• Title/Summary/Keyword: Science & Technology Databases

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Evaluation of Upper Limb Movement and Function in Stroke Patients Using Electromyography : A Review (근전도를 활용한 뇌졸중 환자의 상지 운동 및 기능 평가에 관한 고찰)

  • Lee, Jiyeon;Lee, Gyeong A;Jung, Jae Hyu;Park, Ji-Hyuk
    • Therapeutic Science for Rehabilitation
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    • v.11 no.3
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    • pp.37-50
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    • 2022
  • Objective : This study aimed to investigate the use of electromyography (EMG) to evaluate upper limb movement or function in stroke patients. Methods : We reviewed papers published in journals between January 2018 and December 2021 using PubMed, EMBASE, Scopus, RISS, and KISS. The main keywords of databases were ('stroke' OR 'hemiplegia') AND ('EMG' OR 'electromyography' OR 'electromyogram' OR 'muscle activity') AND ('Upper limb' OR 'Hand'). Results : Fifteen studies were selected, most of which evaluated muscle activity. Interventions performing tasks related to activities of daily living (ADLs), using assistive technology, and interventions that provide repetitive training were most frequently applied. Conclusions : When evaluating upper limb functions using electromyography, it is meaningful to present an evaluation that can be used according to the purpose of the study and to provide a basis for setting up interventions that can utilize electromyography during evaluation.

A Study on Intelligent Value Chain Network System based on Firms' Information (기업정보 기반 지능형 밸류체인 네트워크 시스템에 관한 연구)

  • Sung, Tae-Eung;Kim, Kang-Hoe;Moon, Young-Su;Lee, Ho-Shin
    • Journal of Intelligence and Information Systems
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    • v.24 no.3
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    • pp.67-88
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    • 2018
  • Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

A Comparative Study on the Effective Deep Learning for Fingerprint Recognition with Scar and Wrinkle (상처와 주름이 있는 지문 판별에 효율적인 심층 학습 비교연구)

  • Kim, JunSeob;Rim, BeanBonyka;Sung, Nak-Jun;Hong, Min
    • Journal of Internet Computing and Services
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    • v.21 no.4
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    • pp.17-23
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    • 2020
  • Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

Usefulness of Data Mining in Criminal Investigation (데이터 마이닝의 범죄수사 적용 가능성)

  • Kim, Joon-Woo;Sohn, Joong-Kweon;Lee, Sang-Han
    • Journal of forensic and investigative science
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    • v.1 no.2
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    • pp.5-19
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    • 2006
  • Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

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Identification of Lettuce Germplasms and Commercial Cultivars Using SSR Markers Developed from EST (EST로부터 개발된 SSR 마커를 이용한 상추 유전자원 및 유통품종의 식별)

  • Hong, Jee-Hwa;Kwon, Yong-Sham;Choi, Keun-Jin;Mishra, Raghvendra Kumar;Kim, Doo Hwan
    • Horticultural Science & Technology
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    • v.31 no.6
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    • pp.772-781
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    • 2013
  • The objective of this study was to develop simple sequence repeat (SSR) markers from expressed sequence tags (EST) of lettuce (Lactuca sativa) and identify 9 germplasms from 3 wild species of lettuce and 61 commercial cultivars using the developed EST-SSR markers. A total of 81,330 lettuce ESTs from NCBI databases were used to search for SSR and 4,229 SSR loci were identified. The highest proportion (59.12%, 2500) was represented by trinucleotide, followed by dinucleotide (29.70%, 1256) and hexanucleotide (6.62%, 280) among SSR repeat motifs. Totally 474 EST-SSR primers were developed from EST and a random set of 267 primers was used to assess the genetic diversity among 9 germplasms and 61 cultivars. Out of 267 primers, 47 EST-SSR markers showed polymorphism between 7 cultivars. Twenty-six EST-SSR markers among 47 EST-SSR markers showed high polymorphism, reproducibility, and band clearance. The relationship between 26 markers genotypes and 70 accessions was analyzed. Totally 127 polymorphic amplified fragments were obtained by 26 EST-SSR markers and two to nine SSR alleles were detected for each locus with an average of 4.88 alleles per locus. Average polymorphism information content was 0.542, ranging from 0.269 to 0.768. Genetic distance of clusters ranged from 0.05 to 0.94 between 70 accessions and dendrogram at a similarity of 0.34 gave 7 main clusters. Analysis of genetic diversity revealed by these 26 EST-SSR markers showed that the 9 germplasms and 61 commercial cultivars were discriminated by marker genotypes. These newly developed EST-SSR markers will be useful for cultivar identification and distinctness, uniformity and stability test of lettuce.

