A Review of Intelligent Music Generation Systems

With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects

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Unsupervised Feature Extraction for Reliable Hyperspectral Imagery Clustering via Dual Adaptive Graphs

Hyperspectral imagery (HSI) clustering aims to assign pixel-wise data with large amount of spectral bands into different groups, where each group indicates one of land-cover objects existed in HSI. Without available label information in clustering task, the clustering performance heavily depends on the reliability of unsupervised feature learned from HSI. Nevertheless,when HSI data are corrupted with noise,the conventional feature learning methods often failed. To address this problem, in this paper, a dual graph-based robust unsupervised feature extraction framework for HSI is proposed to realize reliable clustering. Firstly, low-rank reconstruction and projected learning are incorporated into the proposed framework to improve the data quality and obtain their robust structures. Then, a novel learning schemes is designed to learn two reliable graphs from the above robust structures respectively. We show that the scheme can reveals the latent similarity relationships while removing the noise influence. Meanwhile, the two reliable graphs are also integrated into a comprehensive graph with consistent constraint. At last, a joint learning framework is proposed, in which the data quality improvement, reliable graphs and consistent graph are learned iteratively to benefit from each other. After that, the normalized cut technique is applied to the learned consistent graph to obtain the final unsupervised feature. Several experiments are conducted on the two public HIS datasets to show advantage of our proposed method against the existing methods

A Reliable Separation Algorithm of ADS-B Signal Based on Time Domain

With the development of Aerospace Science and technology, automatic dependent surveillance-broadcast has become a core technique in the field of air surveillance. Installing ADS-B receiver on LEO satellite can solve the problem of small coverage of ground receiver, to realize global coverage and monitoring. However, the satellite ADS-B system is faced with serious collision and overlap problems, which has a serious impact on the signal decoding, leading to the wrong decoding or even loss of important information. In this paper, a time-domain ADS-B blind signal separation algorithm is proposed. When there is a certain power difference between the two source signals, the overlap signals are offset by the high-power signal and low-power signal to get the corresponding cancellation signals. According to the superposition mode of different pulses, different bit decision results are obtained according to the amplitude, to recover the source signal. Simulations demonstrate that the proposed algorithm is feasible and has a lower bit error rate

The Nonlocal Sparse Reconstruction Algorithm by Similarity Measurement with Shearlet Feature Vector

Due to the limited accuracy of conventional methods with image restoration, the paper supplied a nonlocal sparsity reconstruction algorithm with similarity measurement. To improve the performance of restoration results, we proposed two schemes to dictionary learning and sparse coding, respectively. In the part of the dictionary learning, we measured the similarity between patches from degraded image by constructing the Shearlet feature vector. Besides, we classified the patches into different classes with similarity and trained the cluster dictionary for each class, by cascading which we could gain the universal dictionary. In the part of sparse coding, we proposed a novel optimal objective function with the coding residual item, which can suppress the residual between the estimate coding and true sparse coding. Additionally, we show the derivation of self-adaptive regularization parameter in optimization under the Bayesian framework, which can make the performance better. It can be indicated from the experimental results that by taking full advantage of similar local geometric structure feature existing in the nonlocal patches and the coding residual suppression, the proposed method shows advantage both on visual perception and PSNR compared to the conventional methods

A Fast Calculation Method for Improving the Steering Arm of Mining Trucks with Macpherson Suspension

The steering arm has recently been frequently broken in a kind of mining truck with Macpherson suspension. To accelerate replacing the broken parts and minimize the economic cost, a fast calculation method for improving the steering arm is proposed in this paper. In this method, the forces on the steering arm are calculated by quasi-static analysis under a low vehicle velocity. Dynamic characteristics of the tire and road are partly included by considering the ranges of the rolling resistance coefficient and friction coefficient from the empirical values, which determines the torque on the steering arm under extreme conditions. The rigid–flexible coupling model for the left steering mechanism in ANSYS Workbench is established and solved to obtain the distribution stress on the steering arm under extreme conditions. Then, the reliability of the simulation results based on this fast calculation method is verified by the experiment. After determining an improvement scheme considering the economic and time cost, the satisfactory strength is obtained. The results illustrate that the strength of the improved steering arm has nearly doubled. Finally, the effectiveness of the improved steering arm is demonstrated by the users’ feedback after it is manufactured, installed, and used

Quantitative Identification of Rural Functions Based on Big Data: A Case Study of Dujiangyan Irrigation District in Chengdu

Urbanization increases the scales of urban spaces and the sizes of their populations, causing the functions in cities and towns to be in short supply. This study carries out functional space identification on the Dujiangyan elite irrigation area based on remote sensing data and point of interest (POI) data from Open Street Map (OSM), enabling the use of POI data to analyze rural functional spaces. Research and development and big data can greatly improve the accuracy of spatial function recognition, but research on rural spaces has limitations regarding the amount of available data. The Dujiangyan Irrigation District has low spatial aggregation levels for functions, scattered functions and linear distributions along roads. The mixing degrees of regional functions are low, the connections between functional elements are insufficient, and the comprehensive functional quality is low. The features of various functional elements in the region are significant, mostly in the discrete distribution mode, and functional compounding has become a trend. Therefore, it is necessary to integrate spatial resources and improve the centrality of cities and towns to realize the optimal allocation of resources and enable the development of surrounding cities and towns