• Title/Summary/Keyword: System Application

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A Study on the Effect of Using Sentiment Lexicon in Opinion Classification (오피니언 분류의 감성사전 활용효과에 대한 연구)

  • Kim, Seungwoo;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.20 no.1
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    • pp.133-148
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    • 2014
  • Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

The Brand Personality Effect: Communicating Brand Personality on Twitter and its Influence on Online Community Engagement (브랜드 개성 효과: 트위터 상의 브랜드 개성 전달이 온라인 커뮤니티 참여에 미치는 영향)

  • Cruz, Ruth Angelie B.;Lee, Hong Joo
    • Journal of Intelligence and Information Systems
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    • v.20 no.1
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    • pp.67-101
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    • 2014
  • The use of new technology greatly shapes the marketing strategies used by companies to engage their consumers. Among these new technologies, social media is used to reach out to the organization's audience online. One of the most popular social media channels to date is the microblogging platform Twitter. With 500 million tweets sent on average daily, the microblogging platform is definitely a rich source of data for researchers, and a lucrative marketing medium for companies. Nonetheless, one of the challenges for companies in developing an effective Twitter campaign is the limited theoretical and empirical evidence on the proper organizational usage of Twitter despite its potential advantages for a firm's external communications. The current study aims to provide empirical evidence on how firms can utilize Twitter effectively in their marketing communications using the association between brand personality and brand engagement that several branding researchers propose. The study extends Aaker's previous empirical work on brand personality by applying the Brand Personality Scale to explore whether Twitter brand communities convey distinctive brand personalities online and its influence on the communities' level or intensity of consumer engagement and sentiment quality. Moreover, the moderating effect of the product involvement construct in consumer engagement is also measured. By collecting data for a period of eight weeks using the publicly available Twitter application programming interface (API) from 23 accounts of Twitter-verified business-to-consumer (B2C) brands, we analyze the validity of the paper's hypothesis by using computerized content analysis and opinion mining. The study is the first to compare Twitter marketing across organizations using the brand personality concept. It demonstrates a potential basis for Twitter strategies and discusses the benefits of these strategies, thus providing a framework of analysis for Twitter practice and strategic direction for companies developing their use of Twitter to communicate with their followers on this social media platform. This study has four specific research objectives. The first objective is to examine the applicability of brand personality dimensions used in marketing research to online brand communities on Twitter. The second is to establish a connection between the congruence of offline and online brand personalities in building a successful social media brand community. Third, we test the moderating effect of product involvement in the effect of brand personality on brand community engagement. Lastly, we investigate the sentiment quality of consumer messages to the firms that succeed in communicating their brands' personalities on Twitter.

The Effect of PNF Technique application Using Thera-Band on the Balance and Gait of Females over 65 years old (세라밴드를 이용한 PNF 기법 적용이 65세 이상 여성노인의 균형과 보행에 미치는 영향)

  • Kang, Dal-Won;Kang, Mi-Kyoung;Kang, Eun-Sil;Go, Yu-Ri;Kim, Da-Woon;Kim, Dae-Yong;Kim, Jung-Eun;Kim, Won-Hwang;Kim, Ja-Yeon;Kim, Hwan;Jung, Dae-In;Kim, Myung-Hoon;Kim, Sang-Yup;Lee, Dong-Jin;Kim, Chan-Kyu;Kim, Hyun-Jin
    • Journal of Korean Physical Therapy Science
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    • v.18 no.1
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    • pp.1-10
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    • 2011
  • Purpose: Study on the effect of the use of Proprioceptive Neuromuscular Facilitation(PNF) method by use of the direction and charge regulation which is the advantage of the Thera-band therapy on the walks and balances in old people and comparison with the result after applying the general PNF technique. Method: The study has been performed on 30 females over 65 years old. The study has been done by dividing the object group in 3 patterns, which are number 1, the comparison group of 10, two ones that are applied the PNF technique using Thera-band and third, the ones that are applied only the PNF technique. For the PNF and the Thera-band using PNF, we have divided the group into Combination of Isotonic technique and the Rhythmical stabilization technique according to the patient's acquaintance pattern and applied them to the patient's body. Evaluation was the balancing ability which was calculated by using the BIODEX Balance system / FRT and for the evaluation of walking ability, we have used the speed of walking for 10M / TUG. Result: In the comparison group of 10, the balancing ability and the walking ability did not change much before and after the experiment, which made it possible to compare the group with the other two easily(p>0.05). For the other two groups, we have recognized the enhancement both in the balancing ability and the walking ability, but they did not know much difference between themselves(p<0.05). Conclusion: Though there were not a big difference in the sense of improvement between the Thera-band using PNF and the PNF technique only, we could infer that these two therapy has enhanced much in the walking and balancing ability for people over 65 and through these result we can foresee that not only using the method shown in this study but also by using many advantages of Thera-band, we could diminish the tiredness of healer, enhance the efficiency of exercise in them and also by forming self training program for older people we could help them build the prevention program from falls.

