Empirical Research on Workload and Work Performance of University Faculty: Moderating Roles of Self-monitoring and Work Characteristics

종하몽 계명대학교 대학원 2023 국내박사

중학교 교사의 업무부담 및 교사효능감과 학교효과성 인식의 관계

김대안 전북대학교 교육대학원 2022 국내석사

The purpose of this study is to analyze perceived difference of workload, teacher efficacy, and school effectiveness of middle school teachers by their demographic variables, and to investigate the effect of workload and teacher efficacy on school effectiveness. To this end, four different research questions were examined as follows: First, What is the difference in perception of workload according to the background characteristics of middle school teachers? Second, What is the difference in perception of teacher efficacy according to the background characteristics of middle school teachers? Third, What is the difference in perception of school effectiveness according to the background characteristics of middle school teachers? Fourth, what is the relationship between workload, teacher efficacy and school effectiveness perception of middle school teacher? To achieve the purpose of this study, the relationship among workload, teacher efficacy, and school effectiveness was analyzed using the data 『Gyeonggi Education Panel Study』, which was conducted by the Gyeonggi Institute of Education. The major findings of this research are as follows: First, it has been shown that the workload of middle school teachers were at a slightly higher level than average. According to a thorough examination of the workload perceived by middle school teachers according to their background variables, teachers who has less experience, teachers who are on a fixed-term contract, teachers who are in charge of a homeroom, teachers who have a lower position, and younger teachers were suffering from a high level of perceived workload. Second, it has been shown that the level of class guidance teacher efficacy and life guidance teacher efficacy perceived by middle school teachers were high. According to the analysis of perceived class guidance teacher efficacy based on the teacher’s background variables, it has been found that teachers with more experience, higher position, higher level of education, higher age, and teachers who are not a homeroom teacher had a higher level of perceived class guidance teacher efficacy. Meanwhile, it has also been found that teachers with more experience, higher age, teachers who are on a fixed-term contract, and chief teachers had a higher level of perceived life guidance teacher efficacy. Third, middle school teachers generally had a high level of perceived job satisfaction, school satisfaction, teaching/learning activity, principal’s leadership, school culture, and cooperative network with local community, which are all sub-factors of school effectiveness. According to the analysis of job satisfaction perceived by middle teacher’s background variables, it has been found that teachers who have more than 30 years of experience, teachers who are not a homeroom teacher, teachers who are on a fixed-term contract, chief teachers, male teachers, and older teachers had a higher level of perceived job satisfaction. Fourth, the workload and school effectiveness of middle school teachers had a low, but significant positive correlation, while the correlation between class guidance teacher efficacy/life guidance teacher efficacy and school effectiveness were somewhat high. According to the result of a multiple regression analysis on the effect of workload on teacher efficacy, it has been found that workload did not have a significant influence on teacher efficacy. In addition, the result of a multiple regression analysis on the influence of workload, class guidance/life guidance teacher efficacy on school effectiveness has revealed that workload had a low but significant positive impact on school effectiveness, while class guidance/life guidance teacher efficacy had a signifiacnt positive impact on school effectiveness. The findings of this study suggests these implications. First, it is necessary to prepare measures to improve and support the treatment of fixed-term teachers and low-experience teachers. Second, supplemen tary measures are needed for institutional support implemented to redu ce teachers’ work. Third, there is a need for a plan to promote teacher efficacy in order to increase school effectiveness.

