• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.583-586
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• 2002

In this research, we propose a flexible conveying system (FCS) which consists of multiple arrays of cells. Each cell is a wheel driven by a two degree-of-freedom mechanism. The direction and velocity of cell are controlled based on the concept of hybrid control under a distributed network. Each cell has its own controller under a subsumption architecture for low-level control. A cell communicates with its four neighboring cells to manipulate n targeted object towards its desired position. The high-level control assigns a desired position and direction of the object to each cell. The path of each object is generated by many supporting cells. Moreover, the FCS can handle multiple objects simultaneously. To study the flexible conveying system, a GUI-based simulator of flexible conveying system is constructed. The simulated results show that the system can handle multiple objects independently and simultaneously under the proposed hybrid control architecture.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2003년도 추계학술대회 및 정기총회
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• pp.201-204
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• 2003

A hybrid artificial intelligence approach based on combining case based reasoning and neural networks is presented. The approach is designed to allow for solving blowing control of BOF(basic oxygen furnace), example of which lie at the core of steelmaking process control systems application in the steel industry. According to this hybrid approach, the system, when faced with a new problem, first retrieves similar cases and neural network is used to solve the problem. Experimental Results indicate that combining case based reasoning and neural network offers an efficient approach to solving control and prediction problem

• Journal of information and communication convergence engineering
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• 제8권5호
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• pp.549-553
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• 2010

Lim et al. proposed a Hybrid Group Key Management scheme for Hierarchical Self-Organizing Sensor Network in 2008 to provide a secure way to pass down the group key for cluster-based communication. This paper presents two practical attacks on the scheme proposed by Lim et al. by tampering sensor nodes of a cluster to recover necessary secret keys and by exploiting the IDS employed by the scheme. The first attack enables a long-term but slow data fabrication while other attack causes more severe DoS on the access to cluster sensor nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권6호
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• pp.1982-2004
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• 2014

Wireless Mesh Networks (WMNs) are emerging as promising, convenient next generation wireless network technology. There is a great need for a secure framework for routing in WMNs and several research studies have proposed secure versions of the default routing protocol of WMNs. In this paper, we propose a security framework for Hybrid Wireless Mesh Protocol (HWMP) in WMNs. Contrary to existing schemes, our proposed framework ensures both end-to-end and point-to-point authentication and integrity to both mutable and non-mutable fields of routing frames by adding message extension fields to the HWMP path selection frame elements. Security analysis and simulation results show that the proposed approach performs significantly well in spite of the cryptographic computations involved in routing.

• 전자공학회논문지B
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• 제31B권12호
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• pp.116-123
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• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• 전자공학회논문지B
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• 제31B권8호
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• pp.211-218
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• 1994

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

• 반도체디스플레이기술학회지
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• 제13권1호
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• pp.73-81
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• 2014

In this paper, high-speed Hybrid MAC (Medium Access Control layer) implementation suitable for smart grid applications is researched. MB-OFDM (Multi-Band Orthogonal Frequency Division Multiplexing) is considered for high-speed communication method in smart grid application. In this paper, the MAC adopts the distributed network managing method. Also, the MB-OFDM merit of high-speed transfer rate of up to 480Mbps must be supported. Hence, this paper presents an efficient hardware-software integration (co-design) method in order to realize a high- speed transmission, and a realizing method of distribution network. Finally, MAC performance and reliability based on MB-OFDM PHY (PHYsical layer) are confirmed through simulation and emulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2089-2091
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• 2001

In this paper, a hybrid controller that consists of a conventional PD controller and a neural network controller which adapts to various control conditions by online learning is used and a new learning algorithm of the neural networks is used to prevent weights of neural network from diverging. A conventional PI controller and the hybrid controller is applied to speed control of 3 phase induction motor. So in comparison with a PD controller, we prove superiority of hybrid controller by experiments.

• 한국정보통신학회논문지
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• 제11권7호
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• pp.1394-1399
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• 2007

본 논문에서는 무선센서 네트워크에서 전송 효율을 높이기 위하여 CSMA/CA 방식과 TDMA 방식을 혼합한 HMAC 프로토콜을 제안한다. 제안된 HMAC에서는 패킷 전송을 원하는 송신 노드가 CSMA/CA 방식을 통하여 수신 노드에게 전송 요청을 하고, 수신 노드에서는 네트워크 토폴로지와 전송되어지는 트래픽 양에 따라 TDMA 방식으로 적절히 슬롯을 할당함으로 여러 수신 노드들 사이에 슬롯 중복 할당으로 인한 충돌을 낮춤으로 전송효율을 높일 수 있다.

• 전력전자학회논문지
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• 제27권1호
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.