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Relationships of Physiological Activity and Anatomical Structure to the Wilting Phenomena in Rice Plant 2. Relationships between the anatomical structure and wilting phenomena of rice variety "Yushin" (수도품종의 위조현상과 생리 및 형태해부학적 구조와의 관련성에 관한 연구 제2보 유신벼의 위조현상발생과 형태해부학적 구조와의 관계)

  • Jong-Hoon Lee
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.25 no.2
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    • pp.6-14
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    • 1980
  • One of the physiological disease, sudden wiliting of Yushin variety suggested that low sunlight, excessive nitrogen application, and highly reduced soil condions either singly or combined, might be possible causes of the disorder. Some visual symptom of sudden wilting are discoloration of leaves, development of nodal roots above the soil surface, total root rot, and lodging. Those observations led to the hypothesis that suffocation of root tissues was a direct cause of the wilting. The oxygen transport characteristics of Yushin, IR262 and Tongil were examined by two methods. First, Soil-cultured plants of the three varieties were subjected to paraffin treatment to decrease the oxygen supply from the air to root tissues through the soil-water system, liquid paraffin was applied to the water surface in the pots at panicle formation stage. In this experiment, sudden wilting was observed of Yushin and IR262 at about a week after the treatment, but Tongil remained green and healthy. Wilting-resistant variety Tongil had higher oxygen release, whereas the susceptible Yushin and IR262 had lower oxygen release. Second, the number and size of the air spaces in each internode were investigated in the 5th internode from the top, all three varieties have a similar number of air spaces, although the air spaces of Thongil were larger. In the 4th internode, Tongil had plenty air spaces, Yushin and one of the Yushin's parents IR262 had scanty or none. The observations indicated that the ability of Yushin and IR262 for oxygen transport is very limited compared with that of Tongil. The limited oxygen supply due to poor development of air space in internode of rice plants may cause suffocation of root tissues, weaken metabolic activity of the tissues, and induce root rot, subsequently inducing sudden wilting and lodging under unfavorable weather, soil and cultural conditions.

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$CO_2$ Transport for CCS Application in Republic of Korea (이산화탄소 포집 및 저장 실용화를 위한 대한민국에서의 이산화탄소 수송)

  • Huh, Cheol;Kang, Seong-Gil;Cho, Mang-Ik
    • Journal of the Korean Society for Marine Environment & Energy
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    • v.13 no.1
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    • pp.18-29
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    • 2010
  • Offshore subsurface storage of $CO_2$ is regarded as one of the most promising options to response severe climate change. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources, to transport to the storage sites and to store $CO_2$ into the offshore subsurface geological structure such as the depleted gas reservoir and deep sea saline aquifer. Since 2005, we have developed relevant technologies for marine geological storage of $CO_2$. Those technologies include possible storage site surveys and basic designs for $CO_2$ transport and storage processes. To design a reliable $CO_2$ marine geological storage system, we devised a hypothetical scenario and used a numerical simulation tool to study its detailed processes. The process of transport $CO_2$ from the onshore capture sites to the offshore storage sites can be simulated with a thermodynamic equation of state. Before going to main calculation of process design, we compared and analyzed the relevant equation of states. To evaluate the predictive accuracies of the examined equation of states, we compare the results of numerical calculations with experimental reference data. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the captured $CO_2$ mixture contains many impurities such as $N_2$, $O_2$, Ar, $H_{2}O$, $SO_{\chi}$, $H_{2}S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification and transport processes. This paper analyzes the major design parameters that are useful for constructing onshore and offshore $CO_2$ transport systems. On the basis of a parametric study of the hypothetical scenario, we suggest relevant variation ranges for the design parameters, particularly the flow rate, diameter, temperature, and pressure.

