SPARK M-SPAN 체력수업의 중-고강도 신체활동 증진 효과 및 원인 분석: 순차적 혼합연구

이규일 ( Gyu-il Lee ) 한국스포츠정책과학원(구 한국스포츠개발원) 2016 체육과학연구 Vol.27 No.4

청소년 건강발달의 바로미터는 중-고강도 신체활동이다. 그러나 청소년들의 중-고강도 신체활동 수준은 감소 추세에 있으며, 우리나라 청소년은 상대적으로 저조한 실정이다. SPARK 프로그램은 신체활동 증진의 증거 기반 프로그램으로 인정받고 있다. 이에, 본 연구에서는 SPARK 중등학교 프로그램(SPARK M-SPAN)을 중학교 체력 단원에 적용해 신체활동 수준을 파악하고, 질적 연구방법을 활용해 수업 방식에 따른 차이의 원인을 살펴보았다. 순차적 혼합연구 모형에 따라, SPARK 체력 수업 집단(남 42, 여 42)과 전통적 체력수업 집단(남 42, 여 42)의 신체활동량을 3차원 가속도계로 측정한 후, 질적 연구를 통해 중-고강도 신체활동차이를 일으키는 원인을 분석하였다. 연구결과, SPARK 체력 수업은 전통적인 체력 수업에 비해 모든 운동 강도별 신체활동(비활동, 저강도, 중강도, 고강도, 매우 고강도)에서 긍정적 결과를 보였으며(<.05), 성별분석 결과 역시 여학생의 중강도 활동을 제외한 강도별 활동에서 유사하게 나타났다(<.05). 특히, SPARK체력 수업의 MVPA는 전통적 수업에 비해 16.2%(약 7분 29초) 증가되었다. 질적 연구 결과, 차이의 이유는 SPARK 체력 수업이 신체적으로 바쁜 수업, 재미있고 성취하는 수업, 긍정적 학습 환경을 구축했기 때문으로 확인되었다. 마지막으로, 신체활동 증진에 기여한 SPARK 프로그램의 교수 전략들을 검토하였고, 향후 연구주제를 제안하였다. Active participation in Moderate to Vigorous Physical Activity(MVPA) is the indicator of healthy development for adolescents. However, Korean adolescents` MVPA have continuously declined, and Korean adolescents have lower levels of MVPA compared to adolescents in other countries. Considering this issue, the purpose of this study is to examine the effectiveness of SPARK M-SPAN program to promote adolescents` MVPA in P.E. classes and to understand how the promotion of adolescents` MVPA occurs. To collect data, this study used Sequential Mixed Method and GT3X accelerometers. A total of 168 adolescents (84 in an experimental group and 84 in a control group) participated in this study for the quantitative data analysis, and six students and a teacher were interviewed for the qualitative data analysis. Paired t-test showed that students in SPARK P.E. classes experienced the significant decreases of sedentary behaviors(-339.6 sec) and low intensity P.A.(-96.9 sec) at p<.05 and the significant increases of moderate(+99.3 sec), vigorous(+252.4 sec), and very vigorous intensity P.A.(+84.7 sec) regardless of gender difference at p<.05 except for female students` moderate intensity P.A.. The qualitative data analysis showed that SPARK classes gave students positive learning environments and led them to experience enjoyment and achievement-orientated learning Key teaching strategies of SPARK program and future research suggestions were provided in the discussion section.

SPARK 프로그램이 발달장애 아동의 정서・행동에 미치는 영향

박준태,심태영 한국스포츠학회 2019 한국스포츠학회지 Vol.17 No.1

본 연구의 목적은 SPARK 프로그램 참여가 발달장애 아동의 정서ㆍ행동에 미치는 영향에 대하여 심층적으로 탐색하는데 있다. 연구 참여자는 A초등학교에서 12주간 SPARK 프로그램에 참여한 발달장애 아동 7명과 그 담임교사 5명을 대상으로 선정하였다. 본 연구의 자료 수집을 연구 참여자의 심층면담, 참여 관찰, 문서 자료를 통하여 실시되었 다. 또한 자료 분석을 반복적 비교분석법을 통하였으며 연구의 진실성 확보를 위하여 삼각검증, 캠코더와 현장기록 확 인, 구성원간 검토의 노력을 꾀하였다. 모든 자료들은 전사하여 텍스트화 되었으며, 의미 추출 과정과 범주화 및 의미생 성 과정에서 NVivo11프로그램 사용하여 결과를 도출하였다. 연구결과, 발달장애 아동이 SPARK 프로그램을 참여를 통하여 첫째, 힘들고 어려운 생활모습 : 부정적 자아상 형성, 대인 관계의 어려움, 주변 도움의 부재를 발견할 수 있었다. 둘째, SPARK를 통한 긍정적인 변화 : 변화하는 자아상, 대인 관계의 개선, 장애에 대한 이해와 배려를 발견할 수 있었다. The purpose of this study was to investigate the emotions of children with developmental disabilities according to participation in the SPARK program. And to explore in depth the effects on behavior. Participants were 7 children with developmental disabilities who participated in the SPARK program for 12 weeks in A elementary school and five homeroom teachers. Data collection of this study was conducted through in – depth interviews, participant observation, and documentary data of participants. In addition, data analysis was conducted through iterative comparative analysis. In order to ensure the authenticity of the study, efforts were made to verify triangulation, camcorder and field records, and review among members. All data were transcribed and textualized, and the results were derived using the Nvivo11 program in the process of semantic extraction, categorization, and meaning generation. Through the SPARK program, children with developmental disabilities were found to have a difficult life style (negative self image formation, interpersonal difficulty, lack of peripheral help). Second, We found children with changed developmental disabilities through SPARK (changing self-image, improving interpersonal relationships, understanding and consideration of disability).

