Propagation-based Algorithm Research Articles

X-ray phase and dark-field computed tomography without optical elements.

X-ray diffusive dark-field imaging, which allows spatially unresolved microstructure to be mapped across a sample, is an increasingly popular tool in an array of settings. Here, we present a new algorithm for phase and dark-field computed tomography based on the x-ray Fokker-Planck equation. Needing only a coherent x-ray source, sample, and detector, our propagation-based algorithm can map the sample density and dark-field/diffusion properties of the sample in 3D. Importantly, incorporating dark-field information in the density reconstruction process enables a higher spatial resolution reconstruction than possible with previous propagation-based approaches. Two sample exposures at each projection angle are sufficient for the successful reconstruction of both the sample density and dark-field Fokker-Planck diffusion coefficients. We anticipate that the proposed algorithm may be of benefit in biomedical imaging and industrial settings.

Efficient cutting path planning for a non-spherical tool based on an iso-scallop height distance field

Iso-scallop height machining means, when machining a freeform surface, the scallop height between any two neighboring tool paths on the surface will be a constant (i.e., the given threshold), which is preferable among various freeform surface machining strategies due to its high machining efficiency as well as better machine tool’s dynamics. However, all the existing iso-scallop height path planning methods pertain to only the ball-end or flat-end types of tools. In recent years, the non-spherical cutting tool has become more and more popular, especially for five-axis machining of complex freeform surfaces, majorly owing to its non-constant curvature which can be utilized to adaptively fit the tool to the surface to both avoid the local gouging and enlarge the cutting width. However, there have been no reported works on iso-scallop height five-axis tool path generation for a non-spherical tool, and, in this paper, we present one. Specifically, we first define and construct two fields on the surface to be machined-the collision-free tool orientation field (vector) and the iso-scallop height distance field (scalar). The iso-lines of the scalar field and their associated tool orientation field vectors then naturally serve as potential iso-scallop height five-axis tool paths, and we present a propagation-based algorithm to construct the desired tool path from the iso-lines. The computer simulation and physical cutting experiments confirm that everywhere on the surface, except maybe near the saddle curves of the scalar filed, the scallop height is exactly the given threshold. By adding the saddle curves as extra tool paths, the final machined surface then is assured of the required scallop height requirement.

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.

Identification of non-linear stochastic systems using a new Hammerstein-Wiener neural network: a simulation study through a non-linear hydraulic process

Hammerstein-Wiener models have been proved to be suitable in modelling a class of typical non-linear dynamic systems. This paper aims at developing a Hammerstein-Wiener Neural Network (HWNN) which formulates a Hammerstein-Wiener mathematical model, in order to identify a nonlinear dynamic system operating in a stochastic environment. A central aspect is that a general situation has been considered including non-invertible non-linearity output and correlation of stochastic disturbances after the dynamic linear block. Different from the existing parameter identification methods, the model is developed to handle two types of learning algorithms that can directly obtain the parameters of the unknown time-varying nonlinear system. Firstly, all neural network weights in HWNN are adapted using a Back Propagation-based Gradient algorithm (BPG). The second method, namely Recursive Least Square Back Propagation based Gradient (RLSBPG) is derived from the BPG algorithm to achieve the parametric estimation of the Hammerstein scheme where the remaining parameters are estimated by the least-squares approach based on fuzzy technique to ameliorate the estimation quality. The convergence analysis of the algorithms is presented, and their performances are tested through a simulation study of a nonlinear hydraulic process.

