Knowledge Graph (KG) strongly support the research of knowledge-driven artificial intelligence. Aiming at this fact, the existing technologies of knowledge graph and knowledge hypergraph were analyzed and summarized. At first, from the definition and development history of knowledge graph, the classification and architecture of knowledge graph were introduced. Second, the existing knowledge representation and storage methods were explained. Then, based on the construction process of knowledge graph, several knowledge graph construction techniques were analyzed. Specifically, aiming at the knowledge reasoning, an important part of knowledge graph, three typical knowledge reasoning approaches were analyzed, which are logic rule-based, embedding representation-based, and neural network-based. Furthermore, the research progress of knowledge hypergraph was introduced along with heterogeneous hypergraph. To effectively present and extract hyper-relational characteristics and realize the modeling of hyper-relation data as well as the fast knowledge reasoning, a three-layer architecture of knowledge hypergraph was proposed. Finally, the typical application scenarios of knowledge graph and knowledge hypergraph were summed up, and the future researches were prospected.

To address the problems that data transmission delay is high and the channel utilization rate is low in the TAB-MAC (Assisted Beamforming MAC (Medium Access Control) protocol for terahertz communication network) for present terahertz Wireless Personal Area Network (T-WPAN), an Efficient and Fast dual-channel MAC protocol for T-WPAN (EF-MAC) was proposed. A test frame was sent to the source node through destination node to reduce an acknowledgment frame, so as to reduce control overhead and test delay. And then a sending and receiving mechanism of node location information was adaptively concelled, the source or destination node obtained the location information of the other node through interaction process previously of RTS/CTS (Request To Send/Clear To Send) frame, and the position of other node had not changed, thus the location information of RTS or CTS frame could be omitted to reduce control overhead. The theoretical analysis and simulation results show that compared with TAB-MAC protocol, the proposed protocol can effectively reduce data transmission delay and improve network throughput.

Duing to the complicated pore structure of low porosity and low permeability reservoirs, the prediction accuracy of the existing Nuclear Magnetic Resonance (NMR) logging permeability model for low porosity and low permeability reservoirs is not high. In order to solve the problem, a permeability prediction method based on Deep Belief Network (DBN) algorithm and Kernel Extreme Learning Machine (KELM) algorithm was proposed. The pre-training of DBN model was first carried out, and then the KELM model was placed as a predictor in the trained DBN model. Finally, the Deep Belief Kernel Extreme Learning Machine Network (DBKELMN) model was formed with supervised training by using the training data. Considering that the proposed model should make full use of the information of the transverse relaxation time spectrum which reflected the pore structure, the transverse relaxation time spectrum of NMR logging after discretization was taken as the input, and the permeability was taken as the output. The functional relationship between the transverse relaxation time spectrum of NMR logging and permeability was determined, and the reservoir permeability was predicted based on the functional relationship. The applications of the example show that the permeability prediction method based on DBN algorithm and KELM algorithm is effective and the Mean Absolute Error (MAE) of the prediction sample is 0.34 lower than that of Schlumberger Doll Researchcenter (SDR) model. The experimental results show that the combination of DBN algorithm and KELM algorithm can improve the prediction accuracy of low porosity and low permeability reservoir, and can be used to the exploration and development of oil and gas fields.

Concerning that there is no research about the relationship between disease classification and functional homogeneity analysis of functional protein module in network medicine, the following research work was carried out. Firstly, a gene relationship network was constructed based on the Mesh database and String9 database. Secondly, the gene relationship network was divided by using optimized modularity-based module classification method (such as BGLL, Nonnegtive Matrix Factorization (NMF) and other clustering algorithms). Thirdly, the GO enrichment analysis was carried out for divided modules, and through the comparison of GO enrichment analysis to the high and low pathogenic topology module, important biology suggests for disease classification could be found from protein functional module characteristics in the aspects of biological process, cellular component, molecular function and so on. Finally, the functional characteristics of topological module for disease classification were analyzed, and the data about the functional features of each module was obtained by the analysis to the properties of the network topology such as average degree, density, and average shortest path length, and further correlativity between disease classification and functional module was revealed.

