• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4220-4241
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• 2017

For the downlink energy-harvesting small cell network, this paper proposes an interference management algorithm based on distributed coalitional game. The cooperative interference management problem of the energy-harvesting small cells is modeled as a coalitional game with transfer utility. Based on the energy harvesting strategy of the small cells, the time sharing mode of the small cells in the same coalition is determined, and an optimization model is constructed to maximize the total system rate of the energy-harvesting small cells. Using the distributed algorithm for coalition formation proposed in this paper, the stable coalition structure, optimal time sharing strategy and optimal power distribution are found to maximize the total utility of the small cell system. The performance of the proposed algorithm is discussed and analyzed finally, and it is proved that this algorithm can converge to a stable coalition structure with reasonable complexity. The simulations show that the total system rate of the proposed algorithm is superior to that of the non-cooperative algorithm in the case of dense deployment of small cells, and the proposed algorithm can converge quickly.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.107-110
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• 2015

이 논문에서 우리는 자원이 제약된 무선 센서네트워크에서 협력게임이론 (coalitional game theory) 기반의 전송파워제어 문제를 다룬다. 우리가 다루고자 하는 전송파워제어 문제는 각 센서의 에너지 효율성을 목적 함수 (objecitve function) 로 갖고 지연시간을 제약조건으로 갖는다. 이 문제는 two-sided one-to-one matching game 으로 모델링하고 core에 속하는 센서쌍의 매칭을 찾아내기 위해 deferred acceptance procedure (DAP)를 적용한다. Core에 속하는 매칭은 다른 센서와 매칭을 해도 현재 매칭 이상보다 좋은 결과를 가져오지 않는 매칭이 된다. 그리고, DAP를 반복해서 적용하게 되면 특정 안정상태에 도달하게 되는데, 그 안정상태에서는 지연시간제약을 만족시키면서 더 이상 에너지 효율성이 향상되지 않는 것을 보인다. 우리의 결과는 클러스터 기반의 센서 그룹방법과 지역 최적의 해 (local optimal solution)와 비교된다.

• Smart Structures and Systems
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• 제29권1호
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.