• The KIPS Transactions:PartC
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• v.17C no.6
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• pp.459-464
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• 2010

Due to the application-specific nature of wireless sensor networks, the sensitivity to such a requirement as data reporting latency may vary depending on the type of applications, thus requiring application-specific algorithm and protocol design paradigms which help us to maximize energy conservation and thus the network lifetime. In this paper, we implement and evaluate a novel delay-adaptive sensor scheduling scheme for energy-saving data gathering which is based on a two phase clustering (TPC), in wireless sensor networks. The TPC is implemented on sensor Mote hardwares. With the help of TPC implemented, sensors selectively use direct links for control and forwarding time critical sensed data and relay links for data forwarding based on the user delay constraints given. Implementation study shows that TPC helps the sensors to increase a significant amount of energy while collecting sensed data from sensors in a real environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.82-88
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• 2018

The decoy defensive weapon system is a one-shot system. Reliability is maintained through periodic inspection and high reliability is required to confirm whether or not the functioning is normal after launch. The maintenance cycle of a decoy was set up without target reliability and reliability prediction during the development period. However, the number of operations in the military has been increasing, necessitating the optimization of the maintenance cycle. Reliability is analyzed using the maintenance data of a decoy operated for several decades and the optimal maintenance cycle is suggested. In chapter 2, data collection and classification methods are presented and analysis methodology is briefly introduced. In chapter 3, the data distribution analysis and fitness verification confirmed that applying the Weibull distribution is the most suitable for the maintenance data of the decoy. In chapter 4, we present the analysis result of percentile, survival probability and MTBF and the optimal maintenance cycle was derived from the reliability analysis. Finally, we suggest the application methods for this paper in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5B
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• pp.801-808
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• 2010

LEACH, one of hierarchical based routing protocols, was proposed for energy efficiency which is the most important requirement of Wireless Sensor Network(WSN). LEACH protocol is composed of a cluster of certain large number of clusters, which have a cluster head and member nodes. Member nodes send sensing data to their cluster heads, and the cluster heads aggregate the sensing data and transmit it to BS. The challenges of LEACH protocol are that cluster heads are not evenly distributed, and energy consumption to transmit aggregated data from Cluster heads directly to BS is excessive. This study, to improve LEACH protocol, suggests LECEEP that transmit data to contiguity cluster head that is the nearest and not far away BS forming chain between cluster head, and then the nearest cluster head from BS transmit aggregated data finally to BS. According to simulation, LECEEP consumes less energy and retains more number of survival node than LEACH protocol.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.283-286
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• 2014

본 논문에서는 카메라형 OCR (Optical Character Reader) 기술을 이용해 오프라인 중고서점의 효율적 장서관리 시스템을 구축하기 위한 디지털 데이터화 관리시스템 방안을 제안한다. OCR은 광학적으로 인식할 수 있는 문자를 컴퓨터가 읽을 수 있도록 하는 기술이다. 원리적으로 문자 한 개를 수십 개의 모눈으로 분할해 특정한 모눈의 흑백 또는 자획형상 특징에 의해 문자를 판독한다. 이 논문에서는 OCR 기술을 활용함으로써 디지털 데이터화의 효과는 물론 적용 환경의 개선효과를 기대해 볼 수 있는 오프라인 중고서점 시장을 목표로 했다. 오프라인 중고서점에서 보유하고 있는 장서의 디지털 데이터화는 기업형 중고서점과의 경쟁에 있어서도 생존을 위해 필요한 요소이다. 카메라형 OCR 기술을 활용한 장서 디지털 데이터화는 오프라인 중고서점 판매자가 도서재고 검색 및 판매 관리 효율을 높이도록 도와줄 뿐 아니라, 도서판매 유형, 소비자 분석과 수요 예측을 가능하게 한다. 또한 소비자에게 오프라인 중고서점에서 보유하고 있는 희귀 장서와 중고서적들을 검색해 구입할 수 있는 편의를 제공할 것이다. 오프라인 중고서점 판매를 촉진하고 활성화시킨다면 출판의 선순환적 구조를 만드는 데 기여할 것으로 예상된다.

