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Conference on Radar Sensor Technology VI

作者： Li, XY Mao, SY Beijing Univ Aeronaut & Astronaut Res Grp 203 Dept EE Beijing 100083 Peoples R China

ISBN: (纸本)0819440698

The co-registration of complex SAR images is a key problem in SAR interferometry, especially when the relative acquisition geometry is not known with sufficient accuracy and the approximate terrain elevation changes cannot be considered a priori(1). This article presents a new multi-stage co-registration strategy using dynamic programming technique for the globally consistent matching. In the primary stage, coarse co-registration results are obtained based on the crosscorrelation algorithm which can always give a robust answer. Then in the following fine registration stages, an optimum scaling factor is used to correct the systematic misregistration errors in the range direction while a pixel-by-pixel dynamic programming procedure is used to give a smooth offset-surface of the azimuth direction, which is usually tied to topography. The dynamic programming technique considers the global compatibility of the match results and optimizes the entire scan area rather than search for the optimum for each point separately, and thus a more smooth and consistent result. Another advantage of this approach is that it gives estimates for co-registration based on a pixel-by-pixel basis. Registration accuracy finer than 1/8 pixel is obtained and both spaceborne and airborne interferometric data provided by DLR are used to show the potential of the proposed co-registration strategy.

关键词： interferometric SAR co-registration dynamic programming technique

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Distributed Beamforming for Randomly Distributed Sensors using Bisection Method and dynamic programming technique

Distributed Beamforming for Randomly Distributed Sensors usi...

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IEEE International Conference on Electronics, Computing and Communication Technologies

作者： Vaikundam, G. Sudha, G. F. Pondicherry Engn Coll Dept Elect & Comm Engg Pondicherry 605014 India

ISBN: (纸本)9781467346092;9781467346078

Beamforming is being used in sensor networks to enhance its communication range. The sensors in networks use their own clock adjustment for distributed beamforming by coordinating with other sensors. For this, the Channel State Information(CSI) is required. Since the CSI is not available at the transmitter side, the Channel Phase tracking is used. A novel bisection based phase tracking method is proposed for compensation of channel propagation delay. This eliminates the probability based method which has more computational complexity. The problem of phase tracking is modeled as a minimization problem. The phase is adjusted iteratively similar to finding roots for an algebraic equation using bisection method. Further, the problem of phase adjustment is simplified by considering phase adjustment of single participating sensor in each time slot using dynamic programming technique, instead of collectively adjusting the phase of all participating sensors. This technique is compared with bilateral uniform, Gaussian and Laplacian distribution function. The initial time delay is considered as bilateral uniform distributed and channel propagation delay is modeled as Rayleigh distributed variables. A computationally efficient algorithm is developed for distributed beamforming and results show 66% reduction in computation complexity.

关键词： Closed Loop Phase Adjustment Distributed Beamforming Bisection Method dynamic programming technique

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Cluster Optimization for Exponential, Right-Triangular, and Uniformly Distributed Data

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IEEE ACCESS 2025年 13卷 36273-36284页

作者： Bulivou, Gabiriele Reddy, Karuna G. Khan, M. G. M. Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

Cluster analysis aims to categorize data objects into cohesive groups based on their intrinsic characteristics, often modeled by probability distributions. This paper presents a novel Mathematical programming-dynamic programming (MP-DP) clustering method developed by the authors, applied to datasets characterized by exponential, right-triangular, and uniform distributions. The MP-DP technique optimizes cluster partitions by leveraging the probability distributions inherent in the data. We conducted a comparative evaluation to assess the performance of MP-DP against four established clustering methodologies: K-Means, Fuzzy C-Means, expectation-maximization, and Genie++ hierarchical clustering. Results from extensive simulations and real-world datasets consistently demonstrate the superior efficacy of MP-DP in achieving optimal clustering outcomes. Specifically, MP-DP excels in handling diverse data distributions and effectively mitigating the effects of noise and uncertainty, thereby enhancing clustering accuracy and reliability. This study highlights the significant advancement offered by MP-DP in clustering research. It underscores the method's potential for applications across various domains, such as healthcare, environmental monitoring, and manufacturing, where robust and efficient data clustering is essential for insightful data analysis and decision-making.