UNDERSTANDING OF THE SPARK EFFECT OF ELECTRON COLLISION BY A CAPACITIVE DISCHARGE IGNITION IN A CONSTANT VOLUME COMBUSTION CHAMBER

Kwonse Kim,Kyung Tae Lee,최문석,Dooseuk Choi 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.1

This work is to investigate the physical effect of plasma discharge in atmospheric air using a capacitive discharge ignition (CDI) system. Also, to specifically investigate the kernel effects of CDI system, this work represents the different characteristics including the spark ignition, electric current, integral energy, spark propagation, flame growth, and kernel distribution comparing with the conventional spark ignition. In the experimental setup, the system is composed of a constant volume combustion chamber (CVCC), spark plug, transformer, capacitor device, mass flow controllers, regulators, high-speed camera, and LabVIEW software and cDAQ. The experiment carried out a wide range as the following conditions: J type spark plug, central type electrode, 1.0 mm plug gap, atmospheric air of initial pressure, 292 K of room temperature, 0.75 ms of spark duration, 420 V of CDI voltage, and 12.5 V of initial transformer voltage. As a result, the spark flame kernel of 400V CDI is increased by MEHV comparing with the conventional spark and the improved effect can be seen in 50 μs. Consequently, the plasma effect of MEHV based on CDI system has a linear characteristic regarding spark kernel growth by capacitance energy comparing with the conventional spark.

2016 쉐보레 더 넥스트 스파크 공력성능 개발

김용년(Yongnyun Kim),강선제(Sunje Kang),송봉하(Bongha Song),김용석(Yongsuk Kim) 한국자동차공학회 2015 한국자동차공학회 학술대회 및 전시회 Vol.2015 No.11

This paper presents the development on the Aerodynamic performance of the 2016 Chevrolet the Next Spark. This 2016 Spark is fully changed on the exterior styling and the architecture comparing from its previous version released in 2009 and it was conducted to improve Aerodynamic performance to support fuel economy and fuel consumption. To reduce the drag of the 2016 Spark, Exterior skin is fully optimized to have best Aerodynamic performance. And several Aerodynamic treatments are applied such as flat underbody, low leakage for cooling flow, and add-on Aerodynamics devices. Biggest contribution on drag of the vehicle is coming from exterior surface and underbody shape. This 2016 Spark was developed to have better drag coefficient on these exterior surface and underbody shape. For reducing drag on the exterior surface, it was conducted to optimize the exterior surface cooperated with Exterior studio from early development stage. In this development, Aero was involved from proportion development of the vehicle and theme development. This new vehicle is able to get 58 counts drag reduction from its initial styling model. And for reducing drag on the underbody structure, this vehicle is applied not to have vertical wall on the underbody structure decreasing pressure load. General vehicle and previous version of the Spark have vertical wall on the underbody structure against flow direction to support vehicle safety, but this 2016 Spark is adopted and designed flat underbody structure considering not hurting vehicle safety in early stage of development. This concept was contributed to reduce drag on the underbody structure. Also, this vehicle is developed low leakage cooling flow between the grill and the radiator. The 2016 Spark is reduced 8% - 19% in each powertrain variant comparing to the previous version. This improved cooling flow leakage contributes drag reduction decreasing non-effective flow goes into the engine room. And this vehicle adopted the enhanced airdam, Aerodynamic friendly OSRVM, D-pillar applique integrated roof spoiler, and edged side corner on taillamp, etc. In this development, Aero spends 344 hours for wind tunnel test of the 2016 Spark. And, there was 38 simulation runs for Aero CFD analysis and there was 5 times architecture change reflecting changed body structure. This development was supported to reduce drag 8.3% from the previous Spark and the 2016 Spark is able to lead Aerodynamic performance in the A segment.

