39 research outputs found
An Incomplete Tensor Tucker decomposition based Traffic Speed Prediction Method
In intelligent transport systems, it is common and inevitable with missing
data. While complete and valid traffic speed data is of great importance to
intelligent transportation systems. A latent factorization-of-tensors (LFT)
model is one of the most attractive approaches to solve missing traffic data
recovery due to its well-scalability. A LFT model achieves optimization usually
via a stochastic gradient descent (SGD) solver, however, the SGD-based LFT
suffers from slow convergence. To deal with this issue, this work integrates
the unique advantages of the proportional-integral-derivative (PID) controller
into a Tucker decomposition based LFT model. It adopts two-fold ideas: a)
adopting tucker decomposition to build a LFT model for achieving a better
recovery accuracy. b) taking the adjusted instance error based on the PID
control theory into the SGD solver to effectively improve convergence rate. Our
experimental studies on two major city traffic road speed datasets show that
the proposed model achieves significant efficiency gain and highly competitive
prediction accuracy
Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks
With the development of blockchain technology, the cryptocurrency based on
blockchain technology is becoming more and more popular. This gave birth to a
huge cryptocurrency transaction network has received widespread attention. Link
prediction learning structure of network is helpful to understand the mechanism
of network, so it is also widely studied in cryptocurrency network. However,
the dynamics of cryptocurrency transaction networks have been neglected in the
past researches. We use graph regularized method to link past transaction
records with future transactions. Based on this, we propose a single latent
factor-dependent, non-negative, multiplicative and graph
regularized-incorporated update (SLF-NMGRU) algorithm and further propose graph
regularized nonnegative latent factor analysis (GrNLFA) model. Finally,
experiments on a real cryptocurrency transaction network show that the proposed
method improves both the accuracy and the computational efficienc
Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization
Stochastic gradient descent (SGD) algorithm is an effective learning strategy
to build a latent factor analysis (LFA) model on a high-dimensional and
incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is
commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e,
learning rate and regularization coefficient, self-adaptation. However, a
standard PSO algorithm may suffer from accuracy loss caused by premature
convergence. To address this issue, this paper incorporates more historical
information into each particle's evolutionary process for avoiding premature
convergence following the principle of a generalized-momentum (GM) method,
thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a
GM-PSO-based LFA (GMPL) model is further achieved to implement efficient
self-adaptation of hyper-parameters. The experimental results on three HDI
matrices demonstrate that the GMPL model achieves a higher prediction accuracy
for missing data estimation in industrial applications
A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model
High-dimensional and incomplete (HDI) data holds tremendous interactive
information in various industrial applications. A latent factor (LF) model is
remarkably effective in extracting valuable information from HDI data with
stochastic gradient decent (SGD) algorithm. However, an SGD-based LFA model
suffers from slow convergence since it only considers the current learning
error. To address this critical issue, this paper proposes a Nonlinear
PID-enhanced Adaptive Latent Factor (NPALF) model with two-fold ideas: 1)
rebuilding the learning error via considering the past learning errors
following the principle of a nonlinear PID controller; b) implementing all
parameters adaptation effectively following the principle of a particle swarm
optimization (PSO) algorithm. Experience results on four representative HDI
datasets indicate that compared with five state-of-the-art LFA models, the
NPALF model achieves better convergence rate and prediction accuracy for
missing data of an HDI data
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the
most appropriate one among numerous functionally identical or similar service
candidates. Quality-of-Service (QoS) describes the non-functional
characteristics of Web services, and it has become the key differentiator for
service selection. However, users cannot invoke all Web services to obtain the
corresponding QoS values due to high time cost and huge resource overhead.
Thus, it is essential to predict unknown QoS values. Although various QoS
prediction methods have been proposed, few of them have taken outliers into
consideration, which may dramatically degrade the prediction performance. To
overcome this limitation, we propose an outlier-resilient QoS prediction method
in this paper. Our method utilizes Cauchy loss to measure the discrepancy
between the observed QoS values and the predicted ones. Owing to the robustness
of Cauchy loss, our method is resilient to outliers. We further extend our
method to provide time-aware QoS prediction results by taking the temporal
information into consideration. Finally, we conduct extensive experiments on
both static and dynamic datasets. The results demonstrate that our method is
able to achieve better performance than state-of-the-art baseline methods.Comment: 12 pages, to appear at the Web Conference (WWW) 202
A novel robot calibration method with plane constraint based on dial indicator
In pace with the electronic technology development and the production
technology improvement, industrial robot Give Scope to the Advantage in social
services and industrial production. However, due to long-term mechanical wear
and structural deformation, the absolute positioning accuracy is low, which
greatly hinders the development of manufacturing industry. Calibrating the
kinematic parameters of the robot is an effective way to address it. However,
the main measuring equipment such as laser trackers and coordinate measuring
machines are expensive and need special personnel to operate. Additionally, in
the measurement process, due to the influence of many environmental factors,
measurement noises are generated, which will affect the calibration accuracy of
the robot. Basing on these, we have done the following work: a) developing a
robot calibration method based on plane constraint to simplify measurement
steps; b) employing Square-root Culture Kalman Filter (SCKF) algorithm for
reducing the influence of measurement noises; c) proposing a novel algorithm
for identifying kinematic parameters based on SCKF algorithm and Levenberg
Marquardt (LM) algorithm to achieve the high calibration accuracy; d) adopting
the dial indicator as the measuring equipment for slashing costs. The enough
experiments verify the effectiveness of the proposed calibration algorithm and
experimental platform
Highly-Accurate Electricity Load Estimation via Knowledge Aggregation
Mid-term and long-term electric energy demand prediction is essential for the
planning and operations of the smart grid system. Mainly in countries where the
power system operates in a deregulated environment. Traditional forecasting
models fail to incorporate external knowledge while modern data-driven ignore
the interpretation of the model, and the load series can be influenced by many
complex factors making it difficult to cope with the highly unstable and
nonlinear power load series. To address the forecasting problem, we propose a
more accurate district level load prediction model Based on domain knowledge
and the idea of decomposition and ensemble. Its main idea is three-fold: a)
According to the non-stationary characteristics of load time series with
obvious cyclicality and periodicity, decompose into series with actual economic
meaning and then carry out load analysis and forecast. 2) Kernel Principal
Component Analysis(KPCA) is applied to extract the principal components of the
weather and calendar rule feature sets to realize data dimensionality
reduction. 3) Give full play to the advantages of various models based on the
domain knowledge and propose a hybrid model(XASXG) based on Autoregressive
Integrated Moving Average model(ARIMA), support vector regression(SVR) and
Extreme gradient boosting model(XGBoost). With such designs, it accurately
forecasts the electricity demand in spite of their highly unstable
characteristic. We compared our method with nine benchmark methods, including
classical statistical models as well as state-of-the-art models based on
machine learning, on the real time series of monthly electricity demand in four
Chinese cities. The empirical study shows that the proposed hybrid model is
superior to all competitors in terms of accuracy and prediction bias
Smart Monitoring and Control in the Future Internet of Things
The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems