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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3523-3543
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• 2022

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.707-710
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• 2005

In this paper, we propose a new communication network for on-chip communication. The network is based on a direct sequence code division multiple access (DS-CDMA) technique. The new communication network is suitable for a parallel processing system and also drastically reduces the I/O pin count. Our network architecture is mainly divided into a CDMA-based network interface (CNI), a communication channel, a synchronizer. The network includes a reverse communication channel for reducing latency. The network decouples computation task from communication task by the CNI. An extreme truncation is considered to simplify the communication link. For the scalability of the network, we use a PN-code reuse method and a hierarchical structure. The network elements have a modular architecture. The communication network is done using fully synthesizable Verilog HDL to enhance the portability between process technologies.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.304-309
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• 2003

Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today's highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system. In order to realize these tasks autonomously, the three dimensional position of target object such as electric line and the stand of insulator must be recognized accurately and rapidly. The approaching of an insulator and the wrenching of a nut task is selected as the typical task of the maintenance of active electric power distribution lines in this paper. Image recognition by multilayer neural network and optimal target position calculation method are newly proposed in order to recognize the center 3 dimensional position of the bolt hole in the stand of insulator. By the proposed image recognition method, it is proved that the center 3 dimensional position of the bolt hole can be recognized rapidly and accurately without regard to the pose of the stand of insulator. Finally the approaching and wrenching task is automatically realized using 6-link electro-hydraulic manipulators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.199-209
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• 2017

Purpose: Caffeine is the most widely consumed psychostimulant. It is often adopted as a tool to modulate brain activations in fMRI studies. However, its pharmaceutical effect on task-induced deactivation has not been fully examined in fMRI. Therefore, the purpose of this study was to examine the effect of caffeine on both activation and deactivation under sustained attention. Materials and Methods: Task fMRI was acquired from 26 caffeine naive healthy volunteers before and after taking caffeine pill (200 mg). Results: Statistical analysis showed an increase in cognition-load dependent task activation but a decrease in load dependent de-activation after caffeine ingestion. Increase of attention and memory task activation and its load-dependence suggest a beneficial effect of caffeine on the brain even though it has no overt behavior improvement. The reduction of deactivation by caffeine and its load-dependence indicate reduced facilitation from task-negative networks. Conclusion: Caffeine affects brain activity in a load-dependent manner accompanied by a disassociation between task-positive network and task-negative network.

• Journal of KIISE
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• v.43 no.8
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• pp.878-882
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• 2016

Automatic generation of captions for an image is a very difficult task, due to the necessity of computer vision and natural language processing technologies. However, this task has many important applications, such as early childhood education, image retrieval, and navigation for blind. In this paper, we describe a Recurrent Neural Network (RNN) model for generating image captions, which takes image features extracted from a Convolutional Neural Network (CNN). We demonstrate that our models produce state of the art results in image caption generation experiments on the Flickr 8K, Flickr 30K, and MS COCO datasets.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.107-112
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• 2004

As the internet service evolves fast recently, guarantee request of QoS (Quality of Service) by characteristic of traffic source as well as high rate of data is going greatest. Accordingly intemet service technology also is changing rapidly, technology that guarantee QoS in existent network service technology is developed or network model that guarantee new QoS is presented. By DiffServ-aware-MPLS network that present in IETF (Internet Engineering Task Force) to guarantee QoS in this treatise does comparative analysis with existent network model, relative show that is superior, and present direction that compose next generation network wish to.

• Management Science and Financial Engineering
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• v.17 no.2
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• pp.39-62
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• 2011

In wireless sensor network, since each sensor is equipped with a limited power, efficient use of the energy is important. One possible network management scheme is to cluster the sensors into several sets, so that the sensors in each of the sets can completely perform the monitoring task. Then the sensors in one set become active to perform the monitoring task and the rest of the sensors switch to a sleep state to save energy. Therefore, we rotate the roles of the active set among the sensors to maximize the network lifetime. In this paper, we suggest an optimal algorithm for the maximum lifetime coverage problem which maximizes the network lifetime. For comparison, we implemented both the heuristic proposed earlier and our algorithm, and executed computational experiments. Our algorithm outperformed the heuristic concerning the obtained network lifetimes, and it found the solutions in a reasonable amount of time.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.1-7
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• 2016

In existing Convolutional Neural Network (CNNs) for object recognition task, there are only few efforts known to reduce the noises from the images. Both convolution and pooling layers perform the features extraction without considering the noises of the input image, treating all pixels equally important. In computer vision field, there has been a study to weight a pixel importance. Seam carving resizes an image by sacrificing the least important pixels, leaving only the most important ones. We propose a new way to combine seam carving approach with current existing CNN model for object recognition task. We attempt to remove the noises or the "unimportant" pixels in the image before doing convolution and pooling, in order to get better feature representatives. Our model shows promising result with CIFAR-10 dataset.