• Journal of Korean Public Health Nursing
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• v.1 no.1
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• pp.45-61
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• 1987

This Study was an attempt to estimate the optimum numbers of Operating Room Nursing Manpower by measuring the amount of service hours required by the patients in Operating Room in relation to the service amount actually provided by the nurses. The major concern of this study was placed on the measurement of Nursing Service Requirements by using the Operating Room (O. R) Patient Acuity System recently developed by M. M. Hart to classify the O. R. patients into four groups according to the degree of the complexity of operative procedure and some other elements which increase nursing activities in respect of patient care; Acuity IV group is the one requires nursing services most, on the other hand Acuity I requires least. nu sing The objectives of this study were as follows; 1. To analyze functions of the nursing personnel in O. R. by time unit and to estimate the average time a nurse can activate for productive functions. 2. To measure the actual amount of nursing times provided by nurses to the surgical patients. 3. To develop a patient classification system in order to measure the amount of Nursing services required by the patients. 4. To calculate an appropriate number of nursing manpower to meet the needs of the patients. In order to conduct the research both selected nurses and patients in 'S' University Hospital were Studied by utilizing the O. R. Patient Acuity System as well as the Classification Chart developed by Association of Operating Room Nurses (A. O. R. N) as a means of classifying functions of O. R. nurses. That is; Functions of the 10 selected O. R. nurses observed during the period of June 30 to July 4, 1986, whereas the amount of nursing services required by or provided to the 974 patients who had received surgeries during the period of June 9 to July 4, 1986. The results of this study were as follows; 1) The actual working hours per a nurse averaged 6.7 hours a day. 2) Each nurse's daily routine schedule consists of $71.4\%$ for Technical Functions, $16.1\%$ for Nonprodective Functions, $6.6\%$ for Assessment and Evaluation, $3.9\%$ for Overseeing and Supervision and the rest $2.0\%$ for Patient Preparation respectively. 3) Preoperative waiting time per a patient was 24.1 minutes on the average; for the first case was 10.7 minutes, whereas for the following cases was 32.0 minutes. 4) Total Operation time for the 974 patients during the period of observation for this study amounted to 2759.6 hours, weekly hour was equivalent to 689.9 hours, Whereas daily operation time averaged 130 hours. Meanwhile the average operation time per patient was 2.8 hours ; for the case of Acuity IV was 5.6 hours, 5. 1 hours for the case of Acuity III, 2.3 hours for Acuity II and 1.1 hours for Acuity I. 5) According to the O. R. Patient Acuity System, $64.5\%$ of the whole patients belonged to Acuity II, $23.7\%$ to Acuity III, 11. $3\%$ to Acuity IV and $0.7\%$ to Acuity I respectively. 6) Required amount of nursing times based on the preoperative waiting time and operation time was 7167.8 person hours, which showed that $5.5\%$ of them needed for preoperative nursing care, whereas the rest $94.5\%$ for intraoperative nursing care. In terms of the O. R. Patient Acuity System, $49.7\%$ of total nursing service requirements was needed for Acuity II patients, $27.4\%$ for Acuity III patients, $17.2\%$ for Acuity IV patients and $0.2\%$ for Acuity I patients. 7) The rate of the nursing services provided against the required nursing times was about $81.4\%$ on the average; some departments, like those of Plastic Surgery, Otolaryngology and Ophthalmology whose patients mostly belonged to Acuity II recorded hegher provision rate than average, whereas other departments of Thoracic Surgery. Neurosurgery and Orthopedic Surgery whose patients belonged to Acuity III and Acuity IV as well as Acuity II recorded lower provision rate than average. 8) Subsequently, required numbers of nursing manpower was 10.7 nurses additionally. Based on the above findings the following recommendations will be made; 1) this study recommends, develops. and adopts an accurate and realistic O. R. Patient Acuity System which can help measure the nursing service requirements objectively to elicit the rationales of allocation of nursing personnels. 2) this study proposes storongly place nurses who take the role of preoperative nursing care exclusively for the waiting patients in O. R. and shortening their waiting time by close communication between the designated O. R. and the ward.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1218-1226
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• 2008

With the development of semiconductor integrated technology and with the increasing use of multimedia functions in computer, more functions have been implemented as hardware. Nowadays, most microprocessors beyond 32 bits generally implement an integer multiplier as hardware. However, as for a divider, only specific microprocessor implements traditional SRT algorithm as hardware due to complexity of implementation and slow speed. This paper suggested an algorithm that uses a multiplier, 'w bit $\times$ w bit = 2w bit', to process $\frac{N}{D}$ integer division. That is, the reciprocal number D is first calculated, and then multiply dividend N to process integer division. In this paper, when the divisor D is '$D=0.d{\times}2^L$, 0.5 < 0.d < 1.0', approximate value of ' $\frac{1}{D}$', '$1.g{\times}2^{-L}$', which satisfies ' $0.d{\times}1.g=1+e$, $e<2^{-w}$', is defined as over reciprocal number and then an algorithm for over reciprocal number is suggested. This algorithm multiplies over reciprocal number '$01.g{\times}2^{-L}$' by dividend N to process $\frac{N}{D}$ integer division. The algorithm suggested in this paper doesn't require additional revision, because it can calculate correct reciprocal number. In addition, this algorithm uses only multiplier, so additional hardware for division is not required to implement microprocessor. Also, it shows faster speed than the conventional SRT algorithm and performs operation by word unit, accordingly it is more suitable to make compiler than the existing division algorithm. In conclusion, results from this study could be used widely for implementation SOC(System on Chip) and etc. which has been restricted to microprocessor and size of the hardware.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Clinical and Experimental Reproductive Medicine
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• v.23 no.3
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• pp.247-268
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• 1996

