752 publications from this institution
We study a situation where a swarm of robots is deployed to handle multiple different targets in a confined unknown area. The targets are found in real time and each target requires a certain amount of resources. An individual robot may not have sufficient capabilities for its execution, therefore an announcement process can start. We address the issue of how each robot responds itself to one of the discovered targets in an efficient way, considering a dynamic scenario where failure of the robots and unreliable communications unpredictably can occur. We propose a network architecture that incorporates a self-regulating mechanism allowing the distribution among the targets, with the minimal exchange of information. We have conducted experiments for evaluating our proposed approach in a simulated environment, considering different network parameters and studying the scalability and the robustness of the proposed model.
In real-world engineering problems, several conflicting objective functions have often to be optimized simultaneously. Typically, the objective functions of these problems are too complex to solve using derivative-based optimization methods. Integration of navigation and radar functionality with communication applications is such a problem. Designing sequences for these systems is a difficult task. This task is further complicated by the following factors: (i) conflicting requirements on autocorrelation and crosscorrelation characteristics; (ii) the associated cost functions might be irregular and may have several local minima. Traditional or gradient based optimization methods may face challenges or are unsuitable to solve such a complex problem. In this paper, we pose simultaneous optimization of autocorrelation and crosscorrelation characteristics of Oppermann sequences as a multiobjective problem. We compare the performance of prominent state-of-the-art multiobjective evolutionary meta-heuristic algorithms to design Oppermann sequences for integrated radar and communication systems.
Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately find the Pareto fronts for a set of test functions. We then solve a bi-objective disc brake design problem, which indeed converges quickly.
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Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.
Precise marketing is one of the trends for the service industry, it means nowadays, many consumers are keen on personalized as well as diversified consuming experience.When it comes to the tourism industry, there are a lot of examples of precise marketing, like wenyi tourist destinations have gained popularity on social media in recent years, many tourist destinations and enterprises try to attract target guest groups with the label of wenyi image, but there are few papers studying on the target consumer groups of this kind of destinations.In this paper, the research team captured the network text from Zhihu, a popular ChineseQ&A platform, and analysed the self-portrait of the target group of the wenyi destination systematically.The coding results reveal that there are many outstanding characteristics of wenyi qingnian in both behavior(237) and psychology(193), providing a reference for relative tourist destinations' precise marketing strategies which are aimed at their target customer group.
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: A drilling system incorporating percussive action is generally preferred for very hard rock to propagate significant cracking and fragmentation. Therefore, improving energy utilization efficiency becomes crucial for increasing the drilling rate. In this study, 3D FDEM simulations were employed to model the piston-bit-rock interaction in percussive drilling, revealing how the impact energy is partitioned and consumed during the impact process. To ensure the accuracy of energy analysis, rigorous validation against experiments was conducted, comparing the bit indentation and rebound velocity, fragment mass, and the range of rock cracking after impact. The results revealed that, for both St Anne limestone and Rhune sandstone, during a single impact, only 2.4-2.6% of the energy was utilized for crack propagation, while 30-70% of the energy was consumed by friction between fragments. As the piston impact energy increases, there is no significant change in the proportion of fracture energy, while the energy lost to friction gradually increases, then remains unchanged. 1. INTRODUCTION With the increasing demand for underground mineral resources and green energy (i.e., geothermal energy), efficient drilling technologies are crucial for reducing the cost of drilling (Zappa et al., 2019). When drilling into hard brittle rock formations, impact-assisted drilling methods achieve faster drilling rates compared to purely cutting drilling technologies. Therefore, for percussion drilling, achieving faster drilling rates to reduce drilling costs has become a focus of many engineers and researchers (Aldannawy et al., 2022; Kahraman et al., 2003). It also becomes crucial to fully understand the cracking mechanism and energy transition during the multi-body interaction (i.e., piston-bit-rock interaction), to improve the drilling rate and the energy utilization efficiency. Several experimental studies have been conducted at various scales to study the rock fragmentation efficiency (i.e., fragment mass per impact) of percussion drilling. Factors such as insert geometry (Qin et al., 2014), impact energy (Saksala et al., 2014), "weight on bit" (Aldannawy et al., 2022) and rock confinement pressure (Li et al., 2021) have been studied for their impact on the rock fragmentation process.
A mathematical model of compaction in sedimentary basins is presented and analyzed. Compaction occurs when accumulating sediments compact under their own weight, expelling pore water in the process. If sedimentation is rapid or the permeability is low, then high pore pressures can result, a phenomenon which is of importance in oil drilling operations. Here we show that one-dimensional compaction can be described in its simplest form by a nonlinear diffusion equation, controlled principally by a dimensionless parameter $\lambda$, which is the ratio of the hydraulic conductivity to the sedimentation rate. Large $\lambda$ corresponds to very permeable sediments, or slow sedimentation, a situation which we term "fast compaction," since the rapid pore water expulsion allows the pore water pressure to equilibrate to a hydrostatic value. On the other hand, small $\lambda$ corresponds to "slow compaction," and the pore pressure is in excess above the hydrostatic value and more nearly equal to the overburden value. We provide analytic and numerical results for both large and small $\lambda$, using also the assumption that the permeability is a strong function of porosity. In particular, we can derive Athy's law (that porosity decreases exponentially with depth) when $\lambda\gg 1$.
Both data mining and machine learning are becoming popular with many different applications. This chapter introduces some techniques in data mining and machine learning, including clustering, support vector machine, and neural networks. We also discuss their links to nature-inspired algorithms for optimization.
The Maximum Likelihood Estimator (MLE) serves an important role in statistics and machine learning. In this article, for i.i.d. variables, we obtain constant-specified and sharp concentration inequalities and oracle inequalities for the MLE only under exponential moment conditions. Furthermore, in a robust setting, the sub-Gaussian type oracle inequalities of the log-truncated maximum likelihood estimator are derived under the second-moment condition.
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Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.
