Research on DoS Attack Detection Method of Modbus TCP in OpenPLC

With the development of new information technologies such as cloud computing, Internet of Things, and mobile Internet of Things, Industry 4.0, Smart Manufacturing and Made in China 2025 have been proposed as the main content of the development of the next industrial revolution. In order to realize these projects with the common characteristics of intelligence, service, and green, a new manufacturing model, digital twin, is proposed, which combines the digital twin with industrial systems, that is, the industrial control virtualization system. However, due to the frequent occurrence of industrial control system security incidents in recent years, the industrial control virtualization system is vulnerable to attacks. The industrial control system is huge and cumbersome. Once attacked, it will cause consequences that affect the whole body. In response to this problem, this article carried out a research on DoS attack detection methods for Modbus TCP in OpenPLC, using OpenPLC as a tool for industrial control system virtualization, building a digital twin system with Raspberry Pi, and launching DoS attacks on the system, combined with Snort Intrusion detection is carried out, and the experimental results show that the built digital twin system can detect DoS attacks in OpenPLC.

A novel model of drug cue-induced behaviours in rhesus macaque subjected to chronic ketamine exposure

To the Editor,Non-human primate(NHP)models are advantageous for mimicking human addiction with high behavioural validity.1 However,current NHP drug addiction models(eg,self-administration)often require a comprehensive behavioural training paradigm,relatively expensive apparatus and invasive surgical procedures.

Multi-focus image fusion based on block matching in 3D transform domain

Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image.This paper presents a fusion framework based on block-matching and 3D(BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform(NSCT), non-subsampled Shearlet transform(NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.

Motion Classification of EMG Signals Based on Wavelet Packet Transform and LS-SVMs Ensemble

This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.

Analysis of Blast Wave Propagation Inside Tunnel

The explosion inside tunnel would generate blast wave which transmits through the longitudinal tunnel. Because of the close-in effects of the tunnel and the reflection by the confining tunnel structure, blast wave propagation inside tunnel is distinguished from that in air. When the explosion happens inside tunnel, the overpressure peak is higher than that of explosion happening in air. The continuance time of the blast wave also becomes longer. With the help of the numerical simulation finite element software LS-DYNA, a three-dimensional nonlinear dynamic simulation analysis for an explosion experiment inside tunnel was carried out. LS-DYNA is a fully integrated analysis program specifically designed for nonlinear dynamics and large strain problems. Compared with the experimental results, the simulation results have made the material parameters of numerical simulation model available. By using the model and the same material parameters, many results were adopted by calculating the model under different TNT explosion dynamites. Then the method of dimensional analysis was used for the simulation results. As overpressures of the explosion blast wave are the governing factor in the tunnel responses, a formula for the explosion blast wave over-pressure at a certain distance from the detonation center point inside the tunnel was derived by using the dimensional analysis theory. By comparing the results computed by the formula with experimental results which were obtained before, the formula was proved to be very applicable at some instance. The research may be helpful to estimate rapidly the effect of internal explosion of tunnel on the structure.

Low Bit Rate Underwater Video Image Compression and Coding Method Based on Wavelet Decomposition

In view of the limited bandwidth of underwater video image transmission,a low bit rate underwater video compression coding method is proposed.Based on the preprocessing process of wavelet transform and coefficient down-sampling,the visual redundancy of underwater image is removed and the computational coefficients and coding bits are reduced.At the same time,combined with multi-level wavelet decomposition,inter frame motion compensation,entropy coding and other methods,according to the characteristics of different types of frame image data,reduce the number of calculations and improve the coding efficiency.The experimental results show that the reconstructed image quality can meet the visual requirements,and the average compression ratio of underwater video can meet the requirements of underwater acoustic channel transmission rate.

Test analysis of stress characteristics on reinforcing rock slope with group anchorage cable

To research the reinforcement effect of a rock slope with group anchorage cablesand the stress characteristics of pre-stressed anchorage cables in the fractured surface,the rock slope calculated model of a wedge block within the double-slide face wasestablished by using the finite difference software according to the actual slope projectcombined with indoor model test.The pre-stress loss rule of the anchorage cable andthe distribution of axial force and the force-transferring mechanism of the anchorage cablewere analyzed during simulation.Also, based on the displacement contour and thesafety factor of the calculated results, the quantitative analysis for the reinforcement effectof the rock slope with group pre-stressed anchorage cable was discovered.The resultscomputed by the software conform with the data in the experiment, which canprove the effectiveness and correctness of parameter selection and model building.

Agent Modeling of User Preferences Based on Fuzzy Classified ANNs in Automated Negotiation

In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.

Security Enhanced Internet of Vehicles with Cloud-Fog-Dew Computing

The Internet of Vehicles(IoV)is becoming an essential factor in the development of smart transportation and smart city projects.The IoV technology consists of the concepts of fog computing and dew computing,which involve on-board units and road side units in the edge network,as well as the concept of cloud computing,which involves the data center that provides service.The security issues are always an important concern in the design of IoV architecture.To achieve a secure IoV architecture,some security measures are necessary for the cloud computing and fog computing associated with the vehicular network.In this paper,we summarize some research works on the security schemes in the vehicular network and cloud-fog-dew computing platforms which the IoV depends on.