Measuring and Quantifying Driver Workload on Limited Access Roads

Liu, Ke ProQuest Dissertations & Theses University of Mich 2019 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Minimizing driver errors should improve driving safety. Driver errors are more common when workload is high than when it is low. Thus, it is of great importance to study driver workload. Knowing the amount of workload at any given time, take-over time can be determined, adaptive in-vehicle systems can be refined, and distracting in-vehicle secondary tasks can be regulated.In this dissertation, a model quantifying workload as a function of traffic, in which workload is proportional to inverse time headway (THW) and time to collision (TTC), was proposed. Two experiments were conducted to investigate how traffic affected driver workload and evaluate the proposed model. The driving scenarios were categorized into static (i.e., no relative movements among vehicles) and dynamic (i.e., there are relative velocities and lane change actions). Three categories of workload measures (i.e., workload rating, occlusion %, and driving performance statistics) were analyzed and compared. A GOMS model was built based upon a timeline model by using timerequired to represent mental resources demanded and timeavailable to represent mental resources available.In static traffic, the workload rating increased with increased number of vehicles around but was unaffected by participant age. The workload ratings decreased with increasing Distance Headways (DHWs) of each vehicle. From greatest to least, the effects were: DHWLead, DHWLeftLead, DHWLeftFollow, DHWFollow. Any surrounding vehicle that was 14.5 m away from the participant resulted in significant greater workload. Drivers tended to compromise longitudinal speed but still maintain lateral position when workload increased. Although occlusion% was less sensitive to scenarios having no lead vehicles, it can nonetheless be well predicted using the proposed workload model in sensitive scenarios. The resulting equations were occlusion% = 0.35 + 0.05/THWLead + 0.02/THWLeftLead - 0.08Age (Rocclusion2=0.91); rating = 1.74 + 1.74/THWLead + 0.20/THWFollow + 0.79/THWLeftLead + 0.28/THWLeftFollow (Rrating2=0.73). In dynamic traffic, drivers experienced greater workload in the faster lane; higher workload level was associated with greater relative velocity between two lanes. Both rating and occlusion% can be described using the proposed model: Anchored rating = 4.53 + 1.215/THWLeftLeadLead + 0.001/THWLeftFollow + 3.069/THWLeadLead + 0.524/THWLead + 0.240/(TTCLeadxTTCLeadLateral) + 30.487/(TTCLeftLeadxTTCLeftLeadLateral) (Rrating2=0.54); Occlusion% = 0.381 + 0.150/THWLeftLeadLead ˗ 0.117/THWLeadLead + 0.021/THWLead + 2.648/(TTCLeftLeadxTTCLeftLeadLateral) (Rocclusion2=0.58). In addition, it was shown that the GOMS model accounted for the observed differences of workload ratings from the empirical data (R2>0.83).In contrast to most previous studies that focus on average long-term traffic statistics (e.g., vehicles/lane/hour), this dissertation provided equations to predict two measures of workload using real-time traffic. The comparisons among three workload measures provided insights into how to choose the desired workload measures in their future research. In GOMS model, the procedural knowledge of rating workload while driving was developed. They should be transferrable to other workload studies and can serve as the primary tool to justify experimental design.Scientifically, the results of this dissertation offer insights into the mechanism of the way that humans perceive workload and the corresponding driving strategies. From the engineering application and practical value perspective, the proposed workload model would help future driving studies by providing a way to quantify driver workload and support the comparison of studies in different situations.

AIR TRAFFIC CONTROLLER WORKLOAD EVALUATIONS FOR TERMINAL AIRSPACE CAPACITY ESTIMATION : 터미널 공역 용량 추정을 위한 항공 교통 관제사 업무량 부하 평가

JEMIMA LOUISE KAMESE Graduate School of Korea Aerospace University 2023 국내석사

The Aviation and Air Transportation Industry is a developing industry that continues to grow at an exponential rate. This growth is attributed to immense technological advancements and constant research into increasing optimization of the air transportation. In turn, safety precautions equivalent to the magnitude of growth are required to ensure that air transportation remains safe. And thus, safety is a paramount aspect and necessity of the Aviation and Air Transportation Industry. Among the various facilitators of Air Traffic Safety, an important one with a key role are the Air Traffic Controllers (ATCos). ATCos ought to make traffic control decisions promptly and swiftly to ensure traffic safety. They are responsible for numerous tasks and the rate at which these tasks are completed, and aircrafts safely guided through the various zones of the airspace to their destination, influences controllers’ workload. Hence, decision-making is undoubtedly a crucial part of air traffic control with a direct impact on Air Traffic Controllers’ workload. This study focuses on workload evaluation for terminal capacity estimation. There are multiple ways that can be applied to estimate terminal capacity and the methods chosen in this research utilize Human-in-the-loop Simulation to assess Air Traffic Controller’s workload through NASA task load index (TLX), the use of the encephalogram (EEG) and Well Clear Score (WCS) with air traffic conditions of varying traffic complexity and density. The results based on these methods are analyzed and used to investigate the most suitable ATCo workload indicators, the factors that influence workload, and the application of these results for increasing or decreasing capacity relative to the ATCo’s capabilities. 항공 운송 산업은 기하급수적인 속도로 성장하고 있는 산업이다. 이러한 성장은 항공 운송의 안전과 기술발전, 운송 산업의 성장 최적화를 증가시키기 위한 연구에 기인하게 된다. 특히, 항공 산업의 안전을 위해서는 성장의 크기에 상응하는 안전 예방 조치가 필요할 것이다. 항공 교통 안전의 핵심적인 역할을 하는 것은 항공 교통 관제사(ATCos)이다. 항공 교통 관제사는 항공기가 출발지부터 목적지까지 안전하게 도착할 수 있도록 신속하고 안전하게 항공기의 운항을 통제하여야한다. 이러한 항공기의 통제를 위해서는 수 많은 관제사의 업무 중, 의사 결정(decision-making)이 중요한 요인이 될 것이다. 본 연구는 단말기 용량 추정을 통해 항공 교통 관제사의 업무량을 추정하는 것에 초점을 맞추고 있다. 단말기를 통해 관제사의 업무량을 추정하는 방법은 여러 가지가 있으나, 본 연구에서는 HITL(Human-in-the-loop Simulation) 실험을 통해 NASA 업무 부하 지수(TLX), 뇌파(EEG) 및 WCS(Well Clear Score)를 분석하여 관제사의 업무량을 추정한다.

