Hasil untuk "Electronic computers. Computer science"

NI Yuheng(倪宇恒), FU Xiaoming(傅孝明)

A novel method for estimating bounds of the maximum isometric distortion between two hexahedral elements is proposed in this paper. Given two hexahedral elements with geometric injections, the proposed approach applies Lipschitz continuity analysis combined with extremum estimation of polynomials. By decomposing the Jacobian matrices of the composite mapping into a combination of Bézier rational polynomials, we establish sufficiency criteria for extrema attainment at parametric domain corners, and construct tight upper and lower bounds for mapping distortion by integrating. Lipschitz bounds of derivatives. Through rigorous testing on extensive datasets, we demonstrate the effectiveness and reliability of our algorithm.提出了一种可估计两个六面体单元之间映射的最大等距扭曲上下界的方法。给定具有几何单射的两个六面体单元，基于Lipschitz连续性分析与多项式最值估计，通过将复合映射的雅可比矩阵分解为Bézier有理多项式的组合形式，建立了参数域在角点处取最值的充分性判据，并结合导数的Lipschitz界构建了映射扭曲的紧致上下界。通过在大量数据集上的严格测试，证明了本文算法的有效性和可靠性。

Feudjio Ghislain, Saha Tchinda Beaudelaire, Romain Atangana et al.

Context: Early detection of ophthalmic diseases, such as drusen and glaucoma, can be facilitated by analyzing changes in the retinal microvascular structure. The implementation of algorithms based on convolutional neural networks (CNNs) has seen significant growth in the automation of disease identification. However, the complexity of these algorithms increases with the diversity of pathologies to be classified. In this study, we introduce a new lightweight algorithm based on CNNs for the classification of multiple categories of eye diseases, using discrete wavelet transforms to enhance feature extraction. Methods: The proposed approach integrates a simple CNN architecture optimized for multi-class and multi-label classification, with an emphasis on maintaining a compact model size. We improved the feature extraction phase by implementing multi-scale decomposition techniques, such as biorthogonal wavelet transforms, allowing us to capture both fine and coarse features. The developed model was evaluated using a dataset of retinal images categorized into four classes, including a composite class for less common pathologies. Results: The feature extraction based on biorthogonal wavelets enabled our model to achieve perfect values of precision, recall, and F1-score for half of the targeted classes. The overall average accuracy of the model reached 0.9621. Conclusion: The integration of biorthogonal wavelet transforms into our CNN model has proven effective, surpassing the performance of several similar algorithms reported in the literature. This advancement not only enhances the accuracy of real-time diagnoses but also supports the development of sophisticated tools for the detection of a wide range of retinal pathologies, thereby improving clinical decision-making processes.

Pu Jiao, Limin Ran

Abstract With the rapid development of science and technology, augmented reality technology provides intelligent and application services. The research is based on imaging techniques using augmented reality technology and camera image capture. Then, it uses screen error algorithms and scale-invariant feature transformation operators to test the quality of scene spatial models. The experimental results demonstrated that the camera significantly improved the frame rate of scene model rendering and could steadily enhance rendering efficiency. For image quality and its influencing factors, binary robust invariant scalable keypoints and scale-invariant feature transformation algorithms in viewpoint changes had the highest recall of 92%. The map drawing module, Hessian matrix, and scale-invariant feature transformation algorithm in the image blurring test achieved the highest recall rate of 98%. This demonstrates the advantage of using a scale-invariant feature transformation operator to capture scene space influence and provide a more accurate spatial model reference for augmented reality technology. This enhances the functional design of the guide system.

Shiuh Tong Lim, Joe Yee Yuan, Khai Wah Khaw et al.

