Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

Improved k-means clustering algorithm

In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower.

On the Sum of a Prime and the k-th Power of a Prime

In this paper, we investigate HUA’s Theorem for short intervals under GRH. Let E k(x)=#{{n≤x;2|n,k is odd, n≠p 1+p k 2}∪{n≤x;2|n,2|k,(p-1)|k, n1(modp),n≠p 1+p k 2}}. Assume GRH. For any k≥2, any A】0 and any 0【ε【14,E k(x+H)-E k(x)≤H(log x) -Aholds for x 12-14k+ε≤H≤x, here the implies constant depends at most on A and ε.

Influential Node Ranking and Invulnerability of Air Traffic Cyber Physical System

To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.

A Modular Representation of K-root-and Jacobson Structure Theorems for Additive Categories

In this paper,we give definition and moduler representation of Kothe root for additive cate gories.Using these results,get inner representation of J-root and fully homomorph class of Jscmisimple additive categories.

Research on control technology of elderly-assistant & walking-assistant robot based on tactile-slip sensation

In this paper,according to the old people's physical characteristics and their technical requirements for comfort and mastery when operating the robot,a control approach driven by tactile and slip senses is investigated to control the elderly-assistant & walking-assistant robot. First,on the basis of the proposed driving control system program of tactile and slip,a detection system of tactile and slip senses are designed. Based on the tactile and slip feature representation and extraction,an improved classification and recognition method is proposed which combines K-nearest neighbor (KNN) algorithm and K-means algorithm. And then,a robot control system based on TMS320F2812 is designed in this paper,including its hardware and software design. Then,a moving control method including the fuzzy adaptive control algorithm is presented for the walking-assistant robot to realize some different moving properties. At last,by the experimental verification in the walking-assistant robot,the research results show that the tactile and slip senses detection and recognition method is effective,and the whole control system has good feasibility and adaptability.

Explicit Solutions for Generalized (2+1)-Dimensional Nonlinear Zakharov-Kuznetsov Equation

The exact solutions of the generalized (2+1)-dimensional nonlinear Zakharov-Kuznetsov (Z-K) equationare explored by the method of the improved generalized auxiliary differential equation.Many explicit analytic solutionsof the Z-K equation are obtained.The methods used to solve the Z-K equation can be employed in further work toestablish new solutions for other nonlinear partial differential equations.

A Portable Fall Detection and Alerting System Based on k-NN Algorithm and Remote Medicine

This paper presents information on a portable fall detection and alerting system mainly consisting of a custom vest and a mobile smart phone. A wearable motion detection sensor integrated with tri-axial accelerometer, gyroscope and Bluetooth is built into a custom vest worn by elderly. The vest can capture the reluctant acceleration and angular velocity about the activities of daily living(ADLs) of elderly in real time. The data via Bluetooth is then sent to a mobile smart phone running a fall detection program based on k-NN algorithm. When a fall occurs the phone can alert a family member or health care center through a call or emergent text message using a built in Global Positioning System. The experimental results show that the system discriminates falls from ADLs with a sensitivity of 95%, and a specificity of 96.67%. This system can provide remote monitoring and timely help for the elderly.

Ground-roll attenuation using modified common-offset–common-reflection-surface stacking

We modified the common-offset-common-reflection-surface （COCRS） method to attenuate ground roll, the coherent noise typically generated by a low-velocity, low-frequency, and high-amplitude Rayleigh wave. The COCRS operator is based on hyperbolas, thus it fits events with hyperbolic traveltimes such as reflection events in prestack data. Conversely, ground roll is linear in the common-midpoint （CMP） and common-shot gathers and can be distinguished and attenuated by the COCRS operator. Thus, we search for the dip and curvature of the reflections in the common-shot gathers prior to the common-offset section. Because it is desirable to minimize the damage to the reflection amplitudes, we only stack the multicoverage data in the ground-roll areas. Searching the CS gathers before the CO section is another modification of the conventional COCRS stacking. We tested the proposed method using synthetic and real data sets from western Iran. The results of the ground-roll attenuation with the proposed method were compared with results of the f-k filtering and conventional COCRS stacking after f-k filtering. The results show that the proposed method attenuates the aliased and nonaliased ground roll better than the f-k filtering and conventional CRS stacking. However, the computation time was higher than other common methods such as f-k filtering.