To realize 10Gbps level wireless access under the condition of terahertz (THz) carrier frequency, a new Medium Access Control (MAC) protocol for THz ultra-high data-rate wireless networks, MAC-T (Medium Access Control for THz) was proposed in this paper. In MAC-T, a new TDMA (Time Division Multiple Access)+CSMA (Carrier Sense Multiple Access) adaptive hybrid MAC access control mechanism and a new superframe structure were designed. Moreover, some key parameters corresponding to terahertz communications were defined. Therefore, MAC-T could make the maximum data transmission rate reach up to 10Gbps or higher. The theoretical analysis and simulation results show that MAC-T can operate normally in terahertz networks and the data rate can reach up to 18.3Gbps which is 2.16 times 5.78Gbps that IEEE 802.15.3c can achieve. Meanwhile, the average access delay of MAC-T is 0.0044s which improves about 42.1% compared with that of IEEE 802.15.3 which is 0.0076s. Thus, MAC-T can provide significant support in the research and application of terahertz ultra-high data-rate wireless networks.

The physical condition of miners escaping from a mine fire in the harmful circumstance is critical to the success of escape. This paper proposed the concept of health degree of escaping miners and analyzed the effect of each harmful factor. A model was built to evaluate all factors' influence on escaping miners based on fuzzy comprehensive evaluation approach, and then a dynamic health degree evaluation method of miners escaping from a mine fire was proposed. Fire Dynamics Simulator (FDS) software was used to simulate a simplified mine fire, and escaping miner's health degree was calculated using the method. The rationality of the method was verified by the experiment. Miner's health degree can evaluate miner's physical state in complex disaster environment synthetically, and it provides a quantifiable basis to guide the decision-making of escape path in the underground disasters.

Concerning the low accuracy and convergence of Glowworm Swarm Optimization (GSO) algorithm when resolving high-dimensional functions, an Improved GSO (IGSO) algorithm with mutation operator and foraging behavior was proposed. Using mutation operator to guide the evolution of glow worms which cannot find their peers in the visible range, the proposed algorithm could enhance the utilization of outliers and improve the overall efficiency. The operator with foraging behavior substantially increased the accuracy and convergence speed by searching accurately in the global optimal field captured by the algorithm. In the meantime, the operator could effectively avoid local optimum and enlarge the global search range of the algorithm in the late stage. The experimental results indicate that IGSO has better ability of global optimization and higher success ratio than GSO according to the tests of eight Benchmarks.

Current research on the Wireless Sensor Network (WSN) routing protocols considers little about the safety of the routers, or merely proposes key management algorithm without combing the practical net model with the algorithm. Therefore, the authors proposed a secure routing algorithm based on hierarchical and level-administration which guaranteed the alive-time as well as the security. In the algorithm, nodes were clustered by level-administration, information was transmitted from low to high level by level, redundancies were reduced by data merging. By combing level-administration and distributed approach to security in sensornets (DSPS) key management, the energy consumption of key management was greatly reduced, which not only extended the alive-time of the network, but also guaranteed the security of the network. Finally, the experimental results in NS2 indicate that this algorithm is suitable for large-scale WSNs, and balances the nodes energy consumption and extends the alive-time of the network.

In the network performance measurement, location error is one of the main factors which influence the accurate network delay measurement. To reduce location error, an improved method for delay measurement was proposed based on Windows Network Driver Interface Specification (NDIS). By using this method, the timestamp position was removed from application to NDIS Intermediate Driver (ID) which was embedded between Miniport Driver (MD) and Protocol Driver (PD), and then the measurement program could calculate the network delay according to those timestamps. Compared with the traditional method, the experimental results show that the proposed method can nearly eliminate location error, and make sure the measurement standard deviation to be lowered than 10μs under different packet-lengths and host-loads. The improved measurement method does not need additional software and hardware, so it has lower measurement cost and can be applied widely.

Most of existing manifold learning algorithms are not capable of dealing with new arrival samples. Although some incremental algorithms are developed via extending a specified manifold learning algorithm, most of them have some disadvantages more or less. In this paper, a novel and more Generalized Incremental Manifold Learning algorithm based on local smoothness is proposed (GIML). GIML algorithm first extracts the local smoothness structure of data set via local PCA. Then the optimal linear transformation, which transforms the local smoothness structure of new arrival sample’s neighborhood to its corresponded low-dimensional embedding coordinates, is computed. Finally the low-dimensinal embedding coordinates of new arrival samples are obtained by the optimal transformation. Extensive and systematic experiments are conducted on both artificial and real image data sets. Experimental results demonstrate that our GIML algotithm is an effective incremental manifold learning algorithm and outperforms other existing algirthms.

