Earticle

신체영역무선통신망(WBAN)에서 클러스터 헤드(CH) 선출 및 최적경로의 라우팅은 에너지 효율 향상과 네트워크 노드 운영수명 연장을 위해 해결해야 할 이슈이다. 이러한 연구를 위해 본 논문에서는 BKOA알고리즘과 그리드 기반 멀티홉 라우팅 프레임워크를 결합한 하이브리드 BKOA-GRID를 제안한다. 시뮬레이션 수행결과 제안된 BKOA-GRID는 PSO, LEACH, EEUC 등 기존 알고리즘보다 노드생존율 90%, 잔류에너지 지속성은 총 에너지의 약 60%를 유지하여 높은 에너지 효율을 보였다.

Cluster head(CH) election and optimal path routing in a Wireless Body Area Network(WBAN) are issues that must be addressed to improve energy efficiency and extend the operating life of network nodes. To address these issues, this paper proposes a hybrid BKOA-GRID (Black Kite Optimization Algorithm-GRID) framework, which integrates the Black Kite Optimization Algorithm with a grid-based multi-hop routing structure. Simulation results demonstrate that the proposed BKOA-GRID exhibits superior energy efficiency compared to existing algorithms such as PSO, LEACH, and EEUC, maintaining a node survival rate of 90% and preserving approximately 60% of the total residual energy.

본 논문에서는 특징점과 필터뱅크, 지문 영상의 품질정보를 이용한 적응적 복합 지문 인식 방법을 제안한다. 블록 단위로 지문영상의 품질을 평가하고 각 블록의 확률 기대값을 산출한다. 필터뱅크와 특징점이 위치한 블록의 영상품질에 대한 기댓값을 각각의 매칭점수에 반영함으로서, 영상 상태에 대해 적응적이고 잡음에 강인한 정합률을 획득할 수 있었다. 제안된 정합 방법은 기존의 특징점만을 이용한 단순 정합 방법보다 높은 정합률을 가진다. 그렇지만 처리 시간은 여러 가지 방법을 복합적으로 적용하므로 증가하는 단점이 발생한다.

In thst paper, we present an adaptive hybrid fingerprint matching method using minutiae, Filterbank and quality information of fingerprint. We estimate the quality of fingerprint images by a unit of block and extract the provability expectation. We apply the expectation about image quality of blocks that Filterbank and minutiae are situated. Therefore, we extract the robust matching rate at noise. The Matching rate of the proposed method is higher than that of other simple method. and matching speed of it is increased because of hybrid methods.

In order to solve the problem of pedestrian detection performance, the described operator was improved. In this paper, semantic local binary pattern (SLBP) and histogram of oriented gradient (HOG) are combined as new feature operator. This feature method would enrich the information and enhance the detection performance. And then histogram intersection kernel support vector machine (HIKSVM) classifier is trained by the augment feature. Because the time cost is too large by the conventional SVM. HIKSVM could make up this drawback, and significantly reduce the training time. The experiments on the INRIA pedestrian dataset show that the method obtained significant improvement in accuracy comparing to HOG descriptors.

Image fusion is an important branch of information fusion, which is widely used in various fields. At present, the image fusion method is mainly aimed at the different frequency information of the images, the images are fused in transform domain. But in practical application, image fusion is used to improve the credibility of the target information and the demand of background information of is not high. Therefore, this paper puts forward an image fusion method combining with image classification. Firstly, the NSCT transform is used to transform the source images, and the K-Means method is used to realize the classification of the target and the background, and the different fusion criteria are used to get the target and the background. The experimental results show that the image fusion based classification method has a better effect on the subjective visual effect and objective evaluation index.

A two-bar plane truss is builtin MATLAB based on mathematical model. Then the authors use genetic algorithm toolbox to solve this problem. The parametric truss model is set up in the finite element analysis software ANSYS. It is analyzed by first-order algorithm. The comparison of two kinds results show the pure genetic algorithm doesn’t always have an advantage over other algorithms. In the end, a joint optimization method is put forward on the basis of genetic algorithm. It combines genetic algorithm based on MATLAB toolbox and numerical algorithm based on quasi-Newton method. This method is illustrated by the numerical example of the two-bar plane truss. The results show this joint optimization method can get the global optimal solution of this problem every time.

In this paper, we propose the new technique adopting power control to MIMO(multi-input multi-output)-OFDMA(orthogonal frequency division multiplexing Access) system with multi-beamformer. The proposed power controlling algorithm for MIMO-OFDMA allocates the transmitting power of each subcarrier based on the CSI(channel state information) and the interference signal. CSI is feedback from base station to mobile station to decide the transmitting power of each subcarrier. Through the proposed technique, we can control iteratively the transmitting power and update the weight of beamformer simultaneously. Therefore, the SNIR of each subcarrier become to converge the target SNIR and the beam is formed toward the desired direction. And the performance of MIMO-OFDMA system with the proposed approach is very improved. The improvement in bit error rate is investigated through computer simulation of a MIMO-OFDMA system with the proposed approach.

