Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution. This paper introduces the diverse kinds of approaches to subjects that tackle diverse kinds of research fields such as model-based MS approach, deep neural network model, image edge detection approach, cross-layer optimization model, LSSVM approach, screen design approach, CPU-GPU hybrid approach and so on. The research on Superintelligence and superconnection for IoT and big data is also described such as ‘superintelligence-based systems and infrastructures’, ‘superconnection-based IoT and big data systems’, ‘analysis of IoT-based data and big data’, ‘infrastructure design for IoT and big data’, ‘artificial intelligence applications’, and ‘superconnection-based IoT devices’.
To deal with the problems of occlusion, pose variations and illumination changes in the object tracking
system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper.
Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS
algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression
model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally,
the geometric center of the tracked areas is considered as the object’s position. The proposed algorithm is
evaluated on several classical videos. The experimental results show that the regression model weighted multitemplates
MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose
Video captioning refers to the process of extracting features from a video and generating video captions using
the extracted features. This paper introduces a deep neural network model and its learning method for
effective video captioning. In this study, visual features as well as semantic features, which effectively express
the video, are also used. The visual features of the video are extracted using convolutional neural networks,
such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction
network proposed in this paper. Further, an attention-based caption generation network is proposed for
effective generation of video captions using the extracted features. The performance and effectiveness of the
proposed model is verified through various experiments using two large-scale video benchmarks such as the
Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).
Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the
pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper.
Combing the image local and global structure information, the triqubit superposition states are used to
represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the
improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is
characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to
achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is
outputted according to the quantum measurement, local gradient maximization and neighborhood chain
code searching. Compared with other methods, the simulation experiments indicate that our algorithm has
less pseudo edges and higher edge detection accuracy.
Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the
networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which
source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size,
end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia
session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise
power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and
calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink
based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets
that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path
which contains the information such as optimal power vector, remaining battery energy vector and number of
packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving
can be accomplished with the proposed cross layer power control algorithm.
Recently, Cyber Physical System (CPS) is one of the core technologies for realizing Internet of Things (IoT).
The CPS is a new paradigm that seeks to converge the physical and cyber worlds in which we live. However,
the CPS suffers from certain CPS issues that could directly threaten our lives, while the CPS environment,
including its various layers, is related to on-the-spot threats, making it necessary to study CPS security.
Therefore, a survey-based in-depth understanding of the vulnerabilities, threats, and attacks is required of
CPS security and privacy for IoT. In this paper, we analyze security issues, threats, and solutions for IoT-CPS,
and evaluate the existing researches. The CPS raises a number challenges through current security markets
and security issues. The study also addresses the CPS vulnerabilities and attacks and derives challenges.
Finally, we recommend solutions for each system of CPS security threats, and discuss ways of resolving
potential future issues.
High-performance computing (HPC) provides to researchers a powerful ability to resolve problems with
intensive computations, such as those in the math and medical fields. When an HPC platform is provided as a
service, users may suffer from unexpected obstacles in developing and running applications due to restricted
development environments and dependencies. In this context, operating system level virtualization can be a
solution for HPC service to ensure lightweight virtualization and consistency in Dev-Ops environments.
Therefore, this paper proposes three types of typical HPC structure for container environments built with
HPC container and Docker. The three structures focus on smooth integration with existing HPC job
framework, message passing interface (MPI). Lastly, the performance of the structures is analyzed with High
Performance Linpack benchmark from the aspect of performance degradation in network communications
An image fusion method is proposed on the basis of depth model segmentation to overcome the
shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep
Boltzmann machine is used to perform the priori learning of infrared and visible target and background
contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative
algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then,
the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the
corresponding rules are used to integrate the coefficients in the light of the segmented background contour.
Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of
MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background
contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits
in objective quantitative indicators.
To build a successful information system, design and development should be carried out from the enterprise
perspective. A complicated business is represented in various ways as technology advances, and many
development methodologies have been studied from the viewpoint of technology and development. Each
domain is independently designed and developed from the enterprise perspective, but there would be
inclusive parts due to the integrated process wherein the definition, design, and development of business are
carried out, and the design is done based on the designer's experience. This study would like to address the
technique of designing screens based on the business process of the applications derived from the business. It
designs the screens that appear when actual applications are completed, including how the data transfer
process in the derived business process is represented and operated on the relevant screens. It designs the
screen which is displayed when the actual application is completed and how the data transfer process in the
derived business process is represented and operated on the relevant screen. In addition, it designs the DFD
representing the overall flow of data for each business to represent the movement procedure between screens
in general. Through the design method proposed in this study, the client's requirement could be confirmed to
reduce the cost for redevelopment, the problem of communication between designers and developers with
various experiences could be reduced, and an efficient design procedure could be provided to persons who
lack design experience.
