• School Mathematics
• /
• v.18 no.4
• /
• pp.749-771
• /
• 2016

The purpose of this study is to analyze mathematical tasks of Korea and the USA textbooks focused on conditions for parallelograms. In this study, structures of task, types of proof and reasoning, and levels of cognitive demand are investigated. The conclusion is as follows: First, with respect to structures of task, structures presented in the USA textbooks are more diverse. Second, with respect to types of proof and reasoning, Korea and the USA prefer IC task and DA task. And task types presented in the USA textbooks are more diverse. Third, with respect to levels of cognitive demand, in both Korea and the USA textbooks, PNC task and PWC task account most. And compared to the USA, Korea prefer algorithms. In addition, we find out implications for reconstruction of Korea textbook. It is as follows: First, with respect to structures of task and types of proof and reasoning, the diversity of composition needs to be raised. Second, with respect to levels of cognitive demand, the concentration in PNC task needs to be declined. And levels of cognitive demand on types of tasks need to be reconsidered. Third, with respect to tasks' topic and material, internal and external connectivities of mathematics need to be strengthened.

• Design & Manufacturing
• /
• v.16 no.3
• /
• pp.1-8
• /
• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• Imaging Science in Dentistry
• /
• v.46 no.4
• /
• pp.245-249
• /
• 2016

Purpose: This study was designed to evaluate differences in the required visibility of anatomic structures according to the diagnostic tasks of implant planning and periapical diagnosis. Materials and Methods: Images of a real skull phantom were acquired under 24 combinations of different exposure conditions in a cone-beam computed tomography scanner (60, 70, 80, 90, 100, and 110 kV and 4, 6, 8, and 10 mA). Five radiologists evaluated the visibility of anatomic structures and the image quality for diagnostic tasks using a 6-point scale. results: The visibility of the periodontal ligament space showed the closest association with the ability to use an image for periapical diagnosis in both jaws. The visibility of the sinus floor and canal wall showed the closest association with the ability to use an image for implant planning. Variations in tube voltage were associated with significant differences in image quality for all diagnostic tasks. However, tube current did not show significant associations with the ability to use an image for implant planning. conclusion: The required visibility of anatomic structures varied depending on the diagnostic task. Tube voltage was a more important exposure parameter for image quality than tube current. Different settings should be used for optimization and image quality evaluation depending on the diagnostic task.

• Wind and Structures
• /
• v.14 no.6
• /
• pp.517-536
• /
• 2011

The assessment of wind-induced motion plays an important role in the development and design of the majority of today's structures that push the limits of engineering knowledge. A vital part of the design is the prediction of wind-induced tall building motion and the assessment of its effects on occupant comfort. Little of the research that has led to the development of the various international standards for occupant comfort criteria have considered the effects of the low-frequency motion on task performance and interference with building occupants' daily activities. It has only recently become more widely recognized that it is no longer reasonable to assume that the level of motion that a tall building undergoes in a windstorm will fall below an occupants' level of perception and little is known about how this motion perception could also impact on task performance. Experimental research was conducted to evaluate the performance of individuals engaged in a manual tracking task while subjected to low level vibration in the frequency range of 0.125 Hz-0.50 Hz. The investigations were carried out under narrow-band random vibration with accelerations ranging from 2 milli-g to 30 milli-g (where 1 milli-g = 0.0098 $m/s^2$) and included a control condition. The frequencies and accelerations simulated are representative of the level of motion expected to occur in a tall building (heights in the range of 100 m -350 m) once every few months to once every few years. Performance of the test subjects with and without vibration was determined for 15 separate test conditions and evaluated in terms of time taken to complete a task and accuracy per trial. Overall, the performance under the vibration conditions did not vary significantly from that of the control condition, nor was there a statistically significant degradation or improvement trend in performance ability as a function of increasing frequency or acceleration.

