• Nuclear Engineering and Technology
• /
• 제53권10호
• /
• pp.3406-3412
• /
• 2021

A real-time digital time-stamp sorting algorithm used in the In-Beam positron emission tomography (In-Beam PET) is presented. The algorithm is operated in the field programmable gate array (FPGA) and a small amount of registers, MUX and memory cells are used. It is developed for sorting the data of annihilation event from front-end circuits, so as to identify the coincidence events efficiently in a large amount of data. In the In-Beam PET, each annihilation event is detected by the detector array and digitized by the analog to digital converter (ADC) in Data Acquisition Unit (DAQU), with a resolution of 14 bits and sampling rate of 50 MS/s. Test and preliminary operation have been implemented, it can perform a sorting operation under the event count rate up to 1 MHz per channel, and support four channels in total, count rate up to 4 MHz. The performance of this algorithm has been verified by pulse generator and ²²Na radiation source, which can sort the events with chaotic order into chronological order completely. The application of this algorithm provides not only an efficient solution for selection of coincidence events, but also a design of electronic circuit with a small-scale structure.

• 대한안전경영과학회지
• /
• 제10권1호
• /
• pp.155-166
• /
• 2008

During last two decades the transportation system has developed into very intelligent system with GIS, GPS and ITS. The practical transportation management system provides real time response module to manage the customer's order. We have surveyed research papers on the real time vehicle routing problem in last two decades to figure out the dynamic vehicle routing problem. The papers are classified by basic routing algorithms and by managing the dynamic events which are the order management, the routing re-optimization, the routing post-optimization and the waiting strategy.

• International journal of thyroidology
• /
• 제11권2호
• /
• pp.61-70
• /
• 2018

Tyrosine kinase inhibitors (TKIs) are widely used for the treatment of advanced radioiodine-refractory thyroid cancer. Although the previous studies including large-scale randomized controlled trials have demonstrated the effects of TKIs in advanced thyroid cancers, it has been found that most patients experienced adverse events (AEs). Unlike other cancers, even patients with advanced thyroid cancers are often asymptomatic. Rather, TKI use can make patients suffer adverse events. Therefore, the use of TKI should be decided after the full consideration of AEs as well as its efficacies. While using TKI, AEs should be monitored, evaluated, and managed appropriately, if AEs develop. In this review, the occurrence, evaluation, and management of AEs of sorafenib, lenvatinib, and vandetanib will be described, which TKIs are most commonly used for the treatment of advanced thyroid cancer. Some suggestions for the management of AEs in the real life are also provided.

• International Journal of Heart Failure
• /
• 제6권2호
• /
• pp.76-81
• /
• 2024

Background and Objectives: Real-world clinical data, outside of clinical trials and expert centers, on adverse events related to the use of SyncCardia total artificial heart (TAH) remain limited. We aim to analyze adverse events related to the use of SynCardia TAH reported to the Food and Drug Administration (FDA)'s Manufacturers and User Defined Experience (MAUDE) database. Methods: We reviewed the FDA's MAUDE database for any adverse events involving the use of SynCardia TAH from 1/01/2012 to 9/30/2020. All the events were independently reviewed by three physicians. Results: A total of 1,512 adverse events were identified in 453 "injury and death" reports in the MAUDE database. The most common adverse events reported were infection (20.2%) and device malfunction (20.1%). These were followed by bleeding events (16.5%), respiratory failure (10.1%), cerebrovascular accident (CVA)/other neurological dysfunction (8.7%), renal dysfunction (7.5%), hepatic dysfunction (2.2%), thromboembolic events (1.8%), pericardial effusion (1.8%), and hemolysis (1%). Death was reported in 49.4% of all the reported cases (n=224/453). The most common cause of death was multiorgan failure (n=73, 32.6%), followed by CVA/other non-specific neurological dysfunction (n=44, 19.7%), sepsis (n=24, 10.7%), withdrawal of support (n=20, 8.9%), device malfunction (n=11, 4.9%), bleeding (n=7, 3.1%), respiratory failure (n=7, 3.1%), gastrointestinal disorder (n=6, 2.7%), and cardiomyopathy (n=3, 1.3%). Conclusions: Infection was the most common adverse event following the implantation of TAH. Most of the deaths reported were due to multiorgan failure. Early recognition and management of any possible adverse events after the TAH implantation are essential to improve the procedural outcome and patient survival.