Pervasive enhancement and required enforcement of the Internet of Things (IoTs) in a distributed massively multiplayer online architecture have effected in massive growth of Big-Data in terms of server over-load. There have been some previous works to overcome the overloading of server works. However, there are lack of considered methods, which is commonly applicable. Therefore, we propose a combing Sparse Auto-Encoder and Load-Balancing, which is ProGReGA for Big-Data of server loads. In the process of Sparse Auto-Encoder, when it comes to selection of the feature-pattern, the less relevant feature-pattern could be eliminated from Big-Data. In relation to Load-Balancing, the alleviated degradation of ProGReGA can take advantage of the less redundant feature-pattern. That means the most relevant of Big-Data representation can work. In the performance evaluation, we can find that the proposed method have become more approachable and stable.

The study on classification methods of hyperspectral image is a focal growing area in remote sensing applications because the wide spectral range, providing a very high spectral resolution, allows the detection and classification surfaces and chemical elements of the observed image. Semi-supervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively have been a new research direction. In this paper we proposed a new semi-supervised classification method of hyperspectral image based on combining Renyi entropy and multinomial logistic regression algorithm. The multinomial logistic regression was performed to describe a direct relationship between the selected sample as and their category. A lot of unlabeled samples are constantly added to the sample data using Renyi entropy algorithm. The test analysis of image classification in test area showed the advantages of classification method based on combining Renyi entropy and multinomial logistic regression algorithm for hyperspectral remote sensing image.

The purpose of this paper is to introduce a general iterative process by viscosity approximation method with parallel method to ap-proximate a common element of the set of solutions of a mixed equilibrium problem and of the set of common fixed points of a finite family of $k_i$-strict pseudo-contractions in a Hilbert space. We obtain a strong convergence theorem of the proposed iterative method for a finite family of $k_i$-strict pseudo-contractions to the unique solution of variational inequality which is the optimality condition for a minimization problem under some mild conditions imposed on parameters. The results obtained in this paper improve and extend the corresponding results announced by Liu (2009), Plubtieng-Panpaeng (2007), Takahashi-Takahashi (2007), Peng et al. (2009) and some well-known results in the literature.

Background/Aims: Noninvasive methods have become increasingly critical in the diagnosis of fibrosis in chronic liver diseases. Herein, we compared the diagnostic performance of serum Mac2 binding protein glycosylation isomer (M2BPGi) and other serological panels for fibrosis in patients with nonalcoholic fatty liver disease (NAFLD) and proposed an improved two-step diagnostic algorithm for advanced fibrosis. Methods: We enrolled 231 patients diagnosed with NAFLD who underwent a liver biopsy. We subsequently evaluated the diagnostic performance of serological panels, including serum M2BPGi, a fibrosis index based on four factors (FIB-4), aspartate aminotransferase-to-platelet ratio index (APRI), and NAFLD fibrosis score (NFS), in predicting the stage of liver fibrosis. We then constructed a two-step algorithm to better differentiate advanced fibrosis. Results: The areas under the receiver operating characteristic curves of serum M2BPGi, FIB-4, APRI, and NFS for advanced fibrosis (≥F3) were 0.823, 0.858, 0.779, and 0.827, respectively. To reduce the performance of unnecessary liver biopsy, we propose a two-step algorithm using FIB-4 as an initial diagnostic tool and serum M2BPGi (≥0.6) as an additional diagnostic method for patients classified as intermediate (23%). Using the proposed algorithm, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.812, 0.814, 0.814, 0.600, and 0.927, respectively. Conclusions: Serum M2BPGi is a simple and effective test for advanced fibrosis in patients with NAFLD. Application of the two-step algorithm based on FIB-4 and M2BPGi proposed here can improve diagnostic performance and reduce unnecessary tests, making diagnosis easily accessible, especially in primary medical centers.

In this paper, we present a new algorithm for high dimensional data reduction based on wavelet decomposition and Genetic Algorithm (GA). Comparative results show the superiority of our algorithm for dimensionality reduction and accuracy improvement.

Cloudsat satellite data is sensitive to snowfall and collected during each month beginning with Dec 2007 and ending Feb 2008. In this study, we attempt to develop a snowfall retrieval algorithm using a combination of radiometer and cloud radar data. We trained data from the relation between brightness temperature measurements from NOAA's Advanced Microwave Sounder Unit-B(AMSU-B) and the radar reflectivity of the 2B-GEOPROF product from W-band(94 GHz) cloud radar onboard Cloudsat and applied it to the Korea peninsula. We use a principal components analysis to quantify the variations that are the result of the radiometric signatures of snowfall from those of the surface. Finally, we quantify the correlation between the higher principal component (orthogonal to surface variability) of the microwave radiances and the precipitation-sensitive CloudSat radar reflectivities. This work summarizes the results of applying this approach to observations over the East Sea during Feb. 2008. The retrieved data show reasonable estimation for snowfall rate compared with Cloudsat vertical image.