Distinguishing node propagation influence by expected index of finite step propagation range

On-line social networks have gradually become an indispensable tool for people. Identifying nodes with high influence in the network as an initial source of communication is of great significance in social perception and rumor control. According to the independent cascade model, in this paper we present an index describing the finite step propagation range expectation as the degree of propagation, and design an efficient recursive algorithm. Based on the local topology information, the index combines the propagation probability to characterize the influence, which can better reflect the propagation influence of a single node. For a single propagation source influence ordering problem, the node degree of propagation and propagation capability are better consistent with each other. And the propagation degree can well describe the propagation influence of nodes under different networks and propagation probabilities. For maximizing the multi-propagation source influence, in this paper we propose a propagation-based heuristic algorithm which is called propagation discount algorithm. This algorithm makes the joint influence of multiple propagation sources maximized. Finally, in this paper we apply the above method to three real networks, showing better effects than the classic indicators and methods. The algorithm has three advantages. First, the expected value of the final propagation range of each node in the small network can be accurately calculated. Second, the degree of propagation fully considers the local topology of the node and belongs to a locality indicator. Third, the indicator combines the effect of propagation probability and yields good outcomes under different networks and propagation probabilities.

A Physical Probabilistic Network Model for Distribution Network Topology Recognition Using Smart Meter Data

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Cyclic switching weighted bit‐flipping decoding for low‐density parity‐check codes

A cyclic switching weighted bit-flipping (CS-WBF) decoding algorithm for low-density parity-check (LDPC) codes is proposed in this study. By carefully choosing two selection criteria of the bit to flip and cyclically switching from one criterion to the other according to some rules during decoding, the proposed scheme can effectively break decoding loops, which often appear for weighted bit-flipping (WBF) algorithms and greatly degrade decoding performance. Simulation results show that CS-WBF provides 0.7–1.3 dB coding gain over various simple improved WBF algorithms, such as reliability ratio-based WBF and improved modified WBF, with almost the same complexity for both regular and irregular LDPC codes. Compared with WBF algorithms that break decoding loops and the belief propagation-based algorithm, CS-WBF can also provide a better complexity-performance trade-off.

A Fast and Efficient Algorithm for Mining Top-k Nodes in Complex Networks

One of the key problems in social network analysis is influence maximization, which has great significance both in theory and practical applications. Given a complex network and a positive integer k, and asks the k nodes to trigger the largest expected number of the remaining nodes. Many mature algorithms are mainly divided into propagation-based algorithms and topology- based algorithms. The propagation-based algorithms are based on optimization of influence spread process, so the influence spread of them significantly outperforms the topology-based algorithms. But these algorithms still takes days to complete on large networks. Contrary to propagation based algorithms, the topology-based algorithms are based on intuitive parameter statistics and static topology structure properties. Their running time are extremely short but the results of influence spread are unstable. In this paper, we propose a novel topology-based algorithm based on local index rank (LIR). The influence spread of our algorithm is close to the propagation-based algorithm and sometimes over them. Moreover, the running time of our algorithm is millions of times shorter than that of propagation-based algorithms. Our experimental results show that our algorithm has a good and stable performance in IC and LT model.

Group detection in complex networks: An algorithm and comparison of the state of the art

Complex real-world networks commonly reveal characteristic groups of nodes like communities and modules. These are of value in various applications, especially in the case of large social and information networks. However, while numerous community detection techniques have been presented in the literature, approaches for other groups of nodes are relatively rare and often limited in some way. We present a simple propagation-based algorithm for general group detection that requires no a priori knowledge and has near ideal complexity. The main novelty here is that different types of groups are revealed through an adequate hierarchical group refinement procedure. The proposed algorithm is validated on various synthetic and real-world networks, and rigorously compared against twelve other state-of-the-art approaches on group detection, hierarchy discovery and link prediction tasks. The algorithm is comparable to the state of the art in community detection, while superior in general group detection and link prediction. Based on the comparison, we also discuss some prominent directions for future work on group detection in complex networks.

Phase Transitions in q-States Signal Reconstruction

Compressed sensing is a new signal acquisition method that acquires signal in a compressed form and then recovers the signal by the use of computational tools and techniques. This means fewer measurements of signal are needed and thus it will save huge amount of time and storage space. We, in this paper, consider the compressed sensing of sparse integer-valued signal (referred as “q-states signal" throughout the paper). In order to accelerate the speed of reconstruction, we adopt the sparse rather than dense measurement matrices. Using methods and tools developed in statistical physics, we locate the reconstruction limit for L0-reconstruction method and propose a belief propagation-based algorithm that can deal with instance with large size and its typical reconstruction performance are also analyzed.