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Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FM-NLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-of-the-art NLM-based denoising algorithms.

Consider a large-scale distributed system in which each computing device is observing triggers from an external source. Distributed Trigger Counting (DTC) algorithm is used to detect the state of the system when the aggregated number of the observed triggers reaches a predefined value. In this paper, we propose a simple and efficient DTC algorithm: Cascading Thresholds (CT). We mathematically show that CT is an optimal DTC algorithm in terms of the total number of exchanged messages among the devices (message complexity). For the maximum number of received messages per device (MaxRcv), CT is sub-optimal. The average message complexity of CT is , and MaxRcv of it is , where is the number of triggers to be detected, is the number of devices, and is the degree of a node in the tree-like structure. Compared to the previous optimal algorithm (TreeFill), CT is much simpler: in our implementation the code size is about 2.5 times smaller. Also, unlike TreeFill CT does not require complicated mechanisms including distributed locking. Experimental results show that CT has a lower message complexity and MaxRcv compared to the previous work (CoinRand and RingRand). Furthermore, CT and TreeFill show a similar performance. From its simplicity, CT is more practical than previous work including TreeFill, CoinRand and RingRand.

To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimized recommendation algorithm for interior design style based on local social network, which includes data acquisition by three-dimensional (3D) model, home-style feature definition, and style association mining. Through the analysis of user behaviors, the user interest model is established accordingly. Combined with the location-based social network of association rule mining algorithm, the association analysis of the 3D model dataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimental results show that the proposed algorithm can complete effective analysis of 3D interior home style with the recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellent application effect.

Given that most of the link prediction algorithms for signed social networks can only complete sign prediction, a novel algorithm is proposed aiming to achieve both link prediction and sign prediction in signed networks. Based on the structural balance theory, the local link tightness and global link tightness are defined respectively by using the structural information of paths with the step size of 2 and 3 between the two nodes. Then the total similarity of the node pair can be obtained by combining them. Its absolute value measures the possibility of the two nodes to establish a link, and its sign is the sign prediction result of the predicted link. The effectiveness and correctness of the proposed algorithm are verified on six typical datasets. Comparison and analysis are also carried out with the classical prediction algorithms in signed networks such as CN-Predict, ICN-Predict, and PSNBS (prediction in signed networks based on balance and similarity) using the evaluation indexes like area under the curve (AUC), Precision, improved AUC', improved Accuracy', and so on. Results show that the proposed algorithm achieves good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms. Moreover, it can achieve a good balance between prediction accuracy and computational complexity.

본 연구에서는 female adult mesh (FASH) 팬텀으로부터 획득한 CT 영상에 FNLM 알고리즘을 적용하여 유사도를 분석하고자 한다. 먼저, GATE (geant4 application for tomographic emission) 프로그램을 이용하여 FASH의 전신 CT 스캔 데이터를 모델링한 뒤 그 데이터로부터 MATLAB 프로그램을 통해 442 × 256 사이즈의 CT axial 영상을 획득하였다. 그리고 획득한 원본 이미지에 표준편차 0.002를 가지는 가우시안 노이즈를 부가함으로서 Noisy 이미지를 획득한 뒤 그 이미지에 fast non-local means (FNLM) 알고리즘을 가중치 조절 인자 h를 0.05로 적용하였다. 본 연구에서는 유사도를 평가하기 위하여 평균 제곱근 편차 (Root Mean Square Error, RMSE)와 최대 신호 대 잡음비 (Peak Signal-to-noise ratio, PSNR)를 사용하였다. 원본이미지와 FNLM 알고리즘이 적용된 영상과의 유사도를 평가 한 결과, 원본이미지와 노이즈 이미지의 유사도에 비하여 RMSE 수치는 1.45배 향상되었으며, PSNR 수치는 1.04배 향상되었다. 결론적으로 FNLM 알고리즘은 영상을 복원하는데 효과가 있음이 증명되었다.

