• The Journal of Korean Association of Computer Education
• /
• v.22 no.6
• /
• pp.11-18
• /
• 2019

Software education is required from elementary schools to prepare students for the fourth industrial revolution, which aims to improve algorithmic thinking. In general, teaching is divided into two stages: using a flowchart to design algorithms and implementing them through programming. However, converting a flowchart into code and checking the results in an educational programming tool is time consuming and requires additional programming activities. This study proposes a tangible coding tool that enables elementary students to convert algorithms designed at the unplugged activity into educational programming tool codes. This tool was developed in order for students to design algorithms at the level of assembling paper blocks and input them into a programming tool by taking a picture. Sixth graders were participated in this activity to evaluate its usability.

• Journal of KIISE:Software and Applications
• /
• v.36 no.7
• /
• pp.560-570
• /
• 2009

RFID, Radio Frequency Identification, is a technology of automated identification replacing bar-code. RFID technology has advantages that it recognizes fast and it is strong to contamination using wireless communication. However, there are difficult problems that should be solved for popularization of RFID. Among of these, tag anti-collision problem is dealed in this paper. It affected the performance of RFID system directly. This paper analyzes conventional algorithms and proposes new algorithms of tag anti-collision. The algorithm proposed was composed of appropriate properties to each phase of distribution and recognition as hybrid between ALOHA-based algorithm and QT-based algorithm. At phase of distribution, the number of tags recognizing at a frame was reduced using ALOHA-based algorithm. It addressed the delay problem because of deep depth of tree. At phase of recognition, it solved ALOHA-based chronic problem that couldn't recognize all the tags sometimes. Moreover, QTR algorithm that recognize by reversed tag IDs was adopted for the performance. The FQTR algorithm proposed in this paper showed brilliant performance as compared with convention algorithms by simulation.

• Journal of KIISE:Software and Applications
• /
• v.36 no.7
• /
• pp.580-589
• /
• 2009

Today, most web pages are being created in the blogspace or evolving into the blogspace. A blog entry (blog page) includes non-traditional features of Web pages, such as trackback links, bloggers' authority, tags, and comments. Thus, the traditional rank algorithms are not proper to evaluate blog entries because those algorithms do not consider the blog specific features. In this paper, a new algorithm called "Blog-Rank" is proposed. This algorithm ranks blog entries by calculating bloggers' reputation scores, trackback scores, and comment scores based on the features of the blog entries. This algorithm is also applied to searching for information related to the users' queries in the blogspace. The experiment shows that it finds the much more relevant information than the traditional ranking algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.1C
• /
• pp.112-121
• /
• 2002

In this paper, we present a design of a hash processor for data security systems. Two standard hash algorithms, Sha-1(American) and HAS-1600(Korean), are implemented on a single hash engine to support real time processing of the algorithms. The hash processor can also be used as a PRNG(Pseudo-random number generator) by utilizing SHA-1 hash iterations, which is being used in the Intel software library. Because both SHA-1 and HAS-160 have the same step operation, we could reduce hardware complexity by sharing the computation unit. Due to precomputation of message variables and two-stage pipelined structure, the critical path of the processor was shortened and overall performance was increased. We estimate performance of the hash processor about 624 Mbps for SHA-1 and HAS-160, and 195 Mbps for pseudo-random number generation, both at 100 MHz clock, based on Samsung 0.5um CMOS standard cell library. To our knowledge, this gives the best performance for processing the hash algorithms.

• Journal of KIISE:Software and Applications
• /
• v.32 no.10
• /
• pp.974-983
• /
• 2005

In genetic algorithms with lotus-based encoding, static gene reordering is to locate the highly related genes closely together. It helps the genetic algorithms to create and preserve the schema of high-quality effectively. In this paper, we propose a static reordering framework for linear locus-based encoding. It differs from existing reorderings in that it is independent of problem-specific knowledge. It makes a complete graph where weights represent the interelationship between each pair of genes. And, it transforms the graph into a unweighted sparse graph by choosing the edges having relatively high weight. It finds a gene reordering by graph search method. Through the wide experiments about several problems, the method proposed in this paper shows significant performance improvement as compared with the genetic algorithm that does not rearrange genes.

• International Journal of Highway Engineering
• /
• v.14 no.3
• /
• pp.163-171
• /
• 2012

Demand Responsive Transit (DRT) services are becoming necessary as part of not only alternative transportation means for elderly and mobility impaired passengers, but also sustainable and flexible transportation options in urban area due to the development of communication technologies and Location Based Services (LBS). It is difficult to investigate the system performance regarding vehicle operational schemes and vehicle routing algorithms due to the lack of commercial software to support door-to-door vehicle simulation for larger area. This study proposes a simulation framework to evaluate innovative and flexible transit systems focusing on various vehicle routing algorithms, which describes data-type requirements for simulating door-to-door service on demand. A simulation framework is applied to compare two vehicle dispatch algorithms, Nearest Vehicle Dispatch (NVD) and Insertion Heuristic (IH) for real-time shared-taxi service in Seoul. System productivity and efficiency of the shared-taxi service are investigated, comparing to the conventional taxi system.

• Journal of KIISE:Software and Applications
• /
• v.32 no.11
• /
• pp.1071-1083
• /
• 2005

By estimating probability distributions of the good solutions in the current population, some researchers try to find the optimal solution more efficiently. Particularly, finite mixtures of distributions have a very useful role in dealing with complex problems. However, it is difficult to choose the number of components in the mixture models and merge superior partial solutions represented by each component. In this paper, we propose a new continuous evolutionary optimization algorithm with distribution estimation by variational Bayesian mixtures of factor analyzers. This technique can estimate the number of mixtures automatically and combine good sub-solutions by sampling new individuals with the latent variables. In a comparison with two probabilistic model-based evolutionary algorithms, the proposed scheme achieves superior performance on the traditional benchmark function optimization. We also successfully estimate the parameters of S-system for the dynamic modeling of biochemical networks.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.9
• /
• pp.401-410
• /
• 2016

3D registration is a computer vision technique of aligning multi-view range images with respect to a reference coordinate system. Various 3D registration algorithms have been introduced in the past few decades. Iterative Closest Point (ICP) is one of the widely used 3D registration algorithms, where various modifications are available nowadays. In the ICP-based algorithms, the closest points are considered as the corresponding points. However, this assumption fails to find matching points accurately when the initial pose between point clouds is not sufficiently close. In this paper, we propose a new method to solve this problem using the 6D distance (3D color space and 3D Euclidean distances). Moreover, a color segmentation-based adaptive sampling technique is used to reduce the computational time and improve the registration accuracy. Several experiments are performed to evaluate the proposed method. Experimental results show that the proposed method yields better performance compared to the conventional methods.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.11
• /
• pp.499-508
• /
• 2015

High utility itemset(HUI) mining refers to the discovery of itemsets with high utilities which are not less than a user-specified minimum utility threshold, by considering both the quantities and weight factors of items in a transaction database. Recently the utility-list based HUI mining algorithms have been proposed to avoid numerous candidate itemsets and the algorithms need the costly join operations. In this paper, we propose a new HUI mining algorithm, using the utility-list with additional attributes of transaction utility and common utility of itemsets. The new algorithm decreases the number of join operations and efficiently prunes the search space. Experimental results on both synthetic and real datasets show that the proposed algorithm outperforms other recent algorithms in runtime, especially when datasets are dense or contain many long transactions.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.11
• /
• pp.455-464
• /
• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.