In this paper, we consider the delay partial differential equation of three dimensions with constant
coefficients. We established the alternating direction difference scheme by the standard finite difference
method, gave the order of convergence of the format and the expression of the difference scheme truncation
In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in
order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small
and medium sized databases but they suffer from serious problems when it comes to using them with large
databases like those encountered in biometrics applications. To address this issue, we proposed in this work a
new training algorithm for PNNs to reduce the hidden layer’s size and avoid over-fitting at the same time.
The proposed training algorithm generates networks with a smaller hidden layer which contains only
representative examples in the training data set. Moreover, adding new classes or samples after training does
not require retraining, which is one of the main characteristics of this solution. Results presented in this work
show a great improvement both in the processing speed and generalization of the proposed classifier. This
improvement is mainly caused by reducing significantly the size of the hidden layer.
As cloud computing has become a widespread technology, malicious attackers can obtain the private
information of users that has leaked from the service provider in the outsourced databases. To resolve the
problem, it is necessary to encrypt the database prior to outsourcing it to the service provider. However, the
most existing data encryption schemes cannot process a query without decrypting the encrypted databases.
Moreover, because the amount of the data is large, it takes too much time to decrypt all the data. For this,
Programmable Order-Preserving Secure Index Scheme (POPIS) was proposed to hide the original data while
performing query processing without decryption. However, POPIS is weak to both order matching attacks
and data count attacks. To overcome the limitations, we propose a group order-preserving data encryption
scheme (GOPES) that can support efficient query processing over the encrypted data. Since GOPES can
preserve the order of each data group by generating the signatures of the encrypted data, it can provide a high
degree of data privacy protection. Finally, it is shown that GOPES is better than the existing POPIS, with
respect to both order matching attacks and data count attacks.
Vibrotactile feedback is widely used in designing non-visual interactions on mobile phones, such as message
notification, non-visual reading, and blind use. In this work, novel vibrotactile codes are presented to
implement a non-visual text reading system for mobile phones. The 26 letters of the English alphabet are
formed in an index table with four rows and seven columns, and each letter is mapped using the codes of
vibrations. Two kinds of vibrotactile codes are designed with the actuator’s on and off states and with specific
lengths (short and long) assigned to each state. To improve the efficiency of tactile perception and user
satisfaction, three user experiments are conducted. The first experiment explores the maximum number of
continuous vibrations and minimum vibration time of the actuator’s on and off states that the human can
perceive. The second experiment determines the minimum interval between continuous vibrations. The
vibrotactile reading system is designed and evaluated in the third experiment according to the results of the
two preceding experiments. Results show that the character reading accuracy reaches 91.7% and the character
reading speed is approximately 617.8 ms. Our method has better reading efficiency and is easier to learn than
the traditional Braille coding method.
A multiple classification system based on a new boosting technique has been approached utilizing different
biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting,
palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric
traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is
comprised of three different super-classifiers to individually perform person identification. The individual
classifiers corresponding to each super-classifier in their turn identify different biometric features and their
conclusions are integrated together in their respective super-classifiers. The decisions from individual superclassifiers
are integrated together through a mega-super-classifier to perform the final conclusion using
programming based boosting. The mega-super-classifier system using different super-classifiers in a compact
form is more reliable than single classifier or even single super-classifier system. The system has been
evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix
for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different
performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable.
Thereby making the system is efficient and effective.
This paper presents a physical storage design method for image access structures using transformation
techniques of multidimensional file organizations in image information systems. Physical storage design is the
process of determining the access structures to provide optimal query processing performance for a given set
of queries. So far, there has been no such attempt in the image information system. We first show that the
number of pages to be accessed decreases as the shape of the given retrieval query region and that of the data
page region become similar in the transformed domain space. Using these properties, we propose a method
for finding an optimal image access structure by controlling the shapes of the page regions. For the
performance evaluation, we have performed many experiments with a multidimensional file organization
using transformation techniques. The results indicate that our proposed method is at least one to maximum
five times faster than the conventional method according to the query pattern within the scope of the
experiments. The result confirms that the proposed physical storage design method is useful in a practical way.
Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the
examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed
simultaneously. However, microarray data have very little sample data and high data dimensionality.
Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction
can eliminate redundancy of data; thus, features used in classification are features that only have a high
correlation with their class. There are two types of dimensional reduction, namely feature selection and
feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection.
The proposed approach can be used to categorize features that have the same characteristics in one cluster, so
that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm
such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected
and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the
simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate
Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is
therefore higher than the approach using Random Forest without clustering.
The object classification using the images’ contents is a big challenge in computer vision. The superpixels’
information can be used to detect and classify objects in an image based on locations. In this paper, we
proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words
(BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it
according to the region score. Further, this information is used to extract local and global features using a
hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the
classification accuracy, the feature fusion technique is applied to combine local and global features vectors
through weight parameter. The support vector machine classifier is a supervised algorithm is used for
classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007
(VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in
high-quality class for independent objects’ locations with a mean average best overlap (MABO) of 0.833 at
1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it
is proved that it gave the better classification results for the non-rigid classes.
Typical search engines may not be the most efficient means of returning images in accordance with user
requirements. With the help of semantic web technology, it is possible to search through images more
precisely in any required domain, because the images are annotated according to a custom-built ontology.
