Naver Corporation

About: Naver Corporation is a company organization based out in Seongnam-si, South Korea. It is known for research contribution in the topics: Terminal (electronics) & Computer science. The organization has 4038 authors who have published 4294 publications receiving 35045 citations. The organization is also known as: NAVER Corporation & NAVER.

Work reality of dental assistant

Abstract: Dental assistant in South Korea, The work is overlapped considerably between dental hygienist and dental practical nurse. Moreover, dental technician, hospital coordinator work in dentistry consultation deeply. It cause friction among work scope of occupation`s type. Accordingly It is purpose to get basic data which is necessary to deduce division of work training and utilization of human resources in oral health distinguish clearly between business occupations. Also compared and analyzed via analysis of frequency and ANOVA above 10 works around something legal work of dental hygienist regarding work reality of dental assistant in dental clinic and hospital. Compare with scaling, representative item about 10 works center on legal work of dental hygienist, is implementing dental technician 9(75%), practical nurse 64(87.67%), etc. 11(64.71%). Dental assistant except dental hygienist is implementing indigenous legal work of dental hygienist. Dental institution secure enough man power, It is suggested necessity for policy means regarding role and work scope of dental assistant.

A Study on the Signal Processing Method for the Hall Sensorless Position Control of ETC Control System using a BLDC Motor

Abstract: This paper describes an signal processing method for the hall sensorless position control of ETC control system using a BLDC motor The proposed ETC control system, which is mainly consisted of a BLDC motor, a throttle plate, a return spring and reduction gear, has a position sensor with an analog voltage output on the throttle valve instead of BLDC motor for detecting rotor position of motor So the additional commutation information is necessarily needed to control the mentioned ETC module In order to estimate and determine the commutation state, it is proposed to properly manipulate the resolution of A/D converter considering the mechanical parameter, that is, the number of motor slot and the ratio of reduction gear Through this method, the estimation of commutation state for operating the system is possible and the discrete signal for commutation is stably obtained The validity of the method is examined through the experimental results

A Study of Intrusion Security Research and Smishing Hacking Attack on a Smartphone

Abstract: Damage is increasing by (Smishing) hacking attack Smishing you use a smart phone after entering 2013. Takeover of personal information and direct financial damage in collaboration with graphics sewing machine hacking attack has occurred. Monetary damage that leads to Internet payment service (ISP) and secure payment system in conjunction with graphics sewing machine hacking attack on a smartphone has occurred. In this paper, I will study analysis in the laboratory examples of actual infringement vinegar sewing machine hacking attack. It is a major power security measures to prevent damage to the secure payment system that a case analysis and practical principle technical nest sewing machine hacking attack, using Smishing. In this paper, I will be to research to be able to through a smart phone, to the online payment safer and more convenient.

Factors Associated with the Prehospital Delay in Acute Myocardial Infarction

Abstract: 급성심근경색환자에서 증상발병후 병원도착지연이 질병과 사망률에 중요한 관련이 있다. 이 연구에서는 환자의 증상 인식 등 응급의료체계 활성화 이전 단계의 의료기관 내원 지연의 원인을 연구하여, 향후 내원지연 시간 감소를 위해 적절한 보건교육 및 치료의 방향을 찾고자 하였다. 연구 샘플은 2009년 6월에서 9월까지 급성심근경색으로 입원한 환자를 대상으로 하였다. 인구통계, 의료기록 및 임상자료는 병원진료기록를 이용하였으며, 병원전 지연은 6시간이전과 이후로 분류하였다. 급성심근경색증 환자들에서 내원시간 지연에 영향을 미치는 요인으로는 고령인 경우, 낮은 사회경제적 위치, 응급의료체계의 이용하지 않고 내원 한 경우와 급성심근경색을 위한 응급처치의 지연등이다. 따라서 사회경제적 위치가 낮은 계층과 고령층에 대한 급성관상동맥 증후군의 증상에 대한 지식을 높이고 증상발생시 의료기관을 빠르게 이용하도록 교육이 필요하다. 【In patients with acute myocardial infarction (AMI), the delay from symptom onset to hospital arrival has a critical effect on morbidity and mortality. This study examined to find out the determinants of the prehospital delay in patients with AMI. The study sample consisted of 597 patients hospitalized with AMI between Jan and Dec 2009. Demographic, medical history, and clinical data were abstracted from the hospital medical records of patients with confirmed AMI, the prehospital delay was categorized as less than or greater than 6 hours. Older age, low socioeconomic status(medical aid), and low use of Emergency medical system were associated with delays in seeking emergency care for Acute myocardial infarction. Education programs to improve patient knowledge of acute coronary syndrome symptoms and promote patient responsiveness with regard to seeking medical care should be used to reduce the prehospital delay time, especially in the low socioeconomic group.】

Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search

Abstract: Although transformers have achieved impressive accuracies in various tasks in natural language processing, they often come with a prohibitive computational cost, that prevents their use in scenarios with limited computational resources for inference. This need for computational efficiency in inference has been addressed by for instance PoWER-BERT (Goyal et al., 2020) which gradually decreases the length of a sequence as it is passed through layers. These approaches however often assume that the target computational complexity is known in advance at the time of training. This implies that a separate model must be trained for each inference scenario with its distinct computational budget. In this paper, we extend PoWER-BERT to address this issue of inefficiency and redundancy. The proposed extension enables us to train a large-scale transformer, called Length-Adaptive Transformer, once and uses it for various inference scenarios without re-training it. To do so, we train a transformer with LengthDrop, a structural variant of dropout, which stochastically determines the length of a sequence at each layer. We then use a multi-objective evolutionary search to find a length configuration that maximizes the accuracy and minimizes the computational complexity under any given computational budget. Additionally, we significantly extend the applicability of PoWER-BERT beyond sequence-level classification into token-level classification such as span-based question-answering, by introducing the idea of Drop-and-Restore. With Drop-and-Restore, word-vectors are dropped temporarily in intermediate layers and restored at the last layer if necessary. We empirically verify the utility of the proposed approach by demonstrating the superior accuracy-efficiency trade-off under various setups, including SQuAD 1.1, MNLI-m, and SST-2. Upon publication, the code to reproduce our work will be open-sourced.