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In the routine inspection process of railway catenary systems, the primary task is to find out the locations of various components accurately. The complex composition of the components in the catenary system and their large dimensional differences make the inspection of small components considerably difficult. Aiming at the problem of the difficulty in locating small components, a new locating method, named asymmetrically effective decoupled head-you only look once (AED-YOLO), for locating small components of the catenary has been proposed in this study. In this method, firstly, a small object detection layer has been added to improve the detection accuracy of the small-sized components such as fastener nuts and bracing wire. Secondly, to reduce missed and false detection errors of small components, the improved bidirectional feature pyramid network with high-order spatial interactions and recursive gated convolution has been used to fuse the features of different scales to further enhance the ability to detect small objects. Finally, an asymmetrically effective decoupled head has been proposed using different decoupled branches to decouple the classification and localization processes, thus further reducing the error in small-sized object classification and location. Experiments performed on the railway catenary dataset collected on-site show that the proposed localization method can efficiently improve detection accuracy, achieving a mean average precision of 93.5%. Thus, compared to the other methods, the proposed method can accurately locate small-sized components.

A novel high-speed transceiver based on 7 bit correlated non-return-to-zero(CNRZ-7) with high-bandwidth-density(bandwidth per unit length) and low-power for Die-to-Die(D2D) communication is proposed. In order to further improve the SNR and the bandwidth of the CNRZ-5 in D2D communication, a CNRZ-7 based transmitter matrix and receiver matrix are proposed firstly, which are derived from Walsh Hadamard(W-H) transform and inverse transformation. In addition, to reduce the power consumption of the transmitter, the encoding driver based on CNRZ-7 transmitter matrix is designed with source series terminated drivers(SST). To further improve the SNR of the receiver, the decoding circuit based on CNRZ-7 receiver matrix is designed with a special multi-input comparators(MIC), which contain equalizer circuits. This transceiver is designed with a 28nm CMOS technology and the core area is 0.66mm2. The post-simulation results show that this transceiver can operate at 280 Gb/s, and the data rate is 35 Gb/s/wire. The worst width of the receiver’s eye-diagrams is 0.45UI when the transceiver operates at a 50 mm PCB channel with a 10dB@20GHz insertion loss, and the total BER is less than 1E-15. The power consumption is 1.1pJ/b under a normal corner.

Real-world problems always change over time. In the past two decades, many researchers have become increasingly interested in studying evolutionary optimization algorithms for dynamic problems owing to their great importance in real-world applications. Currently, there are no unified and mature test functions for dynamic problems, and the existing dynamic test functions are incapable of simulating a real dynamic environment. This study constructs a new benchmark test function generator for simulating dynamic environments that combine existing static benchmark functions and their related properties to form new dynamic test functions. It is simple to construct and conveniently control the dynamic properties by adjusting the corresponding parameters. Through a series of comparisons with the dynamic benchmark of the generalized dynamic benchmark generator (GDBG), the simulation results show that the dynamic test functions constructed by this generator can reflect different types of changing intensity.

The coupling of communication networks plays an indispensable role in modern power systems; however, it also leads to many problems due to cascading failure. Although most current research focuses on the single-layer, non-interacting system and physical layer network, few studies have combined the complex network theory to ignore the influence of communication networks. To address this problem, this paper introduces a cascading failure model of a cyber physical power system (CPPS) that considers the operation characteristics of the communication layer, especially the influence of transmission delay and connectivity on stability control. First, the influence mode of the communication layer failure node acting on the physical layer is analyzed, and the interaction mechanism between the communication layer and physical layer is clarified. Furthermore, the propagation process of failure in the CPPS system is modeled, and the influence of information transmission delay on the stability control process is considered. In this manner, the development process of cascading failure in a CPPS system is described accurately. Finally, according to the loss rate of the physical layer load, the influence of the communication layer node is evaluated to verify the accuracy of the model. This paper also provides a practical model of a CPPS system, which is helpful for building a secure and reliable energy Internet.

