• Journal of Power Electronics
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• v.18 no.3
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• pp.910-922
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• 2018

Conventional battery state-of-charge (SoC) estimation methods either involve sophisticated models or consume considerable computational resource. This study constructs an enhanced coulomb counting method (Ah method) for the SoC estimation of lithium-ion batteries (LiBs) by expanding the Peukert equation for the discharging process and incorporating the Coulombic efficiency for the charging process. Both the rate- and temperature-dependence of battery capacity are encompassed. An SoC mapping approach is also devised for initial SoC determination and Ah method correction. The charge counting performance at different sampling frequencies is analyzed experimentally and theoretically. To achieve a favorable compromise between sampling frequency and accumulation accuracy, a frequency-adjustable current sampling solution is developed. Experiments under the augmented urban dynamometer driving schedule cycles at different temperatures are conducted on two LiBs of different chemistries. Results verify the effectiveness and generalization ability of the proposed SoC estimation method.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.57-64
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• 2017

Numerous software vulnerabilities have been found in the popular operating systems. And recently, robust linear behaviors in software vulnerability discovery process have been noticeably observed among the many popular systems having multi-versions released. Software users need to estimate how much their software systems are risk enough so that they need to take an action before it is too late. Security vulnerabilities are discovered throughout the life of a software system by both the developers, and normal end-users. So far there have been several vulnerability discovery models are proposed to describe the vulnerability discovery pattern for determining readiness for patch release, optimal resource allocations or evaluating the risk of vulnerability exploitation. Here, we apply a linear vulnerability discovery model into Windows operating systems to see the linear discovery trends currently observed often. The applicability of the observation form the paper show that linear discovery model fits very well with aggregate version rather than each version.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.6
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• pp.921-930
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• 2013

From an economic perspective, an enterprise's business activity depends on the efficient use of corporate resources for generating profits. However, on the enterprise side, it is difficult to measure and evaluate the effective use of each resource. This paper suggests an alternative for eliminating process inefficiencies in the consolidation of competitive power in auto parts manufacturing company A. Multitudinous process variables from company A's raw materials-to-shipment process are configured as input resources, and a Data Envelopment Analysis(DEA) is carried out to determine economical benefit of said resources' operation, as well as how products are manufactured. The DEA model offers a non-parametric approach to measuring relative efficiency using input and output factors. Furthermore, AHP is used for logically deciding the importance of each evaluation factor. In general, DEA models have been used for measuring efficiency of the service and public sectors. However, this study focused on measuring the efficiency of SMEs production lines.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.203-210
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• 2014

This study proposes adaptive decision making method having feed-back structure of regression and simulation models to support the quick decision making of production managers by managing and integrating the mutual relationship among historical data. For that, from historical data that have extracted and accumulated from each process, we first selected major constraint resources that are used as independent variables in regression model. The regression model is designed by using the dependent variables (objectives) that defined above by managers and independent variables selected in previous step and simulation model that are composed of constraint resources is designed. In process of simulation run, we obtain the multiple feasible solutions (alternatives) by using meta-heuristic method. Each solution is substituted by regression equation and we found the optimal solution that is minimum of difference between values obtained by regression model and simulation results. The optimal solution is delivered and incorporated to production site and current operation results from production site is used to generate new regression model after that time.

• Korean Business Review
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• v.23 no.2
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• pp.1-29
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• 2010

Traditional human resource management explains human characteristics with motivation, attitude, perception, value, etc., based upon behavioral science approach. And system approach is adopted in analyzing the interaction between people and the environment. However, personnel economics excludes social and psychological variables in the human choice models, and assumes very unrealistic and simple rational decision making process. For environmental variables, personnel economists considers only income and price. Traditional human resource management research model successfully explains the phenomenon for each specific situation, but lacks universality. On the other hand, personnel economics model is strict and analytical, but tends to distort the understanding of the results because of the unrealistic assumptions. These two academic fields may get benefits by introducing the other's research methodologies, theories, and ideas.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.25-34
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• 2013

In this paper, we propose an activity-performer bipartite matrix generation algorithm for analyzing workflow-supported human-resource affiliations in a workflow model. The workflow-supported human-resource means that all performers of the organization managed by a workflow management system have to be affiliated with a certain set of activities in enacting the corresponding workflow model. We define an activity-performer affiliation network model that is a special type of social networks representing affiliation relationships between a group of performers and a group of activities in workflow models. The algorithm proposed in this paper generates a bipartite matrix from the activity-performer affiliation network model(APANM). Eventually, the generated activity-performer bipartite matrix can be used to analyze social network properties such as, centrality, density, and correlation, and to enable the organization to obtain the workflow-supported human-resource affiliations knowledge.

• Journal of the military operations research society of Korea
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• v.35 no.2
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• pp.51-61
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• 2009

Recently, the necessity of VV&A and the importance of M&S are increasing in the national defense area. The purpose of VV&A is to assure a proper development of M&S and to provide users with sufficient information to determine if M&S could meet their demands. Therefore, VV&A process needs to be performed to guarantee the credibility of the M&S. However, the basic guidance and regulation of VV&A are not yet developed in Korea. This paper proposed the VV&A application process in the Korean Wartime Resource Requirement Model, focusing on the close combat situation of the model. The VV&A process provided in this paper can also be applied to other analytical models currently developing in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3034-3055
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• 2014

Wireless Sensor Networks have extensively been utilized for ambient data collection from simple linear structures to dense tiered deployments. Issues related to optimal resource allocation still persist for simplistic deployments including linear and hierarchical networks. In this work, we investigate the case of dimensioning parameters for linear and tiered wireless sensor network deployments with notion of providing extended lifetime and reliable data delivery over extensive infrastructures. We provide a single consolidated reference for selection of intrinsic sensor network parameters like number of required nodes for deployment over specified area, network operational lifetime, data aggregation requirements, energy dissipation concerns and communication channel related signal reliability. The dimensioning parameters have been analyzed in a pipeline monitoring scenario using ZigBee communication platform and subsequently referred with analytical models to ensure the dimensioning process is reflected in real world deployment with minimum resource consumption and best network connectivity. Concerns over data aggregation and routing delay minimization have been discussed with possible solutions. Finally, we propose a node placement strategy based on a dynamic programming model for achieving reliable received signals and consistent application in structural health monitoring with multi hop and long distance connectivity.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.139-146
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• 2002

The third generation mobile communication needs to provide multimedia service with increased data rates. Thus an efficient allocation of radio and network resources is very important. This paper models the 'burst switching' as an efficient radio resource allocation scheme and the performance is compared to the circuit and packet switching. In burst switching, radio resource is allocated to a call for the duration of data bursts rather than an entire session or a single packet as in the case of circuit and packet switching. After a stream of data burst, if a packet does not arrive during timer2 value ($\tau_{2}$), the channel of physical layer is released and the call stays in suspended state. Again if a packet does not arrive for timerl value ($\tau_{1}$) in the suspended state, the upper layer is also released. Thus the two timer values to minimize the sum of access delay and queuing delay need to be determined. In this paper, we focus on the decision of $\tau_{2}$ which minimizes the access and queueing delay with the assumption that traffic arrivals follow Poison process. The simulation, however, is performed with Pareto distribution which well describes the bursty traffic. The computational results show that the delay and the packet loss probability by the burst switching is dramatically reduced compared to the packet switching.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.