Optimizing GPU-accelerated applications using workload scheduling and memory management

박정호 서울대학교 대학원 2020 국내박사

매니코어 프로세서가 탑재된 가속기를 이종 시스템은 고성능이 필요한 다양한 분야에서 널리 활용되고 있다. 그 중 GPU는 가장 널리 사용되는 가속기 중 하나이다. GPU를 효율적으로 활용하기 위해서는 최적화와 메모리 관리가 매우 중요하다. GPU의 하드웨어 구조는 고전적인 CPU와 큰 차이가 있으므로 응용프로그램의 병렬성과 GPU의 구조를 모두 고려한 최적화가 필수적이다. 또한 GPU는 메인 메모리에 비해 매우 적은 메모리를 가지고 있으므로 효율적인 메모리 관리도 필요하다. 본 논문에서는 워크그룹 스케줄링 기법과 메모리 관리 기법을 제시한다. 이 기법은 두 가지 실제 GPU 가속 응용프로그램을 대상으로 구현되었다. 첫번째로 본 논문에서는 컨트롤 플로우 다이버전스(control flow divergence)와 워크로드 불균형을 최소화 하기 위한 워크로드 스케줄링 기법을 제안한다. 컨트롤 플로우 다이번전스와 워크로드 불균형은 GPU 가속 응용프로그램의 성능을 심각하게 저하시킨다. 제안하는 워크로드 스케줄링 기법은 여러개의 통(bin)을 이용하여 작업들을 같거나 비슷한 타입으로 분류한다. 분류된 작업들이 들어있는 통은 각각의 컴퓨트 유닛에서 실행되어 컨트롤 플로우 다이버전스와 작업 불균형을 최소화 한다. 본 논문에서 해당 기법은 저가의 내장형 APU를 활용한 고속의 IPsec 게이트웨이 상에서 예시로 구현된다. APU (Accelerated Processing Unit) 는 한 칩에 일반적인 CPU와 GPU가 탑재된 이종 멀티코어 프로세서이다. 이 프로세서는 물리적으로 CPU와 GPU 사이에 메모리를 공유하는 HSA (Heterogeneous System Architecture)를 지원한다. 기존의 일반적인 GPU에서는 제거하지 못했던 CPU와 GPU의 데이터 전송 오버헤드를 HSA를 이용하여 제거한다. 본 게이트웨이는 OpenCL을 사용하여 GPU 상에서 작동하도록 구현하였으며 제로카피 패킷 입출력을 위해 DPDK를 사용하였다. IPsec 게이트웨이는 다양한 종류의 실제 네트워크 트래픽을 처리해야한다. 이를 위해 본 논문에서는 이러한 패킷 처리 상황에서 GPU의 사용률을 획기적으로 높일 수 있는 패킷 스케줄링 알고리즘을 제안한다. 해당 알고리즘은 APU 뿐만 아니라 일반적인 분산 GPU에서도 작동한다. 두번째로 GPU 메모리 부족 문제를 해결하기 위해 스와핑 메모리 기법을 기반으로 한 GPU 메모리 관리 기법을 제안한다. 딥 러닝은 매우 많은 메모리를 필요로 하므로 제안된 메모리 관리 기법을 적용하기에 매우 좋은 어플리케이션이다. 해당 기법은 tvDNN이라고 이름지어진 공유 라이브러리 형태로 구현되어 딥 러닝시에 GPU 메모리 부족 문제를 해결한다. 이를 이용하여 GPU메모리 공간 보다 훨씬 큰 메모리를 필요로 하는 DNN 모델을 학습 할 수 있다. tvDNN은 두가지 효율적인 메모리 전송 스케줄링 알고리즘을 제시하는데 하나는 ILP를 이용한 알고리즘이고 다른 하나는 휴리스틱 알고리즘이다. 해당 알고리즘은 메모리 전송과 GPU 계산을 최대한 겹치게 하는 스케줄을 만드는 것이다. 또한 메모리 오브젝트 섹셔닝 기법을 제시하여 256이나 512같은 큰 배치 사이즈 에서도 DNN 학습을 가능하게 하였다. tvDNN은 기존 DNN 프레임워크의 소스코드 수정이나 재컴파일이 필요없이 투명하게 동작하는 라이브러리 형태로 작성되었다. Caffe를 이용한 다양한 실험 결과는 기존의 다른 소프트웨어 기반의 메모리 확장 솔루션에 비해 더 좋은 성능을 보여주었다. 또한 기존의 방식에서 불가능했던 256 배치 이상의 입력도 16 GB의 GPU 메모리로 잘 동작함을 보여준다. Heterogeneous systems that contain manycore processors as accelerators are widely used to achieve high performance. GPUs are the most well-known accelerators. The optimizations considering the GPU architecture and parallelism of applications are essential for the efficient use of GPUs. Moreover, GPU memory management is necessary because GPU memory capacity is much less than the main memory. In this thesis, we propose a workload scheduling and memory management technique. We implement two real-world GPU-accelerated applications with workload scheduling and memory management techniques and show the effectiveness of the techniques. First, this thesis proposes the workload scheduling