OpenCL을 활용한 이기종 파이프라인 컴퓨팅 기반 Spark 프레임워크

김대희(Daehee Kim),박능수(Neungsoo Park) 대한전기학회 2018 전기학회논문지 Vol.67 No.2

Apache Spark is one of the high performance in-memory computing frameworks for big-data processing. Recently, to improve the performance, general-purpose computing on graphics processing unit(GPGPU) is adapted to Apache Spark framework. Previous Spark-GPGPU frameworks focus on overcoming the difficulty of an implementation resulting from the difference between the computation environment of GPGPU and Spark framework. In this paper, we propose a Spark framework based on a heterogenous pipeline computing with OpenCL to further improve the performance. The proposed framework overlaps the Java-to-Native memory copies of CPU with CPU-GPU communications(DMA) and GPU kernel computations to hide the CPU idle time. Also, CPU-GPU communication buffers are implemented with switching dual buffers, which reduce the mapped memory region resulting in decreasing memory mapping overhead. Experimental results showed that the proposed Spark framework based on a heterogenous pipeline computing with OpenCL had up to 2.13 times faster than the previous Spark framework using OpenCL.

하둡 및 Spark 기반 공간 통계 핫스팟 분석의 분산처리 방안 연구

김창수,이주섭,황규문,성효진 한국정보과학회 2018 정보과학회논문지 Vol.45 No.2

One of the spatial statistical analysis, hotspot analysis is one of easy method of see spatial patterns. It is based on the concept that "Adjacent ones are more relevant than those that are far away". However, in hotspot analysis is spatial adjacency must be considered, Therefore, distributed processing is not easy. In this paper, we proposed a distributed algorithm design for hotspot spatial analysis. Its performance was compared to standalone system and Hadoop, Spark based processing. As a result, it is compare to standalone system, Performance improvement rate of Hadoop at 625.89% and Spark at 870.14%. Furthermore, performance improvement rate is high at Spark processing than Hadoop at as more large data set. 공간통계 분석중 하나인 핫스팟 분석은 “인접해 있는 것은 멀리 있는 것 보다 더 연관성이 있다”는 법칙에 따라 공간속성이나 사건의 공간 패턴을 쉽게 파악할 수 있는 기법 중 하나 이지만, 공간의 인접성이 고려되어야 하므로 분산 처리하기 용이하지 않다. 본 논문에서는 핫스팟 분석의 분산처리 방안을 기술하고 성능을 하둡 및 인메모리 기반인 Spark으로 평가한 결과 단일 시스템 대비 하둡기반 처리는 625.89%, Spark기반 처리는 870.14%의 성능향상을 확인하였으며, 하둡 기반과 Spark기반의 비교에서는 대용량 데이터 셋을 처리 할수록 Spark기반의 성능향상율이 높아짐을 확인하였다.

Effective density and light absorption cross section of black carbon generated in a spark discharger

Jeong, B.,Lee, J. Pergamon Press 2017 Journal of aerosol science Vol.107 No.-

<P>We measured physical properties and optical properties of the black carbon (BC) generated in a spark discharger which did not produce soluble organic fractions. Effective densities and absorption cross-sections of the fresh BC were estimated using the electrical mobility diameter, the number concentration and the mass concentration. Size distribution measurements using a differential mobility analyzer (DMA) and a condensation particle counter (CPC) showed that the mode diameter and the number concentration increased with increasing spark frequency of the spark discharger. The primary particle size was also measured through the analysis of images observed using a field emission scanning electron microscope (FESEM), decreasing from 36.3 to 15.1 nm with increasing a spark frequency. The effective densities were estimated from 0.054 to L392 g/cm(3) and compared to be lower than those of atmospheric aerosols which might be aged. The absorption cross-sections were estimated as 1.0 x 10(-15) to L7 x 10(-15) m(2) depending on spark frequency. For the BC generated at a fixed constant spark frequency, the absorption cross-section was estimated to be larger as the BC became bigger in electrical mobility diameter. For the BC having the same electrical mobility diameter generated at different spark frequencies, however, the absorption cross-section generated at a faster spark frequency was smaller. Fractal dimension for the BC larger than 160 nm was measured to be 1.79, which is very similar to that for the spark-generated BC studied by other research group. In conclusion, the effective density allowed us to distinguish the core BC from aged BC.</P>

Multi-spark simulation of the electrochemical discharge machining (ECDM) process

Viveksheel Rajput,Mudimallana Goud,Narendra Mohan Suri 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.11

Electrochemical discharge machining (ECDM) has been studied numerically by several researchers using single spark simulation during finite element modeling (FEM) for analyzing the material removal rate (MRR). However, the process includes the stochastic nature of the spark striking that leads to complexities. Because of this randomness, FEM based multi-spark simulation has not attempted till date. This article attempts to develop an improved model based on a randomly oriented multi-sparks for estimating the MRR. Gradually growing spark behavior and Gaussian heat input are utilized for acquiring the temperature distributions within the work-material. The temperature distributions are further processed to evaluate the MRR with consideration of craters overlapping formed during the multi-spark occurrence. The predicted results exhibit a fair agreement with the experimental results. The simulation-based parametric studies are performed on MRR using applied voltage, electrolyte concentration, and energy transference since it influences the total heat input energy given by the sparks.

