1,319 research outputs found
Data Asset Management and Visualization Based on Intelligent Algorithm: Taking Power Equipment Data as An Example
Data asset management is adequate in solving the problem of data silence and data idleness for enterprises. Through intelligent algorithms such as neural network, in-depth learning and block chain, and guided by business needs, it extracts, analyzes and visualizes the existing business precipitation data, and forms scattered and disordered data into valuable information to support the development of the company, so as to activate data assets. Taking the management data of electric power equipment as an example, this paper proposes a method of fusion of multiple intelligent control algorithms. The specific modules include the fusion of heterogeneous data; feature extraction of equipment asset management data based on machine learning; intelligent control of multi-objective optimization environment based on energy consumption data; BIM data visualization based on data classification-energy extraction-neural network (SVM-CART-SAE-DNN) algorithm fusion. The algorithm can effectively improve the efficiency of equipment management and enhance the security and economy of power infrastructure through intelligent control of equipment management
Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network
A topic propagating in a social network reaches its tipping point if the
number of users discussing it in the network exceeds a critical threshold such
that a wide cascade on the topic is likely to occur. In this paper, we consider
the task of selecting initial seed users of a topic with minimum size so that
with a guaranteed probability the number of users discussing the topic would
reach a given threshold. We formulate the task as an optimization problem
called seed minimization with probabilistic coverage guarantee (SM-PCG). This
problem departs from the previous studies on social influence maximization or
seed minimization because it considers influence coverage with probabilistic
guarantees instead of guarantees on expected influence coverage. We show that
the problem is not submodular, and thus is harder than previously studied
problems based on submodular function optimization. We provide an approximation
algorithm and show that it approximates the optimal solution with both a
multiplicative ratio and an additive error. The multiplicative ratio is tight
while the additive error would be small if influence coverage distributions of
certain seed sets are well concentrated. For one-way bipartite graphs we
analytically prove the concentration condition and obtain an approximation
algorithm with an multiplicative ratio and an
additive error, where is the total number of nodes in the social graph.
Moreover, we empirically verify the concentration condition in real-world
networks and experimentally demonstrate the effectiveness of our proposed
algorithm comparing to commonly adopted benchmark algorithms.Comment: Conference version will appear in KDD 201
Numerical Security Analysis of Three-State Quantum Key Distribution Protocol with Realistic Devices
Quantum key distribution (QKD) is a secure communication method that utilizes
the principles of quantum mechanics to establish secret keys. The central task
in the study of QKD is to prove security in the presence of an eavesdropper
with unlimited computational power. In this work, we successfully solve a
long-standing open question of the security analysis for the three-state QKD
protocol with realistic devices, i,e, the weak coherent state source. We prove
the existence of the squashing model for the measurement settings in the
three-state protocol. This enables the reduction of measurement dimensionality,
allowing for key rate computations using the numerical approach. We conduct
numerical simulations to evaluate the key rate performance. The simulation
results show that we achieve a communication distance of up to 200 km.Comment: 14 pages, 5 figure
Performance study of air interface for broadband wireless packet access
Ph.DDOCTOR OF PHILOSOPH
Special Libraries, November 1962
Volume 53, Issue 9https://scholarworks.sjsu.edu/sla_sl_1962/1008/thumbnail.jp
A hybrid algorithm for quadratically constrained quadratic optimization problems
Quadratically Constrained Quadratic Programs (QCQPs) are an important class
of optimization problems with diverse real-world applications. In this work, we
propose a variational quantum algorithm for general QCQPs. By encoding the
variables on the amplitude of a quantum state, the requirement of the qubit
number scales logarithmically with the dimension of the variables, which makes
our algorithm suitable for current quantum devices. Using the primal-dual
interior-point method in classical optimization, we can deal with general
quadratic constraints. Our numerical experiments on typical QCQP problems,
including Max-Cut and optimal power flow problems, demonstrate a better
performance of our hybrid algorithm over the classical counterparts.Comment: 8 pages, 3 figure
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