• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.553-564
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• 2012

Today's security initiatives tend to integrate the physical and information securities which have been run by completely separate departments. That is, the converged security management becomes the core in the security market trend. However, to the best of our knowledge, we cannot find any solutions how to combine these two security events for the converged security. In this paper, we propose an information security object-driven approach which utilizes the physical security events to enhance and improve the information security. For scalability, we also present a systematic method using the analytic hierarchy process finding the meaningful event combinations among the large number of physical security events. In particular, we show the whole implementation processes in detail where we consider the information security object 'illegal computing system access' combined with two physical security devices - access controller and CCTV+video analyzer system.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.774-782
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• 2001

In this paper, we propose a new approach to the autonomous and high-speed indoor navigation of wheeled mobile robots using hybrid system controller. The hierarchical structure of hybrid system presented consists of high-level reasoning process and the low-level motion control process and the environmental interaction. In a discrete event system, the discrete states are defined by the user-defined constraints and the reference motion commands are specified in the abstracted motions. The hybrid control system applied for the nonholonomic mobile robots can combine the motion planning and autonomous navigation with obstacle avoidance in the indoor navigation problem. For the evaluation of the proposed algorithm, the algorithm is implemented to the two-wheel driven mobile robot system. The experimental results show that the hybrid system approach is an effective method for the autonomous navigation in indoor environments.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.119-131
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• 1986

Described in this paper is a modelling methodology for mass production system simulation. The mass production system under consideration consists of various types of flow lines, special purpose production facilities, conveyor lines, palletized carts, and storage facilities. This type of production system is typical in home appliance industry, automobile industry, footwear industry, etc. where a variety of product mix are mass-produced. The modelling methodology is based on the "discrete-event formalism", and an "activity-oriented world view" is adopted to formalize the system description. A distinctive feature of the modelling methodology is that only the static structure (ie, system components) is included in the fixed model. The dynamic structure of the system is specified through a "data-driven" mechanism, which is an extension of the "experimental frame" concept. Each type of system components (ie, flow line, conveyors, carts, etc.) is formally modeled by using Activity Cycle Diagrams. The issue of "model structuring" is also addressed. The modeling methodology has been successfully applied in a real simulation study of a mass production system.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.83-101
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• 2005

Customer retention is one of the major issues in life insurance industry, in which competition is increasingly fierce. There are many things for the life insurers to do many things to retain the customers. One of those things is to make sure to keep in touch with all customers. When an insurance-planner resigned, his/her customers must be taken care of by some planner-assistants. This article outlines the design of Contact Scheduling System (CSS) that supports planner-assistants for contacting the customers. Planner-assistants are unable to share the resigned insurance-planner's experience and knowledge regarding the customer relationship management. The CSS developed by employing both Classification And Regression Tree (CART) technique and Sequential Pattern Mining (SPM) technique has a two-stage process. In the first stage, it segments the customers into eight groups by CART model. Then it generates contact scheduling information consisting of contact-purpose, contact-interval and contact-channel, according to the segment's typical contact pattern. Contact-purpose is derived by schedule-driven, event-driven, or business-rule-driven. Schedule-driven contact is determined by SPM model. In the operation of CSS in a realistic situation, it shows a practicality in supporting planner-assistants to keep in touch with the customers efficiently and effectively.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.111-122
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• 2003

Military wargame simulation models must support the HLA in order to facilitate interoperability with other simulations, and using parallel simulation engines offer efficiency in reducing system overhead generated by propelling interoperability. However, legacy military simulation model engines process events using sequential event-driven method. This is due to problems generated by parallel processing such as synchronous reference to global data domains. Additionally. using legacy simulation platforms result in insufficient utilization of multiple CPUs even if a multiple CPU system is under use. Therefore, in this paper, we propose conversing the simulation engine to an object model-based parallel simulation engine to ensure military wargame model's improved system processing capability, synchronous reference to global data domains, external simulation time processing, and the sequence of parallel-processed events during a crash recovery. The converted parallel simulation engine is designed and implemented to enable parallel execution on a multiple CPU system (SMP).

• Atmosphere
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• v.32 no.3
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• pp.207-221
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• 2022

In Jeju, on January 23, 2016, a cold surge accompanied by heavy snowfall with the most significant amount of 12 cm was the highest record in 32 years. During this period, the temperature of 850 hPa in January was the lowest in 2016. Notably, in 2016, the average surface temperature of January on the Polar cap was the highest since 1991, and 500 hPa geopotential height also showed the highest value. With this condition, the polar vortex in the northern hemisphere meandered and expanded into the subtropics regionally, covering the Korean Peninsula with very high potential vorticity up to 7 Potential Vorticity Unit. As a result, the strong cold advection, mostly driven by a northerly wind, around the Korean Peninsula occurred at over 2𝜎. Previous studies have not addressed this extreme synoptic condition linked to polar vortex expansion due to the unprecedented Arctic warming. We suggest that the occurrence of a strong Ural blocking event after the abrupt warming of the Barents/Karas seas is a major cause of unusually strong cold advection. With a specified mesoscale model simulation with SST (Sea Surface Temperature), we also show that the warmer SST condition near the Korean Peninsula contributed to the heavy snowfall event on Jeju Island.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1369-1378
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• 2017

Pub/Sub (Publish/Subscribe) paradigm is a simple and easy to use model for interconnecting applications in a distributed environment. In general, subscribers register their interests in a topic or a pattern of events and then asynchronously receive events matching their interest, regardless of the events' publisher. In order to build a low latency lightweight pub/sub system for Internet of Things (IoT) services, we propose a GQSFPS (Group Quorum System-based Fog Pub/Sub) system that is a core component in the event-driven service oriented architecture framework for IoT services. The GQSFPS organizes multiple installed pub/sub brokers in the fog servers into a group quorum based P2P (peer-to-peer) topology for the efficient searching and the low latency accessing of events. Therefore, the events of IoT are cached on the basis of group quorum, and the delay-sensitive IoT applications of edge devices can effectively access the cached events from group quorum fog servers in low latency. The performance of the proposed GQSFPS is evaluated through an analytical model, and is compared to the GQPS (grid quorum-based pud/sub system).

• Journal of Service Research and Studies
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• v.4 no.1
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• pp.49-59
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• 2014

Context-aware computing is an emerging paradigm to achieve ubiquitous computing environments by enabling computer systems to understand their situational contexts. A context-aware system uses context to provide relevant information and services to the user depending on the user's task. In this paper, we propose an ontology-based context-aware modeling methodology that transmits low-level contexts acquired by directly accessing various sensors in the physical environments to high-level contexts. With these high-level contexts, context-aware application can provides proactive and intelligent services using ECA (Event-Condition-Action) rules. We implemented a home robot service in smart office environment.