• Asian-Australasian Journal of Animal Sciences
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• v.31 no.7
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• pp.1007-1016
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• 2018

USA beef production is characterized by a diversity of climates, environmental conditions, animal phenotypes, management systems, and a multiplicity of nutritional inputs. The USA beef herd consists of more than 80 breeds of cattle and crosses thereof, and the industry is divided into distinct, but ofttimes overlapping sectors, including seedstock production, cow-calf production, stocker/backgrounding, and feedlot. Exception for male dairy calves, production is predominantly pastoral-based, with young stock spending relatively brief portions of their life in feedlots. The beef industry is very technology driven, utilizing reproductive management strategies, genetic improvement technologies, exogenous growth promoting compounds, vaccines, antibiotics, and feed processing strategies, focusing on improvements in efficiency and cost of production. Young steers and heifers are grain-based diets fed for an average of 5 months, mostly in feedlots of 1,000 head capacity or more, and typically are slaughtered at 15 to 28 months of age to produce tender, well-marbled beef. Per capita beef consumption is nearly 26 kg annually, over half of which is consumed in the form of ground products. Beef exports, which are increasingly important, consist primarily of high value cuts and variety meats, depending on destination. In recent years, adverse climatic conditions (i.e., draught), a shrinking agricultural workforce, emergence of food-borne pathogens, concerns over development of antimicrobial resistance, animal welfare/well-being, environmental impact, consumer perceptions of healthfulness of beef, consumer perceptions of food animal production practices, and alternative uses of traditional feed grains have become increasingly important with respect to their impact on both beef production and demand for beef products. Similarly, changing consumer demographics and globalization of beef markets have dictated changes in the types of products demanded by consumers of USA beef, both domestically and abroad. The industry is highly adaptive, however, and responds quickly to evolving economic signals.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1513-1525
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• 2016

Rapid and accurate phase synchronization is critical for the reliable control of grid-tied inverters. However, the commonly used software phase-locked loop methods do not always satisfy the need for high-speed and accurate phase synchronization under severe grid imbalance conditions. To address this problem, this study develops a novel open-loop phase locking scheme based on a synchronous reference frame. The proposed scheme is characterized by remarkable response speed, high accuracy, and easy implementation. It comprises three functional cascaded blocks: fast orthogonal signal generation block, fast fundamental-frequency positive sequence component construction block, and fast phase calculation block. The developed virtual orthogonal signal generation method in the first block, which is characterized by noise immunity and high accuracy, can effectively avoid approximation errors and noise amplification in a wide range of sampling frequencies. In the second block, which is the foundation for achieving fast phase synchronization within 3 ms, the fundamental-frequency positive sequence components of unsymmetrical grid voltages can be achieved with the developed orthogonal signal construction strategy and the symmetrical component method. The real-time grid phase can be consequently obtained in the third block, which is free from self-tuning closed-loop control and thus improves the dynamic performance of the proposed scheme. The proposed scheme is adaptive to severe unsymmetrical grid voltages with sudden changes in magnitude, phase, and/or frequency. Moreover, this scheme is able to eliminate phase errors induced by harmonics and random noise. The validity and utility of the proposed scheme are verified by the experimental results.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.639-646
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• 2003

Conventional two-tier databases have shown performance limitation in the presence of many concurrent clients. To this end, the three-tier architecture that exploits similarities in client's object access behavior has been proposed. In this system, clients are partitioned into clusters, and object requests can be then served in inter-cluster manner. Introducing an intermediate layer between server(s) and clients enables this. In this paper, we introduce the problem of client realignment in which access behavior changes, and propose on-line client clustering. This system facilitates adaptive reconfiguration and redistribution of sites. The core issue in this paper is to demonstrate the effectiveness of on-line client clustering. We experimentally investigate the performance of the scheme and necessary costs.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.191-198
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• 2007

A mobile ad-hoc network (MANET) is an autonomous, infrastructure-less system that consists of mobile nodes. In MANET, on demand routing protocols are usually used because network topology changes frequently. AODV, which is a representative on demand routing protocol, operates using the routing table of each node that includes next hop of a route for forwarding packets. It maintains the established route if there is not an expiration of route or any link break. In the paper, we propose a partially adaptive route maintenance scheme (AODV-PA) based on AODV, which provides dynamic route modification of initial route for selecting the effective route using not only next hop but also next-hop of next-hop (i.e. 2-hop next node) acquired through route discovery process. In addition, the proposed scheme additionally manages the routing table for preventing exceptional link breaks by route modification using HELLO messages. We use NS 2 for the computer simulation and validate that the proposed scheme is better than general AODV in terms of packet delivery ratio, latency, routing overhead.