In this study, we analyzed the similarity about image applied fast non-local means (FNLM) algorithm to CT images obtained from the female adult mesh (FASH) phantom. First, full-body CT scan data of the FASH was modeled using the genant4 application for tomographic emission (GATE) program, and from that data, a 442 × 256 CT axial image was obtained through the MATLAB program. Subsequently, a noisy image was obtained by adding Gaussian noise with a standard deviation of 0.002 to the acquired original image and the FNLM algorithm which has 0.05 weight was applied to the image. In this study, root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) were used to evaluate the similarity. As a result of evaluating the similarity between the original image and the image with FNLM algorithm, the RMSE level was 1.45 times and PSNR level was 1.04 times higher than the noisy image. In conclusion, we demonstrated that the FNLM algorithm can restore efficiently the noisy images.

본 연구의 목적은 전산화단층촬영 (computed tomography, CT)영상으로부터 각각의 검색창 크기가 설정된 3차원 비지역적 평균 (3D non-local means, 3DNLM) 알고리즘을 적용하여 이에 대한 영상 특성 및 시간 분해능의 경향성을 분석하는 것이다. 이를 위해 Gaussian 및 poisson 노이즈가 부가된 3D Shepp-Logan phantom 영상을 모델링한 후 평활화 상수 및 패치크기가 각각 0.01 및 3×3×3으로 설정된 3DNLM 알고리즘의 검색창 크기를 3×3×3부터 2×2×2 간격으로 15×15×15 크기까지 변경하여 각각 적용하였다. 이에 대한 영상 특성의 정량적 평가를 위해 contrast to noise ratio (CNR) 및 peak signal to noise ratio (PSNR)을 측정하였으며, 시간 분해능 분석을 위해 3DNLM 알고리즘의 연산 시간을 측정하였다. 결과적으로, CNR은 검색창이 증가함에 따라 개선됨을 보였으며, PSNR 은 5×5×5 크기의 검색창에서 가장 개선된 결과를 보였다. 시간분해능의 경우 검색창의 크기가 증가함에 따라 지수함수 적으로 증가함이 확인되었다. 결론적으로, CT 영상으로부터 3DNLM 알고리즘을 효율적으로 적용하기 위해서 영상 특성 및 시간분해능이 모두 고려된 검색창의 크기를 설정하는 것이 중요함을 확인하였다.

The purpose of this study was to analyze the tendency of image characteristics and time resolution by applying a 3D non-local means (3DNLM) algorithm, which each search window size was set, in computed tomography (CT) images. For this purpose, the 3D Shepp-Logan phantom images were modeled by adding Gaussian and poisson noise. Then, search window size of 3DNLM algorithm with smoothing factor and patch size set to 0.01 and 3×3×3, respectively, changed from 3×3×3 to 15×15 ×15 at interval 2×2×2 and applied, respectively. To quantiative evaluate of image characteristics, contrast to noise ratio (CNR) and peak signal to noise ratio (PSNR) were measured, and the operation time of the 3DNLM was measured for time resolution analysis. As a result, the CNR showed improvement as the search window increased, and PSNR showed the most improved in the search window size of 5×5×5. In addition, time resolution was confirmed that it increased exponentially as the search window size increased. In conclusion, we confirmed that setting the search window size considering both image characteristics and time resolution to efficiently apply the 3DNLM algorithm in CT images.

Regarding high-dimensional heterogeneous data, combined with the existing algorithms' poor mining accuracy and parameter sensitivity, this paper proposes a local outlier mining algorithm based on neighborhood density. Use region segmentation to split high-dimensional data into reasonable sub-regions, reducing the difficulty of processing a large amount of high-dimensional data. The kernel neighborhood density is used to replace the average neighborhood density, so that the density calculation has nothing to do with data heterogeneity. Finally, the neighborhood state and outlier state of the data are further determined on the basis of neighborhood density to improve the accuracy of outlier mining. Through artificial and UCI data set simulation results, it shows that data volume and data dimension are the main factors that affect data outlier mining. The accuracy, coverage, and efficiency of the algorithm proposed in this paper are significantly better than those of the comparison algorithm, and it has better adaptability to different types of data sets.