As the fabric defect type of diversity and traditional artificial detection methods inefficient ,in order to detect the fabric defect more effective, A new approach, SGE, based on saliency region feature for fabric defect detection is studied. In this approach, the original image is divided into two parts, one extracts the saliency region feature of fabric defect by improved FSR roughly, another employing the gabor filter and taking the amplitude as an output characteristics, and extracts the saliency region feature of fabric defect by PSR accurately, then by using maximum entropy to segment the saliency region respectively and fused the sub-images. The result is get got by calculating perimeter and area of the contours to removal the isolated points. The experiment selects four types of typical fabric defect images and OpenCV library is used. The experiment result shows that the algorithm, without prior learning，meet the real-time.

Elastic scheduling is designed for real-time systems with variable load. It adjusts the task attributes dynamically to meet the flexibility requirements of system, and it is an effective task scheduling strategy. Concerning the research results and problems of elastic scheduling, an overview of elastic scheduling was given out, and the research progress of the elastic periodic task model, scheduling model and the elastic scheduling algorithm were analyzed. The existing problems in research were investigated, and possible research areas in future were suggested.

In real applications in pattern recognition and computer vison, high dimensional data always lie approximately on a low dimensional manifold. How to improve the performance of clustering algorithm on high dimensional data by using the manifold structure is a research hotspot in machine learning and data mining community. In this paper, a novel clustering algorithm called Nonlinear Discriminant K-means Clustering (NDisKmeans), which has taken the manifold structure of high dimensional into account, is proposed. By introducing the spectracl regularization technology, NDisKmeans first represents the desired low dimensional coordinates as linear combinations of smooth vectors predefined on the data manifold; then maximizes the ratio between inter-clusters scatter and total scatter to cluster the high dimensional data. A convergent iterative procedure is devised to solute the matrix of the combination coefficient and clustering assignment matrix. NDisKmeans overcomes the limilation of linear mapping of DisKmeans algorithm; therefore, it significantly improves the clustering performance. The systematic and extensive experiments on UCI and real world data sets have shown the effectiveness of the proposed NDisKmeans method.

The common routing algorithms tremendously dissipate energy in largescale wireless sensor networks, which goes against the maximization of the network lifetime. A routing model about mobile Agent in sensor networks was drawn out, and then an optimization problem of mobile Agent static route was derived. A chaotic simulated annealing with memory ability and various neighborhood search methods were proposed to optimize the route of mobile Agent in largescale sensor network. The theoretical analysis and experimental results show that the proposed algorithm is superior to other intelligent algorithms in terms of the solutions, the convergence speed, and the computation time. It proves that the proposed approach has obviously prolonged the network lifetime.

Based on the characteristics of glass fragmentation images,a process was proposed.It first combined noise reduction,a traditional edge-detection operator,and threshold segmentation to produce an initial binary image partition.Then it applied the distance function to reconstruct and inverse it.Finally it used the watershed transformation based on chain code to obtain the whole segmentation image of fragmented glass.The final image can be used in testing safety glasses, for example to analyze and count the number of fragments.

A semiautomatic method for medical image segmentation based on active shape model was introduced. In order to improve the segmentation speed and precision, a semiautomatic method was used to model the training set, and a Gaussian Pyramid of images with different resolutions was generated so that multi-resolution image search could be performed. Experiments and analysis show that this method can be used to segment the lung region of medical images and the results are quite better.

Traditional wavelet-based algorithm has a common effect on the images of light nonuniformity and scarcity. Aiming at the shortcoming, a new wavelet-based algorithm for image enhancement was proposed. The image was first decomposed into multi-level wavelet to obtain the scaling coefficients and the multi-level wavelet coefficients. Then, every level of wavelet coefficients was enhanced by different algorithms, and the scaling coefficients were processed by MSR(Multiscale Retinex). Finally, the image of enhancement was obtained via the inverse wavelet transform. Experiments show that the algorithm excels conventional algorithms in the effect of enhancement and the abatement of noise, at the same time, it has an excellent effect on the images of light nonuniformity and scarcity.