• The KIPS Transactions:PartC
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• v.14C no.4
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• pp.365-370
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• 2007

Connected Dominating Set(CDS) has been used as a virtual backbone in wireless ad hoc networks by numerous routing and broadcast protocols. Although computing minimum CDS is known to be NP-hard, many protocols have been proposed to construct a sub-optimal CDS. However, these protocols are either too complicated, needing non- local information, not adaptive to topology changes, or fail to consider the difference of energy consumption for nodes in and outside of the CDS. In this paper, we present two Timer-based Energy-aware Connected Dominating Set Protocols(TECDS). The energy level at each node is taken into consideration when constructing the CDS. Our protocols are able to maintain and adjust the CDS when network topology is changed. The simulation results have shown that our protocols effectively construct energy-aware CDS with very competitive size and prolong the network operation under different level of nodal mobility.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.1
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• pp.39-45
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• 2011

The processes for manufacturing a product differ depending on the characteristics of the product, and the information used or generated by the processes also varies. To implement a flexible and configurable Manufacturing Execution System (MES), a Data Acquisition System (DAS) that takes into consideration the characteristics of the manufacturing system is required. In this study, we design an information acquisition system that can process the information on equipments of a shop floor in real-time and that is adaptive to the changes in the shop floor. The system has a data parser module for flexible processing of the equipment status, a data mapper module to link the equipment status with a manufacturing process, and an SOA-based data integration module to transmit the processed information to other information systems such as MES and ERP. From the results of pilot study, its maintenance is easy even if new equipment or new manufacturing processes are adopted or if the equipments are rearranged.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.409-418
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• 2009

This paper proposes a method to estimate the flow speed of pedestrians in surveillance videos. In the proposed method, the average moving speed of pedestrians is measured by estimating the size of real-world motion from the observed motion vectors. For this purpose, a pixel-to-meter conversion factor is introduced which is calculated from camera parameters. Also, the height information, which is missing because of camera projection, is predicted statistically from simulation experiments. Compared to the previous works for flow speed estimation, our method can be applied to various camera views because it separates scene parameters explicitly. Experiments are performed on both simulation image sequences and real video. In the experiments on simulation videos, the proposed method estimated the flow speed with average error of about 0.08m/s. The proposed method also showed promising results for the real video.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5276-5298
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• 2019

Internet of Things (IoT) based sensor networks have gained unprecedented popularity in recent years. With the exponential explosion of the objects (sensors and mobiles), the bandwidth and the speed of data transmission are dwarfed by the anticipated emergence of IoT. In this paper, we propose a novel heterogeneous IoT model integrated the power line communication (PLC) and WiFi network to increase the network capacity and cope with the rapid growth of the objects. We firstly propose the mean transmission delay calculation algorithm based the 3D Markov chain according to the multi-priority of the objects. Then, the attractor selection algorithm, which is based on the adaptive behavior of the biological system, is exploited. The combined the 3D Markov chain and the attractor selection model, named MASM, can select the optimal path adaptively in the heterogeneous IoT according to the environment. Furthermore, we verify that the MASM improves the transmission efficiency and reduce the transmission delay effectively. The simulation results show that the MASM is stable to changes in the environment and more applicable for the heterogeneous IoT, compared with the other algorithms.

• KCID journal
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• v.18 no.2
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• pp.33-42
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• 2011

Climate change has impacts on not only the average temperature rise but also the intensity and frequency of extreme events such as flood and drought. It is also expected that the damages on agricultural infrastructure will be increased resulting from increased rainfall intensity and frequency caused by climate change. To strengthen the climate change adaptation capacity, it is necessary to identify the vulnerability of a given society's physical infrastructures and to develop appropriate adaptation strategies with infrastructure management because generally facilities related to human settlements are vulnerable to climate changes and establishing an adaptive public infrastructure would reduce the damages and the repair cost. Therefore, development of mitigation strategies for agricultural infrastructure against climatic hazard is very important, but there are few studies on agricultural infrastructure vulnerability assessment and adaptation strategies. The concept of vulnerability, however, is difficult to functionally define due to the fact that vulnerability itself includes many aspects (biological, socioeconomic, etc.) in various sectors. As such, much research on vulnerability has used indicators which are useful for standardization and aggregation. In this study, for the vulnerability assessment for agricultural infrastructure, 3 categories of climate exposure, sensitivity, and adaptation capacity were defined which are composed of 16 sub-categories and 49 proxy variables. Database for each proxy variables was established based on local administrative province. Future studies are required to define the weighting factor and standardization method to calculate the vulnerability indicator for agricultural infrastructure against climate change.