Considering the problem that intrusion detection systems always produced duplicated alarm information, in this paper we propose an iterative self-organization clustering algorithm. It begins with calculating average v...
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Considering the problem that intrusion detection systems always produced duplicated alarm information, in this paper we propose an iterative self-organization clustering algorithm. It begins with calculating average value of classes as the new clustering center on the basis of random selection, merging and dividing dynamically, then finish the clustering procedure through the iteration finally. Experimental results with DARPA1999 testing data set show that the clustering method is more excellent than traditional clustering methods in both aggregation rate and error aggregation rate. Besides, it reduces duplicated alarm effectively and provides assistance to further related work.
Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether ...
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Protein and protein-water hydrogen bonds shape the conformational energy landscape of G Protein-Coupled Receptors, GPCRs. As numerous static structures of GPCRs have been solved, the important question arises whether GPCR structures and GPCR conformational dynamics could be described in terms of conserved hydrogen-bond networks, and alterations of these hydrogen-bond networks along the reaction coordinate of the GPCR. To enable efficient analyses of the hydrogen-bond networks of GPCRs we implemented graph-based algorithms, and applied these algorithms to static GPCR structures from structural biology, and from molecular dynamics simulations of two opioid receptors. We find that static GPCR structures tend to have a conserved, core hydrogen-bond network which, when protein and water dynamics are included with simulations, extends to comprise most of the interior of an inactive receptor. In an active receptor, the dynamic protein-water hydrogen-bond network spans the entire receptor, bridging all functional motifs. Such an extensive, dynamic hydrogen-bond network might contribute to the activation mechanism of the GPCR.
Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' co...
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Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' cooperation ability and lifelong learning ability. Based on students' interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency-inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy.
One of the most popular techniques for computer-assisted solution estimates for magnetics and gravity field data is Werner deconvolution. The approaches frequently produce erratic results and may not always forecast t...
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One of the most popular techniques for computer-assisted solution estimates for magnetics and gravity field data is Werner deconvolution. The approaches frequently produce erratic results and may not always forecast the maximum number of the geologic entity that produces them due to the intrinsic instability of potential field data. This led to the application of the K-means machine learning algorithm to further enhance the detection of the geologic potential field-generated bodies. Two substances that resembled dikes were combined to form a synthetic magnetic model. Random noise was added to the synthetic data, to make the solutions a bit more complex. Werner deconvolution technique was applied to the synthetic model to generate solutions. K-means unsupervised machine learning algorithm was applied to the generated solutions created by the synthetic data. We further applied this algorithm to real data sets from a mining site. The clustering result shows a good spatial corre-spondence with the geologic model, and the method was able to estimate the precise location and depth of the dike bodies. The proposed method is entirely data-driven and has proven to work in the presence of noise.
The era of big data has come into our life, the acceleration of mass data growth, people with the naked eye to observe the work in the data pile with the growth of data becomes more and more laborious, data mining tec...
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The era of big data has come into our life, the acceleration of mass data growth, people with the naked eye to observe the work in the data pile with the growth of data becomes more and more laborious, data mining technology came into being. This paper analyzes the current situation of big data mining, expounds the relevant concepts, characteristics, process and relevant algorithms of data mining, and analyzes the future development direction and trend of data mining technology.
Understanding the spatiotemporal evolution of droughts is critical for food security and water allocation. In this study, we propose an approach to construct the linkage of the propagation from meteorological drought ...
