• 한국정보통신학회논문지
• /
• 제15권7호
• /
• pp.1457-1464
• /
• 2011

본 논문에서는 개선된 CP 알고리즘에서 경쟁층의 수를 효율적으로 설정하기 위해 퍼지 제어 기법을 이용하여 경쟁층의 수를 결정하는 방법을 제안한다. 제안된 방법은 CP 알고리즘에 입력되는 패턴의 정보를 이용하여 퍼지 소속 함수를 설계하고 입력에 대한 소속도를 계산한 후, 퍼지 제어 규칙을 적용하고, Mamdani의 Max_Min 추론 방법으로 추론한다. 퍼지 추론을 통해 최종적으로 얻어진 값을 무게 중심법으로 비퍼지화 하여 최종적으로 개선된 CP 알고리즘의 경쟁층의 수를 결정하는데 적용한다. 제안된 방법의 학습 및 인식 성능을 평가하기 위해, 영문과 같이 다양한 패턴을 실험에 적용한 결과, 제안된 방법이 경쟁층의 수를 결정하는데 효과적임을 확인할 수 있었다.

• 전기학회논문지
• /
• 제65권11호
• /
• pp.1887-1893
• /
• 2016

The VSA(Video Signal Analysis) method is the time-domain approach for estimating ultrasonic attenuation which utilizes the envelop signals from backscattered rf signals. The echogenicity of backscattered ultrasonic signals, however, from deeper depths are distorted when the broadband transmit pulse is used and it degrades the estimation accuracy of attenuation coefficients. We propose the modified VSA method using adaptive bandpass filters according to the centroid shift of echo signals as a pulse propagates. The technique of dual-reference diffraction compensation is also proposed to minimize the estimation errors because the difference of attenuation properties between the reference and sample aggravates the estimation accuracy when the differences are accumulated in deeper depth. The proposed techniques minimize the distortion of relative echogenicity and maximize the signal-to-noise ratio at the given depth. Simulation results for numerical tissue-mimicking phantoms show that the Rectangular-shaped filter with the appropriate center frequency exhibits the best estimation performance and the technique of the dual-reference diffraction compensation dramatically improves accuracy for the region after the beam focus.

• 콘크리트학회지
• /
• 제11권1호
• /
• pp.99-107
• /
• 1999

본 연구에서는 철근콘크리트 고층건물의 시공단계를 고려하여 기둥의 부등축소량을 예측하는데 있어서 슬래브를 통한 양쪽 기둥으로의 비탄성하중 전달현상을 고려하기 위하여 2차원 골조해석을 수행하는 프로그램을 개발하였다. 또한 시간에 따른 해석을 수행함에 있어 단면 중심에서의 변형도와 곡률을 이용하여 균열을 고려한 단면의 성질을 직접 사용하는 방식을 사용하여 정밀도를 저하시키지않는 상태에서 해석시간을 단축하였으며, 축력과 휨의 상호작용에 의한 강성을 적용시켜 철근콘크리트 구조물의 특성을 보다 정확하게 고려하여 주었다. 해석모델로는 ACI 209, CEB-FIP 1990과 B3 모델을 사용하였다. 이렇게 개발된 프로그램의 해석결과를 기존의 간편해석에 의한 결과 및 실제 구조물의 실측치와 비교하여 그 효율성을 입증하였다.

• 한국게임학회 논문지
• /
• 제8권3호
• /
• pp.69-76
• /
• 2008

본 논문에서는 보다 효율적인 렌더링을 위해 물체를 구좌표계(sperical coordinate system) 상에서 표현하는 새로운 렌더링 알고리즘이 제안된다. 먼저 직교 좌표로 표현되어 있는 물체의 정점을 구좌표로 변환하고, 카메라의 가시 절두체(frustum) 영역 내의 정점을 판단하기 위해 삼각형의 무게중심, 색인(index), 메모리 접근(access) 맵 등의 자료구조를 구성한다. 제안된 방법은 카메라에 의해 보여지는 영역, 즉 렌더링되는 물체의 가시 영역에 해당하는 정점만으로 렌더링한다. 따라서 렌더링 파이프라인에서 고려되는 정점의 개수를 크게 줄여 전체적인 시스템 성능이 크게 향상되었음을 실험을 통해서 확인하였다.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
• /
• pp.384-386
• /
• 1998

The use of hand gesture provides an attractive alternative to cumbersome interface devices for human-computer interaction(HCI). Many methods hand gesture recognition using visual analysis have been proposed such as syntactical analysis, neural network(NN), Hidden Markov Model(HMM) and so on. In our research, a HMMs is proposed for alphabetical hand gesture recognition. In the preprocessing stage, the proposed approach consists of three different procedures for hand localization, hand tracking and gesture spotting. The hand location procedure detects the candidated regions on the basis of skin-color and motion in an image by using a color histogram matching and time-varying edge difference techniques. The hand tracking algorithm finds the centroid of a moving hand region, connect those centroids, and thus, produces a trajectory. The spotting a feature database, the proposed approach use the mesh feature code for codebook of HMM. In our experiments, 1300 alphabetical and 1300 untrained gestures are used for training and testing, respectively. Those experimental results demonstrate that the proposed approach yields a higher and satisfying recognition rate for the images with different sizes, shapes and skew angles.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권8호
• /
• pp.3683-3703
• /
• 2018

