• Computers and Concrete
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• v.9 no.5
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• pp.375-387
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• 2012

The objective of this investigation was to develop rules for automatic categorization of concrete quality using selected artificial intelligence methods based on machine learning. The range of tested materials included concrete containing a new waste material - solid residue from coal combustion in fluidized bed boilers (CFBC fly ash) used as additive. The rapid chloride permeability test - Nordtest Method BUILD 492 method was used for determining chloride ions penetration in concrete. Performed experimental tests on obtained chloride migration provided data for learning and testing of rules discovered by machine learning techniques. It has been found that machine learning is a tool which can be applied to determine concrete durability. The rules generated by computer programs AQ21 and WEKA using J48 algorithm provided means for adequate categorization of plain concrete and concrete modified with CFBC fly ash as materials of good and acceptable resistance to chloride penetration.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.280-285
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• 2009

This paper introduced an automatic generation method of falling motions for humanoid robots to minimize a damage. The proposed approach used a PGA based optimization technique to find a set of joint trajectories which minimize a damage of the falling over and down. Injection-migration PGA technique is introduced and compared with EMO and various migration topologies. To verify the proposed method, experiments for falling motions were executed for Sony QRIO robot in Webots simulation environments.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.157-162
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• 2010

We present a new distributed genetic algorithm that can be used to extract useful information from distributed, large data over the network. The main idea of the proposed algorithms is to determine how many and which individuals move between subpopulations at each site adaptively. In addition, we present a method to help individuals from other subpopulations not be weeded out but adapt to the new subpopulation. We used six data sets from UCI Machine Learning Repository to compare the performance of our approach with that of the single, centralized genetic algorithm. As a result, the proposed algorithm produced better performance than the single genetic algorithm in terms of the classification accuracy with the feature subsets.

• Journal of Environmental Science International
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• v.23 no.8
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• pp.1495-1505
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• 2014

The objective of this study was to evaluate survival rate and fish movement (migration) using a tagging approach of passive integrated transponder (PIT) in Juksan Weir, which was constructed as a four major river restoration projects. For this study, survival rates of each fish species and the mobility of fish individuals were analyzed during 2 weeks by the insertion of PIT tags to various fish species in the laboratory. According to tagging tests in the laboratory, the survival rate 37.5% (30 survivals of 80 individuals) after the insertion of PIT tags. The survival rate of Carassius auratus and Hemibarbus labeo was 100% and 80% after the insertion of the tags, respectively, whereas it was only 13.3% for Zacco platypus. In the field experiments of Juksan Weir, 6 species and 157 individuals from 8 species (563 individuals) were detected in the fixed automatic data-logging system, indicating a detection rate of 27.9% in the fishway of Juksan Weir. In the meantime, some species with no or low detection rates in the fixed automatic data-logging system were turn out to be stagnant-type species, which prefer stagnant or standing water to live.

• Journal of Navigation and Port Research
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• v.32 no.4
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• pp.271-277
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• 2008

When performing steering by an autopilot (automatic steering gear), a sensitivity adjustment is mainly determined by P value and D value. These values differ in the optimal combination by model of ship and external forces. This research was carried out simulation case studies and examined movement of Pure Car Carrier, which easily received ship by wind pressure influence in low speed We investigated the relation of horizontal migration(transfer) of ship's body and P-D value. Based on it, four parameters of P-D at approaching berth could be suggested Hence there were suggestions of parameters; Distance to maximum lee point, Time to maximum lee point, Time to return to original course and Time to 300th second. The correlation of these parameters and P-D value were also considered. As a result, we think that this index, like formulated P-D, leads to an easy and safe navigation by utilizing these indices.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.11-21
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• 2013

Recently operating a large scale computing resource like a data center becomes easier because of the virtualization technology that virtualize servers and enable flexible resource provision. The most of public cloud services provides automatic scaling in the form of scale-in or scale-out and these scaling approaches works well to satisfy the service level agreement (SLA) of users. However, a novel scaling approach is required to operate private clouds that has smaller amount of computing resources than vast resources of public clouds. In this paper, we propose a hybrid server scaling architecture and related algorithms using both scale-in and scale-out to achieve higher resource utilization rate for private clouds. We uses dynamic resource allocation and live migration to run our proposed algorithm. Our propose system aims to provide a fine-grain resource scaling by steps. Thus private cloud systems are able to keep stable service and to reduce server management cost by optimizing server utilization. The experiment results show that our proposed approach performs better in resource utilization than the scale-out approach based on the number of users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.10
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• pp.712-720
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• 2001

Algorithms from the research of the layout migration were proposed in the paper. These are automatic recognition algorithm for the VLSI devices from it, graph based construction algorithm to maintain the constraints, dependencies, and design rule between the devices, and high speed compaction algorithm to reduce size of the VLSI area and reuse the design with compacted size for the new technology. Also, this paper describes that why proposed algorithms are essential for the era of the SoC (System on a Chip), design reuse, and IP DB, which are the big concerns in these days. In addition to introduce our algorithms, the benchmark showed that our performance is superior by 27 times faster than that of the commercial one, and has better efficiency by 3 times in disk usage.

• Research in Plant Disease
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• v.24 no.3
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• pp.202-212
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• 2018

Major viruses infecting rice are transmitted by planthoppers such as small brown planthopper (SBPH), brown planthopper (BPH) and white-backed planthopper (WBPH). In this study, we investigated planthoppers captured during 2015 to 2017 by a smart sky net trap (SSNT) system installed in 40 areas in Korea, which is an automatic, rapid and real-time insect surveillance system. The average rates of captured migration plnathoppers was 27.5%, 17.2%, 15.3% and 10.9% in Chungcheongnamdo, Jeollanamdo, Jeollabukdo and Gyeonggido, orderly. The highly migrated month was July for SBPH, July to August for WBPH and August for BPH. To investigate the viruliferous rates of planthoppers of rice during 2015 to 2017, we performed RT-PCR using specific primers for each rice virus. RBSDV was detected from 0.4% in SBPH, while no viruses were detected in BPH and SBPH. Rice planthoppers exist all around in Asia. They can move long distance by wind from southern countries to Korea. Monitoring the migration of rice planthoppers and their viruliferous rates is important to prevent the outbreaks of rice virus diseases.