关键词： Clustering algorithms Probability distribution dynamic programming programming Noise Clustering methods Partitioning algorithms Heuristic algorithms Distributed databases Uncertainty Cluster analysis dynamic programming technique mathematical programming problem optimum cluster partitions probability distribution function

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Aiding a Disaster Spot via Multi-UAV-Based IoT Networks: Energy and Mission Completion Time-Aware Trajectory Optimization

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IEEE INTERNET OF THINGS JOURNAL 2022年第8期9卷 5853-5867页

作者： Yang, Hailiang Ruby, Rukhsana Quoc-Viet Pham Wu, Kaishun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Pusan Natl Univ Korean Southeast Ctr 4th Ind Revolut Leader Educ Busan 46241 South Korea

Unmanned aerial vehicles (UAVs) are one of the effective means to provide emergency communication services in post-disaster areas. In this article, we consider data dissemination in post-disaster areas, where all Internet of Things (IoT) nodes may not have data needs all the time. The energy consumption in data dissemination is one of the key metrics to pay attention to since the charging facilities for UAVs may be limited due to the destruction of existing infrastructure. In addition, UAVs have limited endurance or lifetime, so unnecessarily flying over IoT nodes that may not have data is time consuming. Therefore, given the energy budget and data requirements of the IoT nodes, we formulated a data dissemination problem using multiple UAVs in a post-disaster area while optimizing their trajectory, mission completion time, and energy consumption. After time discretization, the formulated problem is a mixed-integer nonconvex problem and thus difficult to solve in general. For this reason, we jointly use the bisection search technique and the block coordinate descent (BCD) method to solve the entire problem while aiming to optimize the trajectory and mission completion time of the considered UAV as well as the overall energy consumption. In each iteration of the BCD method, we solve the user association, trajectory optimization, and power optimization subproblems one after the other in an alternating fashion. To solve each subproblem, we employ the geometric programming (GP)-based optimization technique that transforms the variables and constraints. Regarding the initial trajectory of the UAV, we utilized dynamic programming techniques based on unsaturated data requirements of IoT nodes. We performed extensive simulations in many realistic environments to verify the effectiveness and efficiency of the proposed data dissemination scheme in post-disaster scenarios.

关键词： Data dissemination Energy consumption Internet of Things Task analysis Minimization Wireless sensor networks Trajectory optimization Bisection search technique completion time minimization convex optimization tools data dissemination using multiple unmanned aerial vehicles (UAVs) dynamic programming technique energy optimization trajectory design

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Incorporating the effects of service quality regulation in decision-making framework of distribution companies

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第18期12卷 4172-4181页

作者： Jooshaki, Mohammad Abbaspour, Ali Fotuhi-Firuzabad, Mahmud Moeini-Aghtaie, Moein Lehtonen, Matti Sharif Univ Technol Dept Elect Engn Tehran Iran Sharif Univ Technol Energy Engn Dept Tehran Iran Aalto Univ Dept Elect Engn & Automat Espoo Finland

Incentive regulations of reliability have made a link between distribution companies' revenue and their service reliability. The companies have to decide how much to spend on various projects to provide an acceptable level of reliability while anticipation of load growth. Planners and decision makers require a comprehensive framework to optimally allocate available budgets to different plans with the highest benefits considering implementation of incentive regulation. This paper proposes a decision framework for the optimum share of expansion and reliability oriented plans in presence of reward-penalty mechanisms. A two-layer optimization model is introduced, where in the outer layer, an iterative algorithm is applied to determine the optimal set of long-term projects including Distributed Generations (DGs) installation. A heuristic optimization algorithm is employed in this layer. Considering long-term plans, in inner optimization layer, the optimal set of mid-term plans including feeder reinforcement, and preventive maintenance actions are determined using algorithms such as Branch-and-Cut and dynamic programming techniques. The model is further implemented on a test distribution network and the results are investigated through various case studies. Obtained results show the strong influence of incentive regulation on reliability indices.