technique to minimize control flow divergence and workload imbalance. The control flow divergence and workload imbalance significantly degrade the performance of the GPU-accelerated applications. The proposed workload scheduling technique uses multiple bins to classify the workloads of the same or similar types. Each bin is executed on a compute unit in a GPU. The technique is implemented on a high-performance IPsec gateway using a low-cost commodity embedded Accelerated processing unit (APU). The IPsec gateway processed different types of packets, which created tremendous control flow divergence and workload imbalance. An APU is a heterogeneous multicore processor that contains general-purpose CPU cores and a GPU in a single chip. It also supports Heterogeneous System Architecture (HSA) that provides coherent physically-shared memory between the CPU and GPU. The gateway is implemented in OpenCL to exploit the GPU and uses zero-copy packet I/O APIs in the DPDK. The IPsec gateway handles the real-world network traffic where each packet has a different workload. The proposed packet scheduling algorithm significantly improves GPU utilization for such traffic. It works not only for APUs but also for discrete GPUs. Second, this thesis proposes a GPU memory management technique, based on a memory swapping mechanism, to address the shortage of GPU memory capacity for applications that require more memory than the memory capacity of GPUs. The technique is implemented as a shared library, called tvDNN, to address the GPU memory restriction problem for deep learning. Deep learning requires a massive amount of GPU memory, so this is a suitable example of implementing the proposed technique. The tvDNN enables us to build a DNN model that requires a larger GPU memory space than that of the target GPU. The tvDNN provides two efficient memory transfer scheduling algorithms based on ILP and heuristics to generate an optimal memory transfer schedule that maximizes the overlap between memory transfers and GPU computations. We also propose a memory object sectioning technique that enables DNN configurations with a huge input batch size, such as 256 and 512. No source code modification or recompilation of the target DNN is required to use the tvDNN. The experimental result shows that the performance of Caffe with the tvDNN is much better than the existing software-based, non-transparent GPU memory management solution. Moreover, the tvDNN enables Caffe to build the VGG-16 with a batch size of 256 or 512 using 16GB of GPU memory, which is impossible with existing solutions.

