• 한국통신학회논문지
• /
• 제37B권9호
• /
• pp.767-777
• /
• 2012

Topology control increases channel efficiency by controlling transmission power of a node, and as a result, network lifetime and throughput are increased. However, reducing transmission range causes a network connectivity problem, especially in mobile networks. When a network loses connectivity, the network topology should be re-configured. However, topology re-configuration consumes lots of energy because every node need to collect neighbor information. As a result, network lifetime may decrease, even though topology control is being used to prolong the network lifetime. Therefore, network connectivity time needs to be increased to expend network lifetime in mobile networks. In this paper, we propose an Adaptive-Redundant Transmission Range (A-RTR) algorithm to address this need. A-RTR uses a redundant transmission range considering a node status and flexibly changes a node's transmission range after a topology control is performed.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2001년도 추계학술대회논문집A
• /
• pp.723-729
• /
• 2001

Electro-hydraulic shift control of a vehicle automatic transmission has been predominantly carried out via an open-loop control based on numerous time-consuming calibrations. Despite remarkable success in practice, the variations of system characteristics inevitably deteriorate the performance of the tuned open-loop controller. As a result, the controller parameters need to be continuously updated in order to maintain satisfactory shift quality. This paper presents a self-learning algorithm for automatic transmission shift control in a construction vehicle during inertia phase. First, an observer reconstructs the turbine acceleration signal (impossible to measure in a construction vehicle) from the readily accessible turbine speed measurement. Then, a control algorithm based on a quadratic function of the turbine acceleration is shown to guarantee the asymptotic convergence (within a specified target bound) of the error between the actual and the desired turbine accelerations. A Lyapunov argument plays a crucial role in deriving adaptive laws for control parameters. The simulation and hardware-in-the-loop simulation (HILS) studies show that the proposed algorithm actually delivers the promise of satisfactory performance despite the system characteristics variations and uncertainties.

• 조명전기설비학회논문지
• /
• 제18권6호
• /
• pp.37-44
• /
• 2004

전력계통분야의 복합 대형화에 유연한 대처와 전력조류의 최적화 도모를 위해 사용되는 FACTS(Flexible AC Transmission System)기기 중 가장 유용한 UPFC(Unified Power Flow Controller)는 선로의 전압을 임의의 크기와 위상을 갖도록 제어하여 선로로 전송되는 유ㆍ무효전력을 총체적으로 보상하는 기능을 갖는다. 이런 UPEC가 계통에 연계되어 운영된다면 송전선로 매개변수가 변하기 때문에 계통의 영향을 많이 받는 거리계전기는 불필요한 오동작이 발생하게 된다. 즉 거리계전기에서 바라본 임피던스 영역(Impedance Zone)이 송전선로에 UPFC 연계시 각각의 보상 값에 의해 상당한 변화를 보임으로, 기존의 방식으로 정정된 Relay Setting Zone과 Adaptive Setting Zone은 현저한 오차가 발생하게 된다. 그러므로 계통에 연계된 UPFC의 운전 조건을 고려한 거리계전기 보호구간의 재설정이 필요하게 된다. 따라서 본 논문의 목적은 학습이 가능한 신경회로망(ANN)을 이용하여 거리계전기 동작의 신속성(Speed)을 기본으로 전력계통의 다양한 환경에 대해 거리계전기 응동 특성을 향상시키는데 있다. 학습 방법으로는 정적 및 동적인 비선형 시스템의 인식과 다변수 시스템에 적용 가능한 역전파 알고리즘(Back-propagation Algorithm)을 사용했다.

• Journal of Information Processing Systems
• /
• 제15권3호
• /
• pp.593-603
• /
• 2019

In wearable healthcare systems, sensor devices can be deployed in places around the human body such as the stomach, back, arms, and legs. The sensors use tiny batteries, which have limited resources, and old sensor batteries must be replaced with new batteries. It is difficult to deploy sensor devices directly into the human body. Therefore, instead of replacing sensor batteries, increasing the lifetime of sensor devices is more efficient. A transmission power control (TPC) algorithm is a representative technique to increase the lifetime of sensor devices. Sensor devices using a TPC algorithm control their transmission power level (TPL) to reduce battery energy consumption. The TPC algorithm operates on a closed-loop mechanism that consists of two parts, such as sensor and sink devices. Most previous research considered only the sink part of devices in the closed-loop. If we consider both the sensor and sink parts of a closed-loop mechanism, sensor devices reduce energy consumption more than previous systems that only consider the sensor part. In this paper, we propose a new approach to consider both the sensor and sink as part of a closed-loop mechanism for efficient energy management of sensor devices. Our proposed approach judges the current channel condition based on the values of various body sensors. If the current channel is not optimal, sensor devices maintain their current TPL without communication to save the sensor's batteries. Otherwise, they find an optimal TPL. To compare performance with other TPC algorithms, we implemented a TPC algorithm and embedded it into sensor devices. Our experimental results show that our new algorithm is better than other TPC algorithms, such as linear, binary, hybrid, and ATPC.

• Journal of Power Electronics
• /
• 제19권3호
• /
• pp.771-783
• /
• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.

• 한국전자통신학회논문지
• /
• 제10권1호
• /
• pp.103-110
• /
• 2015

WBAN(Wireless Body Area Network)는 인체주변 2-3m 영역에서 의료 및 비의료 디바이스들로 구성된 다양한 환자 모니터링 분야를 지원하기 위한 무선센서네트워크이다. WBAN 환경을 위해서는 저전력 소비, QoS, 듀티사이클등의 요구사항을 만족하고 주파수 대역을 효율적으로 분배하며 트래픽 로드에 강하면서 에너지를 절약하는 MAC(Medium Access Control)이 설계되어야 한다. 본 논문에서는 트래픽 로드가 증가할 때를 고려해 에너지에 효율적인 AQ(Adaptive Queuing) MAC 슈퍼프레임 구조를 제안한다. 또한 시뮬레이션 결과 제안하는 AQ(Adaptive Quenuing)MAC를 IEEE 802.15.4 MAC과 비교 하였을 때 전송처리율, 평균MAC 지연율 측면에서 향상된 결과를 얻을 수 있었다.

