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The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.

Aiming at the information security in industrial communication network and industrial network integration requirements, this paper develops in-depth study on the current industrial ethernet, OPC UA, Modbus TCP and Profinet, against the vulnerability in the industrial control network, adopts the security strategy under the combination of security function in the mainstream industrial ethernet and protocol deep analysis, and designs an ethernet security monitor in multi-protocol industry. This monitor is able to identify different network protocols automatically in single or multiple network protocols, maintain surveillance over network status in real-time according to the security configuration strategy, supervise illegal access from unauthorized devices, abnormal message, network abnormalities, etc., track the equipment critical process data, and realize local and remote alarm function. After testing, this monitor does not affect the original industrial control network and can effectively protect the security of industrial control network.

the samples collected in the Helan Mountain of Inner Mongolia were studied by Anatomy and molecular biology and found that strain of ascoma was tan, smooth surface, no obvious verrucous protuberance and edges. Gleba of strains was brown, dense, and ivory of vagus veins; Spherical or rod-shaped ascus in honeycomb arrangement contained 1-4 ascospore with package; the surface of ascospore had a protuberant ornamentation and was spherical. Thickness of double wall was about 1.7μm. Diameter of ascospore was 20 microns (including ascospore grain). The molecular evolution analysis shows that the strain was Choiromyces helanshanensis.

Packet loss rate is an important consideration in the Quality of Service (QoS) measurement for a packet-switched network. The software-defined networking (SDN) technique can conveniently monitor flow statistics. However, the packet loss rate of a link or path cannot be directly measured by inquiring the statistics of an ongoing flow at the starting and ending points because it is impossible to accurately compute and control pairwise sampling moments. In this paper, we propose a two-way link-level packet loss measurement solution for software-defined networks. We solve the flow statistics sampling problem mentioned above by inquiring the statistics of a terminated probe flow. We propose a ring-based packet loss probe structure, which contains every measured directed link once and only once. The proposed probe structure effectively avoids the mutual interference between different probe flows, and thereby improves probe accuracy. The ring is implemented based on the flexible flow match capability of SDN. We further study an optimization problem of ring-based packet loss probe structure that strives to minimize the maximum delay of rings. This optimization problem is very complex, and we approximately solve it using a top-down-top graph partition method. A packet loss positioning method, based on flow statistic inquiries and the symmetry design of the probe ring, is also proposed herein.

It is difficult to timely predict dynamic loads of green buildings in order to optimize operation of its energy supply systems. In this paper, HCMAC (Hyperball CMAC) neural networks are used to build load prediction models of buildings. The model inputs are outdoor meteorological parameters and the personnel distribution, and outputs cold / heat load and electricity load. A Novel fuzzy C-means clustering algorithm is proposed to overcome the drawback that the node number of HCMAC neural network increases exponentially with the increasing of input dimensions, effectively reducing the number of the network nodes, and decreasing the computational burden of neural network parameter learning. Load characteristics of a building are analyzed applying software TRNSYS, and the simulating operation data used for building load models are obtained. Simulation results demonstrated that the presented building load prediction method is an effective data-driven method to be universally applied to modeling of buildings.

In this paper, a highly robust digital video watermarking technique is given. First of all, the watermark information is encrypted by Arnord transform. Then it is embedded in DCT domain by high-frequency coefficients correlation algorithm in RGB frames of source video file. The field tests indicate that the video file with the watermark information is transparent, and strong anti-aggressive.

The thresholding method is a fundamental part for motion detection and other advanced applications,so this step is very important, it must base on a reliable, effective method in order to access to the robustness of the computer intelligent video surveillance system. A new thresholding method which is simple and effective is proposed in the article. Firstly, we propose an imitating uni-Gaussian model thresholding method for motion detection, aiming at the disadvantages of this algorithm existed in the experiment, a predecessor's research is introduced, and then the second mixed thresholding method based on complementary advantages of the two aforesaid methods is proposed. Experiment results show that our final method can obtain clear and complete information of the moving object, and eliminate noise fundamentally for the scene at the same time. Motion detection with this thresholding method is accurate and real-time.