It has been known for a long time that gonadotropins and steroid hormones play a pivotal role in a series of reproductive biological phenomena including the maturation of ovarian follicles and oocytes, ovulation and implantation, maintenance of pregnancy and fetal growth & development, parturition and mammary development and lactation. Recent investigations, however, have elucidated that in addition to these classic hormones, multiple growth factors also are involved in these phenomena. Most growth factors in reproductive organs mediate the actions of gonadotropins and steroid hormones or synergize with them in an autocrine/paracrine manner. The insulin-like growth factor(IGF) system, which is one of the most actively investigated areas lately in the reproductive organs, has been found to have important roles in a wide gamut of reproductive phenomena. In the present communication, published literature pertaining to the intrauterine IGF system will be reviewed preceded by general information of the IGF system. The IGF family comprises of IGF-I & IGF-II ligands, two types of IGF receptors and six classes of IGF-binding proteins(IGFBPs) that are known to date. IGF-I and IGF-II peptides, which are structurally homologous to proinsulin, possess the insulin-like activity including the stimulatory effect of glucose and amino acid transport. Besides, IGFs as mitogens stimulate cell division, and also play a role in cellular differentiation and functions in a variety of cell lines. IGFs are expressed mainly in the liver and messenchymal cells, and act on almost all types of tissues in an autocrine/paracrine as well as endocrine mode. There are two types of IGF receptors. Type I IGF receptors, which are tyrosine kinase receptors having high-affinity for IGF-I and IGF-II, mediate almost all the IGF actions that are described above. Type II IGF receptors or IGF-II/mannose-6-phosphate receptors have two distinct binding sites; the IGF-II binding site exhibits a high affinity only for IGF-II. The principal role of the type II IGF receptor is to destroy IGF-II by targeting the ligand to the lysosome. IGFs in biological fluids are mostly bound to IGFBP. IGFBPs, in general, are IGF storage/carrier proteins or modulators of IGF actions; however, as for distinct roles for individual IGFBPs, only limited information is available. IGFBPs inhibit IGF actions under most in vitro situations, seemingly because affinities of IGFBPs for IGFs are greater than those of IGF receptors. How IGF is released from IGFBP to reach IGF receptors is not known; however, various IGFBP protease activities that are present in blood and interstitial fluids are believed to play an important role in the process of IGF release from the IGFBP. According to latest reports, there is evidence that under certain in vitro circumstances, IGFBP-1, -3, -5 have their own biological activities independent of the IGF. This may add another dimension of complexity of the already complicated IGF system. Messenger ribonucleic acids and proteins of the IGF family members are expressed in the uterine tissue and conceptus of the primates, rodents and farm animals to play important roles in growth and development of the uterus and fetus. Expression of the uterine IGF system is regulated by gonadal hormones and local regulatory substances with temporal and spatial specificities. Locally expressed IGFs and IGFBPs act on the uterine tissue in an autocrine/paracrine manner, or are secreted into the uterine lumen to participate in conceptus growth and development. Conceptus also expresses the IGF system beginning from the peri-implantation period. When an IGF family member is expressed in the conceptus, however, is determined by the presence or absence of maternally inherited mRNAs, genetic programming of the conceptus itself and an interaction with the maternal tissue. The site of IGF action also follows temporal (physiological status) and spatial specificities. These facts that expression of the IGF system is temporally and spatially regulated support indirectly a hypothesis that IGFs play a role in conceptus growth and development. Uterine and conceptus-derived IGFs stimulate cell division and differentiation, glucose and amino acid transport, general protein synthesis and the biosynthesis of mammotropic hormones including placental lactogen and prolactin, and also play a role in steroidogenesis. The suggested role for IGFs in conceptus growth and development has been proven by the result of IGF-I, IGF-II or IGF receptor gene disruption(targeting) of murine embryos by the homologous recombination technique. Mice carrying a null mutation for IGF-I and/or IGF-II or type I IGF receptor undergo delayed prenatal and postnatal growth and development with 30-60% normal weights at birth. Moreover, mice lacking the type I IGF receptor or IGF-I plus IGF-II die soon after birth. Intrauterine IGFBPs generally are believed to sequester IGF ligands within the uterus or to play a role of negative regulators of IGF actions by inhibiting IGF binding to cognate receptors. However, when it is taken into account that IGFBP-1 is expressed and secreted in primate uteri in amounts assessedly far exceeding those of local IGFs and that IGFBP-1 is one of the major secretory proteins of the primate decidua, the possibility that this IGFBP may have its own biological activity independent of IGF cannot be excluded. Evidently, elucidating the exact role of each IGFBP is an essential step into understanding the whole IGF system. As such, further research in this area is awaited with a lot of anticipation and attention.