- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - $\mathbf{1545}\ \mu \mathbf{m}$ Quantum Dot Vertical Cavity Surface Emitting Laser with low threshold
摘要： Approximate Nearest Neighbor (ANN) search has become a popular approach for performing fast and efficient retrieval on very large-scale datasets in recent years, as the size and dimension of data grow continuously. In this paper, we propose a novel vector quantization method for ANN search which enables faster and more accurate retrieval on publicly available datasets. We define vector quantization as a multiple affine subspace learning problem and explore the quantization centroids on multiple affine subspaces. We propose an iterative approach to minimize the quantization error in order to create a novel quantization scheme, which outperforms the state-of-the-art algorithms. The computational cost of our method is also comparable to that of the competing methods.
关键词： vector quantization,Approximate nearest neighbor search,subspace clustering,large-scale retrieval,binary codes
更新于2025-09-23 15:21:01
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A Channel Phase Error Correction Method Based on Joint Quality Function of GF-3 SAR Dual-Channel Images
摘要： Multichannel SAR is an effective approach to solving the contradiction between high azimuth resolution and wide swath. The goal of this paper is to obtain a new and effective method for estimating and compensating the interchannel phase error of the Chinese GF-3 Synthetic aperture radar (SAR). A channel phase error correction method based on the optimal value of the image domain quality function is proposed. In this method, the phase error is initially compensated using the correlation function method. In the ?ne correction of dual-channel phase error, a heuristic search algorithm is used to estimate the residual phase by searching the extremum of the quality function. After phase compensation in the image domain, the azimuth ambiguities caused by the remaining phase are eliminated. The proposed image domain processing method provides a new idea for channel phase error correction. The measured data of high-resolution GF-3 dual-channel ultra?ne imaging mode veri?es the validity of this method.
关键词： joint quality function,Heuristic search algorithm,dual-channel SAR,interchannel phase error,error correction
更新于2025-09-23 15:21:01
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Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm
摘要： Although the combined cooling, heating and power (CCHP) microgrid is feasible for achieving a high energy utilization efficiency, the fluctuation of energy sources, such as a photovoltaic system and multiple loads, may affect the safety, economics and stability in CCHP microgrid operation. For this reason, this paper establishes a mathematical model using a multi-objective optimization mechanism for resolving the in?uence of economy and energy allocation in the mixed photovoltaic type CCHP microgrid. It is based on analytic hierarchy process (AHP) to determine the individual weight of objective function optimization for the multi-objective power capacity allocation. The improved arti?cial bee colony (IABC) based on the whale search and dynamic selection probability can achieve an optimization solution, reaching a stable operation state and reasonable capacity con?guration in the microgrid system. The performance results con?rm that the proposed algorithm is superior to others in both convergence speed and accuracyfor the capacity allocation of the CCHP microgrid.
关键词： AHP,whale search,CCHP,IABC
更新于2025-09-23 15:21:01
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Noisy image block matching based on dissimilarity measure in discrete cosine transform domain
摘要： In this paper, the problem of image block similarity measuring in noisy environment is considered. In different practical applications often is necessary to ?nd groups of similar image blocks within an ample search area. In such situation, the full search algorithm is very slow; apart, its accuracy is low due to the presence of noise. New algorithms for similar image block matching in noisy environment are presented. The algorithms are based on the dissimilarity measure calculated as the distance between image patches in the discrete cosine transform domain. The proposed algorithms perform the hierarchical search for the similar image blocks and hereby have a reduced complexity in comparison to the full search algorithm. Adjusting the radius of the distance calculation for spectral coef?cient matching, the characteristics of the block matching algorithm can easily be adjusted to obtain a better accuracy of the matched block group. A higher accuracy is obtained using the local adaptation of the radius for the distance calculation outperforming the existing algorithms used to ?nd groups of similar blocks in different applications, such as image noise ?ltering and image clustering. The performance of the different block matching algorithms were evaluated on the base of the proposed accuracy measure that uses as a reference the list of patches obtained with the full search algorithm in the absence of noise.
关键词： Dissimilarity measure,hierarchical search,local adaptation,noisy image block matching,discrete cosine transform
更新于2025-09-23 15:21:01
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[IEEE 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT) - Hurgada, Egypt (2019.11.17-2019.11.20)] 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT) - Co-Planar Waveguide Resonator to Mediate Coupling between Superconducting Quantum Bits
摘要： Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.