The application of unmanned aerial vehicle (UAV) has been increasingly popular for its advantages such as convenience and mobility. Thus, its application scenarios have been more and more complex. The UAV must avoid not only stationary obstacles but also dynamic obstacles. Typical UAV path planning algorithms perform well in avoiding static obstacles but poor in dynamic ones. A new dynamic path planning algorithm based on obstacles’ position prediction and modified artificial potential field - HOAP is proposed in this paper. The Markov prediction model is employed to predict the obstacles’ future position with an obstacle grid map. And to resolve the local minima of the typical APF algorithm, a new virtual obstacle method is put forward. What’s more, the attractive force gain coefficient gradient increase method is proposed to solve local oscillation. Simulation results show that the UAV can finally fly a safer path with high accuracy in an unknown environment with static or dynamic obstacles, and avoid local minima or solve local oscillation at the same time.

Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of gene interactions. Applying machine learning methods to microarray gene expression profiles for disease classification is a popular method to predict disease state or outcome. Traditional computational methods that detect genes differentially expressed between cancer and normal samples are ineffective in independent cohorts of patients. However, current methods consider pathways as simple gene sets and include pathway topological information but ignore significant individual genes and interactions between genes, which are essential to infer a more robust pathway activity. In this study, we proposed a novel approach to describe the activity of a pathway that incorporates both the differential expression degree of genes between the case and control and the interaction strength between genes. We applied the method to the classifications of seven cancers. Within-dataset experiments and cross-dataset experiments demonstrated that our novel method achieved robust and superior performance when compared to the five existing methods.

The application of artificial intelligence in auxiliary diagnosis diseases has become a current research hotspot. The traditional method of diagnosing diabetes circulatory complication, diabetic peripheral neuropathy hyperlipidemia, diabetes mellitus peripheral angiopathy, and the comprehensive diseases is to distinguish an inspection report by a professional doctor. Its implementation of the clinical decision support algorithm for medical text data faces a challenge with the confidence level and accuracy. We proposed an expanding learning system to detect diseases above in our medical text data, which cover many kinds of physiological parameters of human, such as hematologic parameters, urine parameters, and biochemical detection. First, the raw data were expanded and corrected. Second, the processed data were fed into a 1D-convolution neural network with dropout and pooling. Our algorithm achieves 80.43%, 80.85%, 91.49%, 82.61%, and 95.60% with testing datasets (46 subjects). The effect of data quantification on model performance also had been researched, and the different data quantification methods would affect model performance on distinguishing different diseases. The proposed auxiliary diagnostic systems that have a highly accurate and robust performance can be used for preliminary diagnosis and referral; therefore, it is not only saving many human resources but also resulting in improved clinical diagnostic efficiency.

Micro-positioning stages, which are capable to meet the emerging industrial trend for performing various kinds of micro-manipulation and micro-assembly tasks are the crucial tools to miniaturize the products under ultra-high precision. The current main practice is to specifically tailor a monolithic positioning stage design for one kind of micro-manipulation application. However, the design and fabrication of such a specific flexure-based micro-positioning stage are expensive in both the time and financial investment. In order to increase the flexibility and functionality of micro-positioning stages, this paper proposes a novel reconfigurable planar micro-positioning stages platform based on different function modules. A functional model-based approach is developed to divide one micro-positioning stage into a set of functionally independent sub-models, which allows flexible composition resulting in an adjustable new stage. The robustness of the proposed platform is demonstrated using some typical micro-positioning stages benchmarking cases. The effectiveness of assembly using flexible function modules is verified to show the benefits of the proposed design platform for reconfigurable micro-positioning stages.

