Recently, big data and artificial intelligence (AI) based on communication systems have become one of the
hottest issues in the technology sector, and methods of analyzing big data using AI approaches are now
considered essential. This paper presents diverse paradigms to subjects which deal with diverse research areas, such as image segmentation, fingerprint matching, human tracking techniques, malware distribution networks, methods of intrusion detection, digital image watermarking, wireless sensor networks, probabilistic neural networks, query processing of encrypted data, the semantic web, decision-making, software engineering, and so on.
In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of
the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi
entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection
algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy
and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At
the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the
local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast
experiments are implemented in different image database. The robustness of the proposed model for
segmentation of images with intensity inhomogeneity and complicated edges is verified.
This paper introduces an efficient fingerprint matching method based on multiple reference minutiae points.
First, we attempt to effectively align two fingerprints by employing multiple reference minutiae points.
However, the corresponding minutiae points between two fingerprints are ambiguous since a minutia of one
fingerprint can be a match to any minutia of the other fingerprint. Therefore, we introduce a novel method
based on linear classification concept to establish minutiae correspondences between two fingerprints. Each
minutiae correspondence represents a possible alignment. For each possible alignment, a matching score is
computed using minutiae and ridge orientation features and the maximum score is then selected to represent
the similarity of the two fingerprints. The proposed method is evaluated using fingerprint databases, FVC2002
and FVC2004. In addition, we compare our approach with two existing methods and find that our approach
outperforms them in term of matching accuracy, especially in the case of non-linear distorted fingerprints.
Furthermore, the experiments show that our method provides additional advantages in low quality fingerprint
images such as inaccurate position, missing minutiae, and spurious extracted minutiae.
A method for detecting foreign substances in mould based on scatter grams was presented to protect moulds
automatically during moulding production. This paper proposes a wavelet transform foreign detection method
based on Monte Carlo analysis mechanism to identify foreign objects in the tube. We use the Monte Carlo
method to evaluate the image, and obtain the width of the confidence interval by the deviation statistical gray
histogram to divide the image type. In order to stabilize the performance of the algorithm, the high-frequency
image and the low-frequency image are respectively drawn. By analyzing the position distribution of the pixel
gray in the two images, the suspected foreign object region is obtained. The experiments demonstrate the
effectiveness of our approach by evaluating the labeled data.
This study is concerned with the mechanism and structure of an optical microscope and an automatic multifocus
algorithm for automatically selecting sharp images from multiple foci of a cell. To obtain precise cell
images quickly, a z-axis actuator with a resolution of 0.1 ?m was designed to control an optical microscope
Moreover, a lighting control system was constructed to select the color and brightness of light that best suit the
object being viewed. Cell images are captured by the instrument and the sharpness of each image is determined
using Gaussian and Laplacian filters. Next, cubic spline interpolation and peak detection algorithms are applied
to automatically find the most vivid points among multiple images of a single object. A cancer cell imaging
experiment using propidium iodide staining confirmed that a sharp multipoint image can be obtained using
this microscope. The proposed system is expected to save time and effort required to extract suitable cell images
and increase the convenience of cell analysis.
Storage system often applies erasure codes to protect against disk failure and ensure system reliability and
availability. Liberation code that is a type of coding scheme has been widely used in many storage systems
because its encoding and modifying operations are efficient. However, it cannot effectively achieve fast recovery
from single disk failure in storage systems, and has great influence on recovery performance as well as response
time of client requests. To solve this problem, in this paper, we present HRSF, a Hybrid Recovery method for
solving Single disk Failure. We present the optimal algorithm to accelerate failure recovery process. Theoretical
analysis proves that our scheme consumes approximately 25% less amount of data read than the conventional
method. In the evaluation, we perform extensive experiments by setting different number of disks and chunk
sizes. The results show that HRSF outperforms conventional method in terms of the amount of data read and
failure recovery time.
Accurate detection, tracking and analysis of human movement using robots and other visual surveillance
systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in
shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which
involved scanning of various sizes of windows across an image. This paper concentrates on employing a stateof-
the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for
color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is
achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme
with validation phase. Localization of human region in each frame is performed by keypoints by casting votes
for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based
framework is used to detect voting behavior. The designed methodology is tested on the video sequences having
3 to 4 persons.