• 대한전기학회논문지:전력기술부문A
• /
• 제49권7호
• /
• pp.332-338
• /
• 2000

In this paper, a robust nonlinear excitation controller is proposed to achieve both voltage regulation and system stability enhancement for single machine-infinite power systems. The proposed method employs backstepping technique and combines this with an adaptation algorithm for estimating the effective reactance of transmission line, thereby leading to adaptive nonlinear control. Simulation results show that power that angle stabilization as well as voltage regulation is achieved in a satisfactory manner, regardless of the system operating conditions and system structure.

• 한국통신학회논문지
• /
• 제32권3A호
• /
• pp.271-280
• /
• 2007

본 논문에서는 동일 채널 간섭이 존재하는 셀룰러 환경에서 OFDM 통신 시스템들이 각기 목표로 하는 전송속도를 달성하기 위한 송신 전력 조절 방안을 제시하였다. 일반적으로 다중 반송파 시스템에 대한 최적 또는 준 최적 송신 전력 할당 방안은 주파수 대역별로 채널 상태에 따라 송신 전력을 조절하는 형태인 것으로 알려져 있으며, 이는 WF(waterfilling)이나 IWF(iterative waterfilling) 형태로 발표된 바 있다. 하지만 반송파별로 송신 전력을 달리하는 방식을 구현하기 위해서는 각 대역별 채널 상태와 관련된 정보를 별도의 궤환 링크(feedback link)를 통해 수신기에서 송신기로 전송해야 한다. 만약 채널 상태가 빠르게 변하거나 반송파의 개수가 크다면 이로 인한 부담이 상당할 수가 있다. 이에 본 논문에서는 이런 부담을 줄이기 위한 방안으로 부반송파마다 동일한 송신 전력을 사용하는 방식에 대한 전력 조절 알고리듬을 제시한다. 그리고 제안한 알고리듬이 수렴한다는 것을 증명하고, 기존의 IWF 방식과 복잡도를 비교하며, 컴퓨터 실험을 통해 수렴 특성을 살펴보고자 한다.

• 한국지능시스템학회논문지
• /
• 제8권2호
• /
• pp.117-127
• /
• 1998

전력계통은 번개, 폭우, 고장등이 여러요인으로 인해 변화하는 동특성을 갖는다. 전송선로의 리액터스가 사고로 인해 변하는 것도 그 대표적인 예이며 전력게통에 불확성을 야기시키는 원인이 된다. 이경우, 이와같은 불확실성에 대해 견실한 성능을 발휘하는 제어기가 필요하다. 한편, 최근의 연구들을 통해 비파라미터적인 불확실서을 갖는 계통에 대해 퍼지제어기가 우수한 성능을 발휘함이 입증되었다. 따라서, 본 논문에서는 선로의 고장 발생시, 전력계통의 발전기 단자전압을 일정값으로 유지하며 계통을 안정화시킬 수 있는 적응퍼지제어기를 궤환선형화 기법에 근거하여 설계하고자 한다. 아울러 본 논문에서는 불확실한 계통에 있어서, 리아프노프 안정도를 보장 받기우해 필요한 불확실한 항의 상계를 퍼지계통에 으해 추정하는 방법을 제안한다. 시뮬레인션 결고, 설계된 제어기가 선로고장에도 불구하고 계통의 전압유지와 과도 안정도를 잘 보장해 줌을 확인할 수 있었다.

• International Journal of Computer Science & Network Security
• /
• 제22권10호
• /
• pp.191-200
• /
• 2022

Constrained Application Protocol (CoAP) is a standardized protocol by the Internet Engineering Task Force (IETF) for the Internet of things (IoT). IoT devices have limited computation power, memory, and connectivity capabilities. One of the significant problems in IoT networks is congestion control. The CoAP standard has an exponential backoff congestion control mechanism, which may not be adequate for all IoT applications. Each IoT application would have different characteristics, requiring a novel algorithm to handle congestion in the IoT network. Unnecessary retransmissions, and packet collisions, caused due to lossy links and higher packet error rates, lead to congestion in the IoT network. This paper presents an adaptive congestion control protocol for CoAP, Adaptive Congestion Control with a Backoff algorithm (ACCB). AACB is an extension to our earlier protocol AdCoCoA. The proposed algorithm estimates RTT, RTTVAR, and RTO using dynamic factors instead of fixed values. Also, the backoff mechanism has dynamic factors to estimate the RTO value on retransmissions. This dynamic adaptation helps to improve CoAP performance and reduce retransmissions. The results show ACCB has significantly higher goodput (49.5%, 436.5%, 312.7%), packet delivery ratio (10.1%, 56%, 23.3%), and transmission rate (37.7%, 265%, 175.3%); compare to CoAP, CoCoA+ and AdCoCoA respectively in linear scenario. The results show ACCB has significantly higher goodput (60.5%, 482%,202.1%), packet delivery ratio (7.6%, 60.6%, 26%), and transmission rate (40.9%, 284%, 146.45%); compare to CoAP, CoCoA+ and AdCoCoA respectively in random walk scenario. ACCB has similar retransmission index compare to CoAp, CoCoA+ and AdCoCoA respectively in both the scenarios.