Bankruptcy Type Prediction Using A Hybrid Artificial Neural Networks Model (하이브리드 인공신경망 모형을 이용한 부도 유형 예측)

  • Jo, Nam-ok;Kim, Hyun-jung;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.79-99
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    • 2015
  • The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

Ontology-Based Process-Oriented Knowledge Map Enabling Referential Navigation between Knowledge (지식 간 상호참조적 네비게이션이 가능한 온톨로지 기반 프로세스 중심 지식지도)

  • Yoo, Kee-Dong
    • Journal of Intelligence and Information Systems
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    • v.18 no.2
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    • pp.61-83
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    • 2012
  • A knowledge map describes the network of related knowledge into the form of a diagram, and therefore underpins the structure of knowledge categorizing and archiving by defining the relationship of the referential navigation between knowledge. The referential navigation between knowledge means the relationship of cross-referencing exhibited when a piece of knowledge is utilized by a user. To understand the contents of the knowledge, a user usually requires additionally information or knowledge related with each other in the relation of cause and effect. This relation can be expanded as the effective connection between knowledge increases, and finally forms the network of knowledge. A network display of knowledge using nodes and links to arrange and to represent the relationship between concepts can provide a more complex knowledge structure than a hierarchical display. Moreover, it can facilitate a user to infer through the links shown on the network. For this reason, building a knowledge map based on the ontology technology has been emphasized to formally as well as objectively describe the knowledge and its relationships. As the necessity to build a knowledge map based on the structure of the ontology has been emphasized, not a few researches have been proposed to fulfill the needs. However, most of those researches to apply the ontology to build the knowledge map just focused on formally expressing knowledge and its relationships with other knowledge to promote the possibility of knowledge reuse. Although many types of knowledge maps based on the structure of the ontology were proposed, no researches have tried to design and implement the referential navigation-enabled knowledge map. This paper addresses a methodology to build the ontology-based knowledge map enabling the referential navigation between knowledge. The ontology-based knowledge map resulted from the proposed methodology can not only express the referential navigation between knowledge but also infer additional relationships among knowledge based on the referential relationships. The most highlighted benefits that can be delivered by applying the ontology technology to the knowledge map include; formal expression about knowledge and its relationships with others, automatic identification of the knowledge network based on the function of self-inference on the referential relationships, and automatic expansion of the knowledge-base designed to categorize and store knowledge according to the network between knowledge. To enable the referential navigation between knowledge included in the knowledge map, and therefore to form the knowledge map in the format of a network, the ontology must describe knowledge according to the relation with the process and task. A process is composed of component tasks, while a task is activated after any required knowledge is inputted. Since the relation of cause and effect between knowledge can be inherently determined by the sequence of tasks, the referential relationship between knowledge can be circuitously implemented if the knowledge is modeled to be one of input or output of each task. To describe the knowledge with respect to related process and task, the Protege-OWL, an editor that enables users to build ontologies for the Semantic Web, is used. An OWL ontology-based knowledge map includes descriptions of classes (process, task, and knowledge), properties (relationships between process and task, task and knowledge), and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. Therefore a knowledge network can be automatically formulated based on the defined relationships, and the referential navigation between knowledge is enabled. To verify the validity of the proposed concepts, two real business process-oriented knowledge maps are exemplified: the knowledge map of the process of 'Business Trip Application' and 'Purchase Management'. By applying the 'DL-Query' provided by the Protege-OWL as a plug-in module, the performance of the implemented ontology-based knowledge map has been examined. Two kinds of queries to check whether the knowledge is networked with respect to the referential relations as well as the ontology-based knowledge network can infer further facts that are not literally described were tested. The test results show that not only the referential navigation between knowledge has been correctly realized, but also the additional inference has been accurately performed.