An innovative tone modeling framework based on deep neural networks in tone recognition was proposed in
this paper. In the framework, both the prosodic features and the articulatory features were firstly extracted as
the raw input data. Then, a 5-layer-deep deep belief network was presented to obtain high-level tone features.
Finally, support vector machine was trained to recognize tones. The 863-data corpus had been applied in
experiments, and the results show that the proposed method helped improve the recognition accuracy
significantly for all tone patterns. Meanwhile, the average tone recognition rate reached 83.03%, which is 8.61%
higher than that of the original method.
Malicious code distribution on the Internet is one of the most critical Internet-based threats and distribution
technology has evolved to bypass detection systems. As a new defense against the detection bypass technology
of malicious attackers, this study proposes the automated tracing of malicious websites in a malware
distribution network (MDN). The proposed technology extracts automated links and classifies websites into
malicious and normal websites based on link structure. Even if attackers use a new distribution technology,
website classification is possible as long as the connections are established through automated links. The use of
a real web-browser and proxy server enables an adequate response to attackers’ perception of analysis
environments and evasion technology and prevents analysis environments from being infected by malicious
code. The validity and accuracy of the proposed method for classification are verified using 20,000 links, 10,000
each from normal and malicious websites.
In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power
quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization
algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia
weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the
outset and effectively search locally later in a study, which improves the overall classification accuracy. The
experimental results show that the improved particle swarm optimization method is more accurate than a grid
search algorithm optimization and other improved particle swarm optimizations with regard to its classification
of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.
User-based and item-based approaches have been developed as the solutions of the movie recommendation
problem. However, the user-based approach is faced with the problem of sparsity, and the item-based approach
is faced with the problem of not reflecting users’ preferences. In order to solve these problems, there is a research
on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem
of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a combining method that
simplifies the combination equation of prior study. This method is relatively free from the problem of sparsity,
since it has less parameters to be calculated. Thus, it can get more accurate results by reflecting the users rating
to calculate the parameters. It is very fast to predict new movie ratings as well. In experiments for the proposed
method, the initial error is large, but the performance gets quickly stabilized after. In addition, it showed about
6% lower average error rate than the existing method using similarity.
Although the research of immune-based anomaly detection technology has made some progress, there are still
some defects which have not been solved, such as the loophole problem which leads to low detection rate and
high false alarm rate, the exponential relationship between training cost of mature detectors and size of selfantigens.
This paper proposed an intrusion detection method based on changes of antibody concentration in
immune response to improve and solve existing problems of immune based anomaly detection technology. The
method introduces blood relative and blood family to classify antibodies and antigens and simulate correlations
between antibodies and antigens. Then, the method establishes dynamic evolution models of antigens and
antibodies in intrusion detection. In addition, the method determines concentration changes of antibodies in
the immune system drawing the experience of cloud model, and divides the risk levels to guide immune
responses. Experimental results show that the method has better detection performance and adaptability than
With the recent advances of memory technologies, high-performance non-volatile memories such as nonvolatile
dual in-line memory module (NVDIMM) have begun to be used as an addition or an alternative to
server-side storages. When these memory bus-connected storages (MBSs) are installed over non-uniform
memory access (NUMA) servers, the distance between NUMA nodes and MBSs is one of the crucial factors
that influence file processing performance, because the access latency of a NUMA system varies depending on
its distance from the NUMA nodes. This paper presents the design and implementation of a high-performance
logical volume manager for MBSs, called MBS-LVM, when multiple MBSs are scattered over a NUMA server.
The MBS-LVM consolidates the address space of each MBS into a single global address space and dynamically
utilizes storage spaces such that each thread can access an MBS with the lowest latency possible. We
implemented the MBS-LVM in the Linux kernel and evaluated its performance by porting it over the tmpfs, a
memory-based file system widely used in Linux. The results of the benchmarking show that the write
performance of the tmpfs using MBS-LVM has been improved by up to twenty times against the original tmpfs
over a NUMA server with four nodes.
Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the
same features with fire, which may result in high false alarms rate. This paper presents a new video-based, image
processing forest fires detection method, which consists of four stages. First, a background-subtraction
algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using CIE
L?a?b? color space. Thirdly, special wavelet analysis is used to differentiate between actual fire and fire-like
objects, because candidate regions may contain moving fire-like objects. Finally, support vector machine is used
to classify the region of interest to either real fire or non-fire. The final experimental results verify that the
proposed method effectively identifies the forest fires.