关键词： security,multi-keyword search,Cloud computing,ranked search,hierarchical clustering,ciphertext search
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting - Atlanta, GA, USA (2019.7.7-2019.7.12)] 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting - Metagrating-Inspired Approach for Suppressing Reflections in H-Plane Waveguide Bends
摘要： The general problem of a queue-aware radio resource management and scheduling design is investigated for wireless communications under quasi-static fading channel conditions. Based on an analysis of the source buffer queuing system, the problem is formulated as a constrained nonlinear discrete programming problem. The state transition matrix of the queuing system determined by the queue-aware scheduler is shown to have a highly dynamic structure, so that the conventional matrix analysis and optimization tools are not applicable. By reformulating the problem into a nonlinear integer programming problem on an integer convex set, a direct search approach is considered. Two types of search algorithms, gradient based and gradient-free, are investigated. An integer steepest-descent search with a sub-sequential interval search algorithm and a constrained discrete Rosenbrock search (CDRS) algorithm is proposed to solve the nonlinear integer problem. Both algorithms are shown to have low complexity and good convergence. The numerical results for a single user resource allocation are presented, which show that both algorithms outperform equal partitioning and random partitioning queue-aware scheduling. The dynamic programming (DP) solution given by the relative value iteration algorithm, which provides the true optima but has high complexity, is used as a benchmark. In the majority of the numerical examples, the performance of the CDRS algorithm is almost identical to that of the DP approach in terms of both the average queue length minimization and the average packet blocking plus packet retransmission minimization, but it is less complex, and thus has better scalability.
关键词： queue-aware,Constrained nonlinear integer programming,scheduling.,convex set,radio resource management,direct search
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Atomic Layer Deposited Al <sub/>x</sub> Ni <sub/>y</sub> O as Hole Selective Contact for Silicon Solar Cells
摘要： Approximate Nearest Neighbor (ANN) search has become a popular approach for performing fast and efficient retrieval on very large-scale datasets in recent years, as the size and dimension of data grow continuously. In this paper, we propose a novel vector quantization method for ANN search which enables faster and more accurate retrieval on publicly available datasets. We define vector quantization as a multiple affine subspace learning problem and explore the quantization centroids on multiple affine subspaces. We propose an iterative approach to minimize the quantization error in order to create a novel quantization scheme, which outperforms the state-of-the-art algorithms. The computational cost of our method is also comparable to that of the competing methods.
关键词： vector quantization,Approximate nearest neighbor search,subspace clustering,large-scale retrieval,binary codes
更新于2025-09-23 15:19:57
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Backtracking search algorithm with L??vy flight for estimating parameters of photovoltaic models
摘要： An accurate mathematical model plays an important role for simulation, evaluation and optimization of photovoltaic (PV) models. The characteristic current equations describing the PV models are implicit, nonlinear and transcendental. Given the features of the characteristic current equations, traditional optimization algorithms are usually easy to converge to local optimal solutions. Thus using metaheuristic methods called modern optimization algorithms to estimate parameters of PV models has been a research hotspot in recent years. Although many metaheuristic methods have been employed to solve this problem, it is still necessary for researchers to propose new optimization algorithms to obtain more accuracy and reliability solutions. This paper presents a new metaheuristic algorithm called backtracking search algorithm with Lévy flight (LFBSA) to estimate the parameters of PV models. Compared with the basic backtracking search algorithm (BSA), LFBSA has the following two remarkable features. Firstly, an information sharing mechanism with Lévy flight is built to enhance population diversity. Secondly, mutation operator based on the hunting mechanism of grey wolves is introduced to increase the chance of LFBSA to escape from local minima. LFBSA is used to estimate parameters of three different PV models. Experimental results show the proposed LFBSA is superior to BSA and the other compared algorithms in terms of accuracy and reliability.
关键词： Photovoltaic modeling,Backtracking search algorithm,Lévy flight,Metaheuristic method
更新于2025-09-23 15:19:57
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Optical Fiber Transducer for Monitoring Single-Phase and Two-Phase Flows in Pipes
摘要： This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M + 1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated.
关键词： cooperative populations,differential evolution,archive search,multiobjective optimization,many-objective optimization
更新于2025-09-23 15:19:57
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Searching the crystal structure of silicon using the generalized scaled hypersphere search method with the rapid nuclear motion approximation
摘要： The scaled hypersphere search method with the rapid nuclear motion approximation was applied for the prediction of the crystal structure of silicon. Five well-known structures and one novel structure were found. The space group of the novel structure found is Imma. By comparing this with the two known silicon structures in the same space group, it is found that one of these well-known Imma structures and the novel Imma structure are similar. According to our ab initio calculations based on the projector augmented wave method with the Perdew–Burke–Ernzerhof functional, the novel structure is a semiconductor, with calculated band gap 0.89 eV.
关键词： scaled hypersphere search method,crystal structure prediction,silicon,semiconductor,rapid nuclear motion approximation
更新于2025-09-23 15:19:57