• Journal of Industrial Technology
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• v.28 no.A
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• pp.105-111
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• 2008

A typical AP (Access Point) selection policy is to select an AP based on the received signal strength indicator (RSSI), ignoring its load. If multiple stations are deployed densely at a particular area, a typical AP selection policy will bring about the overall network throughput degradation. This paper proposes a novel AP selection policy taking into consideration not only signal strength of the APs but also AP loads to avoid Hotspot congestion. An experiment on Alinker implementing proposed AP selection policy, demonstrates that the proposed policy achieves close to optimal load balancing and grants the maximum transmission rate to stations in comparison with SSF (Strongest-Signal-First) and LLF (Least-Loaded-First).

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.93-107
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• 1996

A flexible flow line(FFL) consists of several groups of identical machines. All work-orders flow along the same path through successive machine groups. Thus, we emphasized the balancing of workloads between machine groups in order to maximize total productivity. On the other hand, many different types of work-orders, in varying batch or lot sizes, are produced simultaneously. The mix of work-orders, their lot sizes, and the sequence in which they are produced affect the amount of workload. However, the work-orders and their lot sizes are prefixed and cannot be changed. Because of these reasons, we have developed an optimal route selection model using heuristic search and Min-Max algorithm for balancing the workload between machine groups in the FFL.

• Korean Journal of Biological Psychiatry
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• v.26 no.2
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• pp.33-38
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• 2019

Psychiatric disorder as dysfunctional behavioural syndrome is a paradoxical phenomenon that is difficult to explain evolutionarily because moderate prevalence rate, high heritability and relatively low fitness are shown. Several evolutionary genetic models have been proposed to address this paradox. In this paper, I explain each model by dividing it into selective neutrality, mutation-selection balance, and balancing selection hypothesis, and discuss the advantages and disadvantages of them. In addition, the feasibility of niche specialization and frequency dependent selection as the plausible explanation about the central paradox is briefly discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.526-529
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• 2020

최근 네트워크 환경에 대한 공격이 급속도로 고도화 및 지능화 되고 있기에, 기존의 시그니처 기반 침입탐지 시스템은 한계점이 명확해지고 있다. 이러한 문제를 해결하기 위해서 기계학습 기반의 침입 탐지 시스템에 대한 연구가 활발히 진행되고 있지만 기계학습을 침입 탐지에 이용하기 위해서는 두 가지 문제에 직면한다. 첫 번째는 실시간 탐지를 위한 학습과 연관된 중요 특징들을 선별하는 문제이며 두 번째는 학습에 사용되는 데이터의 불균형 문제로, 기계학습 알고리즘들은 데이터에 의존적이기에 이러한 문제는 치명적이다. 본 논문에서는 위 제시된 문제들을 해결하기 위해서 Hybrid Feature Selection과 Data Balancing을 통한 심층 신경망 기반의 네트워크 침입 탐지 모델을 제안한다. NSL-KDD 데이터 셋을 통해 학습을 진행하였으며, 평가를 위해 Accuracy, Precision, Recall, F1 Score 지표를 사용하였다. 본 논문에서 제안된 모델은 Random Forest 및 기본 심층 신경망 모델과 비교해 F1 Score를 기준으로 7~9%의 성능 향상을 이루었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.249-251
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• 2014

This paper proposes a channel diversity based load-balancing cross-layer routing scheme for Wireless Mesh Networks (WMNs). The proposed scheme deals with channel diversity phase and load balancing phase in WMNs. Channel diversity factor $metric_{ch-d}$ and load balancing factor $f_{load}$ are defined and employed cooperatively as a combined path selection policy.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.702-712
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• 2023

Load balancing plays a crucial role in ensuring the stable operation of information management systems during periods of high user access requests; therefore, load balancing approaches should be reasonably selected. Moreover, appropriate load balancing techniques could also result in an appropriate allocation of system resources, improved system service, and economic benefits. Nginx is one of the most widely used loadbalancing software packages, and its deployment is representative of load-balancing application research. This study introduces Nginx into an educational administration system, builds a server cluster, and compares and sets the optimal cluster working strategy based on the characteristics of the system, Furthermore, it increases the stability of the system when user access is highly concurrent and uses the Nginx reverse proxy service function to improve the cluster's ability to resist illegal attacks. Finally, through concurrent access verification, the system cluster construction becomes stable and reliable, which significantly improves the performance of the information system service. This research could inform the selection and application of load-balancing software in information system services.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.5-21
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• 1994

We have described the optimal process route selection model for the PCB(printed circuit board) auto-insertion line. This PCB assembly line is known as a FFL(flexible flow line) which produces a range of products keeping the flow shop properties. Under FFL environments, we have emphasized the balancing of work-loads in order to maximize total productivity of PCB auto-insertion line. So we have developed a heuristic algorithm based on a work-order selection rule and min-max concept for the job route selection model.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.81-88
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• 2008

In the game balancing. it is so difficult to choose suitable arms among various actions, or arms and to accurately calculate to which level we adjust the balance. The fuzzy method can be properly used in a particular environment which cannot be correctly processed in mathematics or in lessening the time-consuming problems during the accurate number crunching. Because a variety of actions, relations with opponents. previous battle experiences etc. are not easy to be reflected in every occasion, the fuzzy method could be useful in these cases. When the balancing is needed. the data which have been played to that Point are processed by the fuzzy function and calculated to adapt intensity to each action. The ability of characters is regulated in this process. To demonstrate the efficiency of this method. I would like to make clear the excellence of fuzzy method through the following five experiments; a case with invariable ability adjustment, a case adjusted by a randomly chosen action, a case with the strongest weapon selection. a case with the weakest weapon selection and a case with the fuzzy method application.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.57-64
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• 2002

Since most of the current route selection algorithms use the shortest path algorithm, network resources can not be efficiently used also traffics be concentrated on specific paths resulting in congestgion. In this paper we propose the statistical route selections(SRS) algorithm which adopts a statistical mechanism to utilize the network resource efficiently and to avoid congestion. The SRS algorithm handles requests on demand and chooses a path that meets the requested bandwidth. With the advent of the MPLS it becomes possible to establish an explicit LSP which can be used for traffic load balancing. The SRS algorithm finds a set of link utilizations for route selection, computes link weights using statistical mechanism and finds the shortest path from the weights. Our statistical mechanism computes the mean and the variance of link utilizations and selects a route such that it can reduce the variance and the number of congested links and increase the utilization of network resources. Throughout the simulation, we show that the SRS algorithm performs better than other route selection algorithms on several metrics like the number of connection setup failures and the number of congested links.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.