Performance prediction model for data-parallel workload in distributed processing environment

명노영 Graduate School, Korea University 2021 국내박사

Enhancing performance of big data analytics in distributed environment has been issued because most of the big data related applications such as machine learning techniques and streaming services generally utilize distributed computing frameworks. Thus, optimizing performance of those applications at Spark has been actively researched. Optimizing the performance of the applications in a distributed environment is challenging because it not only requires optimizing the applications themselves but also requires tuning the distributed system configuration parameters. The application processing procedure of the Spark is analyzed and modeled. Through the analyzed results, performance optimization schemes for each step of the procedure: inner-stage and outer-stage are proposed. Also, an appropriate partitioning heuristic is proposed by analyzing the relationship between partitioning parallelism and the performance of the applications. A machine-learning-based prediction model is proposed that determines the efficient memory for a given workload and data. To determine the validity of the proposed model, the model is applied to data-parallel workloads which include a deep learning model. The predicted memory values are in close agreement with the actual amount of required memory. Additionally, the whole building time for the proposed model requires a maximum of 44% of the total execution time of a data-parallel workload. The proposed model can improve memory efficiency up to 1.89 times compared with the vanilla Spark setting. A completion time prediction model based on machine learning for the representative deep learning model convolutional neural network (CNN) is proposed by analyzing the effects of data, task, and resource characteristics on performance when executing the model in the Spark cluster. To reduce the time utilized in collecting the data for training the model, the causal relationship between the model features and the completion time based on Spark CNN's distributed data-parallel model is considered. The model features include the configurations of the Data Center OS Mesos environment, configurations of Apache Spark, and configurations of the CNN model. By applying the proposed model to famous CNN implementations, 99.98% prediction accuracy about estimating the job completion time is achieved. In addition to the downscale search area for the model features, extrapolation is leveraged, which significantly reduces the model build time at most to 89% with even better prediction accuracy in comparison to the actual work. Enhancing performance of big data analytics in distributed environment has been issued because most of the big data related applications such as machine learning techniques and streaming services generally utilize distributed computing frameworks. Thus, optimizing performance of those applications at Spark has been actively researched. Optimizing the performance of the applications in a distributed environment is challenging because it not only requires optimizing the applications themselves but also requires tuning the distributed system configuration parameters. The application processing procedure of the Spark is analyzed and modeled. Through the analyzed results, performance optimization schemes for each step of the procedure: inner-stage and outer-stage are proposed. Also, an appropriate partitioning heuristic is proposed by analyzing the relationship between partitioning parallelism and the performance of the applications. A machine-learning-based prediction model is proposed that determines the efficient memory for a given workload and data. To determine the validity of the proposed model, the model is applied to data-parallel workloads which include a deep learning model. The predicted memory values are in close agreement with the actual amount of required memory. Additionally, the whole building time for the proposed model requires a maximum of 44% of the total execution time of a data-parallel workload. The proposed model can improve memory efficiency up to 1.89 times compared with the vanilla Spark setting. A completion time prediction model based on machine learning for the representative deep learning model convolutional neural network (CNN) is proposed by analyzing the effects of data, task, and resource characteristics on performance when executing the model in the Spark cluster. To reduce the time utilized in collecting the data for training the model, the causal relationship between the model features and the completion time based on Spark CNN's distributed data-parallel model is considered. The model features include the configurations of the Data Center OS Mesos environment, configurations of Apache Spark, and configurations of the CNN model. By applying the proposed model to famous CNN implementations, 99.98% prediction accuracy about estimating the job completion time is achieved. In addition to the downscale search area for the model features, extrapolation is leveraged, which significantly reduces the model build time at most to 89% with even better prediction accuracy in comparison to the actual work.

