• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1986년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 17-18 Oct. 1986
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• pp.518-526
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• 1986

This paper gives the detail of the work carried out to develop real-time control software for Flexible Manufacturing Systems. A basic design philosophy to implement such software is proposed. The major features are the partitioning of complicated control actions into simplified ones, structured programming and multi-threaded transaction-based tasks. The software operates on the basis of passing task-to-task messages via mailboxes, causing appropriate actions to be taken by each task. Each task represents a separate subprocess so that the subprocesses can be run simultaneously. The task-to-task message could be easily replaced by computer-to-computer communication, using LAN, demonstrating that the software methods developed produce a flexible designs for control software of an FMS. A method of linking such software to simulation software is suggested as a potentially powerful additional design-tool.

• 전기학회논문지
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• 제60권1호
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• pp.193-200
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• 2011

This paper presents an optimal checkpoint strategy for fault-tolerance in real-time systems. In our environment, multiple real-time tasks with arbitrary periods are scheduled in the system by Rate Monotonic (RM) algorithm, and checkpoints are inserted at a constant interval in each task while the width of interval is different with respect to the task. We propose a method to determine the optimal checkpoint interval for each task so that the probability of completing all the tasks is maximized. Whenever a fault occurs to a checkpoint interval of a task, the execution time of the task would be prolonged by rollback and re-execution of checkpoints. Our scheme includes the schedulability test to examine whether a task can be completed with an extended execution time. A numerical experiment is conducted to demonstrate the applicability of the proposed scheme.

• Journal of information and communication convergence engineering
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• 제18권4호
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• pp.222-229
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• 2020

Recent multi-core processors for smart devices use per-core dynamic voltage and frequency scaling (DVFS) that enables independent voltage and frequency control of cores. However, because the conventional task scheduler was originally designed for per-core DVFS disabled processors, it cannot effectively utilize the per-core DVFS and simply allocates tasks evenly across all cores to core utilization with the same CPU frequency. Hence, we propose a novel task scheduler to effectively utilize percore DVFS, which enables each core to have the appropriate frequency, thereby improving performance and decreasing energy consumption. The proposed scheduler classifies applications into two types, based on performance-sensitivity and allows a performance-sensitive application to have a dedicated core, which maximizes core utilization. The experimental evaluations with a real off-the-shelf smart device showed that the proposed task scheduler reduced 13.6% of CPU energy (up to 28.3%) and 3.4% of execution time (up to 24.5%) on average, as compared to the conventional task scheduler.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3423-3438
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• 2014

In recent years, wearable sensor devices are reshaping the way people live, work, and play. A wearable sensor device is a computer that is subsumed into the personal space of the user, and is always on, and always accessible. Therefore, among the most salient aspects of a wearable sensor device should be a small form factor, long battery lifetime, and real-time characteristics. Thereby, sophisticated applications of a wearable sensor device use real-time operating systems to guarantee real-time deadlines. The deterministic multi-dimensional task scheduling algorithms are implemented on ARC (Actual Remote Control) with relatively limited hardware resources. ARC is a wearable wristwatch-type remote controller; it can also serve as a universal remote control, for various wearable sensor devices. In the proposed algorithms, there is no limit on the maximum number of task priorities, and the memory requirement can be dramatically reduced. Furthermore, regardless of the number of tasks, the complexity of the time and space of the proposed algorithms is O(1). A valuable contribution of this work is to guarantee real-time deadlines for wearable sensor devices.

• Journal of Multimedia Information System
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• 제9권2호
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• pp.75-86
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• 2022

The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.

• Journal of Mechanical Science and Technology
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• 제15권5호
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• pp.544-554
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• 2001

A shop floor can be considered as an important level to develop Computer Integrated Manufacturing system (CIMs). However, a shop floor is a dynamic environment where unexpected events continuously occur, and impose changes to the planned activities. To deal with this problem, a shop floor should adopt an appropriate control system that is responsible for the coordination and control of the manufacturing physical flow and information flow. In this paper, a hybrid control system is described with a shop floor activity methodology called Multi-Layered Task Initiation Diagram (MTD). The architecture of the control model contains three levels: i.e., he shop floor controller (SFC), the intelligent agent controller (IAC) and the equipment controller (EC). The methodology behind the development of the control system is an intelligent multi-agent paradigm that enables the shop floor control system to be an independent, an autonomous, and distributed system, and to achieve an adaptability to change of the manufacturing environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.117-135
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• 2016

Performance enhancement is one of the most important issues in high performance distributed computing systems. In such computing systems, online users submit their jobs anytime and anywhere to a set of dynamic resources. Jobs arrival and processes execution times are stochastic. The performance of a distributed computing system can be improved by using an effective load balancing strategy to redistribute the user tasks among computing resources for efficient utilization. This paper presents a multi-class load balancing strategy that balances different classes of user tasks on multiple heterogeneous computing nodes to minimize the per-class mean response time. For a wide range of system parameters, the performance of the proposed multi-class load balancing strategy is compared with that of the random distribution load balancing, and uniform distribution load balancing strategies using simulation. The results show that, the proposed strategy outperforms the other two studied strategies in terms of average task response time, and average computing nodes utilization.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.333-338
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• 2009

This paper presents a task-based analysis on the number of independent robotic fingers required for compliant manipulations. Based on the stiffness relation between operational space and fingertip space of a multi-fingered object manipulating system, we describe a technique for modulation of the fingertip stiffness without inter-finger coupling so as to achieve the desired stiffness specified in the operational space. Thus, we provides a guide line how many fingers are basically required for successful multi-fingered compliant tasks. Consequently, this paper enables us to assign effectively the number of fingers for various compliant manipulations by robot hands.

• 대한전기학회논문지
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• 제45권1호
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• pp.100-112
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• 1996

This paper presents a design experience of a control language for coordination of a multi-robot system. To effectively program job commands, a Petrinet-type Graphical Robot Language(PGRL) is proposed, where some functions, such as concurrency and synchronization, for coordination among tasks can be easily programmed.In our system, the proposed task commands of PGRL are implemented by employing formal model languages, which are composed of three modules, sensory, data handling, and action module. It is expected that by using our proposed PGRL and formal languages, one can easily describe a job or task, and hence can effectively operate a complex real-time and concurrent system. The control system is being implemented by using VME-based 32-bit microprocessor boards for supervisory, each module controller(arm, hand, leg, sensor data processing module) and a real time multi-tasking operating system(VxWorks). (author). 17 refs., 16 figs., 2 tabs.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.