In the era of 5G and beyond, heterogeneous network orchestration has become a tremendous issue. The dilemma facing future systems is how to allocate integrated resources to satisfy multifarious services, which is an imperative but arduous task in forming a systematic mathematical model and quantifying the model with its multi-layer uncertainty characteristics. Aiming at the statistical representation and optimization in multimodal heterogenous networks for 5G and beyond, we propose a novel hybrid probability process (HPP) as a generalized surrogate model and a weighted degenerated upper confidence bound (WDUCB) criterion for Bayesian optimization (BO). We apply the proposed HPP-WDUCB combination to our developed simulation platform and configure several applications of the integration of space information network in next generation communication systems. And we compared the proposed method with other surrogate models and acquisition strategies from a range of perspectives. The experiment results yield significant applicability and excellent performance in multimodal system representation and optimization which provides an effective statistical modeling and orchestration references for network tuning.

Facial Micro-Expressions (MEs) are spontaneous, involuntary facial movements when a person experiences an emotion but deliberately or unconsciously attempts to conceal his or her genuine emotions. Recently, ME recognition has attracted increasing attention due to its potential applications such as clinical diagnosis, business negotiation, interrogations, and security. However, it is expensive to build large scale ME datasets, mainly due to the difficulty of inducing spontaneous MEs. This limits the application of deep learning techniques which require lots of training data. In this paper, we propose a simple, efficient yet robust descriptor called Extended Local Binary Patterns on Three Orthogonal Planes (ELBPTOP) for ME recognition. ELBPTOP consists of three complementary binary descriptors: LBPTOP and two novel ones Radial Difference LBPTOP (RDLBPTOP) and Angular Difference LBPTOP (ADLBPTOP), which explore the local second order information along the radial and angular directions contained in ME video sequences. ELBPTOP is a novel ME descriptor inspired by unique and subtle facial movements. It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition. In addition, by firstly introducing Whitened Principal Component Analysis (WPCA) to ME recognition, we can further obtain more compact and discriminative feature representations, then achieve significantly computational savings. Extensive experimental evaluation on three popular spontaneous ME datasets SMIC, CASME II and SAMM show that our proposed ELBPTOP approach significantly outperforms the previous state-of-the-art on all three single evaluated datasets and achieves promising results on cross-database recognition. Our code will be made available.

In the past decade, a number of chaos-based watermarking schemes have been proposed. Generally, it is widely accepted that the introduction of chaos effectively enhances the security of digital watermarking. Yet, taking a representative algorithm as an example, this paper reveals vulnerabilities of the chaos-based watermarking schemes against various attacks under Kerckhoff's principle, and the watermark can be successfully removed and decrypted. To achieve higher security, a sub key generation mechanism based on the feature codes of images (SKGM-FCI) is designed. The obtained watermarking key is related to the original images, such that individual sub keys are obtained for different host images. Benefiting from the SKGM-FCI, a blind watermarking scheme based on Discrete Cosine Transform domain of the scrambled image is further proposed and investigated. The theoretical analysis and simulation results validate the effectiveness and the high security level of the proposed scheme.

In diverse applications ranging from social networks to location-based online services to traffic monitoring, data streams are continuously monitored by multiple outlier analysts customized with different parameter settings. Real-time response to such complex outlier analytics in high-speed streaming data has been recognized as critical for many domains. In this paper, we propose a parameter space framework, called PSOD, for online outlier detection over sliding window streams to support a large variety of query requests in parameter space with both diverse pattern and window parameter settings. First, we design an ingenious neighbor table that records the neighbors for each point in different distance intervals and different slides, which enables us to maximally reuse the already acquired neighbor information across the entire parameter space. In addition, we propose a series of shared strategies in sliding window environment to minimize processing cost by eliminating the redundant query requests. Moreover, the PSOD effectively transforms the query group in 4-D parameter space into a periodic query group in 3-D parameter space to minimize the number of queries. Our experimental study on three real-world steaming data demonstrates that our PSOD successfully drives down the CPU costs by more than 100 folds compared with the state-of-the-art method.