조선해양 CAD 환경에서의 선박배관 자동배치 시스템 개발

김신형 서울대학교 대학원 2014 국내박사

In this research, an automatic pipe routing system operated in a shipbuilding CAD environment is developed. The system is aimed to reduce total workload of pipe routing design. Workload estimation and analysis enable us to improve pipe routing design and consequently, diminish overall workload of the system under consideration. This new design process has a graph based optimization algorithm for pipe routing and design support functions. The pipe routing design process is analyzed by employing a GOMS (Goal, Operation, Method, Selection) describing the design process in which designer need to carry out tasks on a CAD system. This GOMS modeling of pipe design work consists of functional unit tasks. Each unit task has a different Goal, Operation, Method and Selection according to GOMS notation and the series of this unit task describes pipe routing design process on a CAD system. A workload of pipe routing design is estimated by applying a NASA-TLX. The NASA-TLX is a kind of subjective measure for mental workload consisting of six subscales that represent independent aspect of workload: Mental, Physical, Temporal Demand, Frustration, Effort, and Performance. This NASA-TLX measurement is applied to the designer who is achieving unit pipe routing works of GOMS analysis. The sum of all workload measurement is the total workload of pipe routing design work. The workload in this research means mainly mental load that occurs in interaction between designer and design task when resource and capacity is limited to achieve a goal. Through NASA-TLX analysis, major workload tasks are identified among all unit design works. Those heavy workload tasks are mainly related to searching available space with narrow and complex constraint and manipulating view in a CAD system. Having this workload analysis, a new pipe routing design process is designed to reduce workload. Compared to old process, the new routing process additionally has automatic routing sub-process and pipe routing modification process characterized by enhanced functions in a CAD system. A main algorithm of automatic routing system is an A* graph algorithm. It is a path finding algorithm with the best-first search strategy and it uses a heuristic cost function that determines which node may visit from a certain node. A sophisticate modification is developed for the strategy of heuristic cost function and for its update rule. The pipe routing design knowledge and practices are considered in the heuristic strategy keeping the monotonic admissibility which (simultaneously) guarantees finding the optimum path in the network. In this modified cost function, a bending pipe which causes higher cost is considered, and space preference which represents design practices such as pipe support sharing, valve accessibility and so on. This automatic pipe routing system is developed in the shipbuilding CAD using API (Application Program Interface) and integrated seamlessly in the pipe design system. This integrity also contributes reducing workload of pipe routing design. To verify the effectiveness of this automatic routing system, the modified pipe routing design process is also modeled into unit design works by GOMS and is evaluated with NASA-TLX to estimated workload. The results show that there are significant reductions in the total workload and pipe routing time to fulfil the same quality of pipe routing output. 2000년대 들어 국내 조선해양 산업은 세계 최고 수준의 경쟁력을 바탕으로 급격한 질적 양적 성장을 이루며 막대한 수출실적과 전후방 연관산업의 부흥 그리고 대규모 고용창출로 국가 경제 성장에 핵심적인 역할을 담당해 왔다. 산업규모의 확대에 따라 생산인력이 늘어나고 산업안전에 관한 인식과 제도가 강화되면서 생산측면의 근로환경은 향상되어 왔다. 하지만 인력공급이 탄력적이지 못한 설계 엔지니어의 업무부하는 꾸준히 증가되어 왔으며 이는 조선해양산업의 경쟁력을 약화 시킬 수 있는 잠재적인 위험요소가 되고 있다. 이에 설계 자동화 시스템 등의 개발과 도입으로 설계부하를 줄여 나가야 할 필요성이 제기되고 있다. 본 연구를 통해서 조선해양 현업CAD환경에서 운용하는 자동 배관배치 시스템을 개발했다. 새로운 배관배치 시스템의 목표는 전체적인 배관배치 설계업무 부하의 감소이며 이를 위해 현재 배관배치 업무의 체계적인 분석을 통해 개선된 설계 프로세스를 정의했다. 개선된 프로세스는 배치업무에 적합하게 고안된 그래프 기반 최적 배치 알고리즘을 사용한 배관 자동배치기능과 설계지원 기능을 가지고 있다. 배관배치 설계업무를 체계적으로 분석하기 위해서 과업수행 심성모델 구성기법인 GOMS(Goal, Operation, Method, Selection)를 적용했다. 이는 설계자가 CAD 시스템을 이용해서 수행하는 설계 행위를 사용자 - CAD시스템 사이의 인터페이스 작업의 관점으로 해석한 것으로 설계업무를 그 진행절차에 따라 기능들의 계층으로 분류하고 이를 다시 단위 기능업무로 나누는 식으로 분석했다. 분석된 단위 배관배치 설계 업무들은 GOMS 표현기법에 따라 각각Goal, Operation Method와 Selection 이 설정되어 있으며 계층구조를 이루고 진행순서에 따라 CAD 시스템상에서의 배관배치 업무절차를 기술하고 있다. GOMS 표현기법에 따라 단위업무들로 분석된 배관배치 업무를 대상으로 주관적 업무부하 측정기법인 NASA-TLX 를 적용하여 정신적 업무부하 측정을 수행했다. 배관배치 중에 설계자의 직접 설문과 관찰을 통해 기존 배관배치 업무의 정신적 부하를 분석하였다. 이와 같은 업무부하 분석을 통해 기존의 배관배치 업무에서 주요한 정신적 부하작업은 주로 경로결정을 위한 공간 검토작업, 정리작업등으로 나타났다. 이를 경감시키는 방안으로서 배치자동화 기능 및 강화된 수정기능을 포함한 배관배치 설계절차를 정의 하고 이를 지원하기 위한 배관배치 자동화 시스템을 개발하였다. 배관배치 자동화 알고리즘은 그래프 최소비용 탐색 알고리즘인 A* 알고리즘을 이용했으며 A*알고리즘에서 휴리스틱 비용추정 부분의 동작 개선을 통해 벤딩 비용과 특정 공간의 배치선호도를 여부를 공간의 진행비용으로 계량화해서 고려할 수 있도록 했다. 임의 공간에 대한 비용정의는 자동배치 중에 기존 파이프 서포트/트레이 공유, 특정 장비로부터의 유격 유지, 특정 위치에서의 밸브 컨트롤 등 다양한 배관배치 설계 프렉티스를 결과에 반영에 쉽게 활용 할 수 있다. 배관배치 자동화 시스템은 조선해양전용 CAD 시스템인 Tribon M3의 API를 이용해서 구현되었으며 배관배치를 위한 모델정보 획득 및 자동배치 결과에 의한 배관모델 자동 생성 및 모델 수정 등이 CAD 시스템 내부에서 수행된다. 개발된 배관배치 자동화 시스템의 적용을 기반으로 구성된 새 업무절차에 대해 GOMS분석 모델링 하고 이들 단위 업무를 NASA-TLX를 적용하는 동일한 평가방식으로 정신적 업무부하를 측정했다. 결론적으로 배관배치 자동 시스템을 포함한 새로운 배관배치 설계 절차가 기존 배관배치 업무 대비 업무부하의 감소가 있었음을 확인 할 수 있었다.