关键词： power distribution economics power distribution reliability power distribution planning iterative methods optimisation combinatorial mathematics preventive maintenance decision making service quality regulation decision-making framework distribution companies incentive regulations financial link load growth optimal available budget allocation expansion plans reliability-oriented plans reward-penalty mechanisms two-layer optimisation model iterative algorithm distributed generations installation heuristic optimisation algorithm optimisation layer feeder reinforcement preventive maintenance actions mid-term plans branch-and-cut technique dynamic programming technique test distribution network reliability indices

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Determining Optimum Strata Boundaries and Sample Sizes for Skewed Population with Log- Normal Distribution

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2015年第5期44卷 1364-1387页

作者： Khan, M. G. M. Rao, Dinesh Ansari, A. H. Ahsan, M. J. Univ S Pacific Sch Comp Informat & Math Sci Suva Fiji Kejriwal Inst Management & Dev Studies Ranchi Bihar India Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India

The method of choosing the best boundaries that make strata internally homogenous as far as possible is known as optimum stratification. To achieve this, the strata should be constructed in such a way that the strata variances for the characteristic under study be as small as possible. If the frequency distribution of the study variable x is known, the optimum strata boundaries (OSB) could be obtained by cutting the range of the distribution at suitable points. If the frequency distribution of x is unknown, it may be approximated from the past experience or some prior knowledge obtained at a recent study. Many skewed populations have log-normal frequency distribution or may be assumed to follow approximately log-normal frequency distribution. In this article, the problem of finding the OSB and the optimum sample sizes within the stratum for a skewed population with log-normal distribution is studied. The problem of determining the OSB is redefined as the problem of determining optimum strata widths (OSW) and is formulated as a Nonlinear programming Problem (NLPP) that seeks minimization of the variance of the estimated population mean under Neyman allocation subject to the constraint that the sum of the widths of all the strata is equal to the range of the distribution. The formulated NLPP turns out to be a multistage decision problem that can be solved by dynamic programming technique. A numerical example is presented to illustrate the application and computational details of the proposed method. A comparison study is conducted to investigate the efficiency of the proposed method with other stratification methods, viz., Dalenius and Hodges' cum root f method, geometric method by Gunning and Horgan, and Lavallee-Hidiroglou method using Kozak's algorithm available in the literature. The study reveals that the proposed technique is efficient in minimizing the variance of the estimate of the population mean and is useful to obtain OSB for a skewed population with log-n

关键词： dynamic programming technique Log-normal distribution Multistage decision problem Nonlinear programming problem Optimum sample size Optimum stratification Stratified sampling Primary 62D05 Secondary 80M50

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Optimal distribution transformer sizing by dynamic programming

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 1998年第3期20卷 161-167页

作者： Chen, CS Wu, TH Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan

The objective of this article is to propose an efficient strategy for transformer planning by taking into account the load characteristics so that the overall life cycle cost of distribution transformers can be reduced. The typical load patterns and load growth of residential, commercial and industrial customers are derived by a load survey system and then used to solve the transformer copper loss and core loss. The three-phase load flow analysis is performed to solve the yearly peak power loss and annual energy loss according to the customer typical load patterns. The transformer peak power loss and energy loss as well as the initial investment cost, installation cost and depreciation cost of distribution transformers are combined to form the overall cost function. The dynamic programming (DP) is then applied to find the optimal capacity and installation strategy of distribution transformers. To verify the accuracy of the proposed methodology, a practical Taipower distribution feeder has been selected for computer simulation. The conventional transformer sizing by considering the customer peak loading is also investigated for comparison, It is found that more than 5%, of overall life cycle cost of distribution transformers can be saved bz, the proposed optimal transformer sizing strategy. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： load survey transformer sizing dynamic programming technique

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Optimal stratification in stratified designs using weibull-distributed auxiliary information

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2019年第12期48卷 3136-3152页

作者： Reddy, Karuna G. Khan, M. G. M. Univ South Pacific Off Deputy Vice Chancellor Res Int & Innovat Suva Fiji Univ South Pacific Sch Comp Informat & Math Sci Suva Fiji Australian Natl Univ ANU Ctr Social Res & Methods Canberra ACT Australia

