Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Intelligent computing : the latest advances, challenges and future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

Evaluation of the Conductivity and Permeability of Rotor Shaft Material through Electrical Runout Using FEM

Material electromagnetic anisotropy of revolution parts poses a potential hazard to the operation of rotary machine (such as rotor) and interferes the vibration monitoring. The maldistribution of electromagnetic property of material is detected by eddy current sensor as electrical runout, which is necessary to be controlled in manufacturing process. In order to mitigate the uneven electromagnetic property of material easier, a new inversion method of evaluating the electromagnetic properties (the conductivity and permeability) of target material through electrical runout using Finite Element Method is proposed in this paper. A 2D axisymmetric finite element analysis model of eddy current sensor is established firstly. Then on the basis of analysis model, the relationship between electrical runout and conductivity & permeability is formulated. In addition, reference number is used to get another formula about electromagnetic property. In this way the conductivity and permeability can be obtained approximately from the electrical runout data by solving two simultaneous equations. As a result, the profiles of the variations of conductivity and permeability of target material are acquired with the method using the electrical runout data which were measured in experiment, which are instructive in retreating the defective revolution parts

The complete mitochondrial genome sequence of Cynoglossus roulei (Pleuronectiformes: Cynoglossidae)

The complete mitogenome of Cynoglossus roulei is 16,598 bp in length, containing 37 genes, among them, ND6 and eight tRNA genes are encoded by L-strand and other genes by H-strand, which are as same as those of typical mitogenome in fishes. The gene rearrangement related to one tRNA gene and CR were found, forming the gene order of CR-Gln-Ile-Met, which is the same as that of mitogenomes in all identified Cynoglossus speices. Phylogenetic tree based on 12 protein coding genes, tRNA and rRNA shows that C. roulei has a closer phylogenetic relationship to C. semilaevis

A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge. In the RBPNN pattern aggregation layer, the outputs of RBPN are selectively summed according to the category of the kernel center, that is, the subcategory features are combined into category features, and finally the image classification is implemented based on Softmax. The functional module of the proposed method is designed specifically for image characteristics, which can highlight the significance of local and structural features of the image, form a non-convex decision-making area, and reduce the requirements for the completeness of the sample set. Applying the proposed method to medical image classification, experiments were conducted based on the brain tumor MRI image classification public dataset and the actual cardiac ultrasound image dataset, and the accuracy rate reached 85.82% and 83.92% respectively. Compared with the three mainstream image classification models, the performance indicators of this method have been significantly improved