중국 헤이룽장성(黑龍江) 교원업무경감 정책에 대한 교사들의 인식 분석연구

이함옥 중앙대학교 대학원 2023 국내석사

The purpose of this study was to investigate how middle school teachers perceive the policy of reducing teachers' workload and to derive implications for improving the practical implementation of workload reduction policies. Based on the work areas of Chinese teachers surveyed in previous studies, 42 items were created considering the content of workload reduction policies. A survey was conducted among teachers working in the Heilongjiang Province, with a total of 101 respondents. After excluding responses with a response time of less than 100 seconds, a total of 90 responses were analyzed. Using descriptive statistical methods such as frequency, percentage, mean, and standard deviation, the study analyzed the perceptions of elementary and middle school teachers regarding the intensity, difficulty, and level of reduction in administrative tasks. Cross-analysis was conducted to examine the differences in perception of workload intensity, difficulty, and reduction among different groups of teachers. Finally, t-tests, One-way ANOVA, and Scheffé post-hoc tests were used to explore the differences in perception of workload intensity, difficulty, and reduction among elementary and middle school teachers. The analysis revealed the following results: Firstly, elementary and middle school teachers perceived the intensity of workload at a level of 3.37, indicating a relatively high intensity of workload. Secondly, they perceived the difficulty of workload at a level of 3.20, indicating a high level of difficulty. Thirdly, after the implementation of workload reduction policies, teachers' perception of the level of reduction in workload was 3.75, indicating a minimal effect of workload reduction. Based on these findings, several implications can be drawn. Firstly, there is an urgent need for standardization of teachers' tasks by the Ministry of Education, educational authorities, and individual schools. Secondly, support for budget and human resources should be expanded to achieve substantial reduction in administrative tasks. Thirdly, there is a need to establish a new mechanism for managing and disseminating workload reduction policies. 본 연구는 교원 업무경감 정책에 대한 중등학교 교원의 업무경감 정책에 대해 어떻게 인식하고 있는지를 알아보는 한편, 정부가 의도적인 목적을 얼마나 달성하는지에 대한 문제 인식에서 출발한다. 헤이룽장성의 중등학교 교원을 대상으로 교원 업무경감 정책에 대한 인식을 분석하여 실질적인 업무경감 정책 개선을 위한 시사점을 도출하기 위해 수행되었다. 이를 위해 선행연구에 조사된 중국 교원의 업무 수행 영역을 바탕으로 업무경감 정책의 내용을 고려하여, 배경 변인이 포함된 42개 문항을 제작하며, 헤이룽장성 지역에 근무 중인 교원을 대상으로 설문을 실시하였다. 조사 결과 회수된 응답자가 101명이며, 응답 시간은 100초 이하인 응답을 제외하고, 총 90부를 분석에 활용하였다. 위의 분석 자료를 토대로, 먼저 초⦁중등학교 교원들이 행정업무의 업무 수행 강도, 곤란도, 감축된 정도에 대한 인식에 대해 빈도, 백분율, 평균, 표준편차와 같은 기술통계 방법을 사용하였고, 초⦁중등학교 교원들이 행정업무의 업무 수행 강도, 곤란도, 감축된 정도에 대한 인식에 대한 인식의 차이를 살펴보기 위하여 집단 간의 차이 검증인 교차분석을 실시하였다. 마지막으로 초⦁중등학교 교원들이 행정업무의 업무 수행 강도, 곤란도, 감축된 정도에 대한 인식에 대한 인식의 차이를 알아보기 위해 t-test, One way ANOVA를 사용하였으며, 사후검증으로 Scheffé 방법을 사용하였다. 분석 결과, 첫째, 초⦁중학교 교사가 업무 수행 강도에 대한 인식 수준은 3.37로 나타났다. 따라서 교사가 업무 수행 강도가 높은 편이다. 둘째, 초⦁중학교 교사가 곤란도에 대한 인식 수준은 3.20으로 나타났다. 따라서 교사가 업무에 대한 곤란도가 높은 편이다. 셋째, 업무경감 정책을 실시한 후에 초⦁중학교 교사가 업무 감축된 정도에 대한 인식 수준은 3.75로 나타났다. 따라서 업무경감 정책을 실시한 후에 업무에 대해 감축된 효과가 미미하다. 이러한 연구결과를 통대로 첫째, 교육부, 교육청, 단위 학교에서 교원 업무에 대해 업무 표준화가 시급하다. 둘째, 실질적인 행정업무 경감을 위한 예산 및 인적자원에 대한 지원을 확대해야 한다. 셋째, 새로운 업무경감 정책 관리⦁전달 메커니즘을 마련해야 할 필요가 있다.