• 한국산학기술학회논문지
• /
• 제17권4호
• /
• pp.122-128
• /
• 2016

본 논문에서는 OTDR 신호에 대한 새로운 이벤트 분석방법을 제안하였다. OTDR 신호는 고주파 잡음을 효율적으로 제거하기 위해 해밍 필터를 거친 다음, 이벤트 검출을 위해 미분 필터를 통과시킨다. 종단을 전 후로 신호레벨이 현저하게 차이가 나는 점을 이용하여 OTDR 신호의 종단 위치를 찾고, 이 범위 내에서 미분 필터를 통과한 신호의 극대점을 조사하여 피크영역을 검출한다. 검출된 피크 영역에서 잡음 정보와 피크 임계값을 이용하여 피크의 진위를 판단한 다음 이를 이용하여 이벤트 정보를 구성한다. 마지막으로 종단 위치 이후에서는 가입자 피크를 검사하여 가입자 이벤트 정보를 구한다. 시뮬레이션 결과를 광선로 구성과 비교한 결과 제안된 방법은 이벤트 발생지점을 17m 이내로 검출하였다. JDSU 장비의 경우, 이벤트 발생지점이 25m 이내, 가입자 사이 간격은 5m 이내로 검출하였고, RadianTech 장비의 경우에는 이벤트 발생 지점이 19m 이내, 가입자 사이의 간격은 5m 이내로 검출하였다. 이러한 결과는 본 논문에서 제안한 신호 분석방법이 광선로망 감시 시스템 운용에 충분히 활용 가능하다는 것을 보여준다고 할 수 있다.

• 디지털융복합연구
• /
• 제18권11호
• /
• pp.539-545
• /
• 2020

이 연구는 코로나 바이러스 사태로 인해 국내 대학에서 실시한 온라인 실시간 수업에 대해 대학생의 수업 참여 효능감이 어떠한지 알아보고자 기획하였다. 이 연구의 참여자는 J 지역 한 대학교에 재학 중이고 실시간 온라인 수업에 참여한 경험이 있는 701명 대학생이다. 이 연구의 도구는 상호작용, 학습향상, 수업적응, 접근성 등 4 영역으로 구성하였다. 연구 결과는 다음과 같다. 첫째, 대학생의 실시간 온라인 수업 참여의 효능감은 접근성이 가장 높고 상호작용이 가장 낮았다. 둘째, 대학생의 실시간 온라인 수업 참여 효능감은 성별, 학년, 전공계열, 접근 도구에 따라 차이가 나타났다. 이 연구는 코로나 바이러스로 인한 실시간 온라인 수업의 활용성을 점검하고, 포스트(post) 코로나 시대 본격적인 원격교육의 도래를 대비하는 방법으로서 실시간 온라인 수업의 적용 가능성을 확인하였다는 데 그 의미가 있다. 마지막으로 시대의 변화를 반영한 대학 전공별 온라인 수업의 운영 방식 및 지원의 고도화를 제언하였다.

• International Journal of Contents
• /
• 제15권4호
• /
• pp.16-26
• /
• 2019

The efficient implementation of various physical actions of agents to respond to dynamically changing situations is essential for the simulation of realistic agents and activities in a cyber world. To achieve a maximum diversity of actions and immediate responsiveness to abrupt changes in situations, we have developed an animation technique in which complex actions are recursively constructed by reusing a set of primitive motions, and agents are designed to react in real-time to abrupt ambient changes by computationally satisfying kinematic constraints on body parts with respect to their goals. Our reusing scheme is extended to visualize the procedure of realistic intricate situations involving many concurring events. Our approach based on motion reuse and recursive assembly has clear advantages in motion variability and action diversity with respect to authoring scalability and motion responsiveness compared to conventional monolithic (static) animation techniques. This diversity also serves to accommodate the characteristic unpredictability of events concurring in a situation due to inherent non-determinism of associated conditions. To demonstrate the viability of our approach, we implement several composite and parallel actions in a dynamically changing example situation involving events that were originally independent until coincidentally inter-coupled therein.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권1호
• /
• pp.169-189
• /
• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• Journal of Korea Trade
• /
• 제27권2호
• /
• pp.96-114
• /
• 2023

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consumption) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2004년도 춘계학술대회
• /
• pp.225-236
• /
• 2004

Every web server comprises a repository of all actions and events that occur on the server. Server logs can be used to quantify user traffic. Intelligent analysis of this data provides a statistical baseline that can be used to determine server load, failed requests and other events that throw light on site usage patterns. This information provides valuable leads on marketing and site management activities. In this paper, we propose a method of design for log analysis system using RTMA(realtime monitoring and analysis) technique.