With appropriate annotations, a search can then, return images according to the context. This paper reports
on the design of a tourism ontology relevant to touristic images. In particular, the image features and the
meaning of the images are described using various properties, along with other types of information relevant
to tourist attractions using the OWL language. The methodology used is described, commencing with
building an image and tourism corpus, creating the ontology, and developing the search engine. The system
was tested through a case study involving the western region of Thailand. The user can search specifying the
specific class of image or they can use text-based searches. The results are ranked using weighted scores based
on kinds of properties. The precision and recall of the prototype system was measured to show its efficiency.
User satisfaction was also evaluated, was also performed and was found to be high.
Interval-valued neutrosophic hesitant fuzzy set (IVNHFS) is an extension of neutrosophic set (NS) and
hesitant fuzzy set (HFS), each element of which has truth membership hesitant function, indeterminacy
membership hesitant function and falsity membership hesitant function and the values of these functions lie
in several possible closed intervals in the real unit interval [0,1]. In contrast with NS and HFS, IVNHFS can be
more flexibly used to deal with uncertain, incomplete, indeterminate, inconsistent and hesitant information.
In this study, I propose the novel correlation coefficient of IVNHFSs and my paper discusses its properties.
Then, based on the novel correlation coefficient, I develop an approach to deal with multi-attribute decisionmaking
problems within the framework of IVNHFS. In the end, a practical example is used to show that the
approach is reasonable and effective in dealing with decision-making problems.
Nowadays, the number of pets in the Republic of Korea (ROK) is continuously growing, and people’s
perception of animals is changing. Accordingly, new systems and services for them are emerging. Despite
such changes, there are still many serious problems such as animal cruelty, abandonment, and factory-type
breeding places. In this study, we have conducted a research on the design of a humane animal care system
and its implementation with Java. The methodology involved in the design will enable managing animals'
safety and health by systematically categorizing and studying each health-related issue for protection.
Moreover, with this methodology, animals can avert risks through periodic examinations, and the analyzed
data will be useful in managing animals efficiently. Thus, this paper proposes a system that monitors whether
the owners actually carry out such obligation. Authors expect this convenient, easily accessible system to lead
to a more humane approach to the animals they own. The authors plan to establish an animal care network
together with local animal associations for the active promotion of the system implemented in this study, in
the hope that the network will be extended nationwide.
Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the
music improvisation process and can solve different optimization problems. In order to further improve the
performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters
including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are
optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved
method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four
complex function optimization and pressure vessel optimization problems were simulated using the
optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and
exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to
find global search and evolutionary speed. Optimization effect simulation results are satisfactory.
Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many textmining
applications such as document summarization and clustering. In this paper, we propose to use two
novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns
that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set
to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share
very few similar patterns and stylistic features while non-keyphrase candidates often share many similar
patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence
graph that could not connect two words that are syntactically related and placed far from each other in a
sentence while the dependency graph can do so. In experiments, we have compared the performances with
different settings of the graphs (co-occurrence and dependency), and with the existing method results.
Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the
To support and protect firefighters in the scene of disaster, this research suggests an Internet of Things (IoT)
system architecture that can be configurable and applicable to firefighting and rescue in various disaster
situations. The proposed approach provides increased adaptability and reusability of systems compared to
existing approaches. To validate the feasibility of the approach, a system of systems based on the architecture
was developed and successfully tested for a specific firefighting and rescue scenario in a given test
For the disadvantages of multi-scale geometric analysis methods such as loss of definition and complex
selection of rules in image fusion, an improved multi-focus image fusion method is proposed. First, the initial
fused image is quickly obtained based on the lifting stationary wavelet transform, and a simple normalized cut
is performed on the initial fused image to obtain different segmented regions. Then, the original image is
subjected to NSCT transformation and the absolute value of the high frequency component coefficient in
each segmented region is calculated. At last, the region with the largest absolute value is selected as the postfusion
region, and the fused multi-focus image is obtained by traversing each segment region. Numerical
experiments show that the proposed algorithm can not only simplify the selection of fusion rules, but also
overcome loss of definition and has validity.
With the increasing of urban electricity demand, making the most use of the power cable carrying capacity has become an important task in power grid system. Contrary to the rated ampacity obtained under extremely conservative conditions, this paper presents the various steady value of cable ampacity by using the changing surrounding parameters under operation, which is based on cable ampacity calculation equation under the IEC-60287 standard. To some degree, the cable ampacity analysis of actual surroundings improves the transmission capacity of cables. This paper reveals the factors that influence cable ampacity such as insulating layer thickness, allowable long-term conductor temperature, the ambient temperature, soil thermal resistance coefficient, and so on, then gives the class of the influence of these parameters on the ampacity, which plays a great role in accurately calculating the real-time ampacity and improving the utilization rate of cable in the complex external environment condition. Furthermore, the transient thermal rating of the cable is analyzed in this paper, and temperature variation of the conductor under different overload conditions is discussed, which provides effective information for the operation and control of the system.
The paper presents a novel embedded multifunctional media sever (EMMS) for mobile devices to receive various media programs. Being different from other contemporary system research, the paper mainly studies how to design a reception solution for terrestrial digital television (DTV) on mobile devices and how to enable mobile devices can receive DTV program, enjoy video-on-demand (VOD), achieve video surveillance and relay Internet video program via local Wi-Fi simultaneously. In the system design, we integrate broadcasting-terrestrial DTV tuner, streaming media re-transmission system, VOD disk, video camera and access interface to the Internet into EMMS, which can either receive terrestrial DTV radio signals and demodulate out digital transport stream (TS), or can read streaming media bit-stream from VOD disk, surveillance camera or access interface to the Internet. The experimental results show the proposed system is stable and quality-efficient. Comparing with the other systems, the proposed system has the least packet loss rate and response time.
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)