Run-time adaptive workload estimation for dynamic voltage scaling

방성용 Graduate School, Yonsei University 2009 국내석사

고성능 시스템에 대한 요구가 증가함에 따라, 시스템이 필요로 하는 전력량이 점차 증대되고 있다. 그러나 배터리(Battery) 기반의 휴대용 임베디드 시스템(Embedded System)의 경우에는 사용할 수 있는 에너지가 한정되어 있기 때문에 이를 효율적으로 활용하여 배터리 사용 시간을 연장하기 위한 많은 저전력(Low-power) 기법들에 대한 연구가 진행되어 왔다. 그 중에 동적 전압 조절 기법(Dynamic Voltage Scaling, DVS)은 프로세서(Processor)의 휴지 시간(Idle Time)을 최대한 이용하여 전압과 주파수의 적절한 조절을 통해 전력 손실을 줄임으로써 저전력을 실현할 수 있는 전도유망한 기법으로 많은 연구가 이루어졌다. 동적 전압 조절 기법 적용을 위해 고려해야 할 중요한 요소는 실제 작업량(Workload)을 얼마나 적확하게 예측할 수 있는가이다. 현재의 작업을 처리하기 이전에 그 작업량에 대한 정확한 예측을 함으로써 그에 따른 최적의 전압과 주파수를 조절할 수 있게 된다. 그러나 실제 미래의 작업에 대한 정확한 예측은 매우 어려운 일이기 때문에 이를 해결하기 위해 과거의 실제 측정값을 통해 미래의 작업량을 최대한 정확하게 예측하기 위한 피드백(Feedback) 기반의 동적 조절 기법들이 대두되었다. 피드백 기법은 기본적으로 예측 단계(Estimation Phase)와 정정 단계(Correction Phase)로 구성된다. 다시 말해서, 과거의 실제 작업량들을 이용하여 다음 단계의 작업량을 예측하고, 현재 작업을 처리한 후에 실측을 통해서 예측에 대한 에러(Error)를 계산하여 이를 줄이기 위한 정정(Correction) 알고리즘(Algorithm)을 수행한다. 본 연구에서는 피드백 알고리즘 중 칼만 필터(Kalman Filter, KF)를 이용한 실시간 적응형 작업량 예측 기법을 개발하였다. 칼만 필터는 연속된 시간 영역(Continuous Time Domain)에서 잡음(Noise)가 첨가된 신호의 원신호(Original Signal)를 빠르게 찾아낼 수 있는 능력이 있다. 이와 같은 칼만 필터를 변형(Modification)하여 멀티미디어 환경에서 변화가 심한 작업량을 빠르게 추적할 수 있도록 하였다. 칼만 필터 또한 두 가지의 단계로 이루어져 있으며 모든 작업의 수행이 끝날 때까지 매 작업마다 두 가지 단계를 반복적으로 수행 한다. 본 연구에서는 ARM926-EJS 프로세서를 기반으로 매 시간마다 정확도를 검증할 수 있는 SoC Designer 시뮬레이터 환경에서 칼만 필터의 효율성을 검증하였다. 기존의 3가지 기법들과 비교하여 전력 소모 감소와 예측의 정확도 측면에서 우월성을 나타내었으며, 동적 전압 조절 기법을 사용하지 않는 기법에 비해 평균 57.5%의 에너지(Energy) 감소를 보여주었다. 그리고 알고리즘이 수행되는 동안 드랍(Drop)되는 작업량의 평균 비율은 6.1% 정도로 우수한 성능을 나타내었다. Dynamic Voltage Scaling (DVS) is a popular energy-saving technique for real-time tasks. The effectiveness of DVS critically depends on accuracy of workload estimation, since DVS exploits the slack or the difference between the deadline and the execution time. Many existing DVS techniques are profile-based and simply utilize the worst-case or average execution time without estimation. Several recent approaches recognize the importance of workload estimation and adopt statistical estimation techniques. However, these approaches still require extensive profiling to extract reliable workload statistics and furthermore cannot effectively handle time-varying workloads. Feedback control based adaptive algorithms have been proposed to handle such non-stationary workloads, but their results are often too sensitive to parameter selection. To overcome these limitations of existing approaches, we propose a novel workload estimation technique for DVS. This technique is based on the Kalman filter and can estimate the processing time of workloads in a robust and accurate manner by adaptively calibrating estimation error by feedback. We tested the proposed method with workloads of various characteristics extracted from eight MPEG video clips. To thoroughly evaluate the performance of our approach, we used both a cycle-accurate simulator and an XScale-based test board. The simulation result demonstrates that the proposed technique outperforms the compared alternatives with respect to the ability to meet given timing and QoS constraints. Furthermore, we found that the accuracy of our approach is almost comparable to the oracle accuracy achievable only by offline analysis. The experimental results indicate that using our approach can reduce energy consumption by 57.5% on average, only with negligible deadline miss ratio around 6.1%. More importantly, the deadline miss ratio of our method is bounded by 11.7% in the worst case, while those of other methods are twice or more than ours.

Flow theory (4-channel model)를 적용한 자율 주행에서 운전자의 몰입도와 정신적 작업부하 평가를 통한 생리학적 이해

박진상 고려대학교 대학원 2020 국내석사

As autonomous driving technology develops, the driver’s role is gradually changing to passengers. Especially, in a semi-autonomous driving environment(level 3), drivers can engage with Non-Driving-Related-Tasks (NDRTs) more. The aim of this study is to examine how the condition change of drivers performing NDRTs is related to mental workload. Investigating how psychological signals change as the engagement that causes mental workload. In addition, this study investigates the effect of driver's engagement conducting NDRTs on driving performance. This study applied a 4-channel model (Quadrant model), one of the models of flow theory, to the semi-autonomous driving environment. To investigate the degree of engagement in the 4 conditions presented in the 4-channel model, the challenge of the NDRTs was composed 2 levels (Simple, Complex), the driver's skill was decided to depending on whether motivation is provided or not. 2 types of NDRTs (Addition, Drawing) demanding cognitive load were given to participants. They used the Flow Show Scale (FSS) to assess their engagement about NDRTs. The mental workload was also measured through NASA-TLX. During the experiment, Heart Rate (HR) and LF/HF ratio were measured by Electrocardiography (ECG). Take Over Reaction time (TOrt) was measured using a driving simulator. As a result, the subjects showed different degrees of engagement in 4 conditions. They also represented different mental workloads under these conditions. In particular, the LF/HF ratio was found to be the most relevant variable for mental workload and engagement. This shows that the highest LF/HF ratio is shown in flow conditions with a moderate mental workload. Also, Take Over reaction time (TOrt) was the slowest in the flow condition compared to other conditions.