Yearly Update of the List of Plant Diseases in Korea (6.2 Edition, 2024) (한국식물병명목록의 연간 현황 보고(6.2판, 2024년 개정본))

  • Jaehyuk Choi;Seon-Hee Kim;Young-Joon Choi;Gyoung Hee Kim;Ju-Yeon Yoon;Byeong-Yong Park;Hyun Gi Kong;Soonok Kim;Sekeun Park;Chang-Gi Back;Hee-Seong Byun;Jang Kyun Seo;Jun Myoung Yu;Dong-Hyeon Lee;Mi-Hyun Lee;Bong Choon Lee;Seung-Yeol Lee;Seungmo Lim;Yongho Jeon;Jaeyong Chun;Insoo Choi;In-Young Choi;Hyo-Won Choi;Jin Sung Hong;Seung-Beom Hong
    • Research in Plant Disease
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    • v.30 no.2
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    • pp.103-113
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    • 2024
  • Since 2009, the Korean Society of Plant Pathology has established the Committee on Common Names of Plant Disease to systematically review and determine plant disease names and related terminologies. The committee published the 6th edition of the List of Plant Diseases in Korea (LPDK) in 2022, and the list has been made publicly accessible online. The online database has significantly enhanced user accessibility, expedited update processes, and improved interoperability with other databases. As a result, the 6.1 edition of the list was released by online LPDK in 2023, detailing new disease names added over the preceding year and revisions to existing names. Subsequently, in 2024, the 6.2 edition was published, encompassing 6,765 diseases caused by 2,503 pathogen taxa across 1,432 host species. The public release of the online database has, however, introduced several challenges and tasks. Addressing these issues necessitates the development of modern, standardized nomenclature guidelines and a robust system for the registration of new disease names. Open communication and collaboration among the diverse members of the Korean Society of Plant Pathology are required to ensure the reliability of the LPDK.

Analysis of Literatures Related to Crop Growth and Yield of Onion and Garlic Using Text-mining Approaches for Develop Productivity Prediction Models (양파·마늘 생산성 예측 모델 개발을 위한 텍스트마이닝 기법 활용 생육 및 수량 관련 문헌 분석)

  • Kim, Jin-Hee;Kim, Dae-Jun;Seo, Bo-Hun;Kim, Kwang Soo
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.23 no.4
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    • pp.374-390
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    • 2021
  • Growth and yield of field vegetable crops would be affected by climate conditions, which cause a relatively large fluctuation in crop production and consumer price over years. The yield prediction system for these crops would support decision-making on policies to manage supply and demands. The objectives of this study were to compile literatures related to onion and garlic and to perform data-mining analysis, which would shed lights on the development of crop models for these major field vegetable crops in Korea. The literatures on crop growth and yield were collected from the databases operated by Research Information Sharing Service, National Science & Technology Information Service and SCOPUS. The keywords were chosen to retrieve research outcomes related to crop growth and yield of onion and garlic. These literatures were analyzed using text mining approaches including word cloud and semantic networks. It was found that the number of publications was considerably less for the field vegetable crops compared with rice. Still, specific patterns between previous research outcomes were identified using the text mining methods. For example, climate change and remote sensing were major topics of interest for growth and yield of onion and garlic. The impact of temperature and irrigation on crop growth was also assessed in the previous studies. It was also found that yield of onion and garlic would be affected by both environment and crop management conditions including sowing time, variety, seed treatment method, irrigation interval, fertilization amount and fertilizer composition. For meteorological conditions, temperature, precipitation, solar radiation and humidity were found to be the major factors in the literatures. These indicate that crop models need to take into account both environmental and crop management practices for reliable prediction of crop yield.