A Study on the Establishment and Application of Landscape Height Based on View and Topographical Features - Focusing on the Maximum Height Regulation District around Bukhan Mountain National Park - (조망 및 지형특성에 따른 경관고도 도출과 적용 방안 - 북한산 국립공원 인근의 최고고도지구를 중심으로 -)

  • Chang, In-Young;Shin, Ji-Hoon;Cho, Woo-Hyun;Shin, Young-Sun;Kim, Eon-Gyung;Kwon, Yoon-Ku;Im, Seung-Bin
    • Journal of the Korean Institute of Landscape Architecture
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    • v.39 no.1
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    • pp.35-45
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    • 2011
  • The landscape of Seoul was dynamically changed and developed with the rapid post-war economic growth. Seoul city designated a height regulation district to preserve and manage the city landscape and protect it from haphazard construction. The designation of a maximum height regulation district has obvious purpose and simple regulations which makes the implementation and management easy to apply yet the altitude restriction lacks an objective basis for its enforcement. Many studies have been done and the current uniform height regulation requires more objective and logical guidelines. This study selected the Bukhan Mountain area, a National Park designated to protect the environment, to present a new landscape height guideline to minimize environmental degradation and to harmonize the artificial and natural landscapes of the area. Document research was done to identify the regulation types(absolute height regulation, view line regulation, oblique line restriction regulation) and principles for height regulation district establishment, acknowledge the current status and issues of the Bukhan Mountain area's maximum height regulation district. Gangbuk-Gu was chosen as the site and survey was conducted on outstanding view points and view corridors of residents. From document research, an appropriate landscape height guideline was selected and applied to Gangbuk-Gu. According to the guideline, suitable heights for buildings were suggested. These were then applied to three-dimensional simulations and a final guideline was suggested.

Application of Machine Learning Algorithm and Remote-sensed Data to Estimate Forest Gross Primary Production at Multi-sites Level (산림 총일차생산량 예측의 공간적 확장을 위한 인공위성 자료와 기계학습 알고리즘의 활용)

  • Lee, Bora;Kim, Eunsook;Lim, Jong-Hwan;Kang, Minseok;Kim, Joon
    • Korean Journal of Remote Sensing
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    • v.35 no.6_2
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    • pp.1117-1132
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    • 2019
  • Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

Strategy to Improve the Productivity of Broilers: Focusing on Pre-Starter Diet (초이사료 배합설계를 통한 육계 생산성 증대방안)

  • Nam, Doo Seok;Lee, Jinyoung;Kong, Changsu
    • Korean Journal of Poultry Science
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    • v.42 no.3
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    • pp.247-256
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    • 2015
  • There are approximately 1,500 broiler farms in Korea, each raising 55,000 birds. Ninety-five percent of the farms are contracted with Integration Company. According to the Korean broiler performance index, broilers in Korea are marketed at 32 days with 1.52 kg of body weight. In contrast, the market age and body weight of broilers are 47 days/2.8 kg in the United States and 42 days/2.5 kg in Europe. Because of the younger market age of the Korean broiler, the pre-starter feed is important. Chicks exhibit poor absorption of dietary nutrients up to 7 days after hatching due to an immature digestive system and low enzyme secretion rate and activity. At the beginning of hatching, chicks obtain their nutrients from the egg yolk sac. It is highly recommended that chicks, after consuming the nutrients in the egg yolk sac, are given supplemented pre-starter feed to increase early growth rates and improve the performance of broiler production. Pre-starter nutrient requirements are not expressed in NRC, so Korean feeding standards for poultry and commercial breeding companies determine the nutrient requirements of pre-starter broiler chickens. Three approaches are followed to formulate specially designed pre-starter feeds for broiler chicks: (i) selective use of raw materials, (ii) proper standards of nutrient supply, and (iii) application of feed additives such as exogenous enzymes. In the selection of raw materials, those with high digestibility can be used. The absorption rate of carbohydrates in grains can be increased through feed processing at high temperature and high pressure. Soy proteins and fish meal can also be added as protein sources. As an energy source, vegetable oils are preferred over animal fats because of the former's high digestibility. It is suggested that the levels of proteins and amino acids are higher in pre-starter feed than in starter feed. With regard to energy, the sources of energy are more important than the levels of energy in feed. Feed additives such as exogenous enzymes can be used to improve nutrient digestibility. In addition, organic acids and plant extracts can be used as alternatives to animal growth promoters to stimulate immunity and prevent diseases. The growth performance of broilers is affected by various factors, such as management and disease control, in addition to the nutritional strategy; however, nutritional strategies play an important role in improving the productivity of broilers. Therefore, nutritional strategies, along with management and disease control, are required for improving the productivity of broilers in Korea.