A new local path planning algorithm for free-ranging robots is proposed. Considering that a laser range finder has the excellent resolution with respect to angular and distance measurements, a simple local path planning algorithm is achieved by a directional weighting method for obtaining a heading direction of nobile robot. The directional weighting method decides the heading direction of the mobile robot by estimating the attractive resultant force which is obtained by directional weighting function times range data, and testing whether the collision-free path and the copen parthway conditions are satisfied. Also, the effectiveness of the established local path planning algorithm is estimated by computer simulation in complex environment.

개미군락시스템(Ant Colony System, ACS)은 조합 최적화 문제를 해결하기 위한 기법으로 생물학적 기반의 메타휴리스틱 접근법이다. 지나간 경로에 대하여 페로몬을 분비하고 통신 매개물로 사용하는 실제 개미들의 추적 행 위를 기반으로 한다. 최적 경로를 찾기 위해서는 보다 다양한 에지들에 대한 탐색이 필요하다. 기존 개미군락시스템 의 지역 갱신 규칙에서는 지나간 에지에 대하여 고정된 페로몬 갱신 값을 부여하고 있다. 그러나 본 논문에서는 현 재 선택한 노드에 대한 이전 iteration 에서 방문한 총 빈도수를 고려한 페로몬 부여 방법을 지역갱신규칙에 사용하고 자 한다. 탐색을 위해서는 부경로를 이용한 ACS알고리즘을 사용하였다. 보다 많은 정보를 탐색에 활용함으로써 기 존의 방법에 비해 지역 최적화에 빠지지 않고 더 나은 해를 찾을 수 있다.

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

최근 무선 센서 네트워크(WSN : Wireless Sensor Network)에서 센서노드의 에너지 소모를 균등화 하고 효율성을 향상시켜 전제 네트워크의 수명을 최대화하기 위한 다양한 계층적 라우팅 프로토콜들이 제안되고 있다. 특히, 멀티-홉 기법이 향상된 에너지 효율성과 실제 적용 가능한 모델로 많은 각광을 받고 있다. 멀티-홉 기법에서는 센서 노드사이 거리에 따라 전송 에너지를 효율적으로 조절하는 것이 가능하다고 가정한다. 이 논문에서는 대표적인 클러스터 알고리 즘인 LEACH에 대하여 분석하고 이 알고리즘의 단점을 보완하고 에너지를 효율적으로 사용할 수 있는 지역-중앙 클러 스터 라우팅 알고리즘을 제안한다. 제안한 클러스터 라우팅 알고리즘과 LEACH의 성능을 시뮬레이션을 통해 성능을 평가하고 분석하고 NS-2 시뮬레이션을 이용하여 성능 결과를 제시한다.

Recently, lot of researches for the multi-level protocol have been done to balance the sensor node energy consumption of WSN and improve the node efficiency to extend the life of the entire network. Especially in multi-hop protocol, a variety of models have been proposed to improve energy efficiency and apply it to WSN protocol. In this paper, we analyze LEACH algorithm and propose new method based on center of local clustering routing algorithm in wireless sensor networks. We also perform NS-2 simulation to show the performance of our model.