Transmitting visual information over a broadcasting network is not only prone to a copyright violation but also
is a forgery. Authenticating such information and protecting its authorship rights call for more advanced data
encoding. To this end, electronic watermarking is often adopted to embed inscriptive signature in imaging data.
Most existing watermarking methods while focusing on robustness against degradation remain lacking of
measurement against security loophole in which the encrypting scheme once discovered may be recreated by
an unauthorized party. This could reveal the underlying signature which may potentially be replaced or forged.
This paper therefore proposes a novel digital watermarking scheme in temporal-frequency domain. Unlike
other typical wavelet based watermarking, the proposed scheme employed the Lorenz chaotic map to specify
embedding positions. Effectively making this is not only a formidable method to decrypt but also a stronger
will against deterministic attacks. Simulation report herein highlights its strength to withstand spatial and
frequent adulterations, e.g., lossy compression, filtering, zooming and noise.
A production system is a management system that supports all activities to perform production operations at
the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the
concept of onsite production systems to fit the entire system and its necessary functional composition. In this
study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step
model for the deployment of a smart manufacturing system by integrating essential functions. The smart
manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected
necessary functions through requirement analysis and benchmarking of advanced companies. The selected
production operation tasks are mapped to the functions of the system and configured into seven modules, and
the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These
methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm
that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead
time and WIP have reduced by about 50%.
During thermal power coal-fired boiler operation, it is very important to detect the pulverized coal
concentration in the air pipeline for the boiler combustion stability and economic security. Because the current
measurement methods used by power plants are often involved with large measurement errors and unable to
monitor the pulverized coal concentration in real-time, a new method is needed. In this paper, a new method
based on microwave circular waveguide is presented. High Frequency Electromagnetic Simulation (HFSS)
software was used to construct a simulation model for measuring pulverized coal concentration in power plant
pipeline. Theoretical analysis and simulation experiments were done to find the effective microwave emission
frequency, installation angle, the type of antenna probe, antenna installation distance and other important
parameters. Finally, field experiment in Jilin Thermal Power Plant proved that with selected parameters, the
measuring device accurately reflected the changes in the concentration of pulverized coal.
The nature of wireless transmission has made wireless sensor networks defenseless against various attacks. This
paper presents warning message counter method (WMC) to detect blackhole attack, grayhole attack and
sinkhole attack in wireless sensor networks. The objective of these attackers are, to draw the nearby network
traffic by false routing information and disrupt the network operation through dropping all the received packets
(blackhole attack), selectively dropping the received packets (grayhole and sinkhole attack) and modifying the
content of the packet (sinkhole attack). We have also attempted light weighted symmetric key cryptography to
find data modification by the sinkhole node. Simulation results shows that, WMC detects sinkhole attack,
blackhole attack and grayhole attack with less false positive 8% and less false negative 6%.
Currently, the crew working on a ship is required to carry a seafarer's book in most countries around the world,
including the Republic of Korea (ROK). Yet, many fishermen working in the international waters of the ROK
do not abide by this rule as the procedure of obtaining it is rather inconvenient or they do not understand the
necessity or the benefits of having it. Also, as the regulation of carrying the certificate has been strengthened, it
is important for them to avoid making a criminal record unintentionally. This study discusses the digitalization
of the seafarer’s book based on several security measures in addition to BLE Beacon-based positioning
technology, which can be useful for the e-Navigation. Normally, seamen’s certificates are recorded by the
captain, medical institution, or issuing authority and then kept in an onboard safe or a certificate cabinet. The
material of the certificates is a cloth that can withstand salinity as the certificate could be contaminated by mold.
In the past, the captains and their crews were uncooperative when the ROK’s maritime police tried to inspect
several ships simultaneously because of the time and cost involved. Thus, a system with which the maritime
police will be able to conveniently manage the crews is proposed.
The 2nd Journal of Information Processing Systems Awards
"Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City"
Pradip Kumar Sharma, Seo Yeon Moon and Jong Hyuk Park (Seoul National University of Science and Technology, Korea)
Publication (Corresponding Author)
Chengyou Wang (Shangdong University, China)