• 대한산업공학회지
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• 제24권4호
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• pp.591-600
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• 1998

The scheduling of parallel computing systems consists of two procedures, the assignment of tasks to each available processor and the ordering of tasks in each processor. The assignment procedure is same with a clustering. The clustering is classified into linear or nonlinear according to the precedence relationship of the tasks in each cluster. The parallel computing system can be modeled with a Directed Acyclic Graph(DAG). By the granularity theory, DAG is categorized into Coarse Grain Type(CDAG) and Fine Grain Type(FDAG). We suggest the linear clustering method for the scheduling of CDAG using the genetic algorithm. The method utilizes a properly that the optimal schedule of a CDAG is one of linear clustering. We present the computational comparisons between the suggested method for CDAG and an existing method for the general DAG including CDAG and FDAG.

• Journal of information and communication convergence engineering
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• 제16권4호
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• pp.264-270
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• 2018

In our research study, we aim at optimizing multiple load in cloud, effective resource allocation and lesser response time for the job assigned. Using Hadoop on datacenter is the best and most efficient analytical service for any corporates. To provide effective and reliable performance analytical computing interface to the client, various cloud service providers host Hadoop clusters. The previous works done by many scholars were aimed at execution of workflows on Hadoop platform which also minimizes the cost of virtual machines and other computing resources. Earlier stochastic hill climbing technique was applied for single parameter and now we are working to optimize multiple parameters in the cloud data centers with proposed heuristic hill climbing. As many users try to priorities their job simultaneously in the cluster, resource optimized workflow scheduling technique should be very reliable to complete the task assigned before the deadlines and also to optimize the usage of the resources in cloud.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.128-130
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• 2021

Smart cities collect data from thousands of IoT-based sensor devices for intelligent application-based services. Centralized cloud servers support application tasks with higher computation resources but introduce network latency. Fog layer-based data centers bring data processing at the edge, but fewer available computation resources and poor task allocation strategy prevent real-time data analysis. In this paper, tasks generated from devices are distributed as high resource and low resource intensity tasks. The novelty of this research lies in deploying a virtual node assigned to each cluster of IoT sensor machines serving a joint application. The node allocates tasks based on the task intensity to either cloud-computing or fog computing resources. The proposed Task Allocation Strategy provides seamless allocation of jobs based on process requirements.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.16-26
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• 2024

Mobile edge computing (MEC) plays a crucial role in improving the performance of resource-constrained mobile devices by offloading computation-intensive tasks to nearby edge servers. However, existing methods often neglect the critical consideration of future task requirements when making offloading decisions. In this paper, we propose an innovative approach that addresses this limitation. Our method leverages recurrent neural networks (RNNs) to predict task sizes for future time slots. Incorporating this predictive capability enables more informed offloading decisions that account for upcoming computational demands. We employ genetic algorithms (GAs) to fine-tune fitness functions for current and future time slots to optimize offloading decisions. Our objective is twofold: minimizing total processing time and reducing energy consumption. By considering future task requirements, our approach achieves more efficient resource utilization. We validate our method using a real-world dataset from Google-cluster. Experimental results demonstrate that our proposed approach outperforms baseline methods, highlighting its effectiveness in MEC systems.

• 감성과학
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• 제23권4호
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• pp.61-72
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• 2020

경제 불안정과 고령화, 경쟁격화 및 개인 가치관의 변화 등 사회 문제가 점점 심각해질 가능성이 있다. 이러한 상황에서 이를 해결 가능한 방안 중 하나로써 감성컴퓨팅 관련 연구가 증가하고 있다. 이에 본 연구는 감성컴퓨팅 연구 키워드를 중심으로 국내 및 글로벌 연구의 지식구조와 주요 키워드, 연구생산 현황 및 국가간 협력관계 및 주요 키워드별 네트워크 등을 파악하였다. 이를 위해 전문 학술데이터 베이스(Scopus)로부터 해당 키워드를 중심으로 논문을 검색하였으며, 서지분석과 네트워크 분석을 실시하였다. 중국과 미국이 Affective computing 분야에서 지식생산이 활발하였고, 한국은 약 10% 정도로 저조한 상황이다. 주요 키워드는 Affective computing을 중핵으로 주로 컴퓨팅 처리 및 감성분석, 인식을 분류하는 연구 및 사용자들의 모델링, 심리 분석이 주요 연구 키워드이다. 국가 간 협력구조는 중국과 미국이 가장 큰 클러스터를 형성하고 있고, 그 외에 영국, 독일, 스위스, 스페인, 캐나다 등이 협력을 주도하고 있다. 한국의 연구협력은 다양하지 않고 연구생산도 저조한 결과를 보였다. Affective computing 분야의 연구발전을 위해 미국, 중국 등 주요국과의 연구협력 강화와 연구파트너의 다양화를 위한 시사점을 결론으로 제언하였다.

• Fisheries and Aquatic Sciences
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• 제7권3호
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• pp.163-170
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• 2004

A fishing simulator for towed fishing gear was investigated in order to mimic the fish behavior in capture process and investigate fishing selectivity. A fish behavior model using a psycho-hydraulic wheel activated by stimuli is established to introduce Lorenz chaos equations and a neural network system and to generate the components of realistic fish capture processes. The fish positions within the specified gear geometry are calculated from normalized intensities of the stimuli of the fishing gear components or neighboring fish and then these are related to the sensitivities and the abilities of the fish. This study is applied to four different towed gears i.e. a bottom trawl, a midwater trawl, a two-boat seine, and an anchovy boat seine and for 17 fish species as mainly caught. The Alpha cluster computer system and Fortran MPI (Message-Passing Interface) parallel programming were used for rapid calculation and mass data processing in this chaotic behavior model. The results of the simulation can be represented as animation of fish movements in relation to fishing gear using Open-GL and C graphic programming and catch data as well as selectivity analysis. The results of this simulator mimicked closely the field studies of the same gears and can therefore be used in further study of fishing gear design, predicting selectivity and indoor training systems.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.2060-2073
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• 2022

Cloud Computing permits users to access vast amounts of services of computing power in a virtualized environment. Providing secure services is essential. There are several problems to real-world optimization that are dynamic which means they tend to change over time. For these types of issues, the goal is not always to identify one optimum but to keep continuously adapting to the solution according to the change in the environment. The problem of scheduling in Cloud where new tasks keep coming over time is unique in terms of dynamic optimization problems. Until now, there has been a large majority of research made on the application of various Evolutionary Algorithms (EAs) to address the issues of dynamic optimization, with the focus on the maintenance of population diversity to ensure the flexibility for adapting to the changes in the environment. Generally, trust refers to the confidence or assurance in a set of entities that assure the security of data. In this work, a dynamic optimization technique is proposed to find an optimal trust weights in cloud during scheduling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2004년도 International Conference on Digital Policy & Management
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• pp.173-184
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• 2004

E-Government security is crucial to the development of e-government. Due to the complexity and characteristics of e-government security, the viable current approaches for security focus on preventing the network intrusion or misusing in advanced and seldom concern of the forensics data attaining for the investigation after the network attack or fraud. We discuss the method for resolving the problem of the e-government security from the different side of view - network forensics approaches? from the thinking of the active protection or defense for the e-government security, which can also improve the ability of emergence response and incident investigation for e-government security.