본 연구는 4차 산업혁명과 윤리규범을 위한 AI알고리즘 규제에 관한 연구로써, 국가행 정과 지방자치를 위한 지능정보화의 방향을 모색하였다. 본 연구는 인공지능 알고리즘에 대한 개념과 현상을 고찰하고, 인공지능에 대한 각국의 전략과 투자, 미국의 인공지능 윤 리원칙과 한국의 인공지능 윤리기준, EU의 인공지능 법안 등을 분석하였다. 본 연구는 4차 산업혁명과 인공지능을 둘러싼 문제로 효율성의 추구에 기인하는 (1) 인 간의 존엄성과 인권문제, (2) 인간의 실업과 생존문제, (3) 효율성과 윤리적 문제 등을 제 시하였다. 또한, 인공지능 알고리즘 규제를 위하여 (1) 인공지능 알고리즘에 대한 법적인 정의와 범위 설정, (2) 인공지능에 대한 기초적인 법률안 마련을 바탕으로, (3) 인공지능 알고리즘 규제와 특례에 대한 논의, (4) 인공지능에 의한 인위적인 의사결정 개입방지, (5) 빅데이터의 활용 혹은 정보유통과정에서 동의 없는 개인정보와 프라이버시 침해방지 등 을 제안한다. 이와 함께, 본 연구는 4차 산업혁명과 인공지능에 의해 유발되는 문제해결을 위해 (1) 새로운 윤리규범의 설정과 (2) 윤리규범의 설정을 위한 거버넌스의 구성, (3) 4차 산업혁 명과 인공지능에 대한 공적인 담론형성, (4) 수용성 확보를 위한 정책홍보와 정책참여의 활성화, (5) 보편적인 개인정보와 프라이버시 침해방지를 위한 강력한 규제법안에 대한 논의의 필요성 등을 제기하였다. 마지막으로, 국가행정과 지방자치의 방향성 설정을 위해 4차 산업혁명의 대응전략 추진을 (1) 중앙-광역-지방정부-기업-NGO-정책전문가집단-지 역주민 등이 협업하는 거버넌스 체계의 구성 및 정책참여의 필요성, (2) 각 참여자별 책 임성 소재를 명확하게 하는 제도적 장치의 설정과 준비, (3) 4차 산업혁명의 대응전략을 위한 다양한 담론조성의 필요성 등을 제시한다. 본 연구는 4차 산업혁명과 인공지능 알고 리즘을 둘러싼 문제들과 새로운 규범정립의 요구들로 4차 산업혁명과 인공지능의 문제에 대한 시사점을 도출하였다. 본 연구는 4차 산업혁명과 윤리규범을 위한 AI알고리즘 규제 의 필요성을 제기하고 연구를 종결하였다.

Due to the limited lifetime of nodes in ad hoc and sensor network, energy efficiency needs to be an important design consideration in any routing algorithm. Most of the existing Ant colony based routing algorithms grantee the packet delivery. However, they suffer from the high power consumption due to the huge number of control messages to establish and maintain a route from a source to a destination. This paper introduces two new power-aware ant colony routing algorithms for mobile ad hoc network under three main concurrent constraints. (1) Localized algorithms where only information about neighbors' nodes is needed. (2) Maximize the algorithms delivery rate. (3) Minimize the energy consumption. Our new algorithms are based on the idea of extracting a sub-graph of the original network topology and combine it with the advantage of ant based routing algorithms. Extensive experiments are conducted to prove that the new algorithms have significant improvement on the network lifetime (up to twice) without affecting the high delivery rate.

Internet is changing customers’ consumption patterns and the manufactures’ sale model. With the development of the computer network technology and the electronic commerce, more and more firms establish the electronic sale channel and get great profits. The huge supply chain network is established through the new idea and the technology. In this paper, we propose an improved local search algorithm- clustering local search algorithm (CLSA) to solve the hybrid location problem. We apply this method to distribution center location model and get the optimal solution. Result shows that this method can avoid the exponential explosion and get a good solution. In numerical analysis, we compare this method with simulated annealing method and ant searching algorithm. The numerical analysis demonstrates that this CLSA method not only classifies simply and flexibly, but also has the characteristic of fast search speed and small search space.

This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO) in terms of the defect that traditional community detection approaches based on genetic algorithm have strong randomness and weak searching ability in the process of community detection. Taking modularity function Q as the objective function, IGALO algorithm adopts label propagation method of one-iteration to initialize population so as to generate initial population with certain precision. Then, anti-destructive one-way crossover strategy is proposed to ensure the crossover operation to develop in the direction of making community structure increase to modularity function. Finally, mutation strategy of node local optimization is proposed to improve the searching efficiency of algorithm. This algorithm effectively overcomes the defect that traditional algorithms have weak searching ability and improves the community detection accuracy. Tests are made on benchmark networks and real-world networks and comparative analysis is also made with various classic algorithms. The results show that IGALO algorithm is effective and feasible.