Spark 기반 공간 분석에서 공간 분할의 성능 비교

양평우(Yang, Pyoung Woo),유기현(Yoo, Ki Hyun),남광우(Nam, Kwang Woo) 대한공간정보학회 2017 대한공간정보학회지 Vol.25 No.1

본 논문은 인 메모리 시스템인 Spark에 기반 한 공간 빅 데이터 분석 프로토타입을 구현하고, 이를 기반으로 공간 분할 알고리즘에 따른 성능을 비교하였다. 클러스터 컴퓨팅 환경에서 빅 데이터의 컴퓨팅 부하를 균형 분산하기 위해, 빅 데이터는 일정 크기의 순차적 블록 단위로 분할된다. 기존의 연구에서 하둡 기반의 공간 빅 데이터 시스템의 경우 일반 순차 분할 방법보다 공간에 따른 분할 방법이 효과적임이 제시되었다. 하둡 기반의 공간 빅 데이터 시스템들은 원 데이터를 그대로 공간 분할된 블록에 저장한다. 하지만 제안된 Spark 기반의 공간 분석 시스템에서는 검색 효율성을 위해 공간 데이터가 메모리 데이터 구조로 변환되어 공간 블록에 저장되는 차이점이 있다. 그러므로 이 논문은 인 메모리 공간 빅 데이터 프로토타입과 공간 분할 블록 저장 기법을 제시하였다, 또한, 기존의 공간 분할 알고리즘들을 제안된 프로토타입에서 성능 비교를 하여 인 메모리 환경인 Spark 기반 빅 데이터 시스템에서 적합한 공간 분할 전략을 제시하였다. 실험에서는 공간 분할 알고리즘에 대한 질의 수행 시간에 대하여 비교를 하였고, BSP 알고리즘이 가장 좋은 성능을 보여주는 것을 확인할 수 있었다. In this paper, we implement a spatial big data analysis prototype based on Spark which is an in-memory system and compares the performance by the spatial split algorithm on this basis. In cluster computing environments, big data is divided into blocks of a certain size order to balance the computing load of big data. Existing research showed that in the case of the Hadoop based spatial big data system, the split method by spatial is more effective than the general sequential split method. Hadoop based spatial data system stores raw data as it is in spatial-divided blocks. However, in the proposed Spark-based spatial analysis system, there is a difference that spatial data is converted into a memory data structure and stored in a spatial block for search efficiency. Therefore, in this paper, we propose an in-memory spatial big data prototype and a spatial split block storage method. Also, we compare the performance of existing spatial split algorithms in the proposed prototype. We presented an appropriate spatial split strategy with the Spark based big data system. In the experiment, we compared the query execution time of the spatial split algorithm, and confirmed that the BSP algorithm shows the best performance.

Real-Time Processing System of E-Commerce User Data Based on Spark Streaming

장도,가문초,김은성,정회경 한국지식정보기술학회 2023 한국지식정보기술학회 논문지 Vol.18 No.1

The advent of the e-commerce era has changed the way people shop, and at the same time, users generate a large amount of data when shopping. These data can be analyzed by offline calculation, but the results of offline analysis lack real-time performance. In this paper, by processing the log data and business data of e-commerce users in real-time, the feedback of the processing results can be quickly realized. The Spark big data computing framework has the advantages of real-time computing capability and high throughput. Spark Streaming, as an extension of Spark core, is the real-time stream processing component of the Spark computing platform. In this paper, the data is processed in real-time through Spark. Through Maxwell, real-time monitoring of business data changes in the MySQL database is performed, and the monitored data is sent to Kafka. Log data is directly sent to Kafka. Spark Streaming consumes the data in Kafka, then performs specific processing on the data according to the requirements, and the processed data is written to the Elasticsearch. In order to achieve exactly once consumption of data, this paper realizes at least one consumption of data by manually submitting offsets. Elasticsearch supports idempotent writes, so it can achieve exactly once consumption of downstream data. Manually submitted offsets are stored in Redis. Finally, specific queries can be performed on the processing results according to business requirements.