• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• 전자공학회논문지CI
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• 제46권2호
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• pp.65-74
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• 2009

신경망의 구조를 최적화하기 위해서는 노드 또는 연결을 잘라내는 가지치기 방법과 노드를 추가해 나가는 구조 증가 방법이 있다. 이 논문은 신경망의 구조 최적화를 위해 가지치기 방법을 사용하며, 최적의 노드 가지치기를 찾기 위해 유전 알고리즘을 사용한다. 기존 연구에서는 입력층과 은닉층의 노드를 따로 최적화 대상으로 삼았다 우리는 두 층의 노드를 하나의 염색체에 표현하여 동시 최적화를 꾀하였다. 자식은 부모의 가중치를 상속받는다 학습을 위해서는 기존의 오류 역전파 알고리즘을 사용한다. 실험은 UCI Machine Learning Repository에서 제공한 다양한 데이터를 사용하였다. 실험 결과 신경망 노드 가지치기 비율이 평균 $8{\sim}25%$에서 좋은 성능을 얻을 수 있었다. 또한 다른 가지치기 및 구조 증가 알고리즘과의 교차검증에 대한 t-검정 결과 그들에 비해 우수한 성능을 보였다.

• 대한전자공학회논문지TC
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• 제43권9호
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• pp.165-174
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• 2006

본 논문에서는 IEEE 802.11 기반 무선 멀티홉 망에서 TCP의 성능을 향상시키기 위하여 새로운 Contention Window(CW) 제어 알고리즘을 제안 하였다. 제안한 Contention Window(CW) 제어 알고리즘은 무선 멀티홉 망에서 빈번히 발생하는 hidden terminal 문제의 영향을 경감시킨다 무선 멀티홉 망에서 발생하는 대부분의 패킷 손실은 패킷의 충돌에 의한 것이 아니라 hidden terminal과 exposed terminal로 인하여 발생된다. 그러나 IEEE 802.11 DFC 알고리즘에서는 전송에 실패한 사용자의 CW를 지수형태로 증가시키므로 해당노드가 전송에 성공할 확률을 더욱 감소시킨다. 이는 전송에 성공한 노드가 연속해서 패킷 전송에 성공할 가능성을 높여주어 burst한 데이터 전송이 일어날 수 있다. 한편, 최대 재전송을 시도한 후에도 데이터를 보내지 못한 노드는 네트워크 계층에서의 경로 재전송을 시도하게 되는데 이로 인해 데이터 전송이 중지되고 성능감소가 일어날 수 있다. 이와 같은 문제를 해결하기 위하여 본 논문에서 제안한 기법에서는 backoff 재전송의 횟수를 증가시키고 적절한 CW의 크기를 설정하는 방안을 제안 하였다. Ns-2를 사용하여 체인 토폴로지와 격자 토폴로지에서의 시뮬레이션을 수행해 제안된 기법이 무선 멀티홉 망에서 TCP 성능을 향상시킴을 확인 하였다.

• 치과방사선
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• 제29권1호
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• pp.149-159
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• 1999

Purpose: The purpose of this study was to evaluate cervical lymph node metastasis of oral squamous cell carcinoma patients by MRI film and neural network system. Materials and Methods: The oral squamous cell carcinoma patients(21 patients. 59 lymph nodes) who have visited SNU hospital and been taken by MRI. were included in this study. Neck dissection operations were done and all of the cervical lymph nodes were confirmed with biopsy. In MR images. each lymph node were evaluated by using 6 MR imaging criteria(size. roundness. heterogeneity. rim enhancement. central necrosis, grouping) respectively. Positive predictive value. negative predictive value. and accuracy of each MR imaging criteria were calculated. At neural network system. the layers of neural network system consisted of 10 input layer units. 10 hidden layer units and 1 output layer unit. 6 MR imaging criteria previously described and 4 MR imaging criteria (site I-node level II and submandibular area. site II-other node level. shape I-oval. shape II-bean) were included for input layer units. The training files were made of 39 lymph nodes(24 metastatic lymph nodes. 10 non-metastatic lymph nodes) and the testing files were made of other 20 lymph nodes(10 metastatic lymph nodes. 10 non-metastatic lymph nodes). The neural network system was trained with training files and the output level (metastatic index) of testing files were acquired. Diagnosis was decided according to 4 different standard metastatic index-68. 78. 88. 98 respectively and positive predictive values. negative predictive values and accuracy of each standard metastatic index were calculated. Results: In the diagnosis of using single MR imaging criteria. the rim enhancement criteria had highest positive predictive value (0.95) and the size criteria had highest negative predictive value (0.77). In the diagnosis of using single MR imaging criteria. the highest accurate criteria was heterogeneity (accuracy: 0.81) and the lowest one was central necrosis (accuracy: 0.59). In the diagnosis of using neural network systems. the highest accurate standard metastatic index was 78. and that time. the accuracy was 0.90. Neural network system was more accurate than any other single MR imaging criteria in evaluating cervical lymph node metastasis. Conclusion: Neural network system has been shown to be more useful than any other single MR imaging criteria. In future. Neural network system will be powerful aiding tool in evaluating cervical node metastasis.