• Journal of Information Technology Services
• /
• v.12 no.1
• /
• pp.309-321
• /
• 2013

In current complex nature of management with task-structures, a method to reach the enterprise's informationalization success is not common. To satisfy these various requirement, improving the usability of information technology (IT) is a key factor which defining the level of organizational requirement first. Imposing an IT-solution which has excess service of the organization's previous task-environment, procedure and scope is not effective to SME-level unit, which unit could not have a formal organization structure and task structure. SME level informationalization will be success if each function realizes easier on the task-employee's viewpoint. Achieving this objective, a solution provider or department must reflect their work characteristics of nature which has least level of work performing resistance. It is most useful system for SME level unit, if a provider develops single programs which based on task activities, and each program can configure network-linking.

• Informatization Policy
• /
• v.23 no.3
• /
• pp.84-116
• /
• 2016

This paper explores alternative organizational models for ICT units of local governments, while analyzing the task contents, organizational structures and work relations of ICT departments in 17 Korean metropolitan provinces. We employed both quantitative and qualitative methods including in-depth interview, open-ended survey, and hard data analyses to collect a wide variety of information on organizational performance, human resource development, and task characteristics of ICT units. As strategies to enhance task contents, organizational structures and work relations of ICT departments, we suggest four ideal ICT organizational models for local governments: Relation Model, Resource Model, Curation Model and Creation Model.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.383-392
• /
• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3638-3657
• /
• 2022

In urban vehicular edge computing (VEC) environments, one edge server always serves many task requests in its coverage which results in the resource-constrained problem. To resolve the problem and improve system utilization, we first design a general hierarchical resource management framework based on typical VEC network structures. Following the framework, a specific interacting protocol is also designed for our decision algorithm. Secondly, a greedy bidding-based multi-hop task scheduling decision algorithm is proposed to realize effective task scheduling in resource-constrained VEC environments. In this algorithm, the goal of maximizing system utility is modeled as an optimization problem with the constraints of task deadlines and available computing resources. Then, an auction mechanism named greedy bidding is used to match task requests to edge servers in the case of multiple hops to maximize the system utility. Simulation results show that our proposal can maximize the number of tasks served in resource constrained VEC networks and improve the system utility.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.4 no.3
• /
• pp.353-360
• /
• 2004

In this paper we describe Task-Based Problem Solving Adapters (TPSAs) for modeling a humam solution (through activity-centered analysis) to a software solution (in form of computer-based artifact). TPSAs are derived from the problem solving pattern or consistent problem solving structures/strategies employed by practitioners while designing solutions to complex problems. The adapters developed by us lead toward human-centeredness in their design and underpinning that help us to address the pragmatic task constraints through a range of technologies like neural networks, fuzzy logic, and genetic algorithms. We also outline an example of applying the TPSAs to develop a working system for assisting sales engineers of an electrical manufacturing firm in preparing indent and monitoring the status of orders in the company.

• Structural Monitoring and Maintenance
• /
• v.2 no.3
• /
• pp.181-197
• /
• 2015

Assessing the condition of paint on civil structures is an important but challenging and costly task, in particular when it comes to large and complex structures. Current practices of visual inspection are labour-intensive and time-consuming to perform. In addition, this task usually relies on the experience and subjective judgment of individual inspectors. In this study, hyperspectral imaging and classification techniques are proposed as a method to objectively assess the state of the paint on a civil or other structure. The ultimate objective of the work is to develop a technology that can provide precise and automatic grading of paint condition and assessment of degradation due to age or environmental factors. Towards this goal, we acquired hyperspectral images of steel surfaces located at long (mid-range) and short distances on the Sydney Harbour Bridge with an Acousto-Optics Tunable filter (AOTF) hyperspectral camera (consisting of 21 bands in the visible spectrum). We trained a multi-class Support Vector Machines (SVM) classifier to automatically assess the grading of the paint from hyperspectral signatures. Our results demonstrate that the classifier generates highly accurate assessment of the paint condition in comparison to the judgement of human experts.