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Understanding the spatiotemporal evolution of droughts is critical for food security and water allocation. In this study, we propose an approach to construct the linkage of the propagation from meteorological drought to agricultural drought to explore the simultaneous spatiotemporal evolution of droughts based on a 3-dimensional clustering algorithm. We first evaluate the improvement of the downscaled high-resolution outputs from the Weather Research and Forecasting (WRF) model to reanalysis data in detecting wet-dry variations in the agro-pastoral ecotone of northern China (APENC). The WRF simulation results well characterize the evolution of droughts with a high correlation coefficient of 0.82 (p<0.05), which implies that the WRF downscaling model provides reliable hydrometeorological results for assessing and analyzing long-term wet-dry variations. Subse-quently, we identify and track the spatiotemporal evolution of each individual drought event in the APENC for the first time. Based on the standardized precipitation index calculated from the WRF outputs, a total of 185 drought clusters were identified during 2000-2017, with 28 drought events lasting at least 3 months duration occurring in the APENC. The characteristics of the different drought events vary considerably. Droughts in the APENC have a larger percentage of propagation to the northeast and northwest and migrated hundreds of ki-lometers from the source sites. The results of the propagation of meteorological droughts to agricultural droughts indicate that 53.75% of the meteorological drought events progressed further to agricultural droughts. Agri-cultural droughts exhibit spatial variations consistent with meteorological droughts. Agricultural droughts have 1.69 months onset lag time, 2.12 months termination lag time, and overall longer duration than meteorological droughts in the APENC. Our findings provide valuable information for the mechanism and prediction of drought propagation.
The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecast...
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The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.
The accurate measurement of slope displacement profiles using a fiber Bragg grating flexible sensor is limited due to the influence of accumulative measurement errors. The measurement errors vary with the deformation ...
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The accurate measurement of slope displacement profiles using a fiber Bragg grating flexible sensor is limited due to the influence of accumulative measurement errors. The measurement errors vary with the deformation forms of the sensor, which dramatically affects the measurement accuracy of the slope displacement profiles. To tackle the limitations and improve the measurement precision of displacement profiles, a segmental correction method based on strain increments clustering was proposed. A K-means clustering algorithm was used to automatically identify the deformation segments of a flexible sensor with different bending shapes. Then, the particle swarm optimization method was adopted to determine the correction coefficients corresponding to different deformation segments. Both finite element simulations and experiments were performed to validate the superiority of the proposed method. The experimental results indicated that the mean absolute errors (MAEs) percentages of the reconstructed displacements using the proposed method for six different bending shapes were 1.87%, 5.28%, 6.98%, 7.62%, 4.16% and 8.31%, respectively, which had improved the accuracy by 26.83%, 18.94%, 29.49%, 26.35%, 7.39%, and 19.65%, respectively. Therefore, it was confirmed that the proposed correction method was competent for effectively mitigating the measurement errors and improving the measurement accuracy of slope displacement profiles, and it presented a vital significance and application promotion value.
One common problem in viral marketing, counter-terrorism and epidemic modeling is the efficient detection of a community that is centered at an individual of interest. Most community detection algorithms are designed ...
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ISBN:
(纸本)9781921770296
One common problem in viral marketing, counter-terrorism and epidemic modeling is the efficient detection of a community that is centered at an individual of interest. Most community detection algorithms are designed to detect all communities in the entire network. As such, it would be computationally intensive to first detect all communities followed by identifying communities where the individual of interest belongs to, especially for large scale networks. We propose a community detection algorithm that directly detects the community centered at an individual of interest, without the need to first detect all communities. Our proposed algorithm utilizes an expanding ring search starting from the individual of interest as the seed user. Following which, we iteratively include users at increasing number of hops from the seed user, based on our definition of a community. This iterative step continues until no further users can be added, thus resulting in the detected community comprising the list of added users. We evaluate our algorithm on four social network datasets and show that our algorithm is able to detect communities that strongly resemble the corresponding real-life communities.
Topology construction, the initial step of the topology control, is an important technique for a wireless ad hoc network to be energy-efficient. In [4], we have proposed to use the relative neighborhood graph (RNG) to...
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ISBN:
(纸本)9781457720536
Topology construction, the initial step of the topology control, is an important technique for a wireless ad hoc network to be energy-efficient. In [4], we have proposed to use the relative neighborhood graph (RNG) to obtain an RNG-based topology in which the transmission ranges between wireless nodes are reduced. Then, among the RNG-based topology, we proposed a green clustering algorithm (GCA) to organize the wireless nodes into a clustered network topology. In this paper, we further analyze the energy consumption in exchanging data packets and cluster maintenance messages. Simulation results confirm that the proposed RGCA (i.e., combining the RNG and GCA) provides a way to construct an energy-efficient cluster topology for wireless ad hoc networks.
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