• Asian Pacific Journal of Cancer Prevention
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• 제17권11호
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• pp.4913-4916
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• 2016

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• 한국통신학회논문지
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• 제32권7B호
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• pp.463-471
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• 2007

Ad hoc 무선망의 동적인 특성으로 인하여 히든노드 문제가 나타날 수 있기 때문에 ad hoc 무선망에서는 분산방식으로 네트워크가 동작되어야 한다. Ad hoc 무선망에서 CSMA/CA는 비동기적 데이터 트래픽을 위해 가장 많이 사용되는 MAC Protocol 중의 하나이다. 그러나 CSMA/CA는 멀티미디어 데이터의 특성을 보장하지 못한다. 또한 경쟁형(Contention)이므로 채널을 먼저 잡은 하나의 스테이션이 채널을 독점해서 사용하여 채널 기아(starvation)로 인한 공평성문제(fairness problem)가 발생한다. 본 논문에서는 무선 ad hoc망에서 멀티미디어 데이터의 특성을 고려하여, QoS 보장을 위한 MAC protocol을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권1호
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• pp.141-156
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• 2011

In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권12호
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• pp.3152-3165
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• 2012

Wireless sensor networks (WSN) often employ asynchronous MAC scheduling, which allows each sensor node to wake up independently without synchronizing with its neighbor nodes. However, this asynchronous scheduling may not deal with collisions due to hidden terminals effectively. Although most of the existing asynchronous protocols exploit a random back-off technique to resolve collisions, the random back-off cannot secure a receiver from potentially repetitive collisions and may lead to a substantial increase in the packet latency. In this paper, we propose a new collision resolution algorithm called Transient Coordinator (TC) for asynchronous WSN MAC protocols. TC resolves a collision on demand by ordering senders' transmissions when a receiver detects a collision. To coordinate the transmission sequence both the receiver and the collided senders perform handshaking to collect the information and to derive a collision-free transmission sequence, which enables each sender to exclusively access the channel. According to the simulation results, our scheme can improve the average per-node throughput by up to 19.4% while it also reduces unnecessary energy consumption due to repetitive collisions by as much as 91.1% compared to the conventional asynchronous MAC protocols. This demonstrates that TC is more efficient in terms of performance, resource utilization, and energy compared to the random back-off scheme in dealing with collisions for asynchronous WSN MAC scheduling.

• 한국지능시스템학회논문지
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• 제7권4호
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• pp.65-73
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• 1997

본 논문에서는 진화연산을 이용하여 동적 귀환 신경망의 구조를 저차원화하는 방법을 제안한다. 일반적으로 진화연산을 개체군을 이용한 탐색 방법으로서 신경회로망의 여러 가지 다른 성질을 동시에 최적화할 필요가 있을 때 유용한 방법이다. 본 연구에서는 동적 귀환 신경망의 구조를 조차원화하기 위하여 진화 프로그래밍으로 신경망의 구조를 탐색하고, 진화전략으로 신경망의 연결강도를 학습시킴으로서 전체적인 구조를 저차원화하였다.신경망의 중간층 노드의 추가/삭제는 돌연변이 확률에 의하여 결정한다. 노드를 삭제할 경우에는 입력 연결강도의 총합이 가장 작은 노드를 삭제하고, 노드를 추가할 경우에는 미리 지정한 확률함스에 따라 노드를 추가한다. 그리고 추가된 노드와 다른 노드와의 연결방법은 서로 영향을 미칠 수 있는 모든 연결강도 중에서 확률적으로 선택하여 연결하였다. 마지막으로 제안한 저차원화 동적 귀환 신경망이 완전 연결된 신경망보다 더 좋은 성능을 얻을 수 있음을 예제로서 본 논문에서는 도립진자의 안정화 및 제어와 로봇 매니퓰레이터의 비주얼 서보잉에 적용하여 컴퓨터 시뮬레이션을 통하여 그 유효성을 확인한다.