Sampling has evolved into a universally accepted approach for gathering information and data mining as it is widely accepted that a reasonably modest- sized sample can sufficiently characterize a much larger population. In stratified sampling designs, the whole population is divided into homogeneous strata in order to achieve higher precision in the estimation. This paper proposes an efficient method of constructing optimum stratum boundaries (OSB) and determining optimum sample size (OSS) for the survey variable. The survey variable may not be available in practice since the variable of interest is unavailable prior to conducting the survey. Thus, themethod is based on the auxiliary variable which is usually readily available from past surveys. To illustrate the application as an example using a real data, the auxiliary variable considered for this problem follows Weibull distribution. The stratification problem is formulated as a Mathematical programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. The solution procedure employs the dynamic programming technique, which results in substantial gains in the precision of the estimates of the population characteristics.

关键词： Optimal stratification mathematical programming problem dynamic programming technique stratified random sampling Weibull distribution

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Determining the optimal allocation of testing resource for modular software system using dynamic programming

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2016年第3期45卷 670-694页

作者： Khan, M. G. M. Ahmad, N. Rafi, L. S. Univ S Pacific Sch Comp Informat & Math Sci Suva Fiji TM Bhagalpur Univ Univ Dept Stat & Comp Applicat Bhagalpur Bihar India Univ S Pacific Coll Fdn Suva Fiji

Reliability is a major concern in the process of software development because unreliable software can cause failure in the computer system that can be hazardous. A way to enhance the reliability of software is to detect and remove the faults during the testing phase, which begins with module testing wherein modules are tested independently to remove a substantial number of faults within a limited resource. Therefore, the available resource must be allocated among the modules in such a way that the number of faults is removed as much as possible from each of the modules to achieve higher software reliability. In this article, we discuss the problem of optimal resource allocation of the testing resource for a modular software system, which maximizes the number of faults removed subject to the conditions that the amount of testing-effort is fixed, a certain percentage of faults is to be removed and a desired level of reliability is to be achieved. The problem is formulated as a non linear programming problem (NLPP), which is modeled by the inflection S-shaped software reliability growth models (SRGM) based on a non homogeneous Poisson process (NHPP) which incorporates the exponentiated Weibull (EW) testing-effort functions. A solution procedure is then developed using a dynamic programming technique to solve the NLPP. Furthermore, three special cases of optimum resource allocations are also discussed. Finally, numerical examples using three sets of software failure data are presented to illustrate the procedure developed and to validate the performance of the strategies proposed in this article. Experimental results indicate that the proposed strategies may be helpful to software project managers for making the best decisions in allocating the testing resource. In addition, the results are compared with those of Kapur etal. (2004), Huang and Lyu (2005), and Jha etal. (2010) that are available in the literature to deal the similar problems addressed in this article. It

关键词： dynamic programming technique Inflection S-shaped models Non linear programming problem Resource allocation problem Software reliability growth model

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Designing stratified sampling in economic and business surveys

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JOURNAL OF APPLIED STATISTICS 2015年第10期42卷 2080-2099页

作者： Khan, M. G. M. Reddy, K. G. Rao, D. K. Univ S Pacific Sch Comp Informat & Math Sci Suva Fiji

In most economic and business surveys, the target variables (e.g. turnover of enterprises, income of households, etc.) commonly resemble skewed distributions with many small and few large units. In such surveys, if a stratified sampling technique is used as a method of sampling and estimation, the convenient way of stratification such as the use of demographical variables (e.g. gender, socioeconomic class, geographical region, religion, ethnicity, etc.) or other natural criteria, which is widely practiced in economic surveys, may fail to form homogeneous strata and is not much useful in order to increase the precision of the estimates of variables of interest. In this paper, a stratified sampling design for economic surveys based on auxiliary information has been developed, which can be used for constructing optimum stratification and determining optimum sample allocation to maximize the precision in estimate.

关键词： economic survey stratified random sampling optimum stratification optimum sample size allocation mathematical programming problem dynamic programming technique

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