• 전자공학회논문지 IE
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• v.47 no.1
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• pp.41-47
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• 2010

It is very important to prolong the lifetime of wireless sensor networks by using their limited energy efficiently, since it is not possible to change or recharge the battery of sensor nodes after deployment. LEACH protocol is a typical routing protocol based on the clustering scheme for the efficient use of limited energy. It is composed of a few clusters, which consist of head nodes and member nodes. Though LEACH starts from the supposition that all nodes have data transferred to a head, there must be some nodes having useless data in actual state. In this paper we propose a power saving scheme by making a member node dormant if previous sensed data and current data is same. We evaluate the performance of the proposed scheme in comparison with original clustering algorithms. Simulation results validate our scheme has better performance in terms of the number of alive nodes as time evolves.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.73-82
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• 2007

This paper proposes how to reduce the amount of data transmitted in each sensor and cluster head in order to lengthen the lifetime of sensor network by data aggregation of the continuous queries. The most important factor of refuting the sensor's energy dissipation is to reduce the amount of messages transmitted. The method proposed is basically to combine clustering, in-network data aggregation and hierarchical filtering. Hierarchical filtering is to divide sensor network by two tiers when filtering it. First tier performs filtering when transmitting the data from cluster member to cluster head, and second tier performs filtering when transmitting the data from cluster head to base station. This method is much more efficient and effective than the previous work. We show through various experiments that our scheme reduces the network traffic significantly and increases the network's lifetime than existing methods.

• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.189-196
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• 2010

The LEACH does not use flooding method for data transmission and this makes low power consumption. So performance of the WSN is increased. On the other hand, QoS based algorithm which use restricted flooding method in WSN also achieves low power consuming rate by reducing the number of nodes that are participated in routing path selection. But when the data is delivered to the sink node, the LEACH choose a routing path which has a small hop count. And it leads that the performance of the entire network is worse. In the paper we propose a QoS based energy efficient clustering and routing algorithm in WSN. I classify the type of packet with two classes, based on the energy efficiency that is the most important issue in WSN. We provide the differentiated services according to the different type of packet. Simulation results evaluated by the NS-2 show that proposed algorithm extended the network lifetime 2.47 times at average. And each of the case in the class 1 and class 2 data packet, the throughput is improved 312% and 61% each.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.59-64
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• 2024

As the aging population grows, the incidence of cancer is increasing. Skin cancer appears externally, but people often don't notice it or simply overlook it. As a result, if the early detection period is missed, the survival rate in the case of late stage cancer is only 7.5-11%. However, the disadvantage of diagnosing, serious skin cancer is that it requires a lot of time and money, such as a detailed examination and cell tests, rather than simple visual diagnosis. To overcome these challenges, we propose an Attention-based CNN model skin cancer classification system. If skin cancer can be detected early, it can be treated quickly, and the proposed system can greatly help the work of a specialist. To mitigate the problem of image data imbalance according to skin cancer type, this skin cancer classification model applies the Over Sampling, technique to data with a high distribution ratio, and adds a pre-learning model without an Attention layer. This model is then compared to the model without the Attention layer. We also plan to solve the data imbalance problem by strengthening data augmentation techniques for specific classes.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1057-1067
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• 2014

Many genetic variants have been identified to be associated with complex diseases such as hypertension, diabetes and cancers throughout genome-wide association studies (GWAS). However, there still exist a serious missing heritability problem since the proportion explained by genetic variants from GWAS is very weak less than 10~15%. Gene-gene interaction study may be helpful to explain the missing heritability because most of complex disease mechanisms are involved with more than one single SNP, which include multiple SNPs or gene-gene interactions. This paper focuses on gene-gene interactions with the survival phenotype by extending the multifactor dimensionality reduction (MDR) method to the accelerated failure time (AFT) model. The standardized residual from AFT model is used as a residual score for classifying multiple geno-types into high and low risk groups and algorithm of MDR is implemented. We call this method AFT-MDR and compares the power of AFT-MDR with those of Surv-MDR and Cox-MDR in simulation studies. Also a real data for leukemia Korean patients is analyzed. It was found that the power of AFT-MDR is greater than that of Surv-MDR and is comparable with that of Cox-MDR, but is very sensitive to the censoring fraction.