method. In this scheme, state feedback is responsible for stabilizing the system. The compensator, in series with the fractional integrator leads to obtain a similar closed-loop transient response like Bode’s ideal transfer function. The effectiveness of the proposed controller in tracking and being robust against parameter uncertainties is demonstrated through simulation. In addition, to show the usefulness of the proposed control scheme, the fractional controller is compared to an integer state feedback control by simulation and through experimentation on the Quanser’s rotary flexible-joint system.     

Multi-directional blending for heterogeneous objects

This paper presents a novel multi-directional blending method for heterogeneous object design. Contrary to earlier studies, this paper introduces material blending through multiple features with different heterogeneous material composition. Feature-based method is used to represent and design heterogeneous objects with multi-directional material composition. The Voronoi diagram of multiple curves is constructed to generate bisector of the geometric domain. Then, metamorphosis from the bounding curve to multiple internal curves is performed using dynamic programming based optimization approach in two steps. First, optimum curve matching between internal curves and enclosing Voronoi cells is obtained. Then, an optimum ruling line alignment and insertion technique between the Voronoi diagram and the bounding curve is developed. Metamorphosis through complex concavities is also achieved. Finally, multi-directional material composition is mapped based on a set of relations.