A W-EAP Algorithm for IEC 61850 Protocol against DoS/Replay Attack

Substation automation system uses IEC 61850 protocol for the data transmission between different equipment manufacturers. However, the IEC 61850 protocol lacks an authentication security mechanism, which will make the communication face four threats: eavesdropping, interception, forgery, and alteration. In order to verify the IEC 61850 protocol communication problems, we used the simulation software to build the main operating equipment in the IEC 61850 network environment of the communication system. We verified IEC 61850 transmission protocol security defects, under DoS attack and Reply attack. In order to enhance security agreement, an improved algorithm was proposed based on identity authentication (W-EAP, Whitelist Based ECC & AES Protocol). Experimental results showed that the method can enhance the ability to resist attacks.

PICOTEES:a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants from Chinese children cohorts

The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, but it requires significant trust between users and providers. To alleviate privacy concerns, the most commonly used strategy is to anonymize sensitive data. Unfortunately, studies have shown that anonymization is insufficient to protect against reidentification attacks. Recently, privacy-preserving technologies have been applied to preserve application utility while protecting the privacy of biomedical data. We present the PICOTEES framework, a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants (https://birthdefectlab.cn:3000/). PICOTEES enables privacy-preserving queries of the phenotype spectrum for a single variant by utilizing trusted execution environment technology, which can protect the privacy of the user's query information, backend models, and data, as well as the final results. We demonstrate the utility and performance of PICOTEES by exploring a bioinformatics dataset. The dataset is from a cohort containing 20,909 genetic testing patients with 3,152,508 variants from the Children's Hospital of Fudan University in China, dominated by the Chinese Han population (>99.9%). Our query results yield a large number of unreported diagnostic variants and previously reported pathogenicity.

Probability tree based passenger flow prediction and its application to the Beijing subway system

In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.

Video structural description technology for the new generation video surveillance systems

The increasing need of video based applications issues the importance of parsing and organizing the content in videos. However, the accurate understanding and manag- ing video contents at the semantic level is still insufficient. The semantic gap between low level features and high level semantics cannot be bridged by manual or semi-automatic methods. In this paper, a semantic based model named video structural description （VSD） for representing and organizing the content in videos is proposed. Video structural descrip- tion aims at parsing video content into the text information, which uses spatiotemporal segmentation, feature selection, object recognition, and semantic web technology. The pro- posed model uses the predefined ontologies including con- cepts and their semantic relations to represent the contents in videos. The defined ontologies can be used to retrieve and organize videos unambiguously. In addition, besides the de- fined ontologies, the semantic relations between the videos are mined. The video resources are linked and organized by their related semantic relations.

Reduction of rain effect on wave height estimation from marine X-band radar images using unsupervised generative adversarial networks

An intelligent single radar image de-raining method based on unsupervised self-attention generative adversarial networks is proposed to improve the accuracy of wave height parameter inversion results.The method builds a trainable end-to-end de-raining model with an unsupervised cycle-consistent adversarial network as an AI framework,which does not require pairs of rain-contaminated and corresponding ground-truth rain-free images for training.The model is trained by feeding rain-contaminated and clean radar images in an unpaired manner,and the atmospheric scattering model parameters are not required as a prior condition.Additionally,a self-attention mechanism is introduced into the model,allowing it to focus on rain clutter when processing radar images.This combines global and local rain clutter context information to output more accurate and clear de-raining radar images.The proposed method is validated by applying it to actualfield test data,which shows that compared with the wave height derived from the original rain-contaminated data,the root-mean-square error is reduced by 0.11 m and the correlation coefficient of the wave height is increased by 14%using the de-raining method.These results demonstrate that the method effectively reduces the impact of rain on the accuracy of wave height parameter estimation from marine X-band radar images.

Convolution without multiplication:A general speed up strategy for CNNs

Convolutional Neural Networks(CNN)have achieved great success in many computer vision tasks.However,it is difficult to deploy CNN models on low-cost devices with limited power budgets,because most existing CNN models are computationally expensive.Therefore,CNN model compression and acceleration have become a hot research topic in the deep learning area.Typical schemes for speeding up the feed-forward process with a slight accuracy loss include parameter pruning and sharing,low-rank factorization,compact convolutional filters and knowledge distillation.In this study,we propose a general acceleration scheme that replaces the floating-point multiplication with integer addition.To this end,we propose a general accelerate scheme,where the floating point multiplication is replaced by integer addition.The motivation is based on the fact that every floating point can be replaced by the summation of an exponential series.Therefore,the multiplication between two floating points can be converted to the addition among exponentials.In the experiment section,we directly apply the proposed scheme to AlexNet,VGG,ResNet for image classification,and Faster-RCNN for object detection.The results acquired from ImageNet and PASCAL VOC show that the proposed quantized scheme has a promising performance,even with only one item of exponential.Moreover,we analyzed the eciency of our method on mainstream FPGAs.The experimental results show that the proposed quantized scheme can achieve acceleration on FPGA with a slight accuracy loss.

Super-resolution enhancement of UAV images based on fractional calculus and POCS

A super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets(POCS)for unmanned aerial vehicles(UAVs)images.The representative problems of UAV images including motion blur,fisheye effect distortion,overexposed,and so on can be improved by the proposed algorithm.The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS.The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform(SIFT)for matching.The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail.The objective indices and subjective effect are compared between the proposed and other methods.The experimental results indicate that the proposed method outperforms other algorithms in most cases,especially in the structure and detail clarity of the reconstructed images.