A multi-view K-multiple-means clustering method

The K-multiple-means(KMM)retains the simple and efficient advantages of the K-means algorithm by setting multiple subclasses,and improves its effect on non-convex data sets.And aiming at the problem that it cannot be applied to the Internet on a multi-view data set,a multi-view K-multiple-means(MKMM)clustering method is proposed in this paper.The new algorithm introduces view weight parameter,reserves the design of setting multiple subclasses,makes the number of clusters as constraint and obtains clusters by solving optimization problem.The new algorithm is compared with some popular multi-view clustering algorithms.The effectiveness of the new algorithm is proved through the analysis of the experimental results.

Clustering： from Clusters to Knowledge

Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.

Study on the Application of K-Means Algorithm Implemented Hadoop Platform to the Library Work in Colleges and Universities

In this paper, the borrowing data of readers is analyzed and studied by taking K-Means algorithm as an example and implementing this algorithm in Hadoop calculation platform, and data mining technology is effectively and closely combined with personalized library service through the experimental data.

CLOSED REDUCTION AND PERCUTANEOUS K-WIRES FIXATION OF DISPLACED SUPRACONDYLAR HUMERUS FRACTURES IN CHILDREN

To observe the effects of closed reduction and percutaneous K wires fixation of displacd supracondylar humerus fracture in children MethodsRetrospective review of fourteen patients who sustained displaced supracondylar fracture of distal humerus treated by closed reduction and percutaneous K wires fixation Results. All patients’ K wires were removed at 4 weeks post operation Their elbow function regained at 8 weeks The average period of followed up was 10 month (varies from 6 to 18 month), all fractures healed very well without any permanent complications Two transient nerves palsy,ulnar and radial nerve each, recovered completely at 12 weeks and 16 weeks post operation respectively Conclusion. Closed reduction and percutaneous K wires fixation is a safe and efficient treatment for displaced humerus surpracondylar fracture in children

CONDITIONAL FACTORIZATION BASED ON LATTICE THEORY FOR -INTEGERS

In this paper,the integer N = pkq is called a <k,1>-integer,if p and q are odd primes with almost the same size and k is a positive integer. Such integers were previously proposed for various cryptographic applications. The conditional factorization based on lattice theory for n-bit <k,1>-integersis considered,and there is an algorithm in time polynomial in n to factor these integers if the least significant 「((2k-1)n)/((3k-1)(k+1))」bits of p are given.

Short-term photovoltaic power prediction using combined K-SVD-OMP and KELM method

For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.

Two-Stage Resource Allocation Scheme for Three-Tier Ultra-Dense Network

In 5 G Ultra-dense Network(UDN), resource allocation is an efficient method to manage inter-small-cell interference. In this paper, a two-stage resource allocation scheme is proposed to supervise interference and resource allocation while establishing a realistic scenario of three-tier heterogeneous network architecture. The scheme consists of two stages: in stage I, a two-level sub-channel allocation algorithm and a power control method based on the logarithmic function are applied to allocate resource for Macrocell and Picocells, guaranteeing the minimum system capacity by considering the power limitation and interference coordination; in stage II, an interference management approach based on K-means clustering is introduced to divide Femtocells into different clusters. Then, a prior sub-channel allocation algorithm is employed for Femtocells in diverse clusters to mitigate the interference and promote system performance. Simulation results show that the proposed scheme contributes to the enhancement of system throughput and spectrum efficiency while ensuring the system energy efficiency.

A Modified Fixed Point Method for the Perona Malik Equation

In this work we present a new method to solve the Perona Malik equation for the image denoising. The method is based on a modified fixed point algorithm which is fast and stable. We discretize the equation using a finite volume method by integrating the equation using a fuzzy measure on the control volume. To make our algorithm move faster in time, we have used an optimized domain decomposition which generalize the wave relaxation method. Several test of noised images illustrate this approach and show the efficiency of the proposed new method.

Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

B→K0^＊（1430）K Decays in Perturbative QCD Approach

In this article, we calculate the branching ratios of B→K0^＊ （1430）K decays by employing the pertur-bative QCD （pQCD） approach at leading order. We perform the evaluations in the two scenarios for the scalar meson spectrum. We find that （i） The leading order pQCD predictions for the branching ratio Br（B^＋→K^＋K0^＊（1430）^0）are in good agreement with the experimental upper limit in both scenarios, while the pQCD predictions for other considered B→K0^＊（1430）K decay modes are also presented and will be tested by the LHC experiments; （ii） The annihilation contributions play an important role in these considered decays, for B^0→K0^＊（1430）^±K^± decays,for example,which are found to be （1-4）×10^-6.