• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.4
• /
• pp.1426-1447
• /
• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• Journal of Information Processing Systems
• /
• v.13 no.5
• /
• pp.1397-1409
• /
• 2017

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

• The KIPS Transactions:PartA
• /
• v.10A no.3
• /
• pp.181-188
• /
• 2003

We propose a heuristic on-line scheduling algorithm for the IRIS (Increasing Reward with Increasing Service) tasks, which has low computation complexity and produces total reward approximated to that of previous on-line optimal algorithms. The previous on-line optimal algorithms for IRIS tasks perform scheduling on all tasks in a system to maximize total reward. Therefore, the complexities of these algorithms are too high to apply them to practical systems handling many tasks. The proposed algorithm doesn´t perform scheduling on all tasks in a system, but on (constant) W´s tasks selected by a predefined task selection policy. The proposed algorithm is based on task selection policies that define how to select tasks to be scheduled. We suggest two simple and intuitive selection policies and a generalized selection policy that integrates previous two selection policies. By narrowing down scheduling scope to only W´s selected tasks, the computation complexity of proposed algorithm can be reduced to O(Wn). However, simulation results for various cases show that it is closed to O(W) on the average.

• Journal of the military operations research society of Korea
• /
• v.11 no.1
• /
• pp.33-46
• /
• 1985

This study develops a scientific method in pilot selection by analysing a divided attention performance between the successful pilots and the failures in a flight training course. To measure the divided attention performance, Dual Task Method is used in which the primary task is a tracking task while the secondary tasks are, 1. short-term memory task 2. choice reaction task 3. judgement task. Result shows that the performance of the pilots is significantly better (p < 0.1) than that of the failures in divided attention performance. In addition, the differences in the divided attention performance between the two groups are increased in proportion to the difficulty of the task and especially in the short term memory, the increment is most dramatic.

• Journal of Korean Institute of Industrial Engineers
• /
• v.10 no.2
• /
• pp.3-16
• /
• 1984

This study develops a scientific method in pilot selection by analysing a divided attention performance between the successful pilots and the failures in a flight training course. To measure the divided attention performance, Dual Task Method is used in which the primary task is a tracking task while the secondary tasks are, 1. short term memory task, 2. choice reaction task and 3. judgement task. Result shows that the performance of the pilots is significantly better (P < 0.1) than that of the failures in dual performance. In addition, the differences in the divided attention performance between the two groups are increased in proportion to the difficulty of the task and especially in the Short Term Memory, the increment is most dramatic.

• The Journal of Korea Robotics Society
• /
• v.6 no.4
• /
• pp.344-352
• /
• 2011

A mobile manipulator is a system with a robotic manipulator mounted on top of a mobile base. It has both indoor and outdoor applications for transporting or transferring materials. When a user gives commands, they are usually at high levels such as "move the object to the table," or "tidy the room." By intelligently decomposing these complex commands into several subtasks, the mobile manipulator can perform the tasks with a greater efficiency. One of the crucial subtasks for these commands is the pick-and-place task. For the mobile manipulator, selection of a good base position and orientation is essential to accomplishing this task. This paper presents an algorithm that determines one of the position and orientation of a mobile manipulator in order to complete the pick-and-place task without human intervention. Its effectiveness are shown for a mobile manipulator with 9 degrees-of-freedom in simulation.

• Journal of Information Processing Systems
• /
• v.19 no.4
• /
• pp.450-464
• /
• 2023

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

• Communications for Statistical Applications and Methods
• /
• v.8 no.1
• /
• pp.193-207
• /
• 2001

When designing a test set, we need to consider constraints on items that are deemed important by item developers or test specialists. The constraints are essentially on the components of the test domain or abilities relevant to a given test set. And so if the test domain could be represented in a more refined form, test construction would be made in a more efficient way. We assume that relationships among task abilities are representable by a causal model and that the item response theory (IRT) is not fully available for them. In such a case we can not apply traditional item selection methods that are based on the IRT. In this paper, we use entropy as an uncertainty measure for making inferences on task abilities and developed an optimal item selection algorithm which reduces most the entropy of task abilities when items are selected from an item pool.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.6
• /
• pp.904-914
• /
• 2008

An action-selection-mechanism(ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing- a- box-in to- a-goal(PBIG) will be illustrated.

• Journal of Internet Computing and Services
• /
• v.8 no.1
• /
• pp.71-78
• /
• 2007

In this paper, an effective task scheduling scheme was proposed for the flexible real time LINUX systems with the selection between EDF(earliest deadline first) and RMS(rate monotonic scheduling). It was known that many task scheduling schemes were analyzed according to the characteristics of scheduling schemes and the guarantee of an earliest deadline scheduler for process utilities.