Wireless sensor networks (WSNs) consist of set of sensor nodes. These sensor nodes are deployed in unattended area which are able to sense, process and transmit data to the base station (BS). One of the primary issues of WSN is energy efficiency. In many existing clustering approaches, initial centroids of cluster heads (CHs) are chosen randomly and they form unbalanced clusters, results more energy consumption. In this paper, an energy efficient clustering protocol to prevent unbalanced clusters based on firefly and midpoint algorithms called EEC-FM has been proposed, where midpoint algorithm is used for initial centroid of CHs selection and firefly is used for cluster formation. Using residual energy and Euclidean distance as the parameters for appropriate cluster formation of the proposed approach produces balanced clusters to eventually balance the load of CHs and improve the network lifetime. Simulation result shows that the proposed method outperforms LEACH-B, BPK-means, Park's approach, Mk-means, and EECPK-means with respect to balancing of clusters, energy efficiency and network lifetime parameters. Simulation result also demonstrate that the proposed approach, EEC-FM protocol is 45% better than LEACH-B, 17.8% better than BPK-means protocol, 12.5% better than Park's approach, 9.1% better than Mk-means, and 5.8% better than EECPK-means protocol with respect to the parameter half energy consumption (HEC).

• Journal of International Society for Simulation Surgery
• /
• 제2권1호
• /
• pp.33-39
• /
• 2015

Purpose The head of infants under 24 months old who has Craniosynostosis grows extraordinarily that makes head shape unusual. To diagnose the Craniosynostosis, surgeon has to inspect computed tomography(CT) images of the patient in person. It's very time consuming process. Moreover, without a surgeon, it's difficult to diagnose the Craniosynostosis. Therefore, we developed technique which detects Craniosynostosis automatically from the CT volume. Materials and Methods At first, rotation correction is performed to the 3D CT volume for detection of the Craniosynostosis. Then, cranial area is extracted using the iterative thresholding method we proposed. Lastly, we diagnose Craniosynostosis by analyzing centroid relationships of clusters of cranial bone which was divided by cranial suture. Results Using this automatical cranial detection technique, we can diagnose Craniosynostosis correctly. The proposed method resulted in 100% sensitivity and 90% specificity. The method perfectly diagnosed abnormal patients. Conclusion By plugging-in the software on CT machine, it will be able to warn the possibility of Craniosynostosis. It is expected that early treatment of Craniosynostosis would be possible with our proposed algorithm.

• 제어로봇시스템학회논문지
• /
• 제10권8호
• /
• pp.673-679
• /
• 2004

In this paper, we develop the displacement measurement system of multiple moving objects based on image processing techniques. The image processing method adopts inertia moment theory for obtaining the centroid measurement of the targets and basic processing algorithm of gray, binary, closing, labeling and so on. To get precise displacement measurement in spite of multiple moving targets, a CGD camera with zoom is used and the position of camera is changed by a pan/tilt system. The fiducial marks on the fixed positions are used as the sensing points for the image processing to recognize the position errors in direction of XY-coordinates. The precise alignment device is pan/tilt of XY-type and the pan/tilt is controlled by DC servomotors which are driven by a microprocessor. Morover, the centers of fiducial marks are obtainted by an inertia moment method. By applying the developed precise position control system for multiple targets, the displacement of multiple moving targets are detected automatically and are also stored in the database system in a real time. By using database system and internet, the displacement datum can be confirmed at a great distance and analyzed. Finally, the effectiveness of developed system is shown in experimental results and realized the precision about 0.12[mm] in the position control of XY-coordinates.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권4호
• /
• pp.1187-1208
• /
• 2022

One of the main goals of wireless sensor networks (WSNs) is to utilize the energy of sensor nodes effectively and maximize the network lifetime. Thus, this paper proposed a routing protocol for WSNs based on virtual force disturbing mobile Sink node (VFMSR). According to the number of sensor nodes in the cluster, the average energy and the centroid factor of the cluster, a new cluster head (CH) election fitness function was designed. At the same time, a hexagonal fixed-point moving trajectory model with the best radius was constructed, and the virtual force was introduced to interfere with it, so as to avoid the frequent propagation of sink node position information, and reduce the energy consumption of CH. Combined with the improved ant colony algorithm (ACA), the shortest transmission path to Sink node was constructed to reduce the energy consumption of long-distance data transmission of CHs. The simulation results showed that, compared with LEACH, EIP-LEACH, ANT-LEACH and MECA protocols, VFMSR protocol was superior to the existing routing protocols in terms of network energy consumption and network lifetime, and compared with LEACH protocol, the network lifetime was increased by more than three times.

• ETRI Journal
• /
• 제44권2호
• /
• pp.286-299
• /
• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.