In the situation in which the number of variables(p) is comparatively larger than the number of sample(n) and there exist variables which have the same effects in the classifier, this paper presents a new variable selection algorithm extending COSSO (COmponent Selection and Smoothing Operator) by utilizing grouped variables. A grouped variable is composed of a component in the kernel expansion of a classifier. The construction of a kernel classifier is carried out by COSSO with the components made from grouped variables. From the results of simulations with synthetic and real data sets, the proposed method is verified to improve the performance of a kernel classifier and enhance the interpretability of the model.

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.

In this paper, a combining trust region and line search algorithm for equality constrained optimization is proposed. At each iteration, we only need to solve the trust region subproblem once, when the trust region trial step can not be accepted, we switch to line search to obtain the next iteration. Hence, the difficulty of repeated solving trust region subproblem in an iterate is avoided. In order to allow the direction of negative curvature, we add second correction step in trust region step and employ nonmonotone technique in line search. The global convergence and local superlinearly rate are established under certain assumptions. Some numerical examples are given to illustrate the efficiency of the proposed algorithm.

스마트 기기의 생활화와 증강현실 활용 증가로 실내 위치 인식 시스템의 수요가 급증함에 따라, BLE(Bluetooth Lower Energy) 비콘 그리고 UWB(Ultra Wide Band) 등을 이용한 실내 측위 시스템이 개발되고 있다. 본 논문에서는 BLE Beacon을 기반으로 RSSI(Received Signal Strength Indicator)를 이용한 삼변측량(Trilateration) 기법을 사용하여 측위 플레이트(Plate)를 생성한다. 이에 IMU(Inertial Measurement Unit) 센서의 방향, 속도, 이동거리 등의 데이터를 이용하여 PDR(Pedestrian Dead Reckoning) 측위 좌표를 산출하여 정확도를 보정한다. 또, BLE 비콘(Beacon)의 RSSI를 적용한 플레이트(Plate) 기법과 PDR 기법이 융합된 정밀 실내 측위 알고리즘을 제안한다. 본 논문에서 제시한 알고리즘을 실제 대형 실내 경기장과 공항에 BLE 비콘을 설치, 실험하여 평균 2.2m 의 오차로 65%의 정확도가 개선됨을 검증하였다.

As the demand for indoor location recognition system has been rapidly increased in accordance with the increasing use of smart devices and the increasing use of augmented reality, indoor positioning systems(IPS) using BLE (Bluetooth Lower Energy) beacons and UWB (Ultra Wide Band) have been developed. In this paper, a positioning plate is generated by using trilateration technique based on BLE Beacon and using RSSI (Received Signal Strength Indicator). The resultant value is used to calculate the PDR-based coordinates using the positioning element of the Inertial Measurement Unit sensor, We propose a precise indoor positioning algorithm that combines RSSI and PDR technique. Based on the plate algorithm proposed in this paper, the experiment have done at large scale indoor sports arena and airport, and the results were successfully verified by 65% accuracy improvement with average 2.2m error.

본 논문에서는 에지를 고려한 순차주사화(EDI: edge dependent interpolation)와 전역 움직임 보상(GMC: globa1 motion compensation)을 결합한 효율적이면서도 안정적인 순차주사화 알고리즘을 제안한다. 일반적으로 에지를 고려한 순차주사화 알고리즘을 사용하면 한 장의 필드를 이용한 다른 순차주사화 알고리즘들을 사용했을 때보다 시각적으로 우수한 결과를 얻을 수 있다. 그러나 한 장의 필드에 담긴 영상 정보에는 한계가 있기 때문에, 한 장의 필드를 이용한 방법을 통해서는 원본 필드로부터 고화질의 순차 주사 영상을 얻을 수 없다. 이에 반해 움직임 정보를 이용한 순차주사화 방법은 공간 영역뿐 아니라 시간 영역의 정보를 사용하므로 한 장의 필드를 이용할 때 보다 더욱 정확하게 원 프레임을 복원해 내지만, 움직임 추정의 정확도에 따라 결과가 크게 좌우되는 단점이 있다. 따라서 제안된 알고리즘에서는 EDI와 GMC를 함께 사용한다. 또한 최상의 결과를 얻기 위해 GMC의 오류를 검출하는 적응적 문턱 알고리즘을 제안한다. 제안된 알고리즘을 사용하면 기존 방법들에 비해 수치상으로도 시각적으로도 뛰어난 결과가 나타나는 것을 실험을 통해 확인할 수 있다.

In this paper, we propose a robust deinterlacing algorithm which combines edge dependent interpolation (EDI) and global motion compensation (GMC). Generally, EDI algorithm shows a visually better performance than any other deinterlacing algorithm using one field. However, due to the restriction of information in one field, a high duality progressive image from Interlaced sources cannot be acquired by intrafield methods. On the contrary, since algorithms based on motion compensation make use of not only spatial information but also temporal information, they yield better results than those of using one field. However, performance of algorithms based on motion compensation depends on the performance of motion estimation. Hence, the proposed algorithm makes use of mixing process of EDI and GMC. In order to obtain the best result, an adaptive thresholding algorithm for detecting the failure of GMC is proposed. Experimental results indicate that the proposed algorithm outperforms the conventional approaches with respect to both objective and subjective criteria.