Most of the local community detection algorithms based on node similarity often simply count the number of common neighbors as the basis of selecting members of the community that cannot accurately measure the value of a common neighbor node in the information transmission of nodes. For this, we use the new concept of a common neighbor contribution; borrowed from the idea about the local modularity, put forward a new fast community detection algorithm. The algorithm accurately selects candidate nodes to join the community, according to the contribution of the common neighbor node, also without calculating local modularity for each common neighbor node, and greatly improved the accuracy and efficiency in merging Members. the experimental results of the computer-generated network and the real networks verified reliability and efficiency of the algorithm.

Concrete CT image segmentation is a hot civil engineering research field in recent years. An algorithm of minimizing the energy of local region is proposed to solve the problem of interaction fails to capture statistical property of natural image and to segment concrete CT image. At first, the algorithm utilizes local region information of image to construct the energy model of local region, establishes a segmentation model of local interaction region based on MRF. Then using loopy belief propagation (LBP) algorithm and other algorithms optimizes global energy. In process of optimization, local region energy is converged, and the label of local region is estimated based on MAP algorithm. Then the information of local region is transferred to the region of neighborhood. The result of experiment shows that comparing with standard LBP algorithm, the new algorithm has a better segmentation result, and efficiently restrained effect on image noise and texture for segmentation. Result of concrete CT image segmentation will simplify the following CT statistical analysis and provide an important method for reaching real meso-structure of concrete’s finite element network.

This research presents novel Particle swarm optimization inspired by gravitational based search method to solve active power dispatch problem in electrical power system planning. The proposed PSO utilizes the operator of social thinking coupled with search capacity of gravity inspired algorithm to formulate and develop technique for active power dispatch problem to satisfy power demand requirements. Optimal scheduling of generators and system constraints to match load demand and losses is successfully done with proposed method. Total operating cost is minimized satisfying various bounds of system with proposed method. Exploration and convergence efficiency are evaluated to checklist the computational efficiency and robustness of the proposed technique. The suggested technique is tested and evaluated on different test systems comprises three, five, six test systems. Test results are compared with other techniques presented in literature .Investigations shows promising results which further benchmark the effectiveness of proposed method to solve complex optimization non linear problems.

Topology potential field is a novel model to describe interaction and association of network nodes, which has attracted plenty of attention in community detection, node importance evaluation and network hot topics detection. The local maximum potential point search is a critical step for this research. Hill-climbing is a traditional algorithm for local maximum point search, which may leave out some local maximum potential points, and search performance is greatly influenced by initial node sequence. Based on the detailed analysis of local maximum potential points' characteristics, this paper presents a novel local maximum potential point search algorithm. The results of simulation experiments showed that the new algorithm has better performance than the traditional hill-climbing method. It can find all local maximum potential points with high search efficiency.

Ship detection is an important application of optical remote sensing image processing. Sea-land segmentation is the key step in ship detection. Traditional sea-land segment methods only based on the gray-level information of an image to choose a gray threshold to segment the image; however, it is very difficult to establish a self-adapting mechanism to select a suitable threshold for different images. Thus, the segmentation result is greatly influenced by the threshold chosen for sea-land segmentation. In this paper, we are integrating the LBP feature information to propose a novel sea-land segmentation algorithm. Moreover, a new ship detection method based on our sea-land segmentation algorithm is proposed for optical remote sensing images. The performance of ship detection is measured in terms of precision and false-alarm-rate. Experimental results show that, as compared to minimum error meth-od, the proposed algorithm can decrease the false-alarm-rate from 23.2% to 9.24%. And compared to Otsu method, the proposed algorithm improve the precision from 82.9% to 90.2%.