Securing objects in the Internet of Things (IoT) is essential. Authentication model is one candidate to secure
an object, but it is only limited to handle a specific type of attack such as Sybil attack. The authentication
model cannot handle other types of attack such as trust-based attacks. This paper proposed two-phase
security protection for objects in IoT. The proposed method combined authentication and statistical models.
The results showed that the proposed method could handle other attacks in addition to Sybil attacks, such as
bad-mouthing attack, good-mouthing attack, and ballot stuffing attack.
Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs
such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information
on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms
are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest
information. However, microblogs have word limits, and it has there is not enough information to analyze for
content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only
measures the similarity between the data represented as a vector space model, but also measures the semantic
similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on
the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering
Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned
ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding
terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of
environment information analysis, we develop a CPU-GPU hybrid system of automatic environment
perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of
three functional modules, namely, multi-sensor data collection and pre-processing, environment perception,
and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing
function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion
information into a global terrain model after filtering redundant and noise data according to the redundancy
removal principle. In the environment perception module, the registered discrete points are clustered into
ground surface and individual objects by using a ground segmentation method and a connected component
labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed
and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates
the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the
captured video images. Texture meshes and color particle models are used to reconstruct the ground surface
and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel
computation method to implement the applied computer graphics and image processing algorithms in parallel.
In recent times, Natural User Interface/Natural User Experience (NUI/NUX) technology has found
widespread application across a diverse range of fields and is also utilized for controlling unmanned aerial
vehicles (UAVs). Even if the user controls the UAV by utilizing the NUI/NUX technology, it is difficult for
the user to easily control the UAV. The user needs an autopilot to easily control the UAV. The user needs a
flight path to use the autopilot. The user sets the flight path based on the waypoints. UAVs normally fly
straight from one waypoint to another. However, if flight between two waypoints is in a straight line, UAVs
may collide with obstacles. In order to solve collision problems, flight records can be utilized to adjust the
generated path taking the locations of the obstacles into consideration. This paper proposes a natural path
generation method between waypoints based on flight records collected through UAVs flown by users.
Bayesian probability is utilized to select paths most similar to the flight records to connect two waypoints.
These paths are generated by selection of the center path corresponding to the highest Bayesian probability.
While the K-means algorithm-based straight-line method generated paths that led to UAV collisions, the
proposed method generates paths that allow UAVs to avoid obstacles.
In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn’t need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFace1, which uses pairs of face images as inputs and maps them to target space so that the L2 norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.
Recently, computational intelligence has received a lot of attention from researchers due to its potential
applications to artificial intelligence. In computer science, computational intelligence refers to a machine’s
ability to learn how to compete various tasks, such as making observations or carrying out experiments. We
adopted a computational intelligence solution to monitoring residual resources in cloud computing environments.
The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so
that the post migration performance of a virtual machine is as consistent with the pre-migration performance
as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual
machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of
service and service level agreement during the migration. We carried out a number of experimental evaluations
to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that
the proposed scheme intelligently measures the similarities between virtual machines in cloud computing
environments without causing performance degradation, whilst preserving the quality of service and service
Intelligent human identification using face information has been the research hotspot ranging from Internet
of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent
access control. Since 2D face images are usually captured from a long distance in an unconstrained environment,
to fully exploit this advantage and make human recognition appropriate for wider intelligent applications
with higher security and convenience, the key difficulties here include gray scale change caused by
illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by
pose or expression variation. To conquer these, many solutions have been proposed. However, most of them
only improve recognition performance under one influence factor, which still cannot meet the real face
recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture
to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted
on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed
algorithm exhibits excellent discriminative ability compared with other existing algorithms.
In this paper, we propose an improved model to provide users with a better long-term prediction of
waterworks operation data. The existing prediction models have been studied in various types of models such
as multiple linear regression model while considering time, days and seasonal characteristics. But the existing
model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient.
Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to
predict data of water purification plant because its time series prediction is highly reliable. However, it is
necessary to reflect the correlation among various related factors, and a supplementary model is needed to
improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced
to select various input variables that have a necessary correlation and to improve long term prediction rate,
thus increasing the prediction rate through the LSTM predictive value and the combined structure. In
addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM,
which then confirms the data as the final predicted outcome.
There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.