Travel insurance serves as a crucial financial safeguard, offering coverage against unforeseen expenses and losses incurred during travel. With the advent of the proliferation of insurance types and the amplified demand for Covid-related coverage, insurance companies face the imperative task of accurately predicting customers’ likelihood to purchase insurance. This can assist the insurance providers in focusing on the most lucrative clients and boosting sales. By employing advanced machine learning techniques, this study aims to forecast the consumer segments most inclined to acquire travel insurance, allowing targeted strategies to be developed. A comprehensive analysis was carried out on a Kaggle dataset comprising prior clients of a travel insurance firm utilizing the K-Nearest Neighbors (KNN), Decision Tree Classifier (DT), Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF) models. Extensive data cleaning was done before model building. Performance evaluation was then based on accuracy, F1 score, and the Area Under Curve (AUC) with Receiver Operating Characteristics (ROC) curve. Inexplicably, KNN outperformed other models, achieving an accuracy of 0.81, precision of 0.82, recall of 0.82, F1 score of 0.80, and an AUC of 0.78. The findings of this study are a valuable guide for deploying machine learning algorithms in predicting travel insurance purchases, thus empowering insurance companies to target the most lucrative clientele and bolster revenue generation.

Zahra Asghari Varzaneh, Soodeh Hosseini

This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.

Swati A. Patil, G. Pradeepini, Thirupathi Rao Komati

Abstract Music Genre Classification (MGC) is a crucial undertaking that categorizes Music Genre (MG) based on auditory information. MGC is commonly employed in the retrieval of music information. The three main stages of the proposed system are data readiness, feature mining, and categorization. To categorize MG, a new neural network was deployed. The proposed system uses features from spectrographs derived from short clips of songs as inputs to a projected scheme building to categorize songs into an appropriate MG. Extensive experiment on the GTZAN dataset, Indian Music Genre(IMG) dataset, Hindustan Music Rhythm (HMR) and Tabala Dataset show that the proposed strategy is more effective than existing methods. Indian rhythms were used to test the proposed system design. The proposed system design was compared with other existing algorithms based on time and space complexity.

Filip Milisav, Vincent Bazinet, Yasser Iturria-Medina et al.

LI Cenhao, CUI Pengjie, YUAN Ye, WANG Guoren

Subgraph matching is an important method of data mining in complex networks. In recent years, the subgraph matching algorithm based on GPU (graphics processing units) has shown obvious speed advantages.However, due to the large scale of graph data and a large number of intermediate results of subgraph matching, the memory capacity of a single GPU soon becomes the main bottleneck for processing subgraph matching algorithm of large graph. Therefore, this paper proposes a multi-GPU programming model for large graph subgraph matching. Firstly, the framework of subgraph matching algorithm based on multi-GPU is proposed, and the cooperative operation of subgraph matching algorithm on multi-GPU is realized, which solves the problem of graph scale of subgraph matching on GPU. Secondly, a dynamic adjustment technique based on query graph is used to deal with cross-partition subgraph sets, which solves the cross-partition subgraph matching problem caused by graph segmentation. Finally, based on the characteristics of SIMT (single instruction multiple threads) architecture on GPU, a priority scheduling strategy is proposed to ensure the internal load balancing of GPU, and a pipeline mechanism of shared memory is designed to optimize the cache contention of multi-core concurrency. Experiments show that the proposed multi-GPU programming model can get the correct matching results on billions of datasets. Compared with the latest GPU-based solution, the proposed algorithm framework can achieve 1.2 to 2.6 times of acceleration ratio.

Paolo Pegolo, Stefano Baroni, Federico Grasselli

Abstract Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li3ClO antiperovskite. By leveraging ab initio, machine learning, and force-field descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.

nyoman purnama

Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50.

Neng Pan, Ruibin Zhang, Tiankai Yang et al.

Abstract In recent years, several progressive studies promote the development of aerial tracking. One of the representative studies is our previous work Fast‐Tracker which is applicable to various challenging tracking scenarios. However, it suffers from two main drawbacks: (1) the oversimplification in target detection by using artificial markers and (2) the contradiction between simultaneous target and environment perception with limited onboard vision. In this study, we upgrade the target detection in Fast‐Tracker to detect and localise a human target based on deep learning and non‐linear regression to solve the former problem. For the latter one, we equip the quadrotor system with 360° active vision on a customised gimbal camera. Furthermore, we improve the tracking trajectory planning in Fast‐Tracker by incorporating an occlusion‐aware mechanism that generates observable tracking trajectories. Comprehensive real‐world tests confirm the proposed system's robustness and real‐time capability. Benchmark comparisons with Fast‐Tracker validate that the proposed system presents better tracking performance even when performing more difficult tracking tasks. The cover image is based on the Original Article Fast‐Tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression by Can Cui et al., https://doi.org/10.1049/csy2.12033.

Xuelei Meng, Yahui Wang, Wanli Xiang et al.

Abstract Both train rescheduling and station track assignment have become hot topics in recent years. It is fundamentally important to do the rescheduling and track assignment work at the same time to avoid the feasibility risk of the re‐scheduled timetable. The purpose of this paper is to design an integrated model for train rescheduling and track assignment in order to provide an integrative plan for the trains to run on the railway sections and go through stations. Based on the existing train rescheduling model, the model is designed by adding the constraints and the optimization goal of track assignment. The goal of track assignment is to maximize the equilibrium of the track usage time, and the constraint is that two trains cannot occupy a same track at the same time. An artificial bee colony algorithm is used to solve the model to get the operation plan. A computing experiment was carried out to prove the effectiveness of the model and the efficiency of the algorithm. The approach presented in this paper can provide a reference for the developers of a railway dispatching system.

Ze Si, Dongmei Fu, Yang Liu et al.

Abstract Diabetic retinopathy (DR) is the main reason that causes preventable blindness. Hard exudate is one of the earliest signs of diabetic retinopathy. Precise detection of hard exudate is helpful for the early diagnosis of diabetic retinopathy. Fully convolutional network (FCN) shows great performance on hard exudate segmentation task. However, there are limitations for fully convolutional network to build long‐range dependencies in different regions of the image. Convolution operator extract features in local area, segmentation results based on local features are likely to be wrong in some cases. Another channel attention method was proposed, and two different attention modules are used in the segmentation model. In this way, long‐range dependencies across different image regions are built efficiently in different stages of feature extraction. In addition, a new loss function is designed to deal with the data imbalance problem in hard exudate segmentation task. The proposed method was evaluated by two public datasets, and the comparative experiments show the effectiveness of the proposed method.

Ahmad Faisal, Frisma Handayanna, Indah Purnamasari

The application of online learning in various educational institutions in the Covid-19 Pandemic has had an impact on behavioral attitudes where many educators and students have also complained about the limited technology facilities, operations, and internet networks in some areas or quotas to access online learning. Followed by the popularity of the Zoom application in supporting education, the authors researched the application of the Technology Acceptance Model (TAM) to the acceptance of the Zoom application in online learning to determine the effect of using applications in online learning with 4 variables accompanied by multiple linear regression hypothesis testing, F-test and T-test. using SPSS. The test results in Perceived Usefulness, Perceived Ease of Use, and Behavioral Intention to Use affect Actual System Usage with significant and positive results of 20.21. Behavioral Intention to Use is more dominant than other variables with a value of 5.31, while the lowest is Perceived Ease of Use with a value of (-0.50).

Md Niamul Bari, Md Zahangir Alam, Abdullah‐Al Mamun et al.

Abstract Optimization of the process parameters for the extraction of a product from a solid substrate after bioconversion is essentially important to maximize the yield. The extraction of citric acid from the solid substrate of oil palm empty fruit bunches (EFB) after bioconversion was initially optimized by following single factor variation. Following this, the extraction parameters were optimized statistically with the help of experimental design by Box‐Behnken Design under Response Surface Methodology through the development of a second order regression model. The statistical analysis of the result showed that in the range studied all three factors, that is, shaking speed, solvent ratio, and shaking time, had a significant influence on the citric acid extraction. The highest amount of citric acid extracted was 337.34 ± 1.1 g/kg‐dry EFB, for which the extraction parameters were a shaking speed of 125 rpm, a shaking time of 58.5 minutes, and a solvent ratio of 10.70. The coefficient of determination observed (R2) from the analysis was .9921, indicating a satisfactory fit of the model with the response. The analysis showed that all the terms of the model were highly significant with the P‐value <.05.

Wendong Wang, Hanhao Li, Dezhi Kong et al.

The utilization of upper extremity exoskeleton robots has been proved to be a scientifically effective approach for rehabilitation training. In the process of rehabilitation training, it is necessary to detect the fatigue degree during rehabilitation training in order to formulate a reasonable training plan and achieve better training efficiency. Based on the integral value of surface electromyography (sEMG), heart rate variability, and instantaneous heart rate, this article proposes a fatigue judgment method for multi-information fusion. Based on the integral value data, the feature extraction of the bioelectrical signals were implemented separately, then the fatigue recognition was conducted using the decision-level data fusion method. The bioelectrical signal acquisition system of electromyogram signals and electrocardiograph signals was developed for upper limb exoskeleton rehabilitation robot, and the acquisition and processing of electromyogram signals and electrocardiograph signals were completed. Finally, the fuzzy logic controller with instantaneous heart rate, heart rate variability, and surface electromyography signal was designed to judge fatigue degree, including the fuzzy device, fuzzy rule selector, and defuzzifier. The moderate fatigue state data were selected for testing, and the experimental results showed that the error of fatigue judgment is 4.3%, which satisfies the requirements of fatigue judgment.

Tian Zhou, Jiajun Shen, Jianjun Zhu et al.

Recently, coprime array has been a popular research field in the application of direction-of-arrival estimation. Compared with uniform linear array, coprime array can be used to expand array aperture with fewer sensors, and it also has a nice direction-of-arrival estimation performance. According to coprime property, the direction of arrival can be obtained by intersecting the candidate estimation sets from several subarrays. However, when the directions of multiple sources meet a particular relation, the unambiguous phase cannot be unwrapped through the coprime array. In this article, a multi-coprime array is proposed to address the problem, utilizing about half number of array elements and reducing hardware complexity compared with uniform linear array. Then, a low-complexity beamforming interferometry algorithm via multi-coprime array is proposed to reduce computational complexity. Numerical results, including simulation and actual data processing, are provided to indicate that the processing on multi-coprime array can successfully resolve phase ambiguity. Specially, when signal-to-noise ratio exceeds about −4 dB and the element number of subarray in multi-coprime array exceeds 15 for the array geometry in this article, the proposed method achieves close estimation performance with fewer sensors compared with uniform linear array.

Xue-wen Chen, Yue Zhou

A fuzzy increment controller is designed aimed at the vibration system of automobile active suspension with seven degrees of freedom (DOF). For decreasing vibration, an active control force is acquired by created Proportion-Integration-Differentiation (PID) controller. The controller’s parameters are adjusted by a fuzzy increment controller with self-modifying parameters functions, which adopts the deviation and its rate of change of the body’s vertical vibration velocity and the desired value in the position of the front and rear suspension as the input variables based on 49 fuzzy control rules. Adopting Simulink, the fuzzy increment controller is validated under different road excitation, such as the white noise input with four-wheel correlation in time-domain, the sinusoidal input, and the pulse input of C-grade road surface. The simulation results show that the proposed controller can reduce obviously the vehicle vibration compared to other independent control types in performance indexes, such as, the root mean square value of the body’s vertical vibration acceleration, pitching, and rolling angular acceleration.

Tobias Denkinger

We use a non-deterministic variant of storage types to develop a framework for the approximation of automata with storage. This framework is used to provide automata-theoretic views on the approximation of multiple context-free languages and on coarse-to-fine parsing.

Matthew J. Samuel

We discuss the theory of certain partially ordered sets that capture the structure of commutation classes of words in monoids. As a first application, it follows readily that counting words in commutation classes is #P-complete. We then apply the partially ordered sets to Coxeter groups. Some results are a proof that enumerating the reduced words of elements of Coxeter groups is #P-complete, a recursive formula for computing the number of commutation classes of reduced words, as well as stronger bounds on the maximum number of commutation classes than were previously